diff --git a/.github/styles/Vocab/Jumpstart/accept.txt b/.github/styles/config/vocabularies/Jumpstart/accept.txt similarity index 100% rename from .github/styles/Vocab/Jumpstart/accept.txt rename to .github/styles/config/vocabularies/Jumpstart/accept.txt diff --git a/.github/styles/Vocab/Jumpstart/reject.txt b/.github/styles/config/vocabularies/Jumpstart/reject.txt similarity index 100% rename from .github/styles/Vocab/Jumpstart/reject.txt rename to .github/styles/config/vocabularies/Jumpstart/reject.txt diff --git a/AI_DISCLAIMER.MD b/AI_DISCLAIMER.MD new file mode 100644 index 00000000..16a3afe2 --- /dev/null +++ b/AI_DISCLAIMER.MD @@ -0,0 +1,5 @@ +# Arc Jumpstart AI Disclaimer + +Some of the documentation and content on the [Arc Jumpstart](https://aka.ms/ArcJumpstart) and it's associated GitHub repositories were created with the assistance of AI tools. To ensure quality, our team follows [Microsoft’s principles for AI-generated content](https://learn.microsoft.com/principles-for-ai-generated-content), focusing on accuracy, transparency, and accountability. All AI-generated content is reviewed and refined by our team to ensure it meets the high standards of the Azure Arc Jumpstart community. + +If you encounter any issues or have suggestions, please open an issue, and we’ll be happy to improve the documentation further. We believe in maintaining an open and trustworthy approach to collaborative documentation and hope this note clarifies our commitment. diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md index f9ba8cf6..22becd54 100644 --- a/CODE_OF_CONDUCT.md +++ b/CODE_OF_CONDUCT.md @@ -7,3 +7,9 @@ Resources: - [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/) - [Microsoft Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) - Contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with questions or concerns + +## AI-Assisted Content Guidelines + +In line with our commitment to accuracy and transparency, we ask that contributors who use AI tools to assist in content creation adhere to [Microsoft’s principles for AI-generated content](https://learn.microsoft.com/principles-for-ai-generated-content). All AI-assisted contributions should be carefully reviewed to ensure they meet our standards for quality, accuracy, and ethical use. + +For more details, please see our [AI disclaimer](./AI_DISCLAIMER.MD). diff --git a/LICENSE b/LICENSE index 5473cca7..8fe5d7bd 100644 --- a/LICENSE +++ b/LICENSE @@ -392,4 +392,13 @@ understandings, or agreements concerning use of licensed material. For the avoidance of doubt, this paragraph does not form part of the public licenses. +======================================================================= + +Acknowledgments + +This project utilizes the Azure OpenAI Service, provided by Microsoft. Use of the service must comply with Microsoft's Azure Terms of Service (https://azure.microsoft.com/support/legal/). + +This project incorporates the Phi-3-Mini-4K-Instruct model, licensed under the MIT License. For full license details, please refer to the original license provided by the model's creators, available at [Phi-3-Mini-4K-Instruct License](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE). + + Creative Commons may be contacted at creativecommons.org. diff --git a/docs/FAQ/_index.md b/docs/FAQ/_index.md index 35765e82..067f684c 100644 --- a/docs/FAQ/_index.md +++ b/docs/FAQ/_index.md @@ -11,6 +11,10 @@ weight: 8 Absolutely! Arc Jumpstart is a community-driven open source project, and contributions are welcomed. To get started, review the [Jumpstart contribution guidelines](../contribution_guidelines/) and our [Code of Conduct](https://aka.ms/JumpstartCOC). +## Are any of the Arc Jumpstart documents created with AI assistance? + +Yes, some of our documentation was created with the assistance of AI tools. Our team follows [Microsoft’s principles for AI-generated content](https://learn.microsoft.com/principles-for-ai-generated-content) and thoroughly reviews AI-assisted content to ensure it meets high standards for quality and accuracy. You can find more details in our [AI disclaimer](https://github.com/Azure/arc_jumpstart_docs/blob/main/AI_DISCLAIMER.MD). + ## Can I use an Azure free trial account with Arc Jumpstart? Yes, you can use an Azure [free trial account](https://azure.microsoft.com/free). However, some Jumpstart features may require additional Azure consumption isn’t provided in a free trial. @@ -47,43 +51,51 @@ We’d love to hear it! Fill out the [submission form](https://aka.ms/LightningG An up-to-date roadmap for Arc Jumpstart scenarios can be found under [the repository GitHub Project](https://aka.ms/JumpstartRoadmap). +## Jumpstart Drops Frequently Asked Questions (FAQ) -# Jumpstart Drops Frequently Asked Questions (FAQ) +### Can I contribute a Drop? -## Can I contribute a Drop? Arc Jumpstart Drops is fully open-source and welcomes all contributions that follow the Jumpstart Drops [contribution process](../contribution_guidelines/). Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant Arc Jumpstart team the rights to use your contribution. For details, visit [Microsoft CLA](https://cla.opensource.microsoft.com). -## Do I need to host my contribution source code inside the Jumpstart Drops GitHub repository? +### Do I need to host my contribution source code inside the Jumpstart Drops GitHub repository? + No, it's not mandatory to host your contribution source code in the Jumpstart Drops repository. You have two options for hosting the code, and you can choose the one that suits your use case better: - **Include the code in the Pull Request (PR)**: You can include the code as part of your PR and host it within the Arc [Jumpstart Drops GitHub repository](https://github.com/Azure/arc_jumpstart_drops). - **Keep the code in your own repository**: Alternatively, you can keep the code in your own repository and provide a reference/URL to it in the Drop [definition file](https://github.com/Azure/arc_jumpstart_drops/blob/main/SCHEMA.md). -## Do I need to authenticate to the Arc Jumpstart website to contribute or use Jumpstart Drops? +### Do I need to authenticate to the Arc Jumpstart website to contribute or use Jumpstart Drops? + No, authentication and logging in to the portal aren't required to contribute or use Jumpstart Drops. However, if you are logged in, we're developing features to provide contributors with insights on their contributions and enhance their experience and submission. -### What's the Jumnpstart Drop licensing schema? -Jumpstart Drops follows the same [MIT](https://github.com/Azure/arc_jumpstart_drops/blob/main/LICENSE) licensing as all the other Arc Jumpstart Products. All contributions should also be under an [MIT](https://github.com/Azure/arc_jumpstart_drops/blob/main/LICENSE) license. +### What's the Jumpstart Drop licensing schema? + +Jumpstart Drops follows the same [MIT](https://github.com/Azure/arc_jumpstart_drops/blob/main/LICENSE) licensing as all the other Arc Jumpstart Products. All contributions should also be under an [MIT](https://github.com/Azure/arc_jumpstart_drops/blob/main/LICENSE) license. -The MIT license is a permissive open-source software license that allows for the use, modification, and distribution of the licensed software, as long as the original copyright notice and license terms are included in any copies or distributions of the software. +The MIT license is a permissive open-source software license that allows for the use, modification, and distribution of the licensed software, as long as the original copyright notice and license terms are included in any copies or distributions of the software. For more information, see [contribution process](../contribution_guidelines/). -## How can I discover a Jumsptart Drop? +### How can I discover a Jumpstart Drop? + To discover a Jumpstart Drop tailored to your needs, leverage the filters and sorting options available on the Arc Jumpstart Drops main page. Start by visiting [Arc Jumpstart Drops](https://arcjumpstart.com/arc_jumpstart_drops) and then use the panel on the right-hand side to filter by criteria such as *Programming* *Language*, *Product*, *Difficulty*, *Industry*, *Topic*, and more. Additionally, you can always use the search bar at the top to search across all Jumpstart content, including Jumpstart Drops. -## How do I use a Jumpstart Drop? -Using Jumsptart Drop is straightforward. Simply download the Jumnpstart Drop by clicking the Download button on the Jumpstart Drop's page. After downloading, you will have a .zip file containing all the necessary source code to run the Jumpstart Drop. Depending on the Jumpstart Drop, you may need to configure some prerequisites, but all instructions should be included in the *index.md* file of the Jumpstart Drop. +### How do I use a Jumpstart Drop? + +Using Jumpstart Drop is straightforward. Simply download the Jumpstart Drop by clicking the Download button on the Jumpstart Drop's page. After downloading, you will have a .zip file containing all the necessary source code to run the Jumpstart Drop. Depending on the Jumpstart Drop, you may need to configure some prerequisites, but all instructions should be included in the *index.md* file of the Jumpstart Drop. + +### How do I submit a Jumpstart Drop? -## How do I submit a Jumpstart Drop? Submitting a Jumpstart Drop is easy, upload your content and create a pull request directly in the Arc Jumpstart Drops repository. Refer to the Jumpstart Drops [contribution process](../contribution_guidelines/) guide for detailed instructions. -## How's support and maintenance handled for Jumpstart Drops? +### How's support and maintenance handled for Jumpstart Drops? + Support and maintenance for Jumpstart Drops are managed through GitHub Issues, where bugs and feature requests can be tracked. The project follows the support approach outlined in [Arc Jumpstart Support](https://github.com/Azure/arc_jumpstart_docs/blob/main/SUPPORT.md). **Important**: _Please note that Jumpstart Drops don't come with official support or warranty, but the community is encouraged to provide assistance to the best of their ability._ -## What tools are used for code maintenance in this project? -All Jumpstart Drop owners are responsible for the code maintenance of their source code. If you chose to host the Jumpstart Drop source code as part of the Jumpstart Drops repository, we will run some check for code maintenance, like **CodeQL** and **GitHub Dependabot** security alerts. We're actively looking into developing new features to help contributors maintain their source code. If you have suggestions, please create an [Issue](https://github.com/Azure/arc_jumpstart_drops/issues/new?assignees=fcabrera23&labels=triage&projects=&template=%F0%9F%90%9Bbug-report.md&title=) with your suggested approach. \ No newline at end of file +### What tools are used for code maintenance in this project? + +All Jumpstart Drop owners are responsible for the code maintenance of their source code. If you chose to host the Jumpstart Drop source code as part of the Jumpstart Drops repository, we will run some check for code maintenance, like **CodeQL** and **GitHub Dependabot** security alerts. We're actively looking into developing new features to help contributors maintain their source code. If you have suggestions, please create an [Issue](https://github.com/Azure/arc_jumpstart_drops/issues/new?assignees=fcabrera23&labels=triage&projects=&template=%F0%9F%90%9Bbug-report.md&title=) with your suggested approach diff --git a/docs/azure_arc_jumpstart/azure_arc_app_svc/aks/aks_app_service_arm_template/_index.md b/docs/azure_arc_jumpstart/azure_arc_app_svc/aks/aks_app_service_arm_template/_index.md index 7eae0eb8..ed08f534 100644 --- a/docs/azure_arc_jumpstart/azure_arc_app_svc/aks/aks_app_service_arm_template/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_app_svc/aks/aks_app_service_arm_template/_index.md @@ -22,7 +22,7 @@ By the end of this scenario, you will have an AKS cluster deployed with an App S git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_app_svc/aks/aks_azure_apimgmt_arm_template/_index.md b/docs/azure_arc_jumpstart/azure_arc_app_svc/aks/aks_azure_apimgmt_arm_template/_index.md index 534a36d2..7d598985 100644 --- a/docs/azure_arc_jumpstart/azure_arc_app_svc/aks/aks_azure_apimgmt_arm_template/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_app_svc/aks/aks_azure_apimgmt_arm_template/_index.md @@ -22,7 +22,7 @@ By the end of this scenario, you will have an AKS cluster deployed with an Azure git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_app_svc/aks/aks_azure_function_arm_template/_index.md b/docs/azure_arc_jumpstart/azure_arc_app_svc/aks/aks_azure_function_arm_template/_index.md index 23542eb7..8752a61b 100644 --- a/docs/azure_arc_jumpstart/azure_arc_app_svc/aks/aks_azure_function_arm_template/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_app_svc/aks/aks_azure_function_arm_template/_index.md @@ -22,7 +22,7 @@ By the end of this scenario, you will have an AKS cluster deployed with an App S git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_app_svc/aks/aks_container_apps_arm_template/_index.md b/docs/azure_arc_jumpstart/azure_arc_app_svc/aks/aks_container_apps_arm_template/_index.md index fa076313..041ef44b 100644 --- a/docs/azure_arc_jumpstart/azure_arc_app_svc/aks/aks_container_apps_arm_template/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_app_svc/aks/aks_container_apps_arm_template/_index.md @@ -30,7 +30,7 @@ In this scenario, you will deploy a [Container Apps environment](https://learn.m git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_app_svc/aks/aks_logic_app_arm_template/_index.md b/docs/azure_arc_jumpstart/azure_arc_app_svc/aks/aks_logic_app_arm_template/_index.md index 033b4e85..9e0d8ecf 100644 --- a/docs/azure_arc_jumpstart/azure_arc_app_svc/aks/aks_logic_app_arm_template/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_app_svc/aks/aks_logic_app_arm_template/_index.md @@ -22,7 +22,7 @@ By the end of this scenario, you will have an AKS cluster deployed with an App S git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_data/aks/aks_dc_vanilla_arm_template/_index.md b/docs/azure_arc_jumpstart/azure_arc_data/aks/aks_dc_vanilla_arm_template/_index.md index 9a90a615..324541a0 100644 --- a/docs/azure_arc_jumpstart/azure_arc_data/aks/aks_dc_vanilla_arm_template/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_data/aks/aks_dc_vanilla_arm_template/_index.md @@ -24,7 +24,7 @@ By the end of this scenario, you will have an AKS cluster deployed with an Azure git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_data/aks/aks_mssql_mi_arm_template/_index.md b/docs/azure_arc_jumpstart/azure_arc_data/aks/aks_mssql_mi_arm_template/_index.md index 82921043..a4b146ad 100644 --- a/docs/azure_arc_jumpstart/azure_arc_data/aks/aks_mssql_mi_arm_template/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_data/aks/aks_mssql_mi_arm_template/_index.md @@ -22,7 +22,7 @@ By the end of this scenario, you will have an AKS cluster deployed with an Azure git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_data/aks/aks_postgresql_arm_template/_index.md b/docs/azure_arc_jumpstart/azure_arc_data/aks/aks_postgresql_arm_template/_index.md index e753d9b8..5c6fb716 100644 --- a/docs/azure_arc_jumpstart/azure_arc_data/aks/aks_postgresql_arm_template/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_data/aks/aks_postgresql_arm_template/_index.md @@ -24,7 +24,7 @@ By the end of this scenario, you will have an AKS cluster deployed with an Azure git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_data/aro/aro_dc_vanilla_arm_template/_index.md b/docs/azure_arc_jumpstart/azure_arc_data/aro/aro_dc_vanilla_arm_template/_index.md index 75690d4f..cf468fb5 100644 --- a/docs/azure_arc_jumpstart/azure_arc_data/aro/aro_dc_vanilla_arm_template/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_data/aro/aro_dc_vanilla_arm_template/_index.md @@ -22,7 +22,7 @@ By the end of this scenario, you will have an ARO cluster deployed with an Azure git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_data/aro/aro_mssql_mi_arm_template/_index.md b/docs/azure_arc_jumpstart/azure_arc_data/aro/aro_mssql_mi_arm_template/_index.md index 38b1b524..a010b8ba 100644 --- a/docs/azure_arc_jumpstart/azure_arc_data/aro/aro_mssql_mi_arm_template/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_data/aro/aro_mssql_mi_arm_template/_index.md @@ -20,7 +20,7 @@ By the end of this scenario, you will have an ARO cluster deployed with an Azure git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_data/aro/aro_postgresql_arm_template/_index.md b/docs/azure_arc_jumpstart/azure_arc_data/aro/aro_postgresql_arm_template/_index.md index 793e622e..3bcf23d1 100644 --- a/docs/azure_arc_jumpstart/azure_arc_data/aro/aro_postgresql_arm_template/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_data/aro/aro_postgresql_arm_template/_index.md @@ -22,7 +22,7 @@ By the end of this scenario, you will have an ARO cluster deployed with an Azure git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_data/day2/aks/aks_mssql_arm_template_ado/_index.md b/docs/azure_arc_jumpstart/azure_arc_data/day2/aks/aks_mssql_arm_template_ado/_index.md index 436c0e3c..e869614b 100644 --- a/docs/azure_arc_jumpstart/azure_arc_data/day2/aks/aks_mssql_arm_template_ado/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_data/day2/aks/aks_mssql_arm_template_ado/_index.md @@ -22,7 +22,7 @@ By the end of this scenario, you will have an Azure DevOps Release pipeline to d - (Optional) [Create new Azure DevOps organization](https://learn.microsoft.com/azure/devops/organizations/accounts/create-organization?view=azure-devops). - (Optional) [Create new Azure DevOps project](https://learn.microsoft.com/azure/devops/organizations/projects/create-project?view=azure-devops&tabs=preview-page). -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_data/day2/aks/aks_mssql_dr/_index.md b/docs/azure_arc_jumpstart/azure_arc_data/day2/aks/aks_mssql_dr/_index.md index 5277a1b4..62bf6eb5 100644 --- a/docs/azure_arc_jumpstart/azure_arc_data/day2/aks/aks_mssql_dr/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_data/day2/aks/aks_mssql_dr/_index.md @@ -22,7 +22,7 @@ By the end of this guide, you will have two Azure Kubernetes Service (AKS) clust git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_data/day2/aks/aks_mssql_mi_ad_auth_arm_template/_index.md b/docs/azure_arc_jumpstart/azure_arc_data/day2/aks/aks_mssql_mi_ad_auth_arm_template/_index.md index 3b795186..532b59e6 100644 --- a/docs/azure_arc_jumpstart/azure_arc_data/day2/aks/aks_mssql_mi_ad_auth_arm_template/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_data/day2/aks/aks_mssql_mi_ad_auth_arm_template/_index.md @@ -20,7 +20,7 @@ By the end of this scenario, you will have an AKS cluster deployed with an Azure git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_data/day2/aks/aks_mssql_migrate/_index.md b/docs/azure_arc_jumpstart/azure_arc_data/day2/aks/aks_mssql_migrate/_index.md index a50bea4f..d0349370 100644 --- a/docs/azure_arc_jumpstart/azure_arc_data/day2/aks/aks_mssql_migrate/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_data/day2/aks/aks_mssql_migrate/_index.md @@ -20,7 +20,7 @@ By the end of this scenario, you will have an AKS cluster deployed with an Azure git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_data/day2/aks/aks_postgresql_arm_template_ado/_index.md b/docs/azure_arc_jumpstart/azure_arc_data/day2/aks/aks_postgresql_arm_template_ado/_index.md index 418ba728..c64022a7 100644 --- a/docs/azure_arc_jumpstart/azure_arc_data/day2/aks/aks_postgresql_arm_template_ado/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_data/day2/aks/aks_postgresql_arm_template_ado/_index.md @@ -22,7 +22,7 @@ By the end of this scenario, you will have an Azure DevOps Release pipeline to d - (Optional) [Create new Azure DevOps organization](https://learn.microsoft.com/azure/devops/organizations/accounts/create-organization?view=azure-devops). - (Optional) [Create new Azure DevOps project](https://learn.microsoft.com/azure/devops/organizations/projects/create-project?view=azure-devops&tabs=preview-page). -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_data/eks/eks_dc_vanilla_terraform/_index.md b/docs/azure_arc_jumpstart/azure_arc_data/eks/eks_dc_vanilla_terraform/_index.md index 4ca78e91..03335d4a 100644 --- a/docs/azure_arc_jumpstart/azure_arc_data/eks/eks_dc_vanilla_terraform/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_data/eks/eks_dc_vanilla_terraform/_index.md @@ -20,7 +20,7 @@ By the end of this scenario, you will have an EKS cluster deployed with an Azure git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_data/eks/eks_mssql_mi_terraform/_index.md b/docs/azure_arc_jumpstart/azure_arc_data/eks/eks_mssql_mi_terraform/_index.md index 6267d567..4780e8ec 100644 --- a/docs/azure_arc_jumpstart/azure_arc_data/eks/eks_mssql_mi_terraform/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_data/eks/eks_mssql_mi_terraform/_index.md @@ -20,7 +20,7 @@ By the end of this scenario, you will have an EKS cluster deployed with an Azure git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_data/eks/eks_postgres_terraform/_index.md b/docs/azure_arc_jumpstart/azure_arc_data/eks/eks_postgres_terraform/_index.md index bcdfe8fb..a909efce 100644 --- a/docs/azure_arc_jumpstart/azure_arc_data/eks/eks_postgres_terraform/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_data/eks/eks_postgres_terraform/_index.md @@ -22,7 +22,7 @@ By the end of this scenario, you will have an EKS cluster deployed with an Azure git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_data/gke/gke_dc_vanilla_terraform/_index.md b/docs/azure_arc_jumpstart/azure_arc_data/gke/gke_dc_vanilla_terraform/_index.md index 193cc937..7535de2f 100644 --- a/docs/azure_arc_jumpstart/azure_arc_data/gke/gke_dc_vanilla_terraform/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_data/gke/gke_dc_vanilla_terraform/_index.md @@ -35,7 +35,7 @@ By the end of this scenario, you will have a GKE cluster deployed with an Azure git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 or higher](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 or higher](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_data/gke/gke_mssql_mi_terraform/_index.md b/docs/azure_arc_jumpstart/azure_arc_data/gke/gke_mssql_mi_terraform/_index.md index 6d207d30..ee1b1592 100644 --- a/docs/azure_arc_jumpstart/azure_arc_data/gke/gke_mssql_mi_terraform/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_data/gke/gke_mssql_mi_terraform/_index.md @@ -36,7 +36,7 @@ By the end of this scenario, you will have a GKE cluster deployed with an Azure git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_data/gke/gke_postgres_terraform/_index.md b/docs/azure_arc_jumpstart/azure_arc_data/gke/gke_postgres_terraform/_index.md index 849a5f7f..52b5c879 100644 --- a/docs/azure_arc_jumpstart/azure_arc_data/gke/gke_postgres_terraform/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_data/gke/gke_postgres_terraform/_index.md @@ -36,7 +36,7 @@ By the end of this scenario, you will have a GKE cluster deployed with an Azure git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_data/kubeadm/kubeadm_azure_dc_vanilla_arm_template/_index.md b/docs/azure_arc_jumpstart/azure_arc_data/kubeadm/kubeadm_azure_dc_vanilla_arm_template/_index.md index 68c2d575..1f6b06cb 100644 --- a/docs/azure_arc_jumpstart/azure_arc_data/kubeadm/kubeadm_azure_dc_vanilla_arm_template/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_data/kubeadm/kubeadm_azure_dc_vanilla_arm_template/_index.md @@ -22,7 +22,7 @@ By the end of this scenario, you will have a Kubeadm Kubernetes cluster deployed git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_data/kubeadm/kubeadm_azure_mssql_mi_arm_template/_index.md b/docs/azure_arc_jumpstart/azure_arc_data/kubeadm/kubeadm_azure_mssql_mi_arm_template/_index.md index df4ddbb2..d5cbefd3 100644 --- a/docs/azure_arc_jumpstart/azure_arc_data/kubeadm/kubeadm_azure_mssql_mi_arm_template/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_data/kubeadm/kubeadm_azure_mssql_mi_arm_template/_index.md @@ -22,7 +22,7 @@ By the end of this scenario, you will have a Kubeadm Kubernetes cluster deployed git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_data/kubeadm/kubeadm_azure_postgresql_arm_template/_index.md b/docs/azure_arc_jumpstart/azure_arc_data/kubeadm/kubeadm_azure_postgresql_arm_template/_index.md index 682a592f..f54d2670 100644 --- a/docs/azure_arc_jumpstart/azure_arc_data/kubeadm/kubeadm_azure_postgresql_arm_template/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_data/kubeadm/kubeadm_azure_postgresql_arm_template/_index.md @@ -22,7 +22,7 @@ By the end of this scenario, you will have a Kubeadm Kubernetes cluster deployed git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_data/microk8s/mk8s_dc_vanilla_arm_template/_index.md b/docs/azure_arc_jumpstart/azure_arc_data/microk8s/mk8s_dc_vanilla_arm_template/_index.md index 49ad177f..eab67f92 100644 --- a/docs/azure_arc_jumpstart/azure_arc_data/microk8s/mk8s_dc_vanilla_arm_template/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_data/microk8s/mk8s_dc_vanilla_arm_template/_index.md @@ -22,7 +22,7 @@ By the end of this scenario, you will have a Microk8s Kubernetes cluster deploye git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_data/microk8s/mk8s_mssql_mi_arm_template/_index.md b/docs/azure_arc_jumpstart/azure_arc_data/microk8s/mk8s_mssql_mi_arm_template/_index.md index cda024ba..fcb00ec4 100644 --- a/docs/azure_arc_jumpstart/azure_arc_data/microk8s/mk8s_mssql_mi_arm_template/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_data/microk8s/mk8s_mssql_mi_arm_template/_index.md @@ -20,7 +20,7 @@ By the end of this scenario, you will have a Microk8s Kubernetes cluster deploye git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_data/microk8s/mk8s_postgresql_arm_template/_index.md b/docs/azure_arc_jumpstart/azure_arc_data/microk8s/mk8s_postgresql_arm_template/_index.md index d5415260..e1f3aae6 100644 --- a/docs/azure_arc_jumpstart/azure_arc_data/microk8s/mk8s_postgresql_arm_template/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_data/microk8s/mk8s_postgresql_arm_template/_index.md @@ -22,7 +22,7 @@ By the end of this scenario, you will have a Microk8s Kubernetes cluster deploye git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/aks/aks_arm_template/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/aks/aks_arm_template/_index.md index 1b53c394..9aec9aed 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/aks/aks_arm_template/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/aks/aks_arm_template/_index.md @@ -20,7 +20,7 @@ The following Jumpstart scenario will guide you on how to use the provided [Azur git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/aks/aks_terraform/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/aks/aks_terraform/_index.md index 9cd7848f..9be9003c 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/aks/aks_terraform/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/aks/aks_terraform/_index.md @@ -20,7 +20,7 @@ The following Jumpstart scenario will guide you on how to use the provided [Terr git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/_index.md deleted file mode 100644 index 918f4836..00000000 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/_index.md +++ /dev/null @@ -1,9 +0,0 @@ ---- -type: docs -title: "AKS on Azure Stack HCI" -linkTitle: "AKS on Azure Stack HCI" -weight: 3 -toc_hide: true -description: >- - If you do not yet have a Kubernetes cluster, the scenarios in this section will guide on creating an AKS cluster on Azure Stack HCI in an automated fashion. ---- \ No newline at end of file diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/01.png b/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/01.png deleted file mode 100644 index 2bb60a0b..00000000 Binary files a/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/01.png and /dev/null differ diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/02.png b/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/02.png deleted file mode 100644 index b775f3f1..00000000 Binary files a/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/02.png and /dev/null differ diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/03.png b/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/03.png deleted file mode 100644 index 112663dc..00000000 Binary files a/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/03.png and /dev/null differ diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/04.png b/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/04.png deleted file mode 100644 index 3a8b484c..00000000 Binary files a/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/04.png and /dev/null differ diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/05.png b/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/05.png deleted file mode 100644 index 22c9bedb..00000000 Binary files a/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/05.png and /dev/null differ diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/06.png b/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/06.png deleted file mode 100644 index 489b828e..00000000 Binary files a/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/06.png and /dev/null differ diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/07.png b/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/07.png deleted file mode 100644 index 0b942221..00000000 Binary files a/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/07.png and /dev/null differ diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/08.png b/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/08.png deleted file mode 100644 index a746e915..00000000 Binary files a/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/08.png and /dev/null differ diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/09.png b/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/09.png deleted file mode 100644 index 74982fa6..00000000 Binary files a/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/09.png and /dev/null differ diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/10.png b/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/10.png deleted file mode 100644 index 3ab43d16..00000000 Binary files a/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/10.png and /dev/null differ diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/_index.md deleted file mode 100644 index 7571577e..00000000 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/_index.md +++ /dev/null @@ -1,203 +0,0 @@ ---- -type: docs -title: "AKS on Azure Stack HCI PowerShell" -linkTitle: "AKS on Azure Stack HCI PowerShell" -weight: 1 -description: > ---- - -## Deploy AKS cluster on Azure Stack HCI and connect it to Azure Arc using PowerShell - -The following Jumpstart scenario will guide you on how to use the provided PowerShell script to deploy an [Azure Kubernetes Service (AKS)](https://learn.microsoft.com/azure/aks/intro-kubernetes) cluster on [Azure Stack HCI](https://learn.microsoft.com/azure-stack/hci/overview) and connected it as an Azure Arc cluster resource. - -Azure Kubernetes Service on Azure Stack HCI is an implementation of AKS on-premises using hyperconverged infrastructure operating system that is delivered as an Azure service. - -This guide will not provide instructions on how to deploy and set up Azure Stack HCI and it assumes you already have a configured cluster. The commands described in this scenario should be run on the management computer or in a host server in a cluster. - -## Prerequisites - -- Clone the Azure Arc Jumpstart repository - - ```shell - git clone https://github.com/microsoft/azure_arc.git - ``` - -- Create Azure service principal (SP) - - To be able to complete the scenario and its related automation, an Azure service principal assigned with the “Contributor” role is required. To create it, login to your Azure account using PowerShell and run the below command. To do this, you will need to run the script from a PowerShell session that has access to your AKS on the Azure Stack HCI environment. - - ```powershell - Connect-AzAccount - $sp = New-AzADServicePrincipal -DisplayName "" -Role 'Contributor' - ``` - - For example: - - ```powershell - $sp = New-AzADServicePrincipal -DisplayName "" -Role 'Contributor' - ``` - - This command will create a variable with a secure string as shown below: - - ```shell - Secret : System.Security.SecureString - ServicePrincipalNames : {XXXXXXXXXXXXXXXXXXXXXXXXXXXX, http://AzureArcK8s} - ApplicationId : XXXXXXXXXXXXXXXXXXXXXXXXXXXX - ObjectType : ServicePrincipal - DisplayName : AzureArcK8s - Id : XXXXXXXXXXXXXXXXXXXXXXXXXXXX - Type : - ``` - - To expose the generated password use this code to export the secret: - - ```powershell - $BSTR = [System.Runtime.InteropServices.Marshal]::SecureStringToBSTR($sp.Secret) - $UnsecureSecret = [System.Runtime.InteropServices.Marshal]::PtrToStringAuto($BSTR) - ``` - - Copy the Service Principal ApplicationId and Secret as you will need it for later in the automation. - - > **Note:** It is optional but highly recommended to scope the SP to a specific [Azure subscription and resource group](https://learn.microsoft.com/powershell/module/az.resources/new-azadserviceprincipal?view=azps-5.4.0). - -- Enable your subscription with the two resource providers for Azure Arc-enabled Kubernetes. Registration is an asynchronous process, and registration may take approximately 10 minutes. - - ```powershell - Register-AzResourceProvider -ProviderNamespace Microsoft.Kubernetes - Register-AzResourceProvider -ProviderNamespace Microsoft.KubernetesConfiguration - Get-AzResourceProvider -ListAvailable | Select-Object ProviderNamespace, RegistrationState | Select-String -Pattern "Microsoft.Kubernetes" - ``` - -- Perform a clean installation of the AksHci PowerShell module. To install the AksHci PowerShell module remove any previous versions by running the below commands: - - ```powershell - Install-PackageProvider -Name NuGet -Force - Install-Module -Name PowershellGet -Force -Confirm:$false -SkipPublisherCheck - Install-Module -Name Az.Accounts -Repository PSGallery - Install-Module -Name Az.Resources -Repository PSGallery - Install-Module -Name AzureAD -Repository PSGallery - Install-Module -Name AksHci -Repository PSGallery - Import-Module Az.Accounts - Import-Module Az.Resources - Import-Module AzureAD - Import-Module AksHci - Exit - ``` - -- Once completed, close all PowerShell windows and verify the installation by tunning the following: - - ```powershell - Get-Command -Module AksHci - ``` - - ![Screenshot AksHci module installation](./01.png) - -## Automation Flow - -For you to get familiar with the automation and deployment flow, below is an explanation. - -- User is editing the PowerShell script environment variables (1-time edit). These variables values are being used throughout the deployment and Azure Arc onboarding. - -- User is running checks on every physical node of Azure Stack HCI to see if all the requirements are satisfied. - -- User is running the PowerShell script to deploy a basic DHCP AKS cluster on Azure Stack HCI and onboard onto Azure Arc. Runtime script will: - - - Configure the Azure Kubernetes Service cluster management services using _Set-AksHciConfig_ cmdlet. - - Register Azure Kubernetes Service on Azure Stack HCI with Azure ysing _Set-AksHciRegistration_ cmdlet. - - Start the deployment of the AKS cluster management services using the _Install-AksHci_ cmdlet. - - Retrieve the Azure Kubernetes Service cluster credentials. - - Create a target cluster with the number of Linux and Windows nodes specified. - - Onboard the AKS cluster to Azure Arc. - -## Deployment - -- Before deploying AKS on Azure Stack HCI, you need to run checks on every physical node to see if all the requirements are satisfied. Open PowerShell as an administrator and run the following command. - - ```powershell - Initialize-AksHciNode - ``` - -- Now that all nodes are ready, you will deploy the AKS control management and the target cluster to your Azure Stack HCI using this [PowerShell script](https://github.com/microsoft/azure_arc/blob/main/azure_arc_k8s_jumpstart/aks_stack_hci/powershell/aks_hci_deploy.ps1). Edit the file to provide the environment variables that match the parameters of your environment: - - - **vnetName:** the name of the vnet to host your AKS on HCI deployment. - - **vSwitchName:** the name of the external virtual switch to connect the virtual machines to. If you already have an external switch on the host, you should pass the name of the switch here. To get the name of your available switches run the command _Get-VMSwitch_. - - **vipPoolStart:** first IP address for the cluster VIP Pool. The IP addresses in the VIP pool will be used for the API Server and for Kubernetes services. - - **vipPoolEnd:** last IP address for the cluster VIP Pool. The IP addresses in the VIP pool will be used for the API Server and for Kubernetes services. - - **k8sNodeIpPoolStart:** The start IP address of a VM pool. The address must be in range of the subnet. - - **k8sNodeIpPoolEnd:** The end IP address of a VM pool. The address must be in range of the subnet. - - **ipAddressPrefix:** The address prefix to use for Static IP assignment. - - **gateway:** The IP address of the default gateway of the subnet. - - **dnsServers:** An array of IP addresses pointing to the DNS servers to be used for the subnet, you should provide at least one. - - **imageDir:** path to the directory where AKS on Azure Stack HCI will store its VHD images, provide a shared path or SMB for multinode - - **cloudConfigLocation:** path to the directory where the cloud agent will store its configuration, provide a shared path or SMB for multi-node. - - **clusterName:** a name for your AKS cluster, **must be lowercase**. - - **linuxNodeCount:** number of Linux node VMs for your cluster. - - **resourceGroup:** resource group to connect your Azure Arc-enabled Kubernetes cluster. - - **location:** Azure region to connect your Azure Arc-enabled Kubernetes cluster. - - **subscriptionId:** subscription to connect your Azure Arc-enabled Kubernetes cluster. - - **appId:** the appID of the service principal created previously. - - **password:** the password of the service principal created. - - **tenant:** your tenantID. - - ![Screenshot showing the AKS on HCI deployment script](./02.png) - -- As an example: - - **vnetName:** 'mgmtvnet' - - **vSwitchName:** 'InternalNAT' - - **vipPoolStart** '192.168.0.150' - - **vipPoolEnd** '192.168.0.250' - - **k8sNodeIpPoolStart** '192.168.0.3' - - **k8sNodeIpPoolEnd** '192.168.0.149' - - **ipAddressPrefix** '192.168.0.0/16' - - **gateway** '192.168.0.1' - - **dnsServers** '192.168.0.1' - - **imageDir** 'C:\AKS-HCI\Image' - - **cloudConfigLocation:** 'C:\AKS-HCI\Config' - - **clusterName:** 'archcidemo' - - **linuxNodeCount:** 1 - - **resourceGroup:** 'Arc-AKS-HCI-Demo' - - **location:** eastus - - **subscriptionId:** 'XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXX' - - **appId:** 'XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXX' - - **password:** 'XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXX' - - **tenant:** 'XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXX' - -- Note that the script will deploy a simple static IP address based cluster on your Azure Stack HCI and there are additional optional parameters that you could use to customize the deployment to your own environment as described [here](https://learn.microsoft.com/azure-stack/aks-hci/kubernetes-walkthrough-powershell). - -- To run the script open PowerShell as an administrator, navigate to the [script folder](https://github.com/microsoft/azure_arc/blob/main/azure_arc_k8s_jumpstart/aks_stack_hci/powershell/) and run: - - ```powershell - .\aks_hci_deploy.ps1 - ``` - - ![Screenshot showing the AKS on HCI deployment script running](./03.png) - - ![Screenshot showing the AKS on HCI deployment script running](./04.png) - - > **Note:** the script may take around 30 minutes to run. - - ![Screenshot showing the script output](./05.png) - -- You should also see the new AKS cluster on Windows Admin Center. - - ![Screenshot showing the AKS on HCI deployment Windows Admin Center](./06.png) - -- Once the script run has finished, the AKS cluster on HCI will be projected as a new Azure Arc-enabled Kubernetes cluster resource. - - ![Screenshot showing Arc-enabled Kubernetes in RG](./07.png) - - ![Screenshot showing Arc-enabled Kubernetes](./08.png) - -## Delete the deployment - -The most straightforward way is to delete the Azure Arc-enabled Kubernetes cluster resource via the Azure portal, just select the cluster and delete it. - -![Screenshot showing how to delete Azure Arc-enabled Kubernetes resource](./09.png) - -To delete the AKS cluster on HCI run the below command. - -```powershell -Remove-AksHciCluster -name archcidemo -``` - -![Screenshot showing how to delete AKS cluster on HCI](./10.png) diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/alibaba/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/alibaba/_index.md index 221e5312..40f00abf 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/alibaba/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/alibaba/_index.md @@ -2,7 +2,7 @@ type: docs title: "Alibaba Cloud Container Service for Kubernetes" linkTitle: "Alibaba Cloud Container Service for Kubernetes" -weight: 7 +weight: 6 description: >- If you are working in a multi-cloud environment, the scenario in this section will guide on creating a Alibaba Cloud Container Service for Kubernetes and onboard it as an Azure Arc-enabled Kubernetes cluster in an automated fashion using Terraform. --- diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/alibaba/alibaba_terraform/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/alibaba/alibaba_terraform/_index.md index cb7857e0..7fcb465e 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/alibaba/alibaba_terraform/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/alibaba/alibaba_terraform/_index.md @@ -18,7 +18,7 @@ The following Jumpstart scenario will guide you on how to use the provided [Terr git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/aro/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/aro/_index.md index 86eca78b..8c58468d 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/aro/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/aro/_index.md @@ -2,7 +2,7 @@ type: docs title: "Azure Red Hat OpenShift" linkTitle: "Azure Red Hat OpenShift" -weight: 4 +weight: 3 description: >- If you do not yet have an Azure Red Hat OpenShift cluster, the scenarios in this section will guide you on how to create an Azure-managed OpenShift cluster in an automated fashion and onboard it as an Azure Arc-enabled Kubernetes cluster. --- diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/aro/aro_arm_template/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/aro/aro_arm_template/_index.md index 9b137163..bbb01433 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/aro/aro_arm_template/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/aro/aro_arm_template/_index.md @@ -18,7 +18,7 @@ The following Jumpstart scenario will guide you on how to use the provided [Azur git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/_index.md index f4ef9914..156eff93 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/_index.md @@ -2,7 +2,7 @@ type: docs title: "Unified Operations Use Cases" linkTitle: "Unified Operations Use Cases" -weight: 13 +weight: 12 description: >- Once you have Kubernetes clusters projected into Azure with Azure Arc, you can start to use native Azure tooling to manage the clusters as native Azure resources. The following scenarios show examples of using Cluster extensions and Azure management tools such as Azure Monitor, GitOps configurations, and Azure Policy. --- diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/aks/aks_gitops_basic/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/aks/aks_gitops_basic/_index.md index f93c91b7..68db146e 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/aks/aks_gitops_basic/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/aks/aks_gitops_basic/_index.md @@ -34,7 +34,7 @@ GitOps on Azure Arc-enabled Kubernetes uses [Flux](https://fluxcd.io/docs/), a p ![Existing Azure Arc-enabled Kubernetes cluster](./02.png) -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/aks/aks_gitops_helm/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/aks/aks_gitops_helm/_index.md index 7ce6fb4c..31bcf1f4 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/aks/aks_gitops_helm/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/aks/aks_gitops_helm/_index.md @@ -34,7 +34,7 @@ GitOps on Azure Arc-enabled Kubernetes uses [Flux](https://fluxcd.io/docs/), a p ![Existing Azure Arc-enabled Kubernetes cluster](./02.png) -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/aks/aks_monitor/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/aks/aks_monitor/_index.md index 1d83ac99..1c454545 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/aks/aks_monitor/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/aks/aks_monitor/_index.md @@ -22,7 +22,7 @@ in this scenario, you will hook the AKS cluster to Azure Monitor by deploying th git clone https://github.com/microsoft/azure_arc.git ``` -* [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +* [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/aks/aks_policy/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/aks/aks_policy/_index.md index 950d1479..2948e92d 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/aks/aks_policy/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/aks/aks_policy/_index.md @@ -22,7 +22,7 @@ The following Jumpstart scenario will guide you on how to enable [Azure Policy f * Fork the ["Hello Arc"](https://github.com/likamrat/hello_arc) demo application repository. -* [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +* [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/archive/aks_stack_hci/aks_hci_gitops_basic/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/archive/aks_stack_hci/aks_hci_gitops_basic/_index.md index 44aa2765..bfc09336 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/archive/aks_stack_hci/aks_hci_gitops_basic/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/archive/aks_stack_hci/aks_hci_gitops_basic/_index.md @@ -1,18 +1,18 @@ --- type: docs -title: "Deploy GitOps configurations and perform basic GitOps flow on AKS on Azure Stack HCI as an Azure Arc Connected Cluster" -linkTitle: "Deploy GitOps configurations and perform basic GitOps flow on AKS on Azure Stack HCI as an Azure Arc Connected Cluster" +title: "Deploy GitOps configurations and perform basic GitOps flow on AKS on Azure Local as an Azure Arc Connected Cluster" +linkTitle: "Deploy GitOps configurations and perform basic GitOps flow on AKS on Azure Local as an Azure Arc Connected Cluster" weight: 1 description: > --- -## Deploy GitOps configurations and perform basic GitOps flow on AKS on Azure Stack HCI as an Azure Arc Connected Cluster +## Deploy GitOps configurations and perform basic GitOps flow on AKS on Azure Local as an Azure Arc Connected Cluster -The following Jumpstart scenario will guide you on how to create GitOps configuration on an Azure Kubernetes Service (AKS) cluster that is running on Azure Stack HCI and which is projected as an Azure Arc connected cluster resource. +The following Jumpstart scenario will guide you on how to create GitOps configuration on an Azure Kubernetes Service (AKS) cluster that is running on Azure Local and which is projected as an Azure Arc connected cluster resource. in this scenario, you will deploy & attach GitOps configuration to your cluster which will also include deploying an "Hello World" Azure Arc web application on your Kubernetes cluster. By doing so, you will be able to make real-time changes to the application and show how the GitOps flow takes effect. -> **Note:** This guide assumes you already deployed an AKS cluster on Azure Stack HCI and connected it to Azure Arc. If you haven't, this repository offers you a way to do so in an automated fashion using [PowerShell](/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/). +> **Note:** This guide assumes you already deployed an AKS cluster on Azure Local and connected it to Azure Arc. If you haven't, this repository offers you a way to do so in an automated fashion using [PowerShell](/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/). ## Prerequisites @@ -32,7 +32,7 @@ in this scenario, you will deploy & attach GitOps configuration to your cluster * [Mozilla Firefox](https://addons.mozilla.org/firefox/addon/tab-auto-refresh/) -* As mentioned, this scenario starts at the point where you already have a connected AKS cluster to Azure Arc that is running on Azure Stack HCI. +* As mentioned, this scenario starts at the point where you already have a connected AKS cluster to Azure Arc that is running on Azure Local. ![Existing Azure Arc-enabled Kubernetes cluster](./01.png) @@ -46,7 +46,7 @@ in this scenario, you will deploy & attach GitOps configuration to your cluster > **Note:** This guide combines automations that uses both Azure PowerShell Az modules as well as Azure CLI, however both of them can and will be run from a PowerShell window. -* [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +* [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version @@ -151,7 +151,7 @@ For you to get familiar with the automation and deployment flow, below is an exp > **Note:** For the purpose of this guide, notice how the _git-poll-interval 3s_ is set. The 3 seconds interval is useful for demo purposes since it will make the git-poll interval to rapidly track changes on the repository but it is recommended to have longer interval in your production environment (default value is 5min). -* Once the script will complete it's run, you will have the GitOps configuration created and all the resources deployed in your Kubernetes cluster running on Azure Stack HCI. +* Once the script will complete it's run, you will have the GitOps configuration created and all the resources deployed in your Kubernetes cluster running on Azure Local. > **Note:** that it takes few min for the configuration change it's Operator state status from "Pending" to Install. diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/archive/aks_stack_hci/aks_hci_gitops_helm/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/archive/aks_stack_hci/aks_hci_gitops_helm/_index.md index 51642ac7..0bcfd3d2 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/archive/aks_stack_hci/aks_hci_gitops_helm/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/archive/aks_stack_hci/aks_hci_gitops_helm/_index.md @@ -1,20 +1,20 @@ --- type: docs -title: "Deploy GitOps configurations and perform Helm-based GitOps flow on AKS on Azure Stack HCI as an Azure Arc Connected Cluster" -linkTitle: "Deploy GitOps configurations and perform Helm-based GitOps flow on AKS on Azure Stack HCI as an Azure Arc Connected Cluster" +title: "Deploy GitOps configurations and perform Helm-based GitOps flow on AKS on Azure Local as an Azure Arc Connected Cluster" +linkTitle: "Deploy GitOps configurations and perform Helm-based GitOps flow on AKS on Azure Local as an Azure Arc Connected Cluster" weight: 2 description: > --- -## Deploy GitOps configurations and perform Helm-based GitOps flow on AKS on Azure Stack HCI as an Azure Arc Connected Cluster +## Deploy GitOps configurations and perform Helm-based GitOps flow on AKS on Azure Local as an Azure Arc Connected Cluster -The following Jumpstart scenario will guide you on how to create [Helm](https://helm.sh/)-based GitOps configuration on an Azure Kubernetes Service (AKS) cluster running on Azure Stack HCI, which is projected as an Azure Arc connected cluster resource. +The following Jumpstart scenario will guide you on how to create [Helm](https://helm.sh/)-based GitOps configuration on an Azure Kubernetes Service (AKS) cluster running on Azure Local, which is projected as an Azure Arc connected cluster resource. in this scenario, you will deploy & attach 2 GitOps configuration to your cluster, a cluster-level config to deploy nginx-ingress controller and a namespace-level config to deploy the "Hello Arc" web application on your Kubernetes cluster. By doing so, you will be able to make real-time changes to the application and show how the GitOps flow takes effect. -> **Note:** This guide assumes you already deployed an AKS cluster on Azure Stack HCI and connected it to Azure Arc. If you haven't, this repository offers you a way to do so in an automated fashion using [PowerShell](/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/). +> **Note:** This guide assumes you already deployed an AKS cluster on Azure Local and connected it to Azure Arc. If you haven't, this repository offers you a way to do so in an automated fashion using [PowerShell](/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/aks_hci_powershell/). ## Prerequisites @@ -48,7 +48,7 @@ By doing so, you will be able to make real-time changes to the application and s > **Note:** This guide combines automations that uses both Azure PowerShell Az modules as well as Azure CLI, however both of them can and will be run from a PowerShell window. - * [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. + * [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version @@ -121,7 +121,7 @@ By doing so, you will be able to make real-time changes to the application and s Exit ``` -* Using a web browser navigate to https://aka.ms/AKS-HCI-Evaluate, complete the registration form, download and save AKS on Azure Stack HCI. Extract the contents of the zip file. +* Using a web browser navigate to https://aka.ms/AKS-HCI-Evaluate, complete the registration form, download and save AKS on Azure Local. Extract the contents of the zip file. * Open PowerShell as administrator, navigate to the folder where you extracted the software and run the following commands: diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/archive/aks_stack_hci/aks_hci_monitor/aks_hci_monitor_extension/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/archive/aks_stack_hci/aks_hci_monitor/aks_hci_monitor_extension/_index.md index 07562d92..aad3f0ba 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/archive/aks_stack_hci/aks_hci_monitor/aks_hci_monitor_extension/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/archive/aks_stack_hci/aks_hci_monitor/aks_hci_monitor_extension/_index.md @@ -1,14 +1,14 @@ --- type: docs -title: "Integrate Azure Monitor for Containers with AKS on Azure Stack HCI as an Azure Arc Connected Cluster" -linkTitle: "Integrate Azure Monitor for Containers with AKS on Azure Stack HCI as an Azure Arc Connected Cluster" +title: "Integrate Azure Monitor for Containers with AKS on Azure Local as an Azure Arc Connected Cluster" +linkTitle: "Integrate Azure Monitor for Containers with AKS on Azure Local as an Azure Arc Connected Cluster" weight: 3 description: > --- -## Integrate Azure Monitor for Containers with AKS on Azure Stack HCI as an Azure Arc Connected Cluster +## Integrate Azure Monitor for Containers with AKS on Azure Local as an Azure Arc Connected Cluster -The following Jumpstart scenario will guide you on how to enable [Azure Monitor for Containers](https://learn.microsoft.com/azure/azure-monitor/insights/container-insights-overview) for an AKS cluster running on Azure Stack HCI that is projected as an Azure Arc connected cluster. +The following Jumpstart scenario will guide you on how to enable [Azure Monitor for Containers](https://learn.microsoft.com/azure/azure-monitor/insights/container-insights-overview) for an AKS cluster running on Azure Local that is projected as an Azure Arc connected cluster. in this scenario, you will hook the AKS cluster on HCI to Azure Monitor by deploying the [Azure Monitor agent](https://learn.microsoft.com/azure/azure-monitor/platform/log-analytics-agent) in order to start collecting telemetry. @@ -26,7 +26,7 @@ The commands below and the script described in this scenario should be run on th git clone https://github.com/microsoft/azure_arc.git ``` -* [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +* [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/gke/gke_gitops_basic/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/gke/gke_gitops_basic/_index.md index d1078c65..b85ac873 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/gke/gke_gitops_basic/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/gke/gke_gitops_basic/_index.md @@ -34,7 +34,7 @@ GitOps on Azure Arc-enabled Kubernetes uses [Flux](https://fluxcd.io/docs/), a p ![Existing Azure Arc-enabled Kubernetes cluster](./02.png) -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/gke/gke_gitops_helm/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/gke/gke_gitops_helm/_index.md index 6299f4af..0078b9c2 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/gke/gke_gitops_helm/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/gke/gke_gitops_helm/_index.md @@ -34,7 +34,7 @@ GitOps on Azure Arc-enabled Kubernetes uses [Flux](https://fluxcd.io/docs/), a p ![Existing Azure Arc-enabled Kubernetes cluster](./02.png) -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/gke/gke_monitor/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/gke/gke_monitor/_index.md index e2e93437..a31b3a92 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/gke/gke_monitor/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/gke/gke_monitor/_index.md @@ -22,7 +22,7 @@ in this scenario, you will hook the GKE cluster to Azure Monitor by deploying th git clone https://github.com/microsoft/azure_arc.git ``` -* [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +* [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/gke/gke_monitor_extension/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/gke/gke_monitor_extension/_index.md index 528d89b5..1c30fb49 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/gke/gke_monitor_extension/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/gke/gke_monitor_extension/_index.md @@ -24,7 +24,7 @@ Kubernetes extensions are add-ons for Kubernetes clusters. The extensions featur git clone https://github.com/microsoft/azure_arc.git ``` -* [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +* [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/gke/gke_policy/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/gke/gke_policy/_index.md index 078e4949..2ceae87d 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/gke/gke_policy/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/gke/gke_policy/_index.md @@ -20,7 +20,7 @@ The following Jumpstart scenario will guide you on how to enable [Azure Policy f git clone https://github.com/microsoft/azure_arc.git ``` -* [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +* [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/kind/local_kind_gitops_helm/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/kind/local_kind_gitops_helm/_index.md index 977aa743..858a9546 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/kind/local_kind_gitops_helm/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/kind/local_kind_gitops_helm/_index.md @@ -40,7 +40,7 @@ By doing so, you will be able to make real-time changes to the application and s ![Existing Azure Arc-enabled Kubernetes cluster](./02.png) -* [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +* [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/microk8s/local_microk8s_gitops_helm/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/microk8s/local_microk8s_gitops_helm/_index.md index 00947f98..d7f4c477 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/microk8s/local_microk8s_gitops_helm/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/microk8s/local_microk8s_gitops_helm/_index.md @@ -34,7 +34,7 @@ GitOps on Azure Arc-enabled Kubernetes uses [Flux](https://fluxcd.io/docs/), a p ![Existing Azure Arc-enabled Kubernetes cluster](./02.png) -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/microk8s/local_microk8s_monitor/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/microk8s/local_microk8s_monitor/_index.md index b816eed6..e18b1991 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/microk8s/local_microk8s_monitor/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/microk8s/local_microk8s_monitor/_index.md @@ -22,7 +22,7 @@ in this scenario, you will hook the microK8s cluster to Azure Monitor by deployi git clone https://github.com/microsoft/azure_arc.git ``` -* [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +* [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/multi_distributions/azure_policy/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/multi_distributions/azure_policy/_index.md index cb4c284f..a58e3b65 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/multi_distributions/azure_policy/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/multi_distributions/azure_policy/_index.md @@ -15,7 +15,7 @@ In this scenario, you will hook the Azure Arc-enabled Kubernetes cluster to Azur > **Note:** This scenario assumes you already deployed a Kubernetes cluster and connected it to Azure Arc. If you haven't, this repository offers you a way to do so in an automated fashion. - **[Azure Kubernetes Service](/azure_arc_jumpstart/azure_arc_k8s/aks/)** -- **[AKS on Azure Stack HCI](/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/)** +- **[AKS on Azure Local](/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/)** - **[Azure Red Hat OpenShift](/azure_arc_jumpstart/azure_arc_k8s/aro/)** - **[Amazon Elastic Kubernetes Service](/azure_arc_jumpstart/azure_arc_k8s/eks/)** - **[Google Kubernetes Engine](/azure_arc_jumpstart/azure_arc_k8s/gke/)** @@ -35,7 +35,7 @@ Kubernetes extensions are add-ons for Kubernetes clusters. The extensions featur git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/multi_distributions/calico/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/multi_distributions/calico/_index.md index 1794cf90..20028be3 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/multi_distributions/calico/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/multi_distributions/calico/_index.md @@ -36,7 +36,7 @@ The Azure Policy will be set to “Audit” mode to check the configuration of e git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the following command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the following command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/multi_distributions/connected_cluster/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/multi_distributions/connected_cluster/_index.md index 9c3ba773..f965312a 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/multi_distributions/connected_cluster/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/multi_distributions/connected_cluster/_index.md @@ -13,7 +13,7 @@ The following Jumpstart scenario will guide you through how to use the [Cluster > **Note:** This scenario assumes you already deployed a Kubernetes cluster and connected it to Azure Arc. If you haven't, this repository offers you a way to do so in an automated fashion. - **[Azure Kubernetes Service](/azure_arc_jumpstart/azure_arc_k8s/aks/)** -- **[AKS on Azure Stack HCI](/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/)** +- **[AKS on Azure Local](/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/)** - **[Azure Red Hat OpenShift](/azure_arc_jumpstart/azure_arc_k8s/aro/)** - **[Amazon Elastic Kubernetes Service](/azure_arc_jumpstart/azure_arc_k8s/eks/)** - **[Google Kubernetes Engine](/azure_arc_jumpstart/azure_arc_k8s/gke/)** @@ -39,7 +39,7 @@ The following Jumpstart scenario will guide you through how to use the [Cluster Connect-AzAccount ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/multi_distributions/container_insights/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/multi_distributions/container_insights/_index.md index 9e8000a0..73da204a 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/multi_distributions/container_insights/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/multi_distributions/container_insights/_index.md @@ -15,7 +15,7 @@ In this scenario, you will hook the Azure Arc-enabled Kubernetes cluster to Azur > **Note:** This scenario assumes you already deployed a Kubernetes cluster and connected it to Azure Arc. If you haven't, this repository offers you a way to do so in an automated fashion. - **[Azure Kubernetes Service](/azure_arc_jumpstart/azure_arc_k8s/aks/)** -- **[AKS on Azure Stack HCI](/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/)** +- **[AKS on Azure Local](/azure_arc_jumpstart/azure_arc_k8s/aks_stack_hci/)** - **[Azure Red Hat OpenShift](/azure_arc_jumpstart/azure_arc_k8s/aro/)** - **[Amazon Elastic Kubernetes Service](/azure_arc_jumpstart/azure_arc_k8s/eks/)** - **[Google Kubernetes Engine](/azure_arc_jumpstart/azure_arc_k8s/gke/)** @@ -35,7 +35,7 @@ Kubernetes extensions are add-ons for Kubernetes clusters. The extensions featur git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/multi_distributions/gitops/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/multi_distributions/gitops/_index.md index b4fc51c6..3a339a47 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/day2/multi_distributions/gitops/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/day2/multi_distributions/gitops/_index.md @@ -36,7 +36,7 @@ In this scenario, we will be using GitOps in an Azure Arc connected Kubernetes c - Fork the [Tigera Azure-arc-demo repository](https://github.com/tigera-solutions/Azure-arc-demo) -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the following command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the following command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/developer/dapr/dapr_statestore/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/developer/dapr/dapr_statestore/_index.md index c8db894d..353feab6 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/developer/dapr/dapr_statestore/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/developer/dapr/dapr_statestore/_index.md @@ -28,7 +28,7 @@ Dapr is a portable, event-driven runtime that makes it easy for any developer to ![Existing Azure Arc-enabled Kubernetes cluster](./02.png) -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/eks/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/eks/_index.md index 9df5a0e1..304ba1fb 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/eks/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/eks/_index.md @@ -2,7 +2,7 @@ type: docs title: "Amazon Elastic Kubernetes Service" linkTitle: "Amazon Elastic Kubernetes Service" -weight: 5 +weight: 4 description: >- If you are working in a multi-cloud environment, the scenario in this section will guide on creating an Amazon Elastic Kubernetes Service (EKS) and onboard it as an Azure Arc-enabled Kubernetes cluster in an automated fashion using Terraform. --- diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/eks/eks_terraform/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/eks/eks_terraform/_index.md index f8a00787..84f05e9c 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/eks/eks_terraform/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/eks/eks_terraform/_index.md @@ -22,7 +22,7 @@ The following Jumpstart scenario will guide you on how to use the provided [Terr * [Install AWS IAM Authenticator](https://docs.aws.amazon.com/eks/latest/userguide/install-aws-iam-authenticator.html) -* [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +* [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/general/onboard_k8s/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/general/onboard_k8s/_index.md index 76f2eccd..713a56be 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/general/onboard_k8s/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/general/onboard_k8s/_index.md @@ -16,7 +16,7 @@ The following Jumpstart scenario will guide you on how to connect an existing Ku * (Optional) To simplify work against multiple k8s contexts, consider using [kubectx](https://github.com/ahmetb/kubectx). -* [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +* [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/gke/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/gke/_index.md index d339b45d..5dcefb10 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/gke/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/gke/_index.md @@ -2,7 +2,7 @@ type: docs title: "Google Kubernetes Engine" linkTitle: "Google Kubernetes Engine" -weight: 6 +weight: 5 description: >- If you are working in a multi-cloud environment, the scenario in this section will guide on creating a Google Kubernetes Engine (GKE) and onboard it as an Azure Arc-enabled Kubernetes cluster in an automated fashion using Terraform. --- diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/gke/gke_terraform/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/gke/gke_terraform/_index.md index 2e6ca527..202d7bda 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/gke/gke_terraform/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/gke/gke_terraform/_index.md @@ -18,7 +18,7 @@ The following Jumpstart scenario will guide you on how to use the provided [Terr git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/kind/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/kind/_index.md index 1dd78d73..c9618b55 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/kind/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/kind/_index.md @@ -2,7 +2,7 @@ type: docs title: "Kind" linkTitle: "Kind" -weight: 9 +weight: 8 description: >- If you do not have a Kubernetes cluster, the scenario in this section will guide on creating a kind (kubernetes in docker) cluster on your local machine and onboard it as an Azure Arc-enabled Kubernetes cluster in an automated fashion. --- \ No newline at end of file diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/kind/local_kind/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/kind/local_kind/_index.md index 615f5b3b..16afba00 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/kind/local_kind/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/kind/local_kind/_index.md @@ -18,7 +18,7 @@ The following Jumpstart scenario will guide you on how to use [kind](https://kin git clone https://github.com/microsoft/azure_arc.git ``` -* [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +* [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/microk8s/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/microk8s/_index.md index d1812df6..e4981eef 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/microk8s/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/microk8s/_index.md @@ -2,7 +2,7 @@ type: docs title: "MicroK8s" linkTitle: "MicroK8s" -weight: 11 +weight: 10 description: >- If you do not have a Kubernetes cluster, the scenario in this section will guide on creating a MicroK8s Kubernetes cluster on your local machine and onboard it as an Azure Arc-enabled Kubernetes cluster in an automated fashion. --- \ No newline at end of file diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/microk8s/local_microk8s/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/microk8s/local_microk8s/_index.md index c71bdfcb..11eea1b8 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/microk8s/local_microk8s/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/microk8s/local_microk8s/_index.md @@ -18,7 +18,7 @@ The following Jumpstart scenario will guide you on how to use [MicroK8s](https:/ git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/pf9/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/pf9/_index.md index 54604e10..bf3d2efc 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/pf9/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/pf9/_index.md @@ -2,7 +2,7 @@ type: docs title: "Platform9 Managed Kubernetes" linkTitle: "Platform9 Managed Kubernetes" -weight: 10 +weight: 9 description: >- The scenario in this section will guide on creating a Platform9 Managed Kubernetes (PMK) cluster on-premise or on the cloud and onboard it as an Azure Arc-enabled Kubernetes cluster in an automated fashion. --- diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/rancher_k3s/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/rancher_k3s/_index.md index 3540f355..e5e25b10 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/rancher_k3s/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/rancher_k3s/_index.md @@ -2,7 +2,7 @@ type: docs title: "Rancher K3s" linkTitle: "Rancher K3s" -weight: 8 +weight: 7 description: >- If you do not have a Kubernetes cluster, the scenarios in this section will guide on creating a Rancher K3s Kubernetes cluster on either your VMware vSphere infrastructure or on an Azure VM and onboard it as an Azure Arc-enabled Kubernetes cluster in an automated fashion using either ARM template or Terraform. --- \ No newline at end of file diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/rancher_k3s/azure_arm_template/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/rancher_k3s/azure_arm_template/_index.md index d72fec38..339fe978 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/rancher_k3s/azure_arm_template/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/rancher_k3s/azure_arm_template/_index.md @@ -18,7 +18,7 @@ The following Jumpstart scenario will guide you on how to use the provided [Azur git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/rancher_k3s/azure_terraform/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/rancher_k3s/azure_terraform/_index.md index 7360573c..7dcb45e9 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/rancher_k3s/azure_terraform/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/rancher_k3s/azure_terraform/_index.md @@ -18,7 +18,7 @@ The following Jumpstart scenario will guide you on how to use the provided [Terr git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_k8s/rancher_k3s/vmware_terraform/_index.md b/docs/azure_arc_jumpstart/azure_arc_k8s/rancher_k3s/vmware_terraform/_index.md index 2a78631a..d8077dcc 100644 --- a/docs/azure_arc_jumpstart/azure_arc_k8s/rancher_k3s/vmware_terraform/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_k8s/rancher_k3s/vmware_terraform/_index.md @@ -18,7 +18,7 @@ The following Jumpstart scenario will guide you on how to use the provided [Terr git clone https://github.com/microsoft/azure_arc.git ``` -* [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +* [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_ml/aks/aks_blob_mnist_arm_template/_index.md b/docs/azure_arc_jumpstart/azure_arc_ml/aks/aks_blob_mnist_arm_template/_index.md index 5ad097cc..7192311a 100644 --- a/docs/azure_arc_jumpstart/azure_arc_ml/aks/aks_blob_mnist_arm_template/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_ml/aks/aks_blob_mnist_arm_template/_index.md @@ -43,7 +43,7 @@ To demonstrate the various architecture components, the ML pipeline we deploy is git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_servers/aws/aws_terraform_al2/_index.md b/docs/azure_arc_jumpstart/azure_arc_servers/aws/aws_terraform_al2/_index.md index a174b83d..f55f5979 100644 --- a/docs/azure_arc_jumpstart/azure_arc_servers/aws/aws_terraform_al2/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_servers/aws/aws_terraform_al2/_index.md @@ -18,7 +18,7 @@ The following Jumpstart scenario will guide you on how to use the provided [Terr git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_servers/aws/aws_terraform_ubuntu/_index.md b/docs/azure_arc_jumpstart/azure_arc_servers/aws/aws_terraform_ubuntu/_index.md index 7f07241b..4e8d39a1 100644 --- a/docs/azure_arc_jumpstart/azure_arc_servers/aws/aws_terraform_ubuntu/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_servers/aws/aws_terraform_ubuntu/_index.md @@ -18,7 +18,7 @@ The following Jumpstart scenario will guide you on how to use the provided [Terr git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_servers/azure/azure_arm_template_linux/_index.md b/docs/azure_arc_jumpstart/azure_arc_servers/azure/azure_arm_template_linux/_index.md index d968f4db..ec11e32d 100644 --- a/docs/azure_arc_jumpstart/azure_arc_servers/azure/azure_arm_template_linux/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_servers/azure/azure_arm_template_linux/_index.md @@ -18,7 +18,7 @@ However, **for demo purposes only**, the below guide will allow you to use and o ## Prerequisites -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_servers/azure/azure_arm_template_win/_index.md b/docs/azure_arc_jumpstart/azure_arc_servers/azure/azure_arm_template_win/_index.md index a28e77f7..869f7257 100644 --- a/docs/azure_arc_jumpstart/azure_arc_servers/azure/azure_arm_template_win/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_servers/azure/azure_arm_template_win/_index.md @@ -18,7 +18,7 @@ However, **for demo purposes only**, the below guide will allow you to use and o ## Prerequisites -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_servers/day2/arc_automanage/arc_automanage_builtin_profiles/_index.md b/docs/azure_arc_jumpstart/azure_arc_servers/day2/arc_automanage/arc_automanage_builtin_profiles/_index.md index 9390c255..ef754372 100644 --- a/docs/azure_arc_jumpstart/azure_arc_servers/day2/arc_automanage/arc_automanage_builtin_profiles/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_servers/day2/arc_automanage/arc_automanage_builtin_profiles/_index.md @@ -24,7 +24,7 @@ By the end of this scenario, you will have an Azure Arc-enabled server with Azur > **Note:** This guide assumes you already deployed VMs or servers that are running on-premises or other clouds and you have connected them to Azure Arc. If you haven't, this repository offers you a way to do so in an automated fashion. -- **[Azure Stack HCI Windows VM](/azure_arc_jumpstart/azure_arc_servers/azure_stack_hci/azure_stack_hci_windows/)** +- **[Azure Local Windows VM](/azure_arc_jumpstart/azure_arc_servers/azure_stack_hci/azure_stack_hci_windows/)** - **[GCP Ubuntu instance](/azure_arc_jumpstart/azure_arc_servers/gcp/gcp_terraform_ubuntu/)** - **[GCP Windows instance](/azure_arc_jumpstart/azure_arc_servers/gcp/gcp_terraform_windows/)** - **[AWS Ubuntu EC2 instance](/azure_arc_jumpstart/azure_arc_servers/aws/aws_terraform_ubuntu/)** @@ -143,7 +143,7 @@ Complete the following steps to clean up your environment. To disable Azure Auto - Remove the virtual machines from each environment by following the teardown instructions from each guide. -- **[Azure Stack HCI Windows VM](/azure_arc_jumpstart/azure_arc_servers/azure_stack_hci/azure_stack_hci_windows/)** +- **[Azure Local Windows VM](/azure_arc_jumpstart/azure_arc_servers/azure_stack_hci/azure_stack_hci_windows/)** - **[GCP Ubuntu instance](/azure_arc_jumpstart/azure_arc_servers/gcp/gcp_terraform_ubuntu/)** - **[GCP Windows instance](/azure_arc_jumpstart/azure_arc_servers/gcp/gcp_terraform_windows/)** - **[AWS Ubuntu EC2 instance](/azure_arc_jumpstart/azure_arc_servers/aws/aws_terraform_ubuntu/)** diff --git a/docs/azure_arc_jumpstart/azure_arc_servers/day2/arc_automanage/arc_automanage_custom_profiles/_index.md b/docs/azure_arc_jumpstart/azure_arc_servers/day2/arc_automanage/arc_automanage_custom_profiles/_index.md index 749251c0..a12d8891 100644 --- a/docs/azure_arc_jumpstart/azure_arc_servers/day2/arc_automanage/arc_automanage_custom_profiles/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_servers/day2/arc_automanage/arc_automanage_custom_profiles/_index.md @@ -24,7 +24,7 @@ By the end of this scenario, you will have an Azure Arc-enabled server with Azur > **Note:** This guide assumes you already deployed VMs or servers that are running on-premises or other clouds and you have connected them to Azure Arc but If you haven't, this repository offers you a way to do so in an automated fashion. -- **[Azure Stack HCI Windows VM](/azure_arc_jumpstart/azure_arc_servers/azure_stack_hci/azure_stack_hci_windows/)** +- **[Azure Local HCI Windows VM](/azure_arc_jumpstart/azure_arc_servers/azure_stack_hci/azure_stack_hci_windows/)** - **[GCP Ubuntu instance](/azure_arc_jumpstart/azure_arc_servers/gcp/gcp_terraform_ubuntu/)** - **[GCP Windows instance](/azure_arc_jumpstart/azure_arc_servers/gcp/gcp_terraform_windows/)** - **[AWS Ubuntu EC2 instance](/azure_arc_jumpstart/azure_arc_servers/aws/aws_terraform_ubuntu/)** @@ -144,7 +144,7 @@ Complete the following steps to clean up your environment. To disable Azure Auto - Remove the virtual machines from each environment by following the teardown instructions from each guide. -- **[Azure Stack HCI Windows VM](/azure_arc_jumpstart/azure_arc_servers/azure_stack_hci/azure_stack_hci_windows/)** +- **[Azure Local Windows VM](/azure_arc_jumpstart/azure_arc_servers/azure_stack_hci/azure_stack_hci_windows/)** - **[GCP Ubuntu instance](/azure_arc_jumpstart/azure_arc_servers/gcp/gcp_terraform_ubuntu/)** - **[GCP Windows instance](/azure_arc_jumpstart/azure_arc_servers/gcp/gcp_terraform_windows/)** - **[AWS Ubuntu EC2 instance](/azure_arc_jumpstart/azure_arc_servers/aws/aws_terraform_ubuntu/)** diff --git a/docs/azure_arc_jumpstart/azure_arc_servers/day2/arc_keyvault/_index.md b/docs/azure_arc_jumpstart/azure_arc_servers/day2/arc_keyvault/_index.md index ba82f17c..2ed7a342 100644 --- a/docs/azure_arc_jumpstart/azure_arc_servers/day2/arc_keyvault/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_servers/day2/arc_keyvault/_index.md @@ -34,7 +34,7 @@ in this scenario, we will focus on securing an Ubuntu web server. The only prere ![Screenshot showing Azure Arc-enabled server](./02.png) -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_servers/day2/arc_ssh/_index.md b/docs/azure_arc_jumpstart/azure_arc_servers/day2/arc_ssh/_index.md index 7031ddac..570d26b0 100644 --- a/docs/azure_arc_jumpstart/azure_arc_servers/day2/arc_ssh/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_servers/day2/arc_ssh/_index.md @@ -31,7 +31,7 @@ The following Jumpstart scenario will guide you on how to enable [SSH access to git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. - Create Azure service principal (SP) diff --git a/docs/azure_arc_jumpstart/azure_arc_servers/gcp/gcp_terraform_ubuntu/_index.md b/docs/azure_arc_jumpstart/azure_arc_servers/gcp/gcp_terraform_ubuntu/_index.md index e4143e39..6cdef3a6 100644 --- a/docs/azure_arc_jumpstart/azure_arc_servers/gcp/gcp_terraform_ubuntu/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_servers/gcp/gcp_terraform_ubuntu/_index.md @@ -18,7 +18,7 @@ The following Jumpstart scenario will guide you on how to use the provided [Terr git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_servers/gcp/gcp_terraform_windows/_index.md b/docs/azure_arc_jumpstart/azure_arc_servers/gcp/gcp_terraform_windows/_index.md index f34198af..aaac4844 100644 --- a/docs/azure_arc_jumpstart/azure_arc_servers/gcp/gcp_terraform_windows/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_servers/gcp/gcp_terraform_windows/_index.md @@ -18,7 +18,7 @@ The following Jumpstart scenario will guide you on how to use the provided [Terr git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_servers/general/onboard_server_linux/_index.md b/docs/azure_arc_jumpstart/azure_arc_servers/general/onboard_server_linux/_index.md index a07188e3..9dfd5bcb 100644 --- a/docs/azure_arc_jumpstart/azure_arc_servers/general/onboard_server_linux/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_servers/general/onboard_server_linux/_index.md @@ -12,7 +12,7 @@ The following Jumpstart scenario will guide you on how to connect an Linux serve ## Prerequisites -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_servers/general/onboard_server_win/_index.md b/docs/azure_arc_jumpstart/azure_arc_servers/general/onboard_server_win/_index.md index e2690dd7..b66b2521 100644 --- a/docs/azure_arc_jumpstart/azure_arc_servers/general/onboard_server_win/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_servers/general/onboard_server_win/_index.md @@ -12,7 +12,7 @@ The following Jumpstart scenario will guide you on how to connect an Windows mac ## Prerequisites -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_servers/scaled_deployment/aws_scaled_ansible/_index.md b/docs/azure_arc_jumpstart/azure_arc_servers/scaled_deployment/aws_scaled_ansible/_index.md index 45db7341..ffe1d964 100644 --- a/docs/azure_arc_jumpstart/azure_arc_servers/scaled_deployment/aws_scaled_ansible/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_servers/scaled_deployment/aws_scaled_ansible/_index.md @@ -24,7 +24,7 @@ This guide can be used even if you do not already have an existing Ansible test git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_servers/scaled_deployment/configuration_manager_scaled_runscript/_index.md b/docs/azure_arc_jumpstart/azure_arc_servers/scaled_deployment/configuration_manager_scaled_runscript/_index.md index 8ba859a7..16889a52 100644 --- a/docs/azure_arc_jumpstart/azure_arc_servers/scaled_deployment/configuration_manager_scaled_runscript/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_servers/scaled_deployment/configuration_manager_scaled_runscript/_index.md @@ -15,7 +15,7 @@ This guide assumes that you already have an installation of [Microsoft Configura - Make sure you have met all the Configuration Manager [prerequisites](https://learn.microsoft.com/mem/configmgr/apps/deploy-use/create-deploy-scripts#prerequisites) in order to use the "Run Script" feature. -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_servers/scaled_deployment/configuration_manager_scaled_tasksequence/_index.md b/docs/azure_arc_jumpstart/azure_arc_servers/scaled_deployment/configuration_manager_scaled_tasksequence/_index.md index 13b61538..8e1cde70 100644 --- a/docs/azure_arc_jumpstart/azure_arc_servers/scaled_deployment/configuration_manager_scaled_tasksequence/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_servers/scaled_deployment/configuration_manager_scaled_tasksequence/_index.md @@ -13,7 +13,7 @@ This guide assumes that you already have an installation of [Microsoft Configura ## Prerequisites -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_servers/scaled_deployment/vmware_scaled_powercli_linux/_index.md b/docs/azure_arc_jumpstart/azure_arc_servers/scaled_deployment/vmware_scaled_powercli_linux/_index.md index 3751b70f..728024f5 100644 --- a/docs/azure_arc_jumpstart/azure_arc_servers/scaled_deployment/vmware_scaled_powercli_linux/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_servers/scaled_deployment/vmware_scaled_powercli_linux/_index.md @@ -20,7 +20,7 @@ This guide assumes you already have an exiting inventory of VMware Virtual Machi git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_servers/scaled_deployment/vmware_scaled_powercli_win/_index.md b/docs/azure_arc_jumpstart/azure_arc_servers/scaled_deployment/vmware_scaled_powercli_win/_index.md index ff5907af..acf1978a 100644 --- a/docs/azure_arc_jumpstart/azure_arc_servers/scaled_deployment/vmware_scaled_powercli_win/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_servers/scaled_deployment/vmware_scaled_powercli_win/_index.md @@ -20,7 +20,7 @@ This guide assumes you already have an exiting inventory of VMware Virtual Machi git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_servers/vagrant/local_vagrant_ubuntu/_index.md b/docs/azure_arc_jumpstart/azure_arc_servers/vagrant/local_vagrant_ubuntu/_index.md index aaa78e95..8fef551f 100644 --- a/docs/azure_arc_jumpstart/azure_arc_servers/vagrant/local_vagrant_ubuntu/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_servers/vagrant/local_vagrant_ubuntu/_index.md @@ -18,7 +18,7 @@ The following Jumpstart scenario will guide you on how to deploy a local **Ubunt git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_servers/vagrant/local_vagrant_windows/_index.md b/docs/azure_arc_jumpstart/azure_arc_servers/vagrant/local_vagrant_windows/_index.md index 0edbd073..25cf7a6a 100644 --- a/docs/azure_arc_jumpstart/azure_arc_servers/vagrant/local_vagrant_windows/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_servers/vagrant/local_vagrant_windows/_index.md @@ -18,7 +18,7 @@ The following Jumpstart scenario will guide you on how to deploy a local **Windo git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_servers/vmware/vmware_terraform_ubuntu/_index.md b/docs/azure_arc_jumpstart/azure_arc_servers/vmware/vmware_terraform_ubuntu/_index.md index c2343286..a33c1546 100644 --- a/docs/azure_arc_jumpstart/azure_arc_servers/vmware/vmware_terraform_ubuntu/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_servers/vmware/vmware_terraform_ubuntu/_index.md @@ -18,7 +18,7 @@ The following Jumpstart scenario will guide you on how to use the provided [Terr git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_servers/vmware/vmware_terraform_winsrv/_index.md b/docs/azure_arc_jumpstart/azure_arc_servers/vmware/vmware_terraform_winsrv/_index.md index b69ed9f3..306201b4 100644 --- a/docs/azure_arc_jumpstart/azure_arc_servers/vmware/vmware_terraform_winsrv/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_servers/vmware/vmware_terraform_winsrv/_index.md @@ -18,7 +18,7 @@ The following Jumpstart scenario will guide you on how to use the provided [Terr git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_sqlsrv/aws/aws_terraform_winsrv/_index.md b/docs/azure_arc_jumpstart/azure_arc_sqlsrv/aws/aws_terraform_winsrv/_index.md index b2b7e20d..c8040509 100644 --- a/docs/azure_arc_jumpstart/azure_arc_sqlsrv/aws/aws_terraform_winsrv/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_sqlsrv/aws/aws_terraform_winsrv/_index.md @@ -19,7 +19,7 @@ By the end of the guide, you will have an AWS EC2 instance installed with Window git clone https://github.com/microsoft/azure_arc.git ``` -* [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +* [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_sqlsrv/azure/azure_arm_template_sqlsrv_winsrv_defender/_index.md b/docs/azure_arc_jumpstart/azure_arc_sqlsrv/azure/azure_arm_template_sqlsrv_winsrv_defender/_index.md index 8a5e447e..c71f888a 100644 --- a/docs/azure_arc_jumpstart/azure_arc_sqlsrv/azure/azure_arm_template_sqlsrv_winsrv_defender/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_sqlsrv/azure/azure_arm_template_sqlsrv_winsrv_defender/_index.md @@ -19,7 +19,7 @@ By the end of the guide, you will have an Azure VM **JS-Client** installed with git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_sqlsrv/gcp/gcp_terraform_winsrv/_index.md b/docs/azure_arc_jumpstart/azure_arc_sqlsrv/gcp/gcp_terraform_winsrv/_index.md index 81ea06a2..29a91b82 100644 --- a/docs/azure_arc_jumpstart/azure_arc_sqlsrv/gcp/gcp_terraform_winsrv/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_sqlsrv/gcp/gcp_terraform_winsrv/_index.md @@ -20,7 +20,7 @@ By the end of the guide, you will have a GCP VM instance installed with Windows git clone https://github.com/microsoft/azure_arc.git ``` -* [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +* [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_sqlsrv/vmware/vmware_terraform_winsrv/_index.md b/docs/azure_arc_jumpstart/azure_arc_sqlsrv/vmware/vmware_terraform_winsrv/_index.md index 56a36a57..790eb3e5 100644 --- a/docs/azure_arc_jumpstart/azure_arc_sqlsrv/vmware/vmware_terraform_winsrv/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_sqlsrv/vmware/vmware_terraform_winsrv/_index.md @@ -20,7 +20,7 @@ By the end of the guide, you will have a VMware vSphere VM installed with Window git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_arc_vsphere/day2/vmware_arm_template_win/_index.md b/docs/azure_arc_jumpstart/azure_arc_vsphere/day2/vmware_arm_template_win/_index.md index ca21d7e8..cfd10e12 100644 --- a/docs/azure_arc_jumpstart/azure_arc_vsphere/day2/vmware_arm_template_win/_index.md +++ b/docs/azure_arc_jumpstart/azure_arc_vsphere/day2/vmware_arm_template_win/_index.md @@ -20,7 +20,7 @@ The following Jumpstart scenario will guide you on how to use the provided ARM t git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_edge_iot_ops/aio_manufacturing/_index.md b/docs/azure_arc_jumpstart/azure_edge_iot_ops/aio_manufacturing/_index.md index 10ea455c..c83a15bd 100644 --- a/docs/azure_arc_jumpstart/azure_edge_iot_ops/aio_manufacturing/_index.md +++ b/docs/azure_arc_jumpstart/azure_edge_iot_ops/aio_manufacturing/_index.md @@ -30,7 +30,7 @@ The following Jumpstart scenario will show how to create an AKS Edge Essentials ## Prerequisites -- [Install or update Azure CLI to version 2.49.0 or above](https://docs.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the following command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0.0 or above](https://docs.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the following command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_edge_iot_ops/aks_edge_essentials_full/_index.md b/docs/azure_arc_jumpstart/azure_edge_iot_ops/aks_edge_essentials_full/_index.md index c9d7f34d..f4bf8b2b 100644 --- a/docs/azure_arc_jumpstart/azure_edge_iot_ops/aks_edge_essentials_full/_index.md +++ b/docs/azure_arc_jumpstart/azure_edge_iot_ops/aks_edge_essentials_full/_index.md @@ -16,7 +16,7 @@ The following Jumpstart scenario will show how to Create an AKS Edge Essentials ## Prerequisites -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_edge_iot_ops/aks_edge_essentials_full_akri/_index.md b/docs/azure_arc_jumpstart/azure_edge_iot_ops/aks_edge_essentials_full_akri/_index.md index dec79cfa..bf688a41 100644 --- a/docs/azure_arc_jumpstart/azure_edge_iot_ops/aks_edge_essentials_full_akri/_index.md +++ b/docs/azure_arc_jumpstart/azure_edge_iot_ops/aks_edge_essentials_full_akri/_index.md @@ -18,7 +18,7 @@ Akri is an open source project for a Kubernetes resource interface that lets you ## Prerequisites -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_edge_iot_ops/aks_edge_essentials_single/_index.md b/docs/azure_arc_jumpstart/azure_edge_iot_ops/aks_edge_essentials_single/_index.md index 0086ff74..e7e32884 100644 --- a/docs/azure_arc_jumpstart/azure_edge_iot_ops/aks_edge_essentials_single/_index.md +++ b/docs/azure_arc_jumpstart/azure_edge_iot_ops/aks_edge_essentials_single/_index.md @@ -20,7 +20,7 @@ However, **for demo purposes only**, the below guide will allow you to use and o ## Prerequisites -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_edge_iot_ops/aks_edge_essentials_single_acsa/_index.md b/docs/azure_arc_jumpstart/azure_edge_iot_ops/aks_edge_essentials_single_acsa/_index.md index 0d69d58a..08d873b0 100644 --- a/docs/azure_arc_jumpstart/azure_edge_iot_ops/aks_edge_essentials_single_acsa/_index.md +++ b/docs/azure_arc_jumpstart/azure_edge_iot_ops/aks_edge_essentials_single_acsa/_index.md @@ -25,7 +25,7 @@ The automation in this scenario deploys Azure Container Storage on an AKS Edge E ## Prerequisites -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_edge_iot_ops/aks_edge_essentials_single_akri/_index.md b/docs/azure_arc_jumpstart/azure_edge_iot_ops/aks_edge_essentials_single_akri/_index.md index 9e9918b4..7f6a21f2 100644 --- a/docs/azure_arc_jumpstart/azure_edge_iot_ops/aks_edge_essentials_single_akri/_index.md +++ b/docs/azure_arc_jumpstart/azure_edge_iot_ops/aks_edge_essentials_single_akri/_index.md @@ -22,7 +22,7 @@ However, **for demo purposes only**, the below guide will allow you to use and o ## Prerequisites -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_edge_iot_ops/aks_edge_essentials_single_vi/_index.md b/docs/azure_arc_jumpstart/azure_edge_iot_ops/aks_edge_essentials_single_vi/_index.md index d68c4cc7..73e3c6ad 100644 --- a/docs/azure_arc_jumpstart/azure_edge_iot_ops/aks_edge_essentials_single_vi/_index.md +++ b/docs/azure_arc_jumpstart/azure_edge_iot_ops/aks_edge_essentials_single_vi/_index.md @@ -20,7 +20,7 @@ The architecture used in this scenario relies on nested virtualization. An Azure - Azure subscription approval to use the Video Indexer enabled by Arc extension. [Register here](https://aka.ms/vi-register). -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_arc_jumpstart/azure_edge_iot_ops/aks_iot_edge_terraform/_index.md b/docs/azure_arc_jumpstart/azure_edge_iot_ops/aks_iot_edge_terraform/_index.md index ca068059..522697eb 100644 --- a/docs/azure_arc_jumpstart/azure_edge_iot_ops/aks_iot_edge_terraform/_index.md +++ b/docs/azure_arc_jumpstart/azure_edge_iot_ops/aks_iot_edge_terraform/_index.md @@ -22,7 +22,7 @@ The following Jumpstart scenario will guide you on how to use the provided [Terr git clone https://github.com/microsoft/azure_arc.git ``` -* [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +* [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_jumpstart_ag/contoso_hypermarket/_index.md b/docs/azure_jumpstart_ag/contoso_hypermarket/_index.md new file mode 100644 index 00000000..952596ec --- /dev/null +++ b/docs/azure_jumpstart_ag/contoso_hypermarket/_index.md @@ -0,0 +1,55 @@ +--- +type: docs +weight: 2 +linkTitle: Contoso Hypermarket +description: >- + Learn how Contoso Hypermarket, an international consumer goods distributor and retailer, implements an AI-enhanced cloud-to-edge strategy with computer vision, next-gen language models, data pipelines, Microsoft Fabric, and Azure Arc. +--- + +# Overview + +Contoso Hypermarket, a leading international consumer goods distributor and retailer, is leading the digital transformation in the retail industry. By leveraging next-generation AI-powered shopper analytics solutions, Contoso Hypermarket employs advanced computer vision technologies to enhance customer experiences and optimize store operations. + +By integrating advanced computer vision into their retail facilities, Contoso Hypermarket can analyze shopper behavior in real-time, providing valuable insights into customer preferences and shopping patterns. These insights enable them to tailor marketing strategies, optimize product placements, and improve inventory management, ensuring that customers find what they need quickly and efficiently. + +The AI-powered solutions also enhance maintenance and operational efficiency. With real-time monitoring and analytics, Contoso Hypermarket can detect errors or anomalies with automated checkout activities, manage equipment and detect malfunctions. This innovative approach drives operational excellence, positioning Contoso Hypermarket as a leader in the retail sector. + +> **Disclaimer:** This Jumpstart Agora scenario utilizes both Azure OpenAI and the _Phi-3-Mini-4K-Instruct_ model from Microsoft to enhance its capabilities in natural language processing and instruction-based interactions. The _Phi-3-Mini-4K-Instruct_ model is licensed under the MIT License, and users are encouraged to review the full license terms. For details, please refer to Arc Jumpstart [MIT License](../../../LICENSE) included in this repository. +> Additionally, this project uses Intel OpenVINO models, which are distributed under the Apache License 2.0. Please refer to the [Intel OpenVINO License](https://github.com/openvinotoolkit/openvino/blob/master/LICENSE) for the applicable terms and conditions governing its usage. + +## Architecture and technology stack + +Contoso Hypermarket uses an AI technology stack, services, and processes to support their digital transformation. A set of reference use-cases is included with the Jumpstart Agora Contoso Hypermarket scenario. + +- **Retrieval-augmented generation** - Use natural language query (NLQ) to work with Contoso industrial assets, infrastructure and data to support frontline workers and corporate personnel. +- **Computer vision for shopper insights** - Computer vision using tuned inference models provides advanced shopper insights like foot traffic patterns, dwell time, product interaction, and demographic analysis. +- **Speech-to-Text** - Converts spoken language into written text to facilitate customer service interactions, transcribe meetings, and support voice-activated commands for in-store assistance. +- **IoT at the Edge** - Manages IoT devices and sensors at the edge with [Azure IoT Operations](https://learn.microsoft.com/azure/iot-operations/overview-iot-operations) to collect and process data in real-time, enabling predictive maintenance, inventory tracking, and enhanced operational efficiency. +- **Predictive inventory and operations analytics with Microsoft 365 Copilot** - Analyzes sales and inventory trends and provides forecasting insights for optimizing inventory and sales. +- **Edge-to-cloud data pipeline** - Seamless data integration between IoT devices at the edge, customer and business data, and [Microsoft Fabric](https://www.microsoft.com/microsoft-fabric), aggregating real-time shopper insights, centralized data management, predictive maintenance, and enhanced decision-making capabilities and enabling advanced data insights. + +## Virtual sandbox edge environment + +Jumpstart Agora provides virtual sandbox environments that simulate edge infrastructure deployments for industry solutions. The automation in the Contoso Hypermarket scenario deploys an Azure Virtual machine to support this "virtual" factory's AI technology. We have incorporated additional features to enhance the 'virtual industry' experience in a lab setting. These enhancements include simulated Real-Time Streaming Protocol (RTSP) feeds, data emulators, MQTT, industrial assets, and data. For more details about the virtual environment, please review the diagram and dedicated guides below. + +![Screenshot of Contoso Hypermarket technology stack and operations architecture diagram](./deployment/img/architecture_diagram.png) + +## Getting started + +To get started with the "Contoso Hypermarket" Jumpstart Agora scenario, we provided you with a dedicated guide for each step of the way. The guides are designed to be as simple as possible but also keep the detailed-oriented spirit of the Jumpstart. + +| **Guide** | **Contoso Hypermarket service or platform** | **Technology stack** | +|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------| +| [Deployment guide](../contoso_hypermarket/deployment/) | Not applicable | Not applicable | +| [Shopper insights using computer vision](../contoso_hypermarket/shopper_insights/) | Real-time footfall inferences and shopper insights | Yolo8, RTSP, OpenCV, Rancher K3s, PostgreSQL, Azure Arc | +| [Jumpstart Cerebral - Commercial and Operations assistance with Gen AI](../contoso_hypermarket/cerebral/) | Natural language query with Jumpstart Cerebral | Azure OpenAI, phi3, gpt35turbo, Rancher K3s, InfluxDB, Microsoft SQL Server on Linux | +| [Commercial and operations assistance with Speech-to-Text](../contoso_hypermarket/speech_to_text/) | Speech-to-Text | Azure AI Speech, Rancher K3s, Azure Arc | +| [Industrial assets observability](../contoso_hypermarket/observability/) | Observability | Prometheus, Grafana | +| [Shopper insights observability](../contoso_hypermarket/observability/) | Observability | Prometheus, Grafana | +| [Kubernetes infrastructure observability](../contoso_hypermarket/observability/) | Observability | Arc-enabled Kubernetes, Rancher K3s, Prometheus, Grafana | +| [Data pipeline and reporting for operational technology (OT)](../contoso_hypermarket/data_pipeline/operational/) | Operational technology (OT) | Azure IoT Operations, Microsoft Fabric, MQTT, Event Hub, Rancher K3s, InfluxDB, PostgreSQL, MQTT simulators | +| [Data pipeline and reporting for commercial sales](../contoso_hypermarket/data_pipeline/commerical/) | Operational technology (OT) | Azure IoT Operations, Microsoft Fabric, MQTT, Event Hub, Rancher K3s, InfluxDB, PostgreSQL, MQTT simulators | +| [Predictive analytics using Microsoft 365 Copilot](../contoso_hypermarket/predictive_analytics/) | Predictive inventory | Microsoft 365 Copilot | +| [Cleanup](../contoso_hypermarket/cleanup/) | Not applicable | Not applicable | +| [Troubleshooting](../contoso_hypermarket/troubleshooting/) | Not applicable | Not applicable | +| [Frequently asked questions (FAQ)](../../faq/) | Not applicable | Not applicable | diff --git a/docs/azure_jumpstart_ag/contoso_hypermarket/cerebral/_index.md b/docs/azure_jumpstart_ag/contoso_hypermarket/cerebral/_index.md new file mode 100644 index 00000000..9e840b18 --- /dev/null +++ b/docs/azure_jumpstart_ag/contoso_hypermarket/cerebral/_index.md @@ -0,0 +1,220 @@ +--- +type: docs +weight: 3 +title: Commercial and Operations assistance with Gen AI +linkTitle: Commercial and Operations assistance with Gen AI +--- + +# Jumpstart Cerebral - Commercial and Operations assistance with Gen AI + +## Overview + +Jumpstart Cerebral logo + +Jumpstart Cerebral is an advanced Generative AI assistant that revolutionizes how store personnel interact with critical information across Contoso Hypermarket's diverse data sources. By combining powerful large language models with specialized databases, Cerebral delivers contextual assistance and intelligent insights through natural language interactions. The system leverages [Retrieval Augmented Generation (RAG)](https://azure.microsoft.com/products/phi) to synthesize information from technical documentation, real-time metrics, and business data into comprehensive, tailored responses. + +Cerebral innovative hybrid architecture optimally balances edge computing with cloud capabilities. Whether processing queries via [Azure OpenAI](https://learn.microsoft.com/azure/ai-services/openai/overview) in the cloud or utilizing [Small Language Models (SLM)](https://azure.microsoft.com/products/phi) at the edge, Cerebral intelligently routes and handles requests based on their complexity and urgency. Through its deep understanding of industry context and user roles, Cerebral acts as an intelligent partner that enables staff to focus on value-adding activities rather than searching through multiple systems. + +> **Note**: For detailed information about Cerebral's Generative AI capabilities, explore the [Azure OpenAI Service documentation](https://learn.microsoft.com/azure/ai-services/openai/overview) and [Microsoft Phi-3 - small language models (SLMs)](https://azure.microsoft.com/blog/introducing-phi-3-redefining-whats-possible-with-slms/) + +## Business challenges + +In today's complex retail environment, organizations face critical operational challenges that impact efficiency and decision-making capabilities. Store managers and staff often struggle with fragmented information spread across multiple systems, leading to delayed responses to critical inventory and operational issues. New employees face steep learning curves as they attempt to navigate various platforms and documentation sources, while maintenance teams lose valuable time searching through scattered technical documentation for troubleshooting procedures. + +Cerebral addresses these pain points by providing a unified, intelligent interface that breaks down information silos and streamlines access to critical data and documentation. By leveraging natural language processing and contextual understanding, it transforms complex data retrieval and decision-making processes into simple, conversational interactions. This approach significantly reduces response times to operational issues, accelerates employee onboarding, and enables more efficient maintenance operations through instant access to relevant technical information. + +## Architecture + +The power of Cerebral lies in its sophisticated hybrid architecture that balances local processing with cloud capabilities. At its core, Cerebral operates on an edge-located [Arc-enabled Kubernetes cluster](https://learn.microsoft.com/azure/azure-arc/kubernetes/overview), which hosts the local language model (Microsoft Phi-3 Mini-4k) for rapid response to common queries. This edge infrastructure connects seamlessly with cloud services through [Azure IoT Operations](https://azure.microsoft.com/products/iot-operations), enabling a robust and scalable system that can handle everything from simple information requests to complex analytical queries. + +Cerebral exposes its functionality through a comprehensive API layer that supports both REST and WebSocket connections, enabling real-time interactions and seamless integration with applications like the Contoso Hypermarket web interface. + +At its core, Cerebral employs a query processing orchestrator that intelligently routes requests to appropriate data sources based on the nature of the query. For commercial data such as sales, inventory, and customer information, the system interfaces with SQL Server. Real-time device metrics and operational data are managed through InfluxDB, a specialized time-series database that captures everything from equipment performance to environmental readings. Technical documentation and operational procedures are accessed through a [Chroma vector database](https://www.trychroma.com/), enabling powerful RAG (Retrieval Augmented Generation) capabilities. + +![Screenshot of Cerebral solution architecture diagram](./img/cerebral_architecture.png) + +The system's AI processing capabilities are designed for flexibility, with the ability to leverage either Azure OpenAI for complex cloud-based processing or Small Language Models (SLM) for edge processing. This hybrid approach ensures optimal performance while maintaining data privacy and enabling offline operations when needed. + +To support development, testing, and demonstrations, Cerebral includes a sophisticated data simulator that generates realistic streams of commercial transactions, device telemetry, and equipment status updates. This simulation capability is crucial for system validation and training scenarios. + +> **Note**: To understand how Cerebral integrates with Contoso Hypermarket's data infrastructure, explore both the [Data pipeline and reporting for commercial sales](../data_pipeline/commercial/_index.md) and the [Data pipeline and reporting for operational technology (OT)](../data_pipeline/operational/_index.md) guides. + +While currently demonstrated within Contoso Hypermarket's retail environment, the architecture is inherently designed for multi-industry adaptation. New data sources, industry verticals, and processing pipelines can be seamlessly integrated, ensuring that Cerebral can evolve to meet the needs of diverse operational contexts while maintaining consistent performance and reliability. + +## Interacting with Cerebral + +Throughout the Contoso Hypermarket interface, whether you're a store manager reviewing sales data, a maintenance technician checking equipment status, or a shopper seeking assistance, Cerebral is your AI-powered assistant ready to help with any query. The system provides an intuitive and seamless way to access information across all store systems and documentation. + +> **Note**: To learn more about using the Contoso Hypermarket web applications and how Cerebral integrates with different operational interfaces, please refer to our [Shopper Insights Guide](../shopper_insights/_index.md). + +### Getting started with Cerebral + +1. **Accessing the Assistant** + - Locate the Cerebral icon in the top-right corner of any page + - Click the icon to open the sliding sidebar interface + - The sidebar will smoothly slide in from the right side of the screen + +![Screenshot of Cerebral location in the the Contoso Hypermarket's web interface](./img/cerebral_location.png) + +2. **Interface Overview** + - The sidebar presents a clean, focused chat interface + - You'll see a welcome message: "Hi, I'm here to help! You can ask me questions using text or voice." + - A text input field is available at the bottom of the panel + - Voice input can be activated using the microphone icon + - For technical users, a "Debug" checkbox is available to view backend processing details + +![Screenshot of Cerebral interface](./img/cerebral_interface.png) + +1. **Interaction Methods** + - Type your question directly into the text field and press enter or click the send button + - Click the microphone icon to use voice input + - Cerebral will process your query and provide relevant information, data, or procedures based on your request + +![Screenshot of Cerebral interface record button](./img/cerebral_interface_audio.png) + +The interface is designed to be non-intrusive while remaining easily accessible throughout your work session. You can minimize the sidebar at any time by clicking the 'X' in the top-right corner, and reopen it whenever you need assistance. + +The interaction is straightforward and natural - simply type your question or click the microphone icon to speak. Cerebral understands natural language queries across a wide range of topics. Below is an example of how a Sales Analyst can ask Cerebral _"What are our top 5 selling products this week?"_: + +![Screenshot of Sales Analyst Cerebral interaction example](./img/sales_analyst_example.png) + +Note that the examples and screenshots shown in this documentation are for illustration purposes only. While the core functionality remains the same, Cerebral's responses are dynamic and contextual to your specific situation and the latest available information and actual results and responses from Cerebral may vary depending on: + +- Current data in your systems +- Real-time operational status +- Specific store configuration +- Updated procedures and documentation + +> **Note**: For additional prompt examples, please refer to the ["Prompt examples"](https://github.com/Azure/arc_jumpstart_docs/blob/canary/docs/azure_jumpstart_ag/contoso_hypermarket/cerebral/cerebral_appendix.md#prompt-examples) section in the Jumpstart Cerebral appendix file. + +Based on your query, Cerebral automatically classifies the type of request and routes it to the appropriate system (documentation, real-time data, or business intelligence) to provide relevant and contextual responses. For demonstration purposes, users can enable the "Debug" checkbox in the interface to view behind-the-scenes details such as: + +- Query classification (documentation, data, or relational) +- Generated database queries +- Data processing steps +- Response construction logic + +![Screenshot of Cerebral interface debug toggle](./img/cerebral_debug_toggle.png) + +> **Note**: For detailed information about data types and how Cerebral processes different sources of information, see the [Unified Data Sources](#unified-data-sources) section. + +## Data integration and query processing + +At the heart of Cerebral's architecture lies a sophisticated data integration system that seamlessly connects diverse information sources through an intelligent query processing pipeline. This unified approach transforms how organizations access and utilize their operational data. + +### Unified data sources + +The system orchestrates three specialized databases, each optimized for specific types of information and query patterns: + +- **InfluxDB** powers Cerebral's real-time operational insights by managing time-series data from store equipment and systems. This specialized database captures everything from refrigeration temperatures to checkout queue lengths, enabling rapid analysis of current conditions and historical trends. Its optimized time-series capabilities ensure swift access to performance metrics and environmental data when seconds matter. + + - Captures real-time metrics from store equipment + - Monitors system performance data + - Tracks operational status + - Stores historical trending data + +> **Note**: For additional details, please refer to the ["MQTT simulated equipment metrics"](https://github.com/Azure/arc_jumpstart_docs/blob/canary/docs/azure_jumpstart_ag/contoso_hypermarket/cerebral/cerebral_appendix.md#mqtt-simulated-equipment-metrics) section in the Jumpstart Cerebral appendix file. + +- **SQL Server** handles all commercial operations data, providing a robust foundation for business intelligence. From transaction processing to inventory management, this relational database ensures accurate tracking of sales patterns, stock levels, and customer interactions, enabling data-driven decision making across the organization. + + - Manages transaction records + - Tracks inventory levels + - Stores customer data + - Handles business intelligence queries + +> **Note**: For additional details on the relational database structure and how it is used by Contoso Hypermarket for commercial and operational needs, please refer to the ["Relational database structure"](https://github.com/Azure/arc_jumpstart_docs/blob/canary/docs/azure_jumpstart_ag/contoso_hypermarket/cerebral/cerebral_appendix.md#relational-database-structure) section in the Jumpstart Cerebral appendix file. + +- **Chroma Vector Database** serves as the foundation for Cerebral's documentation intelligence. By indexing technical manuals, maintenance procedures, and operational guides, it enables sophisticated semantic search capabilities through Retrieval Augmented Generation (RAG). This allows Cerebral to understand the context and intent behind documentation queries, delivering precise and relevant information to users. + + - Stores and indexes technical documentation + - Enables semantic search capabilities + - Manages operational procedures and maintenance guides + - Facilitates contextual information retrieval + +### Available technical manuals + +All technical documentation is automatically indexed and processed by Cerebral's RAG system, enabling natural language queries about any aspect of these systems. Instead of manually searching through PDFs, users can simply ask Cerebral specific questions about equipment operation, maintenance procedures, or troubleshooting steps. + +> **Note**: For a list of the various technical manuals used by Contoso Hypermarket for operational needs, please refer to the ["Available technical manuals"](https://github.com/Azure/arc_jumpstart_docs/blob/canary/docs/azure_jumpstart_ag/contoso_hypermarket/cerebral/cerebral_appendix.md#Available-technical-manuals) section in the Jumpstart Cerebral appendix file. + +### Intelligent query routing + +When a user interacts with Cerebral, their natural language query flows through a sophisticated decision tree that determines the optimal processing path. Questions about maintenance procedures are seamlessly routed to the vector database, equipment status checks are directed to the time-series database, and sales inquiries are processed through the relational database. This intelligent routing ensures that each query is handled by the most appropriate system, delivering fast, accurate responses while maintaining system efficiency. + +For example, when a maintenance technician asks _"How do I calibrate Scale-02?"_, Cerebral recognizes this as a documentation query and leverages RAG to search the vector database for relevant procedures. Conversely, a store manager asking "What were our top-selling products today?" triggers a SQL query to analyze recent transaction data. + +![Screenshot of Cerebral decision tree architecture diagram](./img/cerebral_decision_tree.png) + +## Industry and Role adaptability + +While Cerebral is currently showcased within Contoso Hypermarket's retail environment, its architecture is designed to be inherently multi-industry and role-adaptive. Through a sophisticated prompt catalog system, Cerebral can be customized to understand and respond to the unique contexts of different industries and professional roles. + +Cerebral is design to support various roles and industry, for example, an Inventory Manager in the retail industry, how cares about monitoring the store stock levels. + +> **Note**: For additional details on the the various industries and roles supported by Cerebral, please refer to the ["Industry and Role support"](https://github.com/Azure/arc_jumpstart_docs/blob/canary/docs/azure_jumpstart_ag/contoso_hypermarket/cerebral/cerebral_appendix.md#Industry-and-Role-support) section in the Jumpstart Cerebral appendix file. + +### Example prompts by category + +Cerebral understands variations in phrasing and can maintain context through follow-up prompts. For example, in the "Documentation and Procedures" category, a prompt such _"What are the steps to close the store at the end of the day?"_ will lead to a step-by-step procedure with checklist. For more information about data sources and query processing, see the [Unified Data Sources](#unified-data-sources) section. + +> **Note**: For additional examples of common prompts Cerebral can handle, please refer to the ["Common prompts Cerebral can handle"](https://github.com/Azure/arc_jumpstart_docs/blob/canary/docs/azure_jumpstart_ag/contoso_hypermarket/cerebral/cerebral_appendix.md#Common-prompts-Cerebral-can-handle) section in the Jumpstart Cerebral appendix file. +> + +### Prompt catalog system + +Cerebral's flexibility comes from its extensible prompt catalog, which enables: + +1. **Industry-Specific Context** + - Customized terminology + - Industry-relevant metrics + - Sector-specific compliance requirements + - Domain-specific best practices + +2. **Role-Based Responses** + - Tailored information access + - Role-appropriate technical depth + - Relevant recommendations + - Authorized data visibility + +Below is an example of how prompt customization scheme: + +```json +{ + "industries": { + "retail": { + "roles": { + "store_manager": { + "classification_prompt": "...", + "query_prompt": "...", + "response_prompt": "..." + } + } + }, + "manufacturing": { + "roles": { + "maintenance_engineer": { + "classification_prompt": "...", + "query_prompt": "...", + "response_prompt": "..." + } + } + } + } +} +``` + +## Benefits of multi-industry design + +Cerebral's architecture delivers three key advantages, making it a powerful solution across industries and roles: + +1. **Scalability**: Easily expand to new industries and roles with minimal restructuring, supported by a flexible prompt management system and a growing knowledge base that adapts to new domains. + +2. **Consistency**: Standardized response formats and query handling ensure reliable, intuitive interactions across environments, streamlining maintenance and updates. + +3. **Customization**: Tailor each implementation with industry-specific terminology, role-based access controls, and context-relevant recommendations, providing actionable insights for every user, from store managers to maintenance engineers. + +This combination of scalability, consistency, and customization makes Cerebral adaptable and effective for diverse operational contexts. + +## Next steps + +Now that you have completed the _Jumpstart Cerebral - Gen AI Commercial and Operations assistance_ scenario, it's time to continue to the next one, [Commercial and Operations assistance with Speech-to-Text](../speech_to_text/_index.md). diff --git a/docs/azure_jumpstart_ag/contoso_hypermarket/cerebral/cerebral_appendix.md b/docs/azure_jumpstart_ag/contoso_hypermarket/cerebral/cerebral_appendix.md new file mode 100644 index 00000000..3b8f0196 --- /dev/null +++ b/docs/azure_jumpstart_ag/contoso_hypermarket/cerebral/cerebral_appendix.md @@ -0,0 +1,83 @@ +# Jumpstart Cerebral - Appendix + +## Prompt examples + +The following table details the various Cerebral user prompt examples: + +| Type of Query | Example Question | Sample Response | +|-----------------------|-------------------------------------------------|---------------------------------------| +| Technical Support | "The cash dispenser in POS-01 is stuck. How do I fix it?" | ![Technical support Example](./img/technical_support_example.png) | +| Sales Analysis | "What are our top 5 selling products this week?" | ![Sales Analysis Example](./img/sales_analyst_example.png) | +| Equipment Monitoring | "What's the power usage for HVAC unit 02?" | ![Equipment monitoring](./img/equipment_monitoring.png) | +| Inventory Management | "Show me all products below reorder threshold" | ![Inventory management](./img/inventory.png) | + +## MQTT simulated equipment metrics + +The following table details the various types and metrics being simulated through MQTT for Contoso Hypermarket's operations: + +> **Note**: The simulation generates realistic data streams for each device type, enabling testing, demonstrations, and development. Device IDs are formatted with sequential numbering (e.g., Refrigerator01, Refrigerator02). All metrics are published to the MQTT broker and InfluxDB and can be queried through Cerebral using natural language. + +| Equipment Type | Device Format | Fields Monitored | Example Metrics | +|--------------------|-----------------------|----------------------------------------------------------------------------------|----------------------------------------------| +| Refrigerator | `Refrigerator{01..XX}`| - temperature_celsius
- door_open
- power_usage_kwh | - Current temperature
- Door status (open/closed)
- Power consumption | +| Scale | `Scale{01..XX}` | - weight_kg
- tare_weight_kg | - Current weight
- Tare weight settings | +| POS | `POS{01..XX}` | - items_sold
- total_amount_usd
- payment_method
- failure_type | - Transaction volume
- Sales amount
- Payment types
- Error states | +| SmartShelf | `SmartShelf{01..XX}` | - product_id
- stock_level
- threshold_stock_level
- last_restocked | - Current inventory
- Stock thresholds
- Restock timing | +| HVAC | `HVAC{01..XX}` | - temperature_celsius
- humidity_percent
- power_usage_kwh
- operating_mode | - Air temperature
- Humidity levels
- Energy usage
- Mode (heating/cooling) | +| LightingSystem | `LightingSystem{01..XX}` | - brightness_level
- power_usage_kwh
- status | - Light intensity
- Power consumption
- Operational status | +| AutomatedCheckout | `AutomatedCheckout{01..XX}` | - items_scanned
- total_amount_usd
- payment_method
- errors
- queueLength
- avgWaitTime | - Scanning activity
- Transaction values
- Error states
- Queue metrics | + +## Relational database structure + +The following table details the relational database structure used by Contoso Hypermarket for commercial and operational data: + +| Table Name | Description | Key Fields | Example Data | +|----------------|---------------------------|----------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------| +| Sales | Transaction records | - sale_id VARCHAR(50)
- sale_date DATETIME2
- store_id VARCHAR(10)
- store_city VARCHAR(100)
- product_id VARCHAR(50)
- quantity INT
- item_total DECIMAL(10,2)
- payment_method VARCHAR(50)
- customer_id VARCHAR(50)
- register_id VARCHAR(20) | - 'SAL20240312001'
- '2024-03-12 14:30:00'
- 'SEA'
- 'Seattle'
- 'PROD001'
- 2
- 4.99
- 'credit_card'
- 'CUST123'
- 'REG01' | +| Products | Product catalog | - product_id VARCHAR(50)
- name VARCHAR(200)
- category VARCHAR(100)
- price_min DECIMAL(10,2)
- price_max DECIMAL(10,2)
- stock INT
- photo_path VARCHAR(500) | - 'PROD001'
- 'Red Apple'
- 'Fruits'
- 0.20
- 0.40
- 1000
- '/img/products/apple.jpg' | +| Inventory | Current stock levels | - id INT
- date_time DATETIME2
- store_id VARCHAR(10)
- product_id VARCHAR(50)
- retail_price DECIMAL(10,2)
- in_stock INT
- reorder_threshold INT
- last_restocked DATETIME2 | - 1
- '2024-03-12 15:00:00'
- 'SEA'
- 'PROD001'
- 0.35
- 850
- 200
- '2024-03-11 08:00:00' | +| Stores | Store locations | - store_id VARCHAR(10)
- city VARCHAR(100)
- state VARCHAR(50)
- country VARCHAR(100) | - 'SEA'
- 'Seattle'
- 'WA'
- 'United States' | +| DeviceMetrics | Equipment telemetry history | - id INT
- timestamp DATETIME2
- device_id VARCHAR(50)
- equipment_type VARCHAR(50)
- metric_name VARCHAR(100)
- metric_value DECIMAL(18,4)
- metric_unit VARCHAR(20) | - 1
- '2024-03-12 15:01:00'
- 'HVAC01'
- 'HVAC'
- 'temperature'
- 22.5
- 'celsius' | + +## Available technical manuals + +The following table details the current available technical manuals used by Contoso Hypermarket for operational needs: + +| System | Description | Documentation | +|--------|-------------|---------------| +| Automated Checkout | Complete guide for operation and maintenance of self-checkout systems | [User Manual](https://download.microsoft.com/download/3ae1d7aa-a642-48cf-b848-67b4eaa81292/Automated%20Checkout%20System%20User%20Manual%20for%20Contoso%20Hypermarket.pdf) | +| SmartShelf | Technical documentation for electronic shelf labeling and inventory tracking | [User Manual](https://download.microsoft.com/download/3ae1d7aa-a642-48cf-b848-67b4eaa81292/SmartShelf%20System%20User%20Manual%20for%20Contoso%20Hypermarket.pdf) | +| Refrigeration | Maintenance and operation guides for refrigeration units | [User Manual](https://download.microsoft.com/download/3ae1d7aa-a642-48cf-b848-67b4eaa81292/Refrigeration%20System%20User%20Manual%20for%20Contoso%20Hypermarket.pdf) | +| HVAC | Environmental control system documentation | [User Manual](https://download.microsoft.com/download/3ae1d7aa-a642-48cf-b848-67b4eaa81292/HVAC%20System%20User%20Manual%20for%20Contoso%20Hypermarket.pdf) | +| Scale Systems | Calibration and operation procedures for weighing equipment | [User Manual](https://download.microsoft.com/download/3ae1d7aa-a642-48cf-b848-67b4eaa81292/Scale%20System%20User%20Manual%20for%20Contoso%20Hypermarket.pdf) | + +## Industry and Role support + +The following table details the current supported industries and roles by Cerebral: + +| Industry | Roles | Examples | +|----------|-------|----------| +| Retail | - Store Manager
- Inventory Manager
- Maintenance Worker | - Store performance metrics
- Stock level monitoring
- Equipment maintenance | +| Manufacturing | - Maintenance Engineer
- Shift Supervisor
- Production Manager | - Machine diagnostics
- Production line metrics
- Quality control data | +| Automotive | - Line Supervisor
- Quality Inspector
- Maintenance Technician | - Assembly line monitoring
- Quality assurance checks
- Equipment maintenance | +| Hypermarket | - Store Manager
- Shopper
- Maintenance Worker | - Sales analytics
- Product location
- Facility maintenance | + +## Common prompts Cerebral can handle + +| Category | Prompt Example | Expected Response Type | +|----------|--------------|----------------------| +| Documentation and Procedures | _"What are the steps to close the store at the end of the day?"_ | Step-by-step procedure with checklist | +|| _"How do I perform the daily cleaning routine for the meat department?"_ | Detailed cleaning protocol with safety guidelines | +|| _"What's the emergency shutdown procedure for the refrigeration system?"_ | Emergency procedure with critical steps highlighted | +|| _"Show me the maintenance checklist for the automated checkout machines"_ | Maintenance checklist with technical specifications | +|| _"What are the safety protocols for handling spills in the produce area?"_ | Safety guidelines and cleaning procedures | +| Real-time Operations | _"What's the current capacity of all automated checkouts in use?"_ | Real-time usage metrics and availability status | +|| _"Show me the temperature trends for all refrigeration units in the last hour"_ | Temperature graphs and anomaly indicators | +|| _"Are any SmartShelves reporting low stock alerts right now?"_ | Current stock alerts and locations | +|| _"What's the average wait time at the deli counter currently?"_ | Current wait times and historical comparison | +|| _"How is HVAC-02 performing compared to its normal baseline?"_ | Performance metrics with baseline comparison | +| Commercial Data | _"Which products in the dairy section need restocking?"_ | List of products below threshold with quantities | +|| _"What was our busiest hour for sales yesterday?"_ | Sales volume analysis with peak times | +|| _"Show me the performance of our seasonal products this month"_ | Sales trends and inventory analysis | +|| _"What's the current inventory level for fresh produce?"_ | Current stock levels with reorder recommendations | +|| _"Which payment method was most used in the last week?"_ | Payment method breakdown with 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00000000..643ebea3 --- /dev/null +++ b/docs/azure_jumpstart_ag/contoso_hypermarket/cleanup/_index.md @@ -0,0 +1,22 @@ +--- +type: docs +weight: 10 +title: Cleanup +linkTitle: Cleanup +--- + +# Cleanup deployment + +To clean up your deployment, simply delete the resource group using Azure CLI or Azure portal. + + ```shell + az group delete -n + ``` + + ![Screenshot showing az group delete](./img/az_group_delete.png) + + ![Screenshot showing group delete from Azure portal](./img/portal_delete.png) + +## Next steps + +If you still having issues with the deployment, please refer to the [Troubleshooting](../troubleshooting//) section. Otherwise, if you have additional questions or feedback, please refer to the [FAQ](../../../faq/) section. diff --git a/docs/azure_jumpstart_ag/contoso_hypermarket/cleanup/img/az_group_delete.png b/docs/azure_jumpstart_ag/contoso_hypermarket/cleanup/img/az_group_delete.png new file mode 100644 index 00000000..348782ad Binary files /dev/null and b/docs/azure_jumpstart_ag/contoso_hypermarket/cleanup/img/az_group_delete.png differ diff --git a/docs/azure_jumpstart_ag/contoso_hypermarket/cleanup/img/portal_delete.png b/docs/azure_jumpstart_ag/contoso_hypermarket/cleanup/img/portal_delete.png new file mode 100644 index 00000000..40ac206a Binary files /dev/null and b/docs/azure_jumpstart_ag/contoso_hypermarket/cleanup/img/portal_delete.png differ diff --git a/docs/azure_jumpstart_ag/contoso_hypermarket/data_pipeline/_index.md b/docs/azure_jumpstart_ag/contoso_hypermarket/data_pipeline/_index.md new file mode 100644 index 00000000..5e42f03f --- /dev/null +++ b/docs/azure_jumpstart_ag/contoso_hypermarket/data_pipeline/_index.md @@ -0,0 +1,18 @@ +--- +type: docs +weight: 8 +title: Data Pipelines +linkTitle: Data Pipelines +showOverview: false +--- + +# Data Pipelines + +Contoso Hypermarket aims to leverage store data through cloud-based data pipelines to drive business intelligence. Two main data pipelines, the operational and commercial, have been developed to streamline dataflow from stores nationwide for effective collection, processing, and analysis in the cloud. + +The **operational** data pipeline focuses on optimizing store orders and inventory management, enabling corporate leadership to generate insights that enhance operational efficiency and support informed decision-making. Similarly, the **commercial** data pipeline provides critical insights that help forecast demand and improve inventory management, ultimately enhancing overall business performance and strategic planning across the organization. + +Together, these pipelines give Contoso's leadership a comprehensive view of store operations, helping identify trends and meet customer demand effectively. + +- [Data pipeline and reporting for operational technology (OT)](operational/_index.md) +- [Data pipeline and reporting for commercial sales](commercial/_index.md) diff --git a/docs/azure_jumpstart_ag/contoso_hypermarket/data_pipeline/commercial/_index.md b/docs/azure_jumpstart_ag/contoso_hypermarket/data_pipeline/commercial/_index.md new file mode 100644 index 00000000..ceaecc8f --- /dev/null +++ b/docs/azure_jumpstart_ag/contoso_hypermarket/data_pipeline/commercial/_index.md @@ -0,0 +1,248 @@ +--- +type: docs +weight: 7 +title: Data pipeline and reporting for commercial sales +linkTitle: Data pipeline and reporting for commercial sales +--- + +# Data pipeline and reporting for commercial sales + +## Overview + +One of Contoso's primary objectives is to utilize store data for business intelligence by leveraging cloud capabilities. + +Contoso Hypermarket's commercial data pipeline is designed to streamline and optimize the flow of data related to store orders and inventory management. This robust pipeline integrates data from various stores across the country, ensuring that all relevant information is collected, processed, and analyzed efficiently in the cloud. By leveraging this data pipeline, Contoso Hypermarket can generate valuable commercial insights that are crucial for corporate leadership. These insights help in making informed decisions, improving operational efficiency, and enhancing overall business performance. The data pipeline ensures that the leadership team has a comprehensive view of the store operations, enabling them to identify trends, forecast demand, and manage inventory effectively. + +## Architecture + +Below is an architecture diagram that shows how the data flows from the store edge locations into the Microsoft Fabric to generate near real-time reports of orders received and processed across various supermarket store locations. This architecture includes a local MQTT broker to relay commercial data to the Event Hub in the Azure Cloud. Azure IoT Operations Dataflows are implemented to connect edge locations to the cloud to relay commercial data. + +![Screenshot showing data pipeline architecture](./img/hypermarket-data-pipeline-architecture.png) + +## Edge components in the solution architecture + +The edge location components of the Contoso Hypermarket solution include SQL Server, Cerebral data simulator, Azure IoT Operations, and MQTT Explorer. These components work together to ensure seamless data collection, storage, and transmission from the edge to the cloud. The SQL Server stores commercial data generated by the Cerebral simulator, which mimics real-world operations. Azure IoT Operations manages the secure and efficient dataflow from edge devices to the cloud, while MQTT Explorer allows users to monitor and validate the data pipeline. Together, these components form a robust edge infrastructure that supports real-time data processing and analysis for Contoso Hypermarket. + +### SQL Server + +The SQL Server at the edge location is responsible for storing and managing the commercial data generated by the Cerebral simulator. It ensures that the data is readily available for processing and analysis. The SQL Server is configured to handle large volumes of data efficiently, providing a reliable storage solution for the edge components. Follow instructions below to view SQL Server pods running on the Edge Kubernetes cluster and connect to SQL Server using Azure Data Studio (ADS) and SQL Server Management Studio (SSMS). + +- Log into the _Ag-VM-Client_ VM using the username and password provided in the deployment. +- On the desktop locate _Windows Terminal_ shortcut and double click to open the terminal window. + + ![Screenshot showing Windows Terminal desktop shortcut](./img/hypermarket-locate-terminal-window.png) + +- Run _kubectx_ command to verify current Kubernetes context configured. Make sure it shows _seattle_, if not change the context by running _kubectx seattle_ command to switch Kubernetes context. + + ![Screenshot showing current Kubernetes context](./img/hypermarket-terminal-kubectx.png) + +- Locate the SQL Server pods running on the Edge Kubernetes cluster by running the following command in the terminal window: + + ```sh + kubectl get pods -n contoso-hypermarket + ``` + + ![Screenshot showing SQL Server pods](./img/hypermarket-locate-terminal-sqlserver-pods.png) + +- As part of the Contoso Hypermarket deployment SQL Server connections to these pods are created in ADS and _SQL Server Endpoints_ shortcut on the desktop to connect using SSMS using the SQL Server endpoints and credentials to connect. + +### Connect to SQL Server using ADS + +- Open the Azure Data Studio from the desktop shortcut. + + ![Screenshot showing Azure Data Studio](./img/hypermarket-locate-data-studio.png) + +- Notice pre-configured SQL Server connections of the edge locations Chicago and Seattle. Click on 1) Connections to view these connections. + + ![Screenshot showing Azure Data Studio connections](./img/hypermarket-data-studio-connections.png) + +- Click on _Seattle_ SQL Server connection to verify the database and tables created for storing commercial data by expanding _RetailDB_ database and tables. + + ![Screenshot showing Seattle RetailDB database and tables](./img/hypermarket-data-studio-seattle.png) + +- Run queries to validate the data being stored and ensure it matches the expected format. + + ![Screenshot showing SQL Server query](./img/hypermarket-data-studio-seattle-query-sales.png) + +- View _Sales_ table data from the query results. + + ![Screenshot showing sales table data](./img/hypermarket-data-studio-seattle-view-sales.png) + +- Explore other tables and data as needed. + +### Connect to SQL Server using SSMS + +- Locate _SQL Server Endpoints_ shortcut on the Desktop and open file in notepad to view SQL Server endpoints and credentials and keep it handy to connect using SSMS. + + ![Screenshot showing SQL Server Endpoints shortcut](./img/hypermarket-locate-sql-endpoints.png) + + ![Screenshot showing SQL Server Endpoints details](./img/hypermarket-sql-endpoints-details.png) + +- Locate _SQL Server Management Studio_ shortcut on the Desktop and double-click to connect SQL Server databases in _Seattle_ edge location. + + ![Screenshot showing locating SSMS shortcut on the Desktop](./img/hypermarket-locate-ssms.png) + +- Use _Seattle_ edge location SQL Server details and connect to the SQL Server as shown below. + + ![Screenshot showing connect to Seattle SQL Server using SSMS](./img/hypermarket-locate-ssms.png) + +- Locate RetailDB database, expand tables, and query _Sales_ table to view sales data. + + ![Screenshot showing Seattle SQL Server RetailDB database details](./img/hypermarket-ssms-database-details.png) + +### Cerebral data simulator + +The Cerebral data simulator is a critical component of the Contoso Hypermarket solution, designed to generate realistic commercial and operational data. This simulator helps in testing and validating the data pipeline by creating various scenarios that mimic real-world operations. Cerebral simulator is pre-configured to generate simulation data at regular intervals and can be monitored using MQTT Explorer installed on _Ag-Client-VM_. + +- Use the simulator to test different scenarios and validate the data pipeline's performance. + +- Screenshot below shows the Cerebral Simulator pod running on the edge Kubernetes cluster. + + ![Screenshot showing Cerebral data simulator](./img/hypermarket-cerebral-simulator-pod.png) + +- Monitor the data being generated using MQTT Explorer as described in the following sections to ensure it is being transmitted to the MQTT broker. + +### Azure IoT Operations + +Azure IoT Operations is responsible for managing the dataflow from the edge devices to the cloud. It ensures that the commercial data generated by the Cerebral simulator is transmitted securely and efficiently to the Azure Event Hubs Namespace. + +- Access the [Azure IoT Operations portal](https://iotoperations.azure.com/sites/_/_/_/instances) to view IoT clusters deployed. + + ![Screenshot showing Azure IoT Operations portal](./img/hypermarket-iot-operations-portal.png) + +- Select one of the instances to view dataflow endpoints and dataflows that are being used to relay data from edge to cloud. + + ![Screenshot showing Azure IoT Operations dashboard](./img/hypermarket-iot-operations-dashboard.png) + +- Click on Dataflow endpoints to view endpoints created for data pipeline. + + ![Screenshot showing Azure IoT Operations dataflow endpoints](./img/hypermarket-iot-operations-dataflow-endpoints.png) + +- Click on Dataflows to view dataflows created for data pipeline. + + ![Screenshot showing Azure IoT Operations dataflows](./img/hypermarket-iot-operations-dataflows.png) + +- Monitor the dataflow and ensure that the commercial data is being transmitted without any issues. Refer Event Hub section below for more details on monitoring messages delivered by dataflows. + +- Troubleshoot any connectivity issues and ensure the data pipeline is functioning correctly. + +### MQTT Explorer + +The MQTT Explorer is a desktop tool included in the Contoso Hypermarket setup. It allows end users to connect to the MQTT listener running in IoT Operations at the edge, observe messages generated by the Cerebral simulator, and send test messages to validate the data pipeline during troubleshooting. + +- Open the MQTT explorer desktop shortcut, it is already configured to connect to the MQTT listener on the cluster. + + ![Screenshot showing opening MQTT explorer on the desktop](./img/hypermarket-locate-mqtt-explorer.png) + +- Notice pre-configured MQTT connections to connect to the MQTT listeners running in Azure IoT Operations. Select one of the edge site and click _Connect_ + + ![Screenshot showing MQTT explorer with connections](./img/hypermarket-mqtt-explorer-connect.png) + +- Once connected, you will start seeing simulated data being transmitted with various metrics from the plant assets. + 1. Topic for the commercial data message publication. + 2. Message received from the Cerebral simulator. + 3. Previous message published in the commercial topic. + + ![Screenshot showing the simulated commercial data](./img/hypermarket-mqtt-explorer-commercial-events.png) + +## Azure services in the solution architecture + +### Azure Event Hubs Namespace + +Azure Event Hubs Namespace is a key component in the Contoso Hypermarket data pipeline, facilitating the seamless delivery of messages from Azure IoT Operations to the Event Hub. This service ensures that commercial data generated at the edge locations is securely and efficiently transmitted to the cloud. Once the data reaches the Event Hub, it is subsequently ingested into the Microsoft Fabric KQL Database for further processing and analysis. By leveraging Azure Event Hubs Namespace, Contoso Hypermarket can achieve real-time data streaming and maintain a robust and scalable data pipeline that supports their business intelligence and operational needs. Follow the steps below to view messages received from MQTT broker through Azure IoT Operations Dataflow. + +- Log into [Azure Portal](https://portal.azure.com/) and go to the resource group where Contoso Hypermarket is deployed. +- Locate Azure Event Hubs Namespace resource created in the deployment and open resource. + + ![Screenshot showing the Event Hubs Namespace](./img/hypermarket-locate-eventhub-namespace.png) + +- Click on Event Hub name as shown in the screenshot below to open Event Hub instance. + + ![Screenshot showing the Event Hubs](./img/hypermarket-eventhub-namespace-overview.png) + +- Click on _Data Explorer (preview)_ to view events. Select _Newest position_ option under Event position and click _View events_. + + ![Screenshot showing events in the Event Hub](./img/hypermarket-eventhub-data-explorer.png) + +- Find any Event with Event body contains _subject="topic/sales"_ and click on the Event and review message JSON. These Events are received from MQTT broker using Azure IoT Operations Dataflow. + + ![Screenshot showing events in the Event Hub](./img/hypermarket-eventhub-events.png) + +### Microsoft Fabric + +[Microsoft Fabric](https://learn.microsoft.com/fabric/get-started/microsoft-fabric-overview) is a unified platform that integrates various data services to streamline data management and analytics. It includes Event House for event streaming, KQL Database for querying large datasets, and Power BI for creating interactive dashboards. The Realtime dashboard provides up-to-the-minute insights, while Notebook offers a collaborative environment for data exploration and analysis. + +### Fabric workspace + +The [Fabric workspace](https://learn.microsoft.com/fabric/get-started/workspaces) is a collaborative environment within Microsoft Fabric where data engineers, data scientists, and business analysts can work together on data projects. It provides tools for data ingestion, transformation, and analysis, enabling teams to build and manage data pipelines, create machine learning models, and develop interactive reports. The workspace supports version control, allowing users to track changes and collaborate effectively on data-driven initiatives.As part of the Contoso Hypermarket deployment Fabric workspace created with all the items required to support data pipeline. Follow the instructions below to access Microsoft Fabric workspaces + +- Access Microsoft Fabric using [URL](https://app.fabric.microsoft.com/) and complete login using the identity used deploying Hypermarket. + +![Screenshot showing overview of Microsoft Fabric home page](./img/microsoft-fabric-home.png) + +- Click on _Workspaces_ as shown in the screenshot above and open the workspace created as part of post deployment steps. + +> **Note:** If you haven't completed the post deployment step to setup Microsoft Fabric workspace, go to the deployment guide and complete Microsoft Fabric Setup. + +![Screenshot showing list of Microsoft Fabric workspace](./img/hypermarket-fabric-workspace-open.png) + +- Screenshot below shows Contoso Hypermarket Fabric workspace and items to support the scenario. + +![Screenshot showing overview of Microsoft Fabric workspace](./img/fabric-workspace.png) + +### KQL Database + +The KQL Database in Microsoft Fabric is a powerful tool designed for querying and analyzing large datasets. In the context of Contoso Hypermarket, the KQL Database is used to ingest commercial data flowing from edge locations to Azure Event Hubs Namespace. This data includes store orders and inventory information, which is then processed and stored in the KQL Database. The database allows for efficient querying and analysis of this data, enabling the generation of real-time insights and reports. By leveraging the KQL Database, Contoso Hypermarket can quickly access and analyze critical commercial data to support decision-making and operational efficiency. + +Data ingested into KQL Database is used by the Power BI reports create for commercial data insights and analysis by the Contoso leadership team. + +- In the Fabric workspace, locate KQL Database created and click on the KQL Database name to open database details view. + + ![Screenshot showing locating KQL Database in the workspace](./img/hypermarket-fabric-open-kql-database.png) + +- Below is the screenshot of KQL Database created in the Fabric workspace and it's details. + 1. KQL Database name + 2. Database tables used in the Contoso Hypermarket for data ingestion and reports + 3. Data pipeline data ingestion over the time + 4. Database tables ingestion details + 5. KQL Database details + + ![Screenshot showing overview of Microsoft Fabric workspace](./img/fabric-kql-database-overview.png) + +- Navigate through different KQL Database features like functions, Data streams to understand more about these features. + +### Power BI Report + +The Power BI report created for Contoso Hypermarket provides comprehensive insights and analytics on commercial data collected from various store locations. This report leverages the data ingested into the KQL Database, offering interactive visualizations and dashboards that help the leadership team monitor key performance indicators, track sales trends, and analyze inventory levels. By utilizing Power BI's robust data visualization capabilities, Contoso Hypermarket can make data-driven decisions, identify opportunities for improvement, and enhance overall business performance. The report is designed to be user-friendly, enabling stakeholders to explore the data and gain valuable insights with ease. Follow the steps below to location and view Power BI report created for Contoso Hypermarket. + +- In the Fabric workspace, locate Power BI report created and click on the Power BI report name to open the report view. + + ![Screenshot showing locating Power BI report in the workspace](./img/hypermarket-fabric-open-powerbi-report.png) + +- Screenshot below shows Contoso Hypermarket weekly sales report across the stores. + + ![Screenshot showing Power BI report](./img/hypermarket-fabric-powerbi-report.png) + +### Sales forecast Jupyter Notebook + +The Microsoft Fabric Notebooks are an integral part of the Contoso Hypermarket's data pipeline, specifically designed to forecast sales and assist the leadership team in making critical business decisions. These notebooks provide a collaborative environment where data scientists and analysts can develop and execute complex data models using Python and KQL. By leveraging historical sales data and advanced machine learning algorithms, the notebooks generate accurate sales forecasts that help the leadership team anticipate future demand, optimize inventory levels, and plan marketing strategies. The interactive nature of the notebooks allows for real-time data exploration and scenario analysis, enabling Contoso's leadership to make informed decisions that drive business growth and operational efficiency. + +- In the Microsoft Fabric workspace locate Jupyter notebook and open to view notebook in the Edge browser. + + ![Screenshot showing Microsoft Fabric Jupyter Notebook](./img/hypermarket-locate-fabric-notebook.png) + +- Notebook is pre-configured to connect to the KQL Database within the workspace. + + ![Screenshot showing Microsoft Fabric Jupyter Notebook](./img/hypermarket-fabric-notebook-overview.png) + + ![Screenshot showing Jupyter Notebook with KQL Database configuration](./img/hypermarket-fabric-notebook-kql.png) + +- Follow instructions provided in the Notebook to run experiments and forecast sales. + + ![Screenshot showing sales forecast for Chicago store](./img/hypermarket-fabric-notebook-sales-forecast.png) + +- Try changing the forecast model to use different product than the one used in the Notebook to learn more. + +## Next steps + +Now that you have completed the first data pipeline scenario, it's time to continue to the next scenario, ["Data pipeline and reporting for operational technology (OT)"](../operational/). diff --git a/docs/azure_jumpstart_ag/contoso_hypermarket/data_pipeline/commercial/img/fabric-kql-database-overview.png b/docs/azure_jumpstart_ag/contoso_hypermarket/data_pipeline/commercial/img/fabric-kql-database-overview.png new file mode 100644 index 00000000..8dab9464 Binary files /dev/null and b/docs/azure_jumpstart_ag/contoso_hypermarket/data_pipeline/commercial/img/fabric-kql-database-overview.png differ diff --git a/docs/azure_jumpstart_ag/contoso_hypermarket/data_pipeline/commercial/img/fabric-workspace.png b/docs/azure_jumpstart_ag/contoso_hypermarket/data_pipeline/commercial/img/fabric-workspace.png new file mode 100644 index 00000000..0210c23c Binary files /dev/null and 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a/docs/azure_jumpstart_ag/contoso_hypermarket/data_pipeline/operational/_index.md b/docs/azure_jumpstart_ag/contoso_hypermarket/data_pipeline/operational/_index.md new file mode 100644 index 00000000..0195006b --- /dev/null +++ b/docs/azure_jumpstart_ag/contoso_hypermarket/data_pipeline/operational/_index.md @@ -0,0 +1,161 @@ +--- +type: docs +weight: 6 +title: Data pipeline and reporting for operational technology (OT) +linkTitle: Data pipeline and reporting for operational technology (OT) +--- + +# Data pipeline and reporting for operational technology (OT) + +## Overview + +One of Contoso's biggest objectives is how to use the data coming from their stores and visualize it for business intelligence by leveraging the power of the cloud. + +Contoso Hypermarket's operational data pipeline is designed to streamline and optimize the flow of data related to store orders and inventory management. This robust pipeline integrates data from various stores across the country, ensuring that all relevant information is collected, processed, and analyzed efficiently in the cloud. By leveraging this data pipeline, Contoso Hypermarket can generate valuable operational insights that are crucial for corporate leadership. These insights help in making informed decisions, improving operational efficiency, and enhancing overall business performance. The data pipeline ensures that the leadership team has a comprehensive view of the store operations, enabling them to identify trends, forecast demand, and manage inventory effectively. + +## Architecture + +Below is an architecture diagram that shows how the data flows from the store edge locations into the Microsoft Fabric to generate near real-time reports of operational data of the equipment across various supermarket store locations. This architecture includes a local MQTT broker to relay operational data to the Event Hub in the Azure Cloud. Azure IoT Operations Dataflows are implemented to connect edge locations to the cloud to relay operational data. + +![Screenshot showing data pipeline architecture](./img/hypermarket-data-pipeline-architecture.png) + +## Edge components in the solution architecture + +The edge location components of the Contoso Hypermarket solution include SQL Server, Cerebral data simulator, Azure IoT Operations, and MQTT Explorer. These components work together to ensure seamless data collection, storage, and transmission from the edge to the cloud. The SQL Server stores operational data generated by the Cerebral simulator, which mimics real-world operations. Azure IoT Operations manages the secure and efficient dataflow from edge devices to the cloud, while MQTT Explorer allows users to monitor and validate the data pipeline. Together, these components form a robust edge infrastructure that supports real-time data processing and analysis for Contoso Hypermarket. + +### Cerebral data simulator + +The Cerebral data simulator is a critical component of the Contoso Hypermarket solution, designed to generate realistic commercial and operational data. This simulator helps in testing and validating the data pipeline by creating various scenarios that mimic real-world operations. Cerebral simulator is pre-configured to generation simulation data at regular intervals and can be monitored using MQTT Explorer installed on _Ag-Client-VM_. + +- Use the simulator to test different scenarios and validate the data pipeline's performance. + +- Screenshot below shows the Cerebral Simulator pod running on the edge Kubernetes cluster. + + ![Screenshot showing Cerebral data simulator](./img/hypermarket-cerebral-simulator-pod.png) + +- Monitor the data being generated using MQTT Explorer as described in the following sections to ensure it is being transmitted to the MQTT broker. + +### Azure IoT Operations + +Azure IoT Operations is responsible for managing the dataflow from the edge devices to the cloud. It ensures that the operational data generated by the Cerebral simulator is transmitted securely and efficiently to the Azure Event Hubs Namespace. + +- Access the [Azure IoT Operations portal](https://iotoperations.azure.com/sites/_/_/_/instances) to view IoT clusters deployed. + + ![Screenshot showing Azure IoT Operations portal](./img/hypermarket-iot-operations-portal.png) + +- Select one of the instances to view Dataflow endpoints and Dataflow that are being used to relay data from edge to cloud. + + ![Screenshot showing Azure IoT Operations dashboard](./img/hypermarket-iot-operations-dashboard.png) + +- Click on Dataflow endpoints to view endpoints created for data pipeline. + + ![Screenshot showing Azure IoT Operations dataflow endpoints](./img/hypermarket-iot-operations-dataflow-endpoints.png) + +- Click on Dataflows to view dataflows created for data pipeline. + + ![Screenshot showing Azure IoT Operations dataflows](./img/hypermarket-iot-operations-dataflows.png) + +- Monitor the dataflow and ensure that the operational data is being transmitted without any issues. Refer Event Hub section below for more details on monitoring messages delivered by dataflows. + +- Troubleshoot any connectivity issues and ensure the data pipeline is functioning correctly. + +### MQTT Explorer + +The MQTT Explorer is a desktop tool included in the Contoso Hypermarket setup. It allows end users to connect to the MQTT listener running in Azure IoT Operations at the edge, observe messages generated by the Cerebral simulator, and send test messages to validate the data pipeline during troubleshooting. + +- Open the MQTT explorer desktop shortcut, it is already configured to connect to the MQTT listener on the cluster. + + ![Screenshot showing opening MQTT explorer on the desktop](./img/hypermarket-locate-mqtt-explorer.png) + +- Notice pre-configured MQTT connections to connect to the MQTT listeners running in Azure IoT Operations. Select one of the edge sites and click _Connect_ + + ![Screenshot showing MQTT explorer with connections](./img/hypermarket-mqtt-explorer-connect.png) + +- Once connected, you will start seeing simulated data being transmitted with various metrics from the plant assets. + 1. Topic for the operational data message publication. + 2. Message received from the Cerebral simulator. + 3. Previous message published in the operational topic. + + ![Screenshot showing the simulated operational data](./img/hypermarket-mqtt-explorer-operational-events.png) + +## Azure services in the solution architecture + +### Azure Event Hubs Namespace + +Azure Event Hubs Namespace is a key component in the Contoso Hypermarket data pipeline, facilitating the seamless delivery of messages from Azure IoT Operations to the Event Hub. This service ensures that operational data generated at the edge locations is securely and efficiently transmitted to the cloud. Once the data reaches the Event Hub, it is subsequently ingested into the Microsoft Fabric KQL Database for further processing and analysis. By leveraging Azure Event Hubs Namespace, Contoso Hypermarket can achieve real-time data streaming and maintain a robust and scalable data pipeline that supports their business intelligence and operational needs. Follow the steps below to view messages received from MQTT broker through Azure IoT Operations Dataflow. + +- Log into [Azure Portal](https://portal.azure.com/) and go to the resource group where Contoso Hypermarket is deployed. +- Locate Azure Event Hubs Namespace resource created in the deployment and open resource. + + ![Screenshot showing the Event Hubs Namespace](./img/hypermarket-locate-eventhub-namespace.png) + +- Click on Event Hub name as shown in the screenshot below to open Event Hub instance. + + ![Screenshot showing the Event Hubs](./img/hypermarket-eventhub-namespace-overview.png) + +- Click on _Data Explorer (preview)_ to view events. Select _Newest position_ option under Event position and click _View events_. + + ![Screenshot showing events in the Event Hub](./img/hypermarket-eventhub-data-explorer.png) + +- Find any Event with Event body contains _subject="iot/devices"_ and click on the Event and review message JSON. These Events are received from MQTT broker using Azure IoT Operations Dataflow. + + ![Screenshot showing events in the Event Hub](./img/hypermarket-eventhub-events.png) + +### Microsoft Fabric + +[Microsoft Fabric](https://learn.microsoft.com/fabric/get-started/microsoft-fabric-overview) is a unified platform that integrates various data services to streamline data management and analytics. It includes Event House for event streaming, KQL Database for querying large datasets, and Power BI for creating interactive dashboards. The Real-Time Dashboard provides up-to-the-minute insights, while Notebook offers a collaborative environment for data exploration and analysis. + +### Fabric workspace + +The [Fabric workspace](https://learn.microsoft.com/fabric/get-started/workspaces) is a collaborative environment within Microsoft Fabric where data engineers, data scientists, and business analysts can work together on data projects. It provides tools for data ingestion, transformation, and analysis, enabling teams to build and manage data pipelines, create machine learning models, and develop interactive reports. The workspace supports version control, allowing users to track changes and collaborate effectively on data-driven initiatives.As part of the Contoso Hypermarket deployment Fabric workspace created with all the items required to support data pipeline. Follow the instructions below to access Microsoft Fabric workspaces + +- Access Microsoft Fabric using [URL](https://app.fabric.microsoft.com/) and complete login using the identity used deploying Hypermarket. + +![Screenshot showing overview of Microsoft Fabric home page](./img/microsoft-fabric-home.png) + +- Click on _Workspaces_ as shown in the screenshot above and open the workspace created as part of post deployment steps. + +> **Note:** If you haven't completed the post deployment step to setup Microsoft Fabric workspace, go to the deployment guide and complete Microsoft Fabric Setup. + +![Screenshot showing list of Microsoft Fabric workspace](./img/hypermarket-fabric-workspace-open.png) + +- Screenshot below shows Contoso Hypermarket Fabric workspace and items to support the scenario. + +![Screenshot showing overview of Microsoft Fabric workspace](./img/fabric-workspace.png) + +### KQL Database + +The KQL Database in Microsoft Fabric is a powerful tool designed for querying and analyzing large datasets. In the context of Contoso Hypermarket, the KQL Database is used to ingest operational data flowing from edge locations to Azure Event Hubs Namespace. This data includes store orders and inventory information, which is then processed and stored in the KQL Database. The database allows for efficient querying and analysis of this data, enabling the generation of real-time insights and reports. By leveraging the KQL Database, Contoso Hypermarket can quickly access and analyze critical operational data to support decision-making and operational efficiency. + +Data ingested into the KQL Database is used by the Power BI reports created for operational data insights and analysis by the Contoso leadership team. + +- In the Fabric workspace, locate KQL Database created and click on the KQL Database name to open database details view. + + ![Screenshot showing locating KQL Database in the workspace](./img/hypermarket-fabric-open-kql-database.png) + +- Below is the screenshot of KQL Database created in the Fabric workspace and it's details. + 1. KQL Database name + 2. Database tables used in the Contoso Hypermarket for data ingestion and reports + 3. Data pipeline data ingestion over the time + 4. Database tables ingestion details + 5. KQL Database details + + ![Screenshot showing overview of Microsoft Fabric workspace](./img/fabric-kql-database-overview.png) + +- Navigate through different KQL Database features like functions, Data streams to understand more about these features. + +### Operational Telemetry Real-Time Dashboard Report + +The Operational Telemetry Real-Time Dashboard Report in Microsoft Fabric provides Contoso Hypermarket with up-to-the-minute insights into their store operations. This interactive dashboard leverages data ingested into the KQL Database, displaying key metrics and trends related to equipment performance. By visualizing this data in real-time, the dashboard enables the leadership team to make informed decisions quickly, respond to operational issues promptly, and optimize overall business performance. The report's intuitive interface and powerful analytics capabilities ensure that critical operational data is always accessible and actionable. + +- Go to Contoso Hypermarket Fabric workspace to open Real-Time dashboard reports + + ![Screenshot showing overview of Microsoft Fabric workspace](./img/hypermarket-fabric-locate-realtime-dashboard.png) + +- Screenshot below shows Operational Telemetry Real-Time Dashboard Report + + ![Screenshot showing overview of Microsoft Fabric workspace](./img/fabric-ot-dashboard-report.png) + +## Next steps + +Now that you have completed the data pipeline scenarios, it's time to continue to the next scenario, ["Predictive Analytics using Microsoft 365 Copilot"](../../predictive_analytics/). diff --git a/docs/azure_jumpstart_ag/contoso_hypermarket/data_pipeline/operational/img/fabric-kql-database-overview.png b/docs/azure_jumpstart_ag/contoso_hypermarket/data_pipeline/operational/img/fabric-kql-database-overview.png new file mode 100644 index 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This automation automatically configures the Contoso Hypermarket scenario environment, including the infrastructure, the Contoso Hypermarket AI applications, CI/CD artifacts, observability components, and cloud architecture. The diagram below details the high-level architecture that's deployed and configured as part of the automation. + +![Screenshot of Contoso Hypermarket technology stack and operations architecture diagram](./img/architecture_diagram.png) + +Deploying the "Contoso Hypermarket" scenario consists of the following steps (once prerequisites are met). + + 1. Deploy infrastructure - The user deploys a Bicep file that creates the infrastructure in an Azure resource group. + 2. Bicep template deploys multiple Azure resources including the Client virtual machine. + 3. Client VM uses a custom script extension to run the Bootstrap PowerShell script which initializes the environment. + 4. Bootstrap script injects the Logon script to the Client VM. + 5. User logs in to the _Agora-VM-Client_ Azure virtual machine. + 6. After login the _Agora-VM-Client_ PowerShell scripts automatically run that configure the infrastructure, applications and CI/CD. These scripts will take some time to run. + +Once automation is complete, users can immediately start enjoying the Contoso Hypermarket experience. + +![Screenshot showing deployment flow architecture diagram](./img/deployment_workflow.png) + +## Prerequisites + +- [Install or update Azure CLI to version 2.65.0 or above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the following command to check your current installed version. + + ```shell + az --version + ``` + +- Login to Azure CLI using the _`az login`_ command. + +- Ensure that you have selected the correct subscription you want to deploy Agora to by using the _`az account list --query "[?isDefault]"`_ command. If you need to adjust the active subscription used by Azure CLI, follow [this guidance](https://learn.microsoft.com/cli/azure/manage-azure-subscriptions-azure-cli#change-the-active-subscription). + +- Register necessary Azure resource providers by running the following commands. + +### Option 1: PowerShell + + ```powershell + $providers = @( + "Microsoft.Kubernetes", + "Microsoft.KubernetesConfiguration", + "Microsoft.ExtendedLocation", + "Microsoft.HybridCompute", + "Microsoft.OperationsManagement", + "Microsoft.DeviceRegistry", + "Microsoft.EventGrid", + "Microsoft.IoTOperationsOrchestrator", + "Microsoft.IoTOperations", + "Microsoft.Fabric", + "Microsoft.SecretSyncController" + ) + + foreach ($provider in $providers) { + az provider register --namespace $provider --wait + } + ``` + +### Option 2: Shell + +```shell + providers=( + "Microsoft.Kubernetes" + "Microsoft.KubernetesConfiguration" + "Microsoft.ExtendedLocation" + "Microsoft.HybridCompute" + "Microsoft.OperationsManagement" + "Microsoft.DeviceRegistry" + "Microsoft.EventGrid" + "Microsoft.IoTOperationsOrchestrator" + "Microsoft.IoTOperations" + "Microsoft.Fabric" + "Microsoft.SecretSyncController" + ) + + for provider in "${providers[@]}"; do + az provider register --namespace "$provider" --wait + done + ``` + +> **Note:** The Jumpstart scenarios are designed with as much ease of use in mind and adhering to security-related best practices whenever possible. It's optional but highly recommended to scope the service principal to a specific [Azure subscription and resource group](https://learn.microsoft.com/cli/azure/ad/sp?view=azure-cli-latest) as well as considering using a [less privileged service principal account](https://learn.microsoft.com/azure/role-based-access-control/best-practices). + +- Clone the Azure Arc Jumpstart repository + + ```shell + git clone https://github.com/microsoft/azure_arc.git + ``` + +### Regions and capacity + +- Agora deploys multiple Azure services that are available in specific regions across the globe like Azure OpenAI and Azure IoT Operations. The list of supported regions per service is always expanding as Azure grows. At the moment, Agora must be deployed to one of the following regions to make sure you have a successful deployment. **Deploying Agora outside of these regions may result in unexpected results, deployment errors as some of the services deployed might not support that region.** + + - East US + - East US 2 + - West US 2 + - West US 3 + - West Europe + +> **Note:** Every subscription has different capacity restrictions and quotas so it's very critical to ensure you have sufficient vCPU quota available in your selected Azure subscription and the region where you plan to deploy Agora. If you encounter any capacity constraints error , please try another region from the list above. + +- Contoso Hypermarket allows an option to deploy standard or GPU-enabled worker nodes for the K3s Kubernetes clusters. + - If you select the option to use standard SKUs in the parameters file, **Agora requires 32 Ds-series vCPUs and 8 Bs-series vCPUs.** + - If you select the option to use GPU-enabled SKUs in the parameters file, then you can select one of a pre-defined list of GPU-enabled Virtual machines based on your subscription's available quotas. + +- You can use the below Azure CLI command to check your vCPU utilization. + + ```shell + az vm list-usage --location --output table + ``` + +> **Note:** Depending on your Azure Subscription, you might be restricted to deploy GPU-enabled SKUs. Please check your utilization and quota availability before using the GPU option. + +- Contoso Hypermarket deploys Azure AI services (OpenAI and speech-to-text models). You can check your utilization for Azure Open AI quotas using the following command. + + ```shell + az cognitiveservices usage list -l -o table --query "[].{Name:name.value, currentValue:currentValue, limit:limit}" + ``` + + ![Screenshot showing cognitive services usage](./img/check_ai_usage.png) + +> **Note:** Depending on your Azure Subscription, you might be restricted to deploy Cognitive Services accounts and/or Azure OpenAI models. Please check your utilization and quota availability before proceeding with the deployment. + +## Deployment: Bicep deployment via Azure CLI + +- Upgrade to latest Bicep version + + ```shell + az bicep upgrade + ``` + +- Edit the [main.parameters.json](https://github.com/microsoft/azure_arc/blob/main/azure_jumpstart_ag/contoso_hypermarket/bicep/main.parameters.json) template parameters file and supply some values for your environment. + - _`tenantId`_ - Your Azure tenant id + - _`windowsAdminUsername`_ - Client Windows VM Administrator username + - _`windowsAdminPassword`_ - Client Windows VM Password. Password must have 3 of the following: 1 lower case character, 1 upper case character, 1 number, and 1 special character. The value must be between 12 and 123 characters long. + - _`deployBastion`_ - Option to deploy using Azure Bastion instead of traditional RDP. Set to _`true`_ or _`false`_. + - _`fabricCapacityAdmin`_ - Microsoft Fabric capacity admin (admin user ins the same Entra ID tenant). + - _`deployGPUNodes`_ - Option to deploy GPU-enabled worker nodes for the K3s clusters. + - _`k8sWorkerNodesSku`_ The K3s worker nodes VM SKU. If _`deployGPUNodes`_ is set to true, a GPU-enabled VM SKU needs to be provided in this parameter (Example: _`Standard_NV6ads_A10_v5`_). + + ![Screenshot showing example parameters](./img/parameters_bicep.png) + +- You will need to get the Azure Custom Location Resource Provider (RP) Object ID (OID) and export it as an environment variable. This is required to enable [Custom Location](https://learn.microsoft.com/azure/azure-arc/platform/conceptual-custom-locations) on your cluster. + +> **Note:** You need permissions to list all the service principals. + +### Option 1: Bash + + ```shell + customLocationRPOID=$(az ad sp list --filter "displayname eq 'Custom Locations RP'" --query "[?appDisplayName=='Custom Locations RP'].id" -o tsv) + ``` + +### Option 2: PowerShell + + ```powershell + $customLocationRPOID=(az ad sp list --filter "displayname eq 'Custom Locations RP'" --query "[?appDisplayName=='Custom Locations RP'].id" -o tsv) + ``` + +- Now you will deploy the Bicep file. Navigate to the local cloned [deployment folder](https://github.com/microsoft/azure_arc/tree/main/azure_jumpstart_ag/contoso_hypermarket/bicep) and run the below command: + + ```shell + az login + az group create --name "" --location "" + az deployment group create -g "" -f "main.bicep" -p "main.parameters.json" -p customLocationRPOID="$customLocationRPOID" + ``` + +> **Note:** If you see any failure in the deployment, please check the [troubleshooting guide](../troubleshooting/). + +## Start post-deployment automation + +Once your deployment is complete, you can open the Azure portal and see the Agora scenario resources inside your resource group. You will be using the _Agora-Client-VM_ Azure virtual machine to explore various capabilities of Agora. You will need to remotely access _Agora-Client-VM_. + + ![Screenshot showing all deployed resources in the resource group](./img/deployed_resources.png) + + > **Note:** For enhanced Agora security posture, RDP (3389) and SSH (22) ports aren't open by default in Agora deployments. You will need to create a network security group (NSG) rule to allow network access to port 3389, or use [Azure Bastion](https://learn.microsoft.com/azure/bastion/bastion-overview) or [Just-in-Time (JIT)](https://learn.microsoft.com/azure/defender-for-cloud/just-in-time-access-usage?tabs=jit-config-asc%2Cjit-request-asc) access to connect to the VM. + +### Connecting to the Agora Client virtual machine + +Various options are available to connect to _Agora-Client-VM_, depending on the parameters you supplied during deployment. + +- [RDP](../deployment/#connecting-directly-with-rdp) - available after configuring access to port 3389 on the _Agora-NSG-Prod_, or by enabling [Just-in-Time access (JIT)](../deployment/#connect-using-just-in-time-access-jit). +- [Azure Bastion](../deployment/#connect-using-azure-bastion) - available if _`true`_ was the value of your _`deployBastion`_ parameter during deployment. + +#### Connecting directly with RDP + +By design, Agora doesn't open port 3389 on the network security group. Therefore, you must create an NSG rule to allow inbound 3389. + +- Open the _Agora-NSG-Prod_ resource in Azure portal and click "Add" to add a new rule. + + ![Screenshot showing adding a new inbound security rule](./img/nsg_add_rule.png) + +- Select My IP address from the dropdown. + + Screenshot showing adding a new allow RDP inbound security rule + +
+ + ![Screenshot showing all inbound security rule](./img/nsg_rdp_all_rules.png) + + ![Screenshot showing connecting to the VM using RDP](./img/rdp_connect.png) + +#### Connect using Azure Bastion + +- If you have chosen to deploy Azure Bastion in your deployment, use it to connect to the VM. + + ![Screenshot showing connecting to the VM using Bastion](./img/bastion_connect.png) + + > **Note:** When using Azure Bastion, the desktop background image isn't visible. Therefore some screenshots in this guide may not exactly match your experience if you are connecting to _Agora-Client-VM_ with Azure Bastion. + +#### Connect using just-in-time access (JIT) + +If you already have [Microsoft Defender for Cloud](https://learn.microsoft.com/azure/defender-for-cloud/just-in-time-access-usage?tabs=jit-config-asc%2Cjit-request-asc) enabled on your subscription and would like to use JIT to access the Client VM, use the following steps: + +- In the Client VM configuration pane, enable just-in-time. This will enable the default settings. + + ![Screenshot showing how to enable JIT](./img/enable_jit.png) + + ![Screenshot showing connecting to the VM using RDP](./img/connect_jit.png) + +### The Logon scripts + +- Once you log into the _Agora-Client-VM_, multiple automated scripts will open and start running. These scripts usually take around thirty minutes to finish, and once completed, the script windows will close automatically. At this point, the deployment is complete. + + ![Screenshot showing Agora-Client-VM](./img/automation.png) + +- Deployment is complete! Let's begin exploring the features of Contoso Hypermarket! + + ![Screenshot showing complete deployment](./img/contoso_hypermarket_complete.png) + + ![Screenshot showing Agora resources in Azure portal](./img/rg_complete.png) + +### Setup Microsoft Fabric workspace + +Due to some limitations of automating Microsoft Fabric items in the Fabric workspace using a managed identity, users deploying Contoso Hypermarket have to run a setup script manually using the end user credentials that are used to access Microsoft Fabric workspace. + +In order to create the Microsoft Fabric workspace, the tenant in which the workspace is created must have one of the following settings enabled. Users can verify these settings in [Microsoft Fabric Admin Portal](https://app.powerbi.com/admin-portal/tenantSettings?experience=power-bi). If you don't have permissions to access Microsoft Fabric Admin Portal, please contact your Entra ID tenant to confirm these settings. + +1. Enabled for the entire organization. +2. User must be a member of the security group allowed to create workspace. +3. Not a member of the excluded security groups. + +> **Note**: Microsoft Fabric don't support access as a guest user. Users will be redirected to their home tenant upon log into Microsoft Fabric. + + ![Screenshot showing Fabric tenant settings for workspace](./img/fabric-tenant-settings.png) + +Once you log into _Agora-Client-VM_ using any of the method described above follow the steps below to run the script + +- Open Windows Explorer and navigate to _C:\Ag\Fabric_ folder and make sure there are two files located in the folder. + + ![Screenshot showing Fabric workspace setup files](./img/fabric-script-files.png) + +- Open Windows PowerShell command-line tool from Start menu + + ![Screenshot showing PowerShell command-line tool launch](./img/ag-client-launch-powershell.png) + +- Change directory to _C:\Ag\Fabric_ + +- Run the script file as shown below and follow the onscreen instructions to login to the Entra ID tenant. + + ![Screenshot showing location of fabric setup PowerShell script](./img/fabric-run-script.png) + +- Users are prompted to complete authentication using a device code to log into the Entra ID tenant to create fabric workspace. + + ![Screenshot showing device authentication URL and code](./img/fabric-device-authentication.png) + +- Open Edge browser and access the URL copied above and enter the code to complete authentication. + + ![Screenshot showing device code prompt in the browser](./img/fabric-enter-device-code.png) + +- Select subscription when prompted to select as shown below and press enter + + ![Screenshot showing subscription selection](./img/fabric-select-subsription.png) + +- Once the script executed successfully without any issues, output of the script looks like below. If there are any errors open the script log file to review any issues and follow troubleshooting instructions below in this document. + + ![Screenshot showing results of successful script execution](./img/fabric-script-output.png) + +- Access Contoso Hypermarket workspace by log into [Microsoft Fabric](https://app.fabric.microsoft.com/) and complete authentication. Fabric home page will look like below + + ![Screenshot showing Microsoft Fabric home page](./img/fabric-home-page.png) + +- Click on Power BI to view workspaces and access Contoso Hypermarket workspace + + ![Screenshot showing choosing Power BI](./img/fabric-select-powerbi.png) + +- Click on Workspaces to view all available workspaces. Contoso Hypermarket workspaces have created with the naming standard _contoso-hypermarket-_, where _naming_guid_ can be found in the Azure Portal resource group. + + ![Screenshot showing view workspaces](./img/fabric-view-workspaces.png) + +- Open the workspace created to view Fabric items + + ![Screenshot showing open workspace](./img/fabric-open-workspace.png) + +- Screenshot below shows all the items created for the Contoso Hypermarket. + + ![Screenshot showing workspace items](./img/fabric-workspace-items.png) + +- Withing the workspace locate Semantic model credentials and click on ellipsis as shown below open settings. + + ![Screenshot showing Semantic model](./img/fabric-semantec-model.png) + +- Select _Settings_ to open settings and edit credentials. + + ![Screenshot showing Semantic model settings](./img/fabric-semantec-model-open-settings.png) + +- Under _Data Source settings_ click on _Edit credentials_ + + ![Screenshot showing Semantic model edit credentials](./img/fabric-semantec-model-edit-credentials.png) + +- Select options as shown in the screenshot below and click _Sign in_. Complete the sign in using sames credentials used to login to Microsoft Fabric. + + ![Screenshot showing Semantic model sign in](./img/fabric-semantec-model-credentials-signin.png) + +- In the Fabric workspace click _Refresh now_ icon as shown below and wait for the model to refresh with logged in credentials. + + ![Screenshot showing Semantic model refresh](./img/fabric-semantec-model-refresh.png) + +- After successful refresh, Semantic model status looks like as shown in the screenshot below. This will allow Contoso Hypermarket commercial dashboard report to access data source and render report. + + ![Screenshot showing Semantic model refresh status](./img/fabric-semantec-model-refresh-status.png) + +## Next steps + +Once deployment is complete its time to start experimenting with the various scenarios under the “Contoso Hypermarket” experience, starting with the ["Shopper insights using computer vision"](../shopper_insights/). diff --git a/docs/azure_jumpstart_ag/contoso_hypermarket/deployment/img/ag-client-launch-powershell.png b/docs/azure_jumpstart_ag/contoso_hypermarket/deployment/img/ag-client-launch-powershell.png new file mode 100644 index 00000000..8490a6b2 Binary files /dev/null and b/docs/azure_jumpstart_ag/contoso_hypermarket/deployment/img/ag-client-launch-powershell.png differ diff --git a/docs/azure_jumpstart_ag/contoso_hypermarket/deployment/img/architecture_diagram.png b/docs/azure_jumpstart_ag/contoso_hypermarket/deployment/img/architecture_diagram.png new 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and improve efficiency. Real-time dashboards provide insights into asset performance, such as refrigerators and HVAC systems, through an edge-to-cloud pipeline, enabling predictive maintenance, reducing downtime, and extending asset lifespan. + +In-store, computer vision and point-of-sale data optimize product placement and employee efficiency, while customizable dashboards support data-driven decisions and trend analysis. This framework enhances resource optimization, ensures reliable system health, and delivers a seamless retail experience. + +- [Industrial assets health and Point-of-sale (PoS) assets health dashboards](assets/_index.md) +- [Shopper insights dashboards and monitoring](shopper_insights/_index.md) +- [Infrastructure observability for Kubernetes and Arc-enabled Kubernetes](infrastructure/_index.md) diff --git a/docs/azure_jumpstart_ag/contoso_hypermarket/observability/assets/_index.md b/docs/azure_jumpstart_ag/contoso_hypermarket/observability/assets/_index.md new file mode 100644 index 00000000..c405e773 --- /dev/null +++ b/docs/azure_jumpstart_ag/contoso_hypermarket/observability/assets/_index.md @@ -0,0 +1,68 @@ +--- +type: docs +weight: 5 +title: Industrial assets observability +linkTitle: Industrial assets observability +--- + +# Industrial assets observability + +## Overview + +Maintenance workers, equipment managers, and production engineers at Contoso Hypermarket use real-time interactive dashboards to operate, monitor, maintain, and service all aspects of industrial assets such as refrigerators, HVAC. Real-time data and insights are available through dashboards they can proactively address potential issues, perform predictive maintenance, and ensure the optimal performance of industrial assets. This lowers downtime and extends the lifespan of the equipment. + +Industrial assets data is collected and managed using [Azure IoT Operations](https://learn.microsoft.com/azure/iot-operations/overview-iot-operations) and sent through an [edge-to-cloud data pipeline](../../data_pipeline/) where it can then be visualized using [Grafana](https://grafana.com/) or other tools.. + +## View industrial assets health in the Contoso Hypermarket application + +Maintenance workers and equipment managers can view the status and health of Contoso Hypermarket's critical production assets such as refrigerators, ovens, and [HVAC](https://en.wikipedia.org/wiki/Heating,_ventilation,_and_air_conditioning) units. Follow the instructions below to see this experience in action. + +- Open Microsoft Edge on the _Ag-Client_VM_ desktop and then click on the bookmarks toolbar and find the bookmark folder for Main UI. + + ![A screenshot showing Microsoft edge and the bookmarks folder with the bookmarks for Main UI](./img/bookmarks.png) + +- Click on the link for Maintenance Workers + + ![A screenshot showing the Contoso Hypermarket homepage](./img/homepage.png) + +- In this screen, maintenance workers can view the health of ovens, refrigerators, and point-of-sale assets such as automated checkouts or smart shelves. + + ![A screenshot showing the maintenance worker dashboard](./img/maintenance_dashboard.png) + +>**Note:** The suggested prompts user interface widgets at the top of the screen are cosmetic only and aren't implemented in this release of Contoso Hypermarket. + +- Users can also interact with Contoso Hypermarket using natural language. Click on the [Jumpstart Cerebral](../../cerebral/) icon in the upper right to ask a question about the status of industrial assets. + + ![A screenshot showing Jumpstart Cerebral location in the app](./img/cerebral_icon.png) + +## View industrial assets health in a Grafana dashboard + +Users also have the ability to use [Grafana](https://grafana.com/) to build, view and customize dashboards for industrial assets telemetry. + +- Open Microsoft Edge and click on the Grafana bookmark. + + ![A screenshot showing Microsoft Edge and the Grafana bookmark](./img/grafana_bookmark.png) + +- Navigate to the Industrial Assets Health dashboard. + + ![A screenshot showing the Industrial Assets Health menu of dashboards](./img/grafana_menu.png) + +- From this view, users can view real-time telemetry from industrial assets and filter by store and time dimension. + + ![A screenshot showing the industrial assets Grafana dashboard](./img/grafana_dashboard.png) + +## Observability architecture components + +The Contoso Hypermarket observability architecture uses [Azure IoT Operations](https://learn.microsoft.com/azure/iot-operations/overview-iot-operations) to collect and send industrial assets telemetry using MQTT, an industry standard protocol. Assets including refrigerators, ovens, HVACs, and retail smart shelves send telemetry through the [MQTT Broker](https://learn.microsoft.com/azure/iot-operations/manage-mqtt-broker/overview-iot-mq) using an [edge-to-cloud data pipeline](../../data_pipeline/operational/_index.md). + +- [**Azure IoT Operations**](https://learn.microsoft.com/azure/iot-operations/overview-iot-operations): Facilitates the connection and management of IoT devices, enabling real-time monitoring and control of industrial assets. +- [**MQTT**](https://learn.microsoft.com/azure/iot-operations/manage-mqtt-broker/overview-iot-mq): A lightweight messaging protocol used for efficient communication between IoT devices and the cloud. +- [**Prometheus**](https://prometheus.io/docs/introduction/overview/): An open-source monitoring and alerting toolkit used to collect and store metrics from industrial assets. +- [**Grafana**](https://grafana.com/): A multi-platform open-source analytics and interactive visualization web application used to create dashboards and graphs from the metrics collected by Prometheus. +- [**Azure Arc-enabled Kubernetes**](https://learn.microsoft.com/azure/azure-arc/kubernetes/overview): Manage and configure Kubernetes clusters running anywhere using Azure. + +![Screenshot of the architecture diagram for observability](./img/arch_diagram.png) + +## Next steps + +Now that you have completed the _Industrial assets_ scenario, it's time to continue to the next one, [Shopper insights observability](../shopper_insights/). diff --git a/docs/azure_jumpstart_ag/contoso_hypermarket/observability/assets/img/arch_diagram.png b/docs/azure_jumpstart_ag/contoso_hypermarket/observability/assets/img/arch_diagram.png new file mode 100644 index 00000000..afd37373 Binary files /dev/null and b/docs/azure_jumpstart_ag/contoso_hypermarket/observability/assets/img/arch_diagram.png differ diff --git a/docs/azure_jumpstart_ag/contoso_hypermarket/observability/assets/img/bookmarks.png 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new file mode 100644 index 00000000..7607eb4a --- /dev/null +++ b/docs/azure_jumpstart_ag/contoso_hypermarket/observability/infrastructure/_index.md @@ -0,0 +1,90 @@ +--- +type: docs +weight: 6 +title: Kubernetes infrastructure observability +linkTitle: Kubernetes infrastructure observability +--- + +# Kubernetes infrastructure observability + +## Overview + +Infrastructure observability is essential for the success of Contoso Hypermarket's cloud-to-edge strategy. By using various tools for infrastructure observability, Contoso Hypermarket is able to monitor and visualize the health of their Kubernetes and Arc-enabled Kubernetes environments. This allows them to proactively oversee the health and performance of their infrastructure, identify potential issues, and make data-driven decisions to optimize their operations. With infrastructure observability, Contoso can ensure that their cloud and edge infrastructure remains reliable, efficient, and resilient. + +[Prometheus](https://prometheus.io/) is an efficient open-source monitoring system that collects and stores metrics from various sources in real-time. It offers a flexible query language for analyzing the collected metrics and provides robust alerting capabilities. Meanwhile, [Grafana](https://grafana.com/) is a powerful open-source data visualization and analytics platform that allows users to create interactive and customizable dashboards to visualize the collected metrics in real-time. + +By using Prometheus and Grafana for infrastructure observability, Contoso benefits in three key ways: + +- First, Prometheus's efficient data collection ensures that Contoso can monitor critical performance indicators and resource utilization in real-time. +- Second, Grafana offers a user-friendly interface for visualizing the collected metrics, enabling Contoso to create interactive and customizable dashboards. These dashboards provide valuable insights into their infrastructure’s health and performance, facilitate trend identification, and support informed decision-making. +- Third, the combination of Prometheus and Grafana aids in troubleshooting and root cause analysis. + +## Architecture + +The architecture of the observability infrastructure stack uses the [Kube Prometheus Stack](https://github.com/prometheus-community/helm-charts/tree/main/charts/kube-prometheus-stack). This stack is comprised of Kubernetes manifests, Grafana dashboards, and Prometheus rules, which are easily deployed and provide end-to-end monitoring of Kubernetes clusters. + +In the store locations, Prometheus instances are deployed to periodically collect metrics from the K3s cluster. A separate Grafana instance is set up to centralize monitoring and visualization. The Prometheus instances send metrics data to this central Grafana instance. Grafana dashboards are then configured to display relevant metrics, enabling operators and administrators to monitor the health and performance of the entire infrastructure. + +![Screenshot showing Observability infrastructure stack architecture](./img/observability-diagram.png) + +## Grafana dashboards + +Grafana's dashboards in Contoso's implementation provide a visually appealing and customizable interface for monitoring their infrastructure. With Grafana, they can create intuitive and interactive dashboards that display key metrics and insights, empowering them to make data-driven decisions and quickly identify any issues or trends within their cloud to edge infrastructure. The following Grafana dashboards are automatically deployed as part of advanced automation for all the manufacturing plants: + +- **Kubernetes / Views / Global**: The Kubernetes Global Dashboard in Grafana offers a concise overview of your Kubernetes cluster, allowing you to quickly assess its overall health and performance. The dashboard includes panels that highlight key metrics, such as total cluster CPU, RAM, and network utilization, as well as resource usage across namespaces and nodes. Additionally, it tracks the number of resource types used in the cluster and helps detect misconfigured application resources by comparing real usage with requested and limited resources. + +- **Node Exporter Full**: The Kubernetes Nodes Dashboard in Grafana provides a detailed view of node-level metrics and resources in your Kubernetes cluster. It enables you to monitor CPU and RAM usage, track pods running on each node, and identify any resource anomalies or performance issues. The dashboard also offers system-level metrics such as system load, context switches, and file descriptors, allowing for troubleshooting and optimization. Additionally, it provides insights into storage capacity, volumes, and I/O operations on the nodes, aiding in the effective management of storage resources. + +> **Note:** The Kubernetes dashboards included in this guide are based on the work of publicly available dashboards. We would like to express our appreciation for authors' efforts in creating this insightful dashboard that enhances our monitoring capabilities for Kubernetes. The original dashboards can be found at _[Node Exporter Full](https://grafana.com/grafana/dashboards/1860-node-exporter-full)_ and _[Grafana Dashboards Kubernetes](https://github.com/dotdc/grafana-dashboards-kubernetes)_. + +## Accessing dashboards + +To view the health of the Kubernetes clusters, access the Grafana dashboards by doing the following: + +- Connect to the Client VM _Ag-VM-Client_ using the instruction in the [deployment guide](../deployment/) + +- Open the Edge browser, expand Grafana in the Favorites Bar and select **Grafana** + + ![Screenshot showing accessing Grafana](./img/grafana-bookmark.png) + +- Login using the Windows Admin Username and Password you provided when you created your deployment. + + ![Screenshot showing Login for Grafana](./img/grafana-login.png) + +- Click on the `Kubernetes / Views / Global` dashboard to review the health of the two clusters. + + ![Screenshot showing Grafana dashboards](./img/grafana-starred-dashboards.png) + + ![Screenshot showing Grafana Cluster dashboard](./img/grafana-chicago-global.png) + +- Review the different panels in the dashboard to see the metrics collected from the Kubernetes cluster. + + ![Screenshot showing Grafana Cluster dashboard panels for CPU](./img/grafana-chicago-global-cpu.png) + + ![Screenshot showing Grafana Cluster dashboard panels for Network](./img/grafana-chicago-global-network.png) + +- Click `Home` to go back to the home page and review the other dashboards available. + + ![Screenshot showing Grafana Home page](./img/grafana-dashboards-home.png) + +- Click `Kubernetes / Views / Node Exporter` dashboard to review the cluster node metrics. + + ![Screenshot showing Grafana Home page](./img/grafana-starred-dashboards-node.png) + + ![Screenshot showing Grafana Node Exporter dashboard](./img/grafana-chicago-node.png) + +- Review the different panels in the dashboard to see the metrics collected from the Kubernetes cluster. + + ![Screenshot showing Grafana Node Exporter dashboard panels quick metrics](./img/grafana-node-quick.png) + + ![Screenshot showing Grafana Node Exporter dashboard panels basic metrics](./img/grafana-node-basic.png) + + ![Screenshot showing Grafana Node Exporter dashboard panels disk](./img/grafana-node-disks.png) + +- Repeat the same steps to access the `Seattle` cluster by selecting this cluster from the _datasource_ dropdown. + + ![Screenshot showing Grafana Monterrey Dashboards](./img/grafana-datasource-cluster.png) + +## Next steps + +Now that you have completed the _Kubernetes observability_ scenario, it's time to continue to the next one, ["Data pipeline and reporting for commercial sales"](../../data_pipeline/commercial/). diff --git a/docs/azure_jumpstart_ag/contoso_hypermarket/observability/infrastructure/img/grafana-bookmark.png b/docs/azure_jumpstart_ag/contoso_hypermarket/observability/infrastructure/img/grafana-bookmark.png new file mode 100644 index 00000000..be97c3f0 Binary files /dev/null and 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b/docs/azure_jumpstart_ag/contoso_hypermarket/observability/shopper_insights/_index.md @@ -0,0 +1,68 @@ +--- +type: docs +weight: 5 +title: Shopper insights observability +linkTitle: Shopper insights observability +--- + +# Shopper insights observability + +## Overview + +Contoso Hypermarket uses computer vision in their retail store locations to improve store sales, optimize product placements, and increase efficiency for store employees. Store managers and other users can view data collected by the [shopper insights](../../shopper_insights/) computer vision system and from point-of-sale and other retail industry assets. + +Retail industry assets data is collected and managed using [Azure IoT Operations](https://learn.microsoft.com/azure/iot-operations/overview-iot-operations) and sent through an [edge-to-cloud data pipeline](../../data_pipeline/) where it can then be visualized using [Grafana](https://grafana.com/) or other tools. + +## View shopper insights in the Contoso Hypermarket application + +Store managers can use the Contoso Hypermarket application to view data from the shopper insights platform. + +- Open Microsoft Edge on the _Ag-Client_VM_ desktop and then click on the bookmarks toolbar and find the bookmark folder for Main UI. + + ![A screenshot showing Microsoft edge and the bookmarks folder with the bookmarks for Main UI](./img/bookmarks.png) + +- Click on the link for Store Managers + + ![A screenshot showing the Contoso Hypermarket homepage](./img/homepage.png) + +- In this screen, store managers can view the status of ovens, refrigerators, and point-of-sale assets such as automated checkouts or smart shelves. + + ![A screenshot showing the maintenance worker dashboard](./img/store_manager_dashboard.png) + +- Users can also interact with Contoso Hypermarket using natural language. Click on the [Jumpstart Cerebral](../../cerebral/) icon in the upper right to ask a question about the status of industrial assets. + + ![A screenshot showing Jumpstart Cerebral location in the app](./img/cerebral_icon.png) + + ![A screenshot showing Jumpstart Cerebral answering a question about industrial assets](./img/cerebral_question.png) + +## View shopper and point-of-sale insights in a Grafana dashboard + +Users also have the ability to use [Grafana](https://grafana.com/) to build, view and customize dashboards for industrial assets telemetry. + +- Open Microsoft Edge and click on the Grafana bookmark. + + ![A screenshot showing Microsoft Edge and the Grafana bookmark](./img/grafana_bookmark.png) + +- Navigate to the Industrial Assets Health dashboard. + + ![A screenshot showing the Industrial Assets Health menu of dashboards](./img/dashboard_menu.png) + +- From this view, users can view real-time telemetry from industrial assets and filter by store and time dimension. + + ![A screenshot showing the industrial assets Grafana dashboard](./img/grafana_dashboard.png) + +## Observability architecture components + +The Contoso Hypermarket observability architecture uses [Azure IoT Operations](https://learn.microsoft.com/azure/iot-operations/overview-iot-operations) to collect and send industrial assets telemetry using MQTT, an industry standard protocol. Assets including refrigerators, ovens, HVACs, and retail smart shelves send telemetry through the [MQTT Broker](https://learn.microsoft.com/azure/iot-operations/manage-mqtt-broker/overview-iot-mq)] using an [edge-to-cloud data pipeline](../../data_pipeline/operational/_index.md). + +![Screenshot of the architecture diagram for observability](./img/arch_diagram.png) + +- [**Azure IoT Operations**](https://learn.microsoft.com/azure/iot-operations/overview-iot-operations): Facilitates the connection and management of IoT devices, enabling real-time monitoring and control of industrial assets. +- [**MQTT**](https://learn.microsoft.com/azure/iot-operations/manage-mqtt-broker/overview-iot-mq): A lightweight messaging protocol used for efficient communication between IoT devices and the cloud. +- [**Prometheus**](https://prometheus.io/docs/introduction/overview/): An open-source monitoring and alerting toolkit used to collect and store metrics from industrial assets. +- [**Grafana**](https://grafana.com/): A multi-platform open-source analytics and interactive visualization web application used to create dashboards and graphs from the metrics collected by Prometheus. +- [**Azure Arc-enabled Kubernetes**](https://learn.microsoft.com/azure/azure-arc/kubernetes/overview): Manage and configure Kubernetes clusters running anywhere using Azure. + +## Next steps + +Now that you have completed the _Shopper insights observability_ scenario, it's time to continue to the next one, [Kubernetes observability](../infrastructure/). diff --git a/docs/azure_jumpstart_ag/contoso_hypermarket/observability/shopper_insights/img/arch_diagram.png b/docs/azure_jumpstart_ag/contoso_hypermarket/observability/shopper_insights/img/arch_diagram.png new file mode 100644 index 00000000..afd37373 Binary files /dev/null and b/docs/azure_jumpstart_ag/contoso_hypermarket/observability/shopper_insights/img/arch_diagram.png differ diff --git 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mode 100644 index 00000000..e85672ee Binary files /dev/null and b/docs/azure_jumpstart_ag/contoso_hypermarket/observability/shopper_insights/img/store_manager_dashboard.png differ diff --git a/docs/azure_jumpstart_ag/contoso_hypermarket/predictive_analytics/_index.md b/docs/azure_jumpstart_ag/contoso_hypermarket/predictive_analytics/_index.md new file mode 100644 index 00000000..a3fae631 --- /dev/null +++ b/docs/azure_jumpstart_ag/contoso_hypermarket/predictive_analytics/_index.md @@ -0,0 +1,111 @@ +--- +type: docs +weight: 9 +title: Predictive analytics using Microsoft 365 Copilot +linkTitle: Predictive analytics using Microsoft 365 Copilot +--- + +# Predictive Analytics using Microsoft 365 Copilot + +## Overview + +Contoso Hypermarket uses Microsoft 365 Copilot to analyze vast amounts of data and generate actionable insights. By integrating Copilot into their existing systems, Contoso can predict trends, optimize inventory, and improve customer satisfaction. Copilot acts as another employee managers and staff can interact with. + +## Working with Spreadsheets + +Despite the advancements in technology, spreadsheets remain a crucial tool for many businesses, including Contoso Hypermarket. Microsoft 365 Copilot seamlessly integrates with older formats like spreadsheets, allowing Contoso to: + +- Import and Analyze Data: Copilot can import data from spreadsheets, analyze it, and provide insights without the need for manual data entry. +- Generate Reports: Automatically generate comprehensive reports based on the data in spreadsheets, saving time and reducing errors. +- Predictive Modeling: Use historical data from spreadsheets to build predictive models that forecast future trends and demands. + +## Key Benefits + +Using Copilot in this way offers several benefits including: + +- Efficiency: Automates data analysis and report generation, freeing up valuable time for employees. +- Accuracy: Reduces the risk of human error in data analysis and reporting. +- Scalability: Handles large datasets and complex calculations with ease. + +## Prerequisites + +This scenario requires access to Microsoft 365 Copilot. For information on enabling this within your organization, please refer to [this](https://learn.microsoft.com/copilot/microsoft-365/microsoft-365-copilot-enable-users) document. + +## Getting Started + +> **Note:** AI-generated content may be incorrect. In addition, the Screenshots and Copilot responses may differ. + +To begin, download the three spreadsheets: + +- [*retail_inventory_sample*](https://download.microsoft.com/download/0832a0b6-bf27-4a3f-bf65-b3404233f9cb/retail_inventory_sample_20241025.xlsx) - this simulates inventory and sales data for Contoso Hypermarket. +- [*footfall_sample*](https://download.microsoft.com/download/3fe05ab3-2aa7-4c59-8260-d90a92888432/footfall_sample_20241025.xlsx) - this simulates footfall data derived from in-store cameras. Footfall is the traffic count inside the store and is useful for store managers to understand what parts of the store get the most visitors. Refer to the [Contoso Hypermarket footfall documentation](../footfall/) to understand how footfall is implemented in this scenario. +- [*contoso_roasters*](https://download.microsoft.com/download/3fe05ab3-2aa7-4c59-8260-d90a92888432/contoso_roasters_20241025.xlsx) - this is a production log of coffee roasting + +Let's explore the data contained in these spreadsheets. The **retail inventory sample** data contains data on the stocked products, their location in the store, and the amount of product sold for each day. The fields representing stock location (e.g. stock_location_1, stock_location_2, etc.) are intended to convey the specific location in the store where a product was placed. + +![Screenshot from Excel of retail inventory sample spreadsheet](./img/02-excel-inventory.png) + +Inside the **footfall** spreadsheet are counts of the total footfall for specific locations in the store (e.g. footfall_location_1, footfall_location_2, etc.) as well as the total footfall for the store. + +![Screenshot from Excel of footfall sample spreadsheet](./img/03-excel-footfall.png) + +The **Contoso Roasters** spreadsheet contains information about the coffee roasting of Contoso Hypermarket. It tracks the variety of green coffee roasted and specific attributes about the roast itself, like the roast level (e.g. City Plus, Full City, etc.). + +![Screenshot from Excel of the Contoso Roasters spreadsheet](./img/04-excel-contoso-roasters.png) + +## Using Microsoft 365 Copilot + +To use the below prompts, visit [Microsoft 365 Copilot](https://copilot.microsoft.com/). As shown in the image below, click on the paperclip icon. + +![Screenshot showing spreadsheets being uploaded](./img/05-upload-spreadsheets-to-copilot.png) + +Next, select **upload from device** + +![Screenshot showing the upload from device icon](./img/06-upload-from-device.png) + +Browse to the spreadsheets you downloaded to upload them. + +![Screenshot showing browsing to the downloaded spreadsheets](./img/09-upload-spreadsheets.png) + +With these spreadsheets uploaded, now you can enter prompts. Copy and paste one of the prompts from the table above, or create your own prompt. + +![Screenshot of example prompt](./img/07-example-prompt.png) + +View the resulting prompt. The example below shows the response to, "based on the sales data in this spreadsheet, how many bananas have I sold?" + +![Screenshot of example response](./img/08-example-response.png) + +Take note that Copilot can infer meaning and context from the data in the spreadsheet. For example, in the Screenshot below, Copilot lists the two variety of Mexican coffee beans roasted (Chiapas Tulija and Chiapas Sierra Mariscal) without additional prompting to explain that both are Mexican coffee beans. + +![Screenshot of Copilot response on coffee roasting](./img/10-copilot-response-coffee.png) + +## Prompts + +The following table provides example prompts and expected results. Note that the output will likely vary somewhat from this table, but the overall analysis should be similar. + +| Prompt Text | Expected Result | Source Data | Remarks | +|-------------|-----------------|-------------|---------| +| based on the sales data in this spreadsheet, how many bananas have I sold? | we've sold 1,339 bananas | *retail_inventory_sample* | note that we did not specify the specific product name; just bananas | +| based on the sales data for apples, which locations in the store sell the most apples? | Stock Locations 1, 2, and 3 are the top locations for selling apples, each selling 25 apples per day, while Stock Location 4 sells 15 apples per day | *retail_inventory_sample* | +| Provide a summary of coffee roasting production. | Overview of coffee roasting production metrics. | *contoso_roasters* | Check for any missing logs | +| how many chocolate chip cookies did I sell each day? | a list of cookies sold per day | *retail_inventory_sample* | +| how many chocolate chip cookies went unsold each day?| a count of the cookies left unsold each day | *retail_inventory_sample* | +| based on the chocolate chip cookie sales data, which day of the week generates the most cookie sales? | Copilot identifies that most cookies sales are on Friday | *retail_inventory_sample* | +| which day of the week generates the most unsold chocolate chip cookies? | Copilot identifies that Thursdays have the most unsold cookies | *retail_inventory_sample* | +| which location in the store produces the least amount of sales? | Copilot identifies that Stock Location 4 generates the least amount of sales | *retail_inventory_sample* | +| which store location sells the most cookies? | Copilot identifies that Stock Location 2 sells the most cookies | *retail_inventory_sample* | +| this spreadsheet contains footfall data for each store location. Footfall is the number of people at each store location. Based on this data, which store location has the most footfall? | Copilot indicates that Stock Location 2 has the most footfall | *footfall_sample* | +| what correlations do you observe between footfall and sales | Copilot observes a correlation between higher footfall traffic with higher sales and that locations with consistent footfall have consistent sales | *retail_inventory_sample* and *footfall_sample* | note that it is making correlation between these two spreadsheets without explicitly joining tables | +| if I can increase footfall to 800, how many chocolate chip cookies should I expect to sell? | Copilot assumes a linear relationship between footfall and sales and estimates we could sell 100 cookies per day with increased footfall | *retail_inventory_sample* and *footfall_sample* | while not intended to replace the role of a data scientist, Copilot is able to suggest an estimate based on the relationship between the data in both spreadsheets | +| using the roast_start_time and cooling_end_time columns, what's the total amount of hours and minutes my Probat roaster has been used so far? | Copilot lists the roast duration for each day and adds of the total roast time | *contoso_roasters* | this is helpful for tracking periodic maintenance tasks that need to occur | +| which roast profile should I use for City Plus roasts that produces the least amount of moisture loss? | Copilot identifies roast profile_cp_01 as the roast that produces the least amount of moisture loss| *contoso_roasters* | everything else equal, it's desirable to use the roast profile that produces the least amount of moisture loss because it means there's more salable product for an equally good product | +| how much energy have I used for all of my roasts this month? | Copilot adds the data in the for power consumption| *contoso_roasters* | note that Copilot is able to identify the pertinent field from the spreadsheet to answer this question | +| how many kilograms of Tanzanian beans have I roasted? | Copilot lists the two days where this bean type was roasted and lists the total amount roasted | *contoso_roasters* | +| how many kilograms of beans from Mexico have I roasted this month? | Copilot identifies the two varieties of Mexican beans roaster and lists the total amount roasted | *contoso_roasters* | note that Copilot does not need to be explicitly told about the Mexican bean varieties | +| which coffee stays on the store shelves the longest? | Copilot identifies that the Tanzanian beans is unsold and stays on the shelves the longest | *contoso_roasters* | +| if current sales trends of coffee continues, how much Mexican beans should I order for roasting? | Copilot estimates average daily sales, projects it over a month, and suggests a quantity of Mexican beans to order | *contoso_roasters* and *retail_inventory_sample* | +| how much beans from Tanzania should I order if I except a 10% increase in demand for these beans over the next month? | Copilot looks at the two different roasts of Tanzanian beans and estimates the demand for each based on a 10% increase in demand | *contoso_roasters* and *retail_inventory_sample* | + +## Next steps + +Now that you have successfully completed all of the Contoso Motors scenarios, continue to the next step to learn how to [cleanup the deployment](../cleanup/). diff --git a/docs/azure_jumpstart_ag/contoso_hypermarket/predictive_analytics/img/01-prompt.png b/docs/azure_jumpstart_ag/contoso_hypermarket/predictive_analytics/img/01-prompt.png new file mode 100644 index 00000000..f00b999d Binary files /dev/null and b/docs/azure_jumpstart_ag/contoso_hypermarket/predictive_analytics/img/01-prompt.png differ diff --git a/docs/azure_jumpstart_ag/contoso_hypermarket/predictive_analytics/img/02-excel-inventory.png b/docs/azure_jumpstart_ag/contoso_hypermarket/predictive_analytics/img/02-excel-inventory.png new file mode 100644 index 00000000..14faff95 Binary files /dev/null and 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operations. In-store cameras detect shopper behavior such as foot traffic, time spent in certain zones of the store, and shopper-specific identifiers that personalize the shopping experience. + +### Shopper insights for store managers + +Store managers can view foot traffic for a store using the Store Manager dashboard. This includes identifying high traffic areas within the store, which helps in optimizing store layout and product placement. In-store cameras can be mapped to specific "zones" which are then used to sort foot traffic into specific groups of shopper foot traffic. + +- Open Microsoft Edge on the _Ag-Client_VM_ desktop and then click on the bookmarks toolbar and find the bookmark folder for Main UI. + + ![A screenshot showing Microsoft edge and the bookmarks folder with the bookmarks for Main UI](./img/bookmarks.png) + +- Click on the link for Store Managers + + ![A screenshot showing the Contoso Hypermarket homepage](./img/homepage.png) + +#### Configure cameras + +The specific region of the camera field-of-view that will be sent for inference can be controlled on a per-camera basis. This is done by managing cameras, zones and camera regions. Cameras are placed in retail store locations to monitor shopper behaviors and inventory. Cameras can be assigned a "zone" which represents a physical area of the store. Cameras also have defined "regions" which represent a defined bounding box inside the camera field of view that will be sent for inference. + +![A screenshot showing cameras that have been added](./img/manage_cameras.png) + +- To add new camera, click "Add Camera" button which will open a side panel to provide details about the new camera. Two [simulated cameras](https://github.com/kerberos-io/virtual-rtsp) are provided as part of the deployment which display videos of a typical grocery store. + + ![A screenshot showing add camera step](./img/add_camera.png) + +- Zones represents a physical area of the store. To create zones, click the "Zones" tab, then click the "Add Zone" button. This will start the a set up wizard for zones. + + ![A screenshot showing "Zones" tab highlighted](./img/zones.png) + +- Upload a floor plan. You can find a sample floor plan in the "C:\Ag\" folder on _Ag_Client_VM_. Click next when ready to continue. + + ![A screenshot showing Upload floor plan step with "Next" button highlighted](./img/upload_floor_plan.png) + +- Zones are created by clicking and dragging the mouse on the floor plan image. You can provide your own floor plan image if desired. + + ![A screenshot showing the draw floor zones page](./img/draw_floor_zones.png) + +- To draw a zone on a floor plan, click any area (any area under the toolbar in the screenshot show above) and then drag the mouse down to create a rectangle. Once the rectangle is created, the selected area will be highlighted on the floor plan and a panel on the right lets you provide a zone label and select a particular camera for the new zone that's being created. Click done when ready. + + ![A screenshot draw floor zones page showing a drawn rectangle](./img/draw_floor_zones_with_rectangle.png) + +- After setting up the zone, the next is to setup a camera region bounding box. Once a camera is selected, the video feed from that camera appears. The camera region will allow user to create bounding box within the camera's video feed. The bounding box is the region of view sent for inference jobs. + + ![A screenshot a selected camera with its video feed](./img/setup_camera_region.png) + +- To create bounding box, click any where on the video and then drag the mouse to draw a rectangle. Once a rectangle is drawn on the video feed, a purple bordered box with light purple tint will appear depicting the bounding box. This is show in the following screenshot: + + ![A screenshot a selected camera with its video feed with bounding box](./img/setup_camera_region_with_rectangle.png) + +- Click the "Next" button to move to the Review Summary page, then click Save to complete the camera setup wizard. + + ![A screenshot showing review summary](./img/review_summary.png) + +### Architecture + +Contoso Hypermarket uses an [adaptive cloud](https://techcommunity.microsoft.com/blog/azurearcblog/a-guide-to-adaptive-cloud-at-microsoft-ignite-2024/4285028) application architecture to use AI in their business and day-to-day store operations. collect and use shopper insights about their retail locations. The diagram illustrates the overall architecture of the Shopper Insights system and the relation of the computer vision APIs relative to other solution components. + +![A diagram depicting the shopper insights system architecture](./img/app_architecture.png) + +#### Video inference pipeline + +Video inference is handled by two APIs using a common pattern. + +- **Footfall API**: Detects humans in a specified region of a camera using [YOLOv8](https://docs.ultralytics.com/models/yolov8/) and makes the inference results available via API call. +- **Shopper Insights API**: Detects and identifies individuals in a specified region of a camera using [YOLOv8](https://docs.ultralytics.com/models/yolov8/) and predicts the individuals age using [OpenVINO](https://docs.openvino.ai). It has the ability to track multiple people simultaneously, maintaining unique IDs for each detected person and providing metrics about customer movements, demographics, and interactions within defined areas. + +![A diagram depicting the footfall inference workflow](./img/footfall_diagram.png) + +- Footfall API - Uses basic object detection with YOLOv8 and the COCO dataset to detect people and other objects in a retail setting +- Shopper Insights API - Uses OpenVINO™ to provide metrics about customer movements, demographics, and interactions within defined areas. It has the ability to track multiple people simultaneously, maintaining unique IDs for each detected person. + +#### Models Used + +Person re-identification is a critical computer vision task that involves recognizing and tracking the same individual across different camera views or time periods. The following models are used in the footfall and shopper insights APIs. + +- Yolo8 Detection Model - [yolov8n.pt]([https://docs.openvino.ai/2022.3/omz_models_model_person_detection_retail_0013.html](https://docs.ultralytics.com/models/yolov8/#supported-tasks-and-modes)) + - Detects people in video frames + - Generates unique feature vectors for tracked individuals + - Tracks people movement and time in zones + - Provides real-time demographic/footfall insights + +- Age Recognition Model - [age-gender-recognition-retail-0013](https://docs.openvino.ai/2022.3/omz_models_model_age_gender_recognition_retail_0013.html) + - Estimates age of detected persons + - Groups ages into decades for statistics + - Provides real-time demographic insights + +## Next steps + +Now that you have completed the _Shopper insights using computer vision_ scenario, it's time to continue to the next one, ["Cerebral: AI-Powered Retail Assistant"](../cerebral/). diff --git 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This feature enables Contoso Hypermarket shoppers and employees to ask questions through voice input, which are processed through Jumpstart Cerebral to provide real-time responses. The solution uses a combination of local and cloud-based AI services to ensure efficient processing, accuracy, and scalability. + +### What is Speech to Text? + +Speech to Text (STT) is a technology that converts spoken language into written text using advanced algorithms and machine learning models. The process involves capturing audio input, preprocessing it to remove noise, extracting relevant features, recognizing speech patterns, and generating text output. This a technology that is widely to enable seamless interaction between humans and machines. + +## Architecture + +Below is an architecture diagram that shows how the audio data flows from the user's microphone and into the STT system to generate real-time transcriptions of spoken language. This architecture includes a local instance of [OpenAI Whisper](https://github.com/openai/whisper) running at the edge in the Contoso Hypermarket, [Azure AI Speech](https://learn.microsoft.com/azure/ai-services/speech-service/overview) in the Azure cloud, and various components to process and route the audio data. The microphone captures audio input, which is processed locally or sent to the cloud for transcription. The transcribed text is then used to generate responses and provide real-time assistance to users. + +![Screenshot showing the Speech To Text architecture diagram](./img/stt_flow.png) + +### User Flow + +> **Warning**: Due to the lack of HTTPS in the Contoso Hypermarket Main UI deployment, some browsers may limit access to the microphone. To ignore the browser's secure origin policy, follow these steps: +> 1. Navigate to `edge://flags/#unsafely-treat-insecure-origin-as-secure` in the Microsoft Edge browser. +> 2. Find and enable the "Insecure origins treated as secure" section. +> 3. Add any addresses you want to ignore the secure origin policy for. Remember to include the port number if required. +> 4. Save and restart Edge. + +> **Note**: This is for development purposes only. To avoid this limitation, the app will need to be hosted on HTTPS for users to be able to use their microphone or camera. + +1. **Microphone Audio Feed Captures User Questions** – The system starts by capturing audio from the user via a microphone connected to the main interface. +2. **Audio Processor** – An audio processing function detects if there is voice input. Once detected, the audio is converted to a blob format suitable for submission to the Jumpstart Cerebral system. +3. The **Jumpstart Cerebral STT Connector** submits the audio blob to the Jumpstart Cerebral API (`/Cerebral/Api/Stt`) for transcription. +4. The **Jumpstart Cerebral API** endpoint receives the requests, and handles it to the **STT function**. This logic checks whether a local model is available. If a local model is available, it uses [OpenAI Whisper](https://github.com/openai/whisper) for transcription; otherwise, it routes the request to [Azure AI Speech](https://learn.microsoft.com/azure/ai-services/speech-service/overview) within Azure services. + - **Local Model Available**: The system uses the OpenAI Whisper model locally deployed on the edge infrastructure. Whisper is a general-purpose speech recognition model. This approach minimizes latency and maintains data privacy by keeping data processing on-premises. + - **No Local Model**: If a local model is not present or available, the audio is sent to [Azure AI Speech](https://learn.microsoft.com/azure/ai-services/speech-service/overview) for transcription. This is a service of the [Azure AI Services](https://azure.microsoft.com/products/ai-services) family that provides high-accuracy transcription with a scalable cloud-based model. +5. **Transcription Processing** – Once the transcription is received, it is verified for accuracy and returned to the main user interface. +6. **UI Rendering** – The transcription result is displayed in the user interface, allowing the user to view the text representation of their question. +7. **Question Processing** – The transcribed question is then sent to the **Jumpstart Cerebral API**, which triggers a **"Process Question"** event. This event initiates further processing to deliver the appropriate response, following the [Jumpstart Cerebral processing](./../cerebral/_index.md) flow. + +## Comparing Cloud vs Edge STT + +### OpenAI Whisper + +[OpenAI Whisper](https://github.com/openai/whisper) is a general-purpose speech recognition model that excels at various tasks, including multilingual speech recognition, speech translation, and language identification. Whisper has been trained on a large dataset of diverse audio samples, which enables it to handle different languages, accents, and speech nuances effectively. + +Whisper is deployed locally in the Contoso Hypermarket's STT architecture to provide quick and accurate transcription without requiring internet connectivity. This local deployment allows for: + +- **Reduced Latency**: Since transcription is processed on-premises, there is minimal delay in response time. +- **Data Privacy**: By keeping data on local infrastructure, the system minimizes exposure to external networks, enhancing data security. +- **Multilingual Support**: Whisper can automatically recognize and transcribe multiple languages, making it versatile in a multicultural retail environment. + +### Azure AI Speech Service + +[Azure AI Speech](https://learn.microsoft.com/azure/ai-services/speech-service/overview) is a cloud-based speech recognition service provided by Microsoft. When the local Whisper model is unavailable, the Contoso Hypermarket system routes audio data to Azure AI Speech for transcription. This service offers high-accuracy transcription with the added benefits of Azure's scalability and reliability. + +Key features of Azure AI Speech used in this architecture include: + +- **Scalability**: Azure Speech can handle large-scale requests and can be adjusted based on demand, making it suitable for a dynamic retail environment. +- **Accuracy and Language Support**: Azure AI Speech provides high-accuracy transcription for numerous languages and dialects, ensuring inclusivity for a diverse customer base. +- **Integration with Azure services**: Azure AI Speech integrates seamlessly with other Azure services, allowing for efficient deployment, monitoring, and maintenance. + +This hybrid approach—using OpenAI Whisper locally and Azure AI Speech when necessary—ensures that Contoso Hypermarket can maintain consistent transcription quality while optimizing for performance, data privacy, and cost. + +## Speech To Text Experience + +### Key Steps + +1. **Access the Interface**: The users accesses the **Contoso Hypermarket** interface on a local workstation or device. +2. **Access Jumpstart Cerebral**: The users clicks the Jumpstart Cerebral logo in the right-side part of the header to open the Cerebral chat panel. + + ![Click on Jumpstart Cerebral logo](./img/init_header.png) + +3. **Activate Recording**: The user clicks the "Record" button in the UI to begin capturing audio input. If **Microphone Access** was not granted before, the user needs to accept enabling this access. + + ![Highlighted Record button and Browser permission enablement](./img/start_recording.png) + +4. **Stop Recording**: The user clicks the "Stop" button to end the audio capture. The recorded audio is then processed for checks and transcriptions. + + ![Highlighted Record Stop button](./img/stop_record.png) + +5. **STT Process**: The captured audio is processed, checked for voice content, and then sent through the STT connector. + + ![Highlighted audio created](./img/stop_record.png) + +6. The user can play the recorded audio for better understanding + + ![Show how to click on audio sent](./img/audio_created.png) + +7. **Real-time Response**: The transcribed text is displayed in the main interface for immediate feedback. + + ![Transcribed question](./img/transcription.png) + +8. **Process Question Event**: The question is submitted to the Cerebral API, triggering further analysis and response generation. + +## Next steps + +Now that you have completed the _Commercial and Operations assistance with Speech-to-Text_ scenario, it's time to continue to the next one, [Web UI and AI Inference flow](../observability/assets/). diff --git a/docs/azure_jumpstart_ag/contoso_hypermarket/speech_to_text/img/audio_created.png b/docs/azure_jumpstart_ag/contoso_hypermarket/speech_to_text/img/audio_created.png new file mode 100644 index 00000000..e03af165 Binary files /dev/null and b/docs/azure_jumpstart_ag/contoso_hypermarket/speech_to_text/img/audio_created.png differ diff --git a/docs/azure_jumpstart_ag/contoso_hypermarket/speech_to_text/img/init_header.png b/docs/azure_jumpstart_ag/contoso_hypermarket/speech_to_text/img/init_header.png new file mode 100644 index 00000000..58c416b3 Binary files /dev/null and b/docs/azure_jumpstart_ag/contoso_hypermarket/speech_to_text/img/init_header.png differ diff --git a/docs/azure_jumpstart_ag/contoso_hypermarket/speech_to_text/img/placeholder.jpg b/docs/azure_jumpstart_ag/contoso_hypermarket/speech_to_text/img/placeholder.jpg new file mode 100644 index 00000000..6615c0e5 Binary files /dev/null and b/docs/azure_jumpstart_ag/contoso_hypermarket/speech_to_text/img/placeholder.jpg differ diff --git a/docs/azure_jumpstart_ag/contoso_hypermarket/speech_to_text/img/start_recording.png b/docs/azure_jumpstart_ag/contoso_hypermarket/speech_to_text/img/start_recording.png new file mode 100644 index 00000000..2b4f4258 Binary files /dev/null and b/docs/azure_jumpstart_ag/contoso_hypermarket/speech_to_text/img/start_recording.png differ diff --git a/docs/azure_jumpstart_ag/contoso_hypermarket/speech_to_text/img/stop_record.png b/docs/azure_jumpstart_ag/contoso_hypermarket/speech_to_text/img/stop_record.png new file mode 100644 index 00000000..175c27e1 Binary files /dev/null and b/docs/azure_jumpstart_ag/contoso_hypermarket/speech_to_text/img/stop_record.png differ diff --git a/docs/azure_jumpstart_ag/contoso_hypermarket/speech_to_text/img/stt_flow.png b/docs/azure_jumpstart_ag/contoso_hypermarket/speech_to_text/img/stt_flow.png new file mode 100644 index 00000000..2bb1ada0 Binary files /dev/null and b/docs/azure_jumpstart_ag/contoso_hypermarket/speech_to_text/img/stt_flow.png differ diff --git a/docs/azure_jumpstart_ag/contoso_hypermarket/speech_to_text/img/transcription.png b/docs/azure_jumpstart_ag/contoso_hypermarket/speech_to_text/img/transcription.png new file mode 100644 index 00000000..c621ac7d Binary files /dev/null and b/docs/azure_jumpstart_ag/contoso_hypermarket/speech_to_text/img/transcription.png differ diff --git a/docs/azure_jumpstart_ag/contoso_hypermarket/troubleshooting/_index.md b/docs/azure_jumpstart_ag/contoso_hypermarket/troubleshooting/_index.md new file mode 100644 index 00000000..78b044b2 --- /dev/null +++ b/docs/azure_jumpstart_ag/contoso_hypermarket/troubleshooting/_index.md @@ -0,0 +1,52 @@ +--- +type: docs +weight: 11 +title: Troubleshooting +linkTitle: Troubleshooting +--- + +# Troubleshooting + +## Basic troubleshooting + +Occasionally deployments of Jumpstart Agora Contoso Hypermarket may fail at various stages. Common reasons for failed deployments include: + +- Invalid Azure credentials such as service principal id, service principal secret, service principal Azure tenant ID, or custom location resource provider id provided in _main.parameters.json_ file. + +- Not enough vCPU quota available in your target Azure region - check vCPU quota and ensure you have at least 32 available vCPU. + - You can use the command _`az vm list-usage --location --output table`_ to check your available vCPU quota. + + ![Screenshot showing capacity constraints error](./img/capacity_constraints.png) + + ![Screenshot showing az vm list-usage](./img/az_vm_list_usage.png) + +- Target Azure region doesn't support all required Azure services - ensure you are running Agora in one of the supported regions listed in the [deployment guide](../deployment/). + +- Not enough AI services quota in your target subscription and region - check AI services quota using the command _`az cognitiveservices usage list -l -o table --query "[].{Name:name.value, currentValue:currentValue, limit:limit}"`_. + + ![Screenshot showing ai services restrictions error](./img/aiServices_quota.png) + +### Exploring logs from the _Ag-VM-Client_ virtual machine + +Occasionally, you may need to review log output from scripts that run on the _Ag-VM-Client_ virtual machine in case of deployment failures. To make troubleshooting easier, the Agora deployment scripts collect all relevant logs in the _C:\Ag\Logs_ folder on _Ag-VM-Client_. A short description of the logs and their purpose can be seen in the list below: + +| Log file | Description | +| ------- | ----------- | +| _C:\Ag\Logs\AgLogonScript.log_ | Output from the primary PowerShell script that drives most of the automation tasks. | +| _C:\Ag\Logs\ArcConnectivity.log_ | Output from the tasks that onboard servers and Kubernetes clusters to Azure Arc. | +| _C:\Ag\Logs\AzCLI.log_ | Output from Azure CLI login. | +| _C:\Ag\Logs\AzPowerShell.log_ | Output from the installation of PowerShell modules. | +| _C:\Ag\Logs\Bookmarks.log_ | Output from the configuration of Microsoft Edge bookmarks. | +| _C:\Ag\Logs\Bootstrap.log_ | Output from the initial bootstrapping script that runs on _Ag-VM-Client_. | +| _C:\Ag\Logs\ClusterSecrets.log_ | Output of secret creation on Kubernetes clusters. | +| _C:\Ag\Logs\GitOps-Ag-*.log_ | Output of scripts that collect GitOps logs on the remote Kubernetes clusters. | +| _C:\Ag\Logs\installK3s-Ag-K3s*.log_ | Output of scripts that configure the K3s clusters. | +| _C:\Ag\Logs\Observability.log_ | Output from the script that configures observability components of the solution. | +| _C:\Ag\Logs\Tools.log_ | Output from the tasks that set up developer tools on _Ag-VM-Client_. | +| _C:\ArcBox\Logs\WinGet-provisioning-*.log_ | Output from WinGet.ps1 which installs WinGet and install the bootstrap packages. | + + ![Screenshot showing Agora logs folder on AG-Client](./img/logs_folder.png) + +### Authorization errors when deploying Azure IoT Operations + +If you see authorization errors during the automation, please make sure to review the [prerequisites](../deployment/#prerequisites) in the deployment guide diff --git a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/troubleshooting/img/aad_quota_exceeded.png b/docs/azure_jumpstart_ag/contoso_hypermarket/troubleshooting/img/aad_quota_exceeded.png similarity index 100% rename from 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a/docs/azure_jumpstart_ag/contoso_hypermarket/troubleshooting/img/aiServices_quota.png b/docs/azure_jumpstart_ag/contoso_hypermarket/troubleshooting/img/aiServices_quota.png new file mode 100644 index 00000000..2f7b8eeb Binary files /dev/null and b/docs/azure_jumpstart_ag/contoso_hypermarket/troubleshooting/img/aiServices_quota.png differ diff --git a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/troubleshooting/img/az_vm_list_usage.png b/docs/azure_jumpstart_ag/contoso_hypermarket/troubleshooting/img/az_vm_list_usage.png similarity index 100% rename from docs/azure_jumpstart_ag/manufacturing/contoso_motors/troubleshooting/img/az_vm_list_usage.png rename to docs/azure_jumpstart_ag/contoso_hypermarket/troubleshooting/img/az_vm_list_usage.png diff --git a/docs/azure_jumpstart_ag/contoso_hypermarket/troubleshooting/img/capacity_constraints.png b/docs/azure_jumpstart_ag/contoso_hypermarket/troubleshooting/img/capacity_constraints.png new file mode 100644 index 00000000..6cbb84b9 Binary files /dev/null and b/docs/azure_jumpstart_ag/contoso_hypermarket/troubleshooting/img/capacity_constraints.png differ diff --git a/docs/azure_jumpstart_ag/contoso_hypermarket/troubleshooting/img/logs_folder.png b/docs/azure_jumpstart_ag/contoso_hypermarket/troubleshooting/img/logs_folder.png new file mode 100644 index 00000000..5ea45a64 Binary files /dev/null and b/docs/azure_jumpstart_ag/contoso_hypermarket/troubleshooting/img/logs_folder.png differ diff --git a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/_index.md b/docs/azure_jumpstart_ag/contoso_motors/_index.md similarity index 96% rename from docs/azure_jumpstart_ag/manufacturing/contoso_motors/_index.md rename to docs/azure_jumpstart_ag/contoso_motors/_index.md index 9ce616ac..85c5e85c 100644 --- a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/_index.md +++ b/docs/azure_jumpstart_ag/contoso_motors/_index.md @@ -1,12 +1,12 @@ --- type: docs +weight: 3 linkTitle: "Contoso Motors" -weight: 1 description: >- Learn how Contoso Motors, a leading global automobile manufacturer, implements an AI-enhanced cloud-to-edge strategy with Azure Arc, IoT services, AKS hybrid, artificial intelligence, software distribution and data pipelines. --- -## Contoso Motors overview +## Overview Contoso Motors continues to lead the automotive industry with its unwavering commitment to innovation and employee safety. Positioned at the forefront of the industrial revolution, Contoso Motors seamlessly integrates cutting-edge technologies into every aspect of their operations, embodying the essence of Industrial IoT (Industry 4.0). @@ -27,13 +27,13 @@ Contoso Motors uses an AI technology stack, services, and processes to support t - **Computer Vision for Object and Defect Detection** - Leveraging computer vision algorithms to detect and classify objects on the manufacturing line, as well as identifying defects with high accuracy and speed, streamlining quality control processes. - **IT/OT Operations and Observability** - Integrating IT and operational technology (OT) systems to enhance visibility and control over manufacturing operations, enabling real-time monitoring, analysis, and optimization for increased productivity and reliability. -![Applications and technology stack architecture diagram](./img/architecture_diagram.png) +![Screenshot showing Contoso Motors applications and technology stack architecture diagram](./img/architecture_diagram.png) ## Virtual edge environment Jumpstart Agora provides virtual sandbox environments that simulate edge infrastructure deployments for industry solutions. The automation in the Contoso Motors scenario deploys an Azure Virtual machine to support this "virtual" factory's AI technology. Additional features are included to further enhance the "virtual industry" experience in a lab setting, including simulated RTSP feeds, data emulators, and MQTT and OPC UA devices and data. Review the diagram and dedicated guides below to learn more about the virtual environment. -![Applications and technology stack architecture diagram](./img/simulation_stack.png) +![Screenshot showing Contoso Motors virtual edge environment architecture diagram](./img/simulation_stack.png) ## Getting started diff --git a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/ai_inferencing/_index.md b/docs/azure_jumpstart_ag/contoso_motors/ai_inferencing/_index.md similarity index 87% rename from docs/azure_jumpstart_ag/manufacturing/contoso_motors/ai_inferencing/_index.md rename to docs/azure_jumpstart_ag/contoso_motors/ai_inferencing/_index.md index d1b2feb2..fff09630 100644 --- a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/ai_inferencing/_index.md +++ b/docs/azure_jumpstart_ag/contoso_motors/ai_inferencing/_index.md @@ -4,7 +4,7 @@ weight: 3 title: Web UI and AI Inference flow linkTitle: Web UI and AI Inference flow summary: | - Contoso Motors leverages AI-powered manufacturing with a Kubernetes-based infrastructure which provides a flexible and scalable design that can be easily extended. In this scenario, Contoso Motors wants to implement different AI inferencing models for various use cases, leveraging OpenVINO™ Model Server (OVMS), a high-performance inference serving software that allows users to deploy and serve multiple AI models. This scenario also explains the architecture of the AI inference flow and the steps involved in the inference/processing. + Contoso Motors leverages AI-powered manufacturing with a Kubernetes-based infrastructure which provides a flexible and scalable design that can be easily extended. In this scenario, Contoso Motors wants to implement different AI inference models for various use cases, leveraging OpenVINO™ Model Server (OVMS), a high-performance inference serving software that allows users to deploy and serve multiple AI models. This scenario also explains the architecture of the AI inference flow and the steps involved in the inference/processing. serviceOrPlatform: INFRASTRUCTURE technologyStack: - AI @@ -28,7 +28,7 @@ The Edge AI inference flow can be divided into two configuration steps and three The models and configurations needed for the OVMS deployment are hosted in a storage account in Azure. During deployment, the **Model Downloader** job pulls these models and configurations by running the ***ovms_config.sh*** script, which downloads all necessary model files and stores them in the **ovms-pvc** persistent volume claim for the OVMS pods to access and serve. All models need to be placed and mounted in a particular directory structure and according to the rules described in [OpenVINO™ - Model Serving](https://docs.openvino.ai/2022.3/ovms_docs_models_repository.html). - For more information, see *[job.yaml](https://github.com/microsoft/jumpstart-agora-apps/blob/main/contoso_manufacturing/operations/charts/ovms/templates/job.yaml)* and *[ovms_config.sh](https://raw.githubusercontent.com/microsoft/jumpstart-agora-apps/manufacturing/contoso_manufacturing/deployment/configs/ovms_config.sh)*. + For more information, see *[job.yaml](https://github.com/microsoft/jumpstart-agora-apps/blob/main/contoso_manufacturing/operations/charts/ovms/templates/job.yaml)* and *[ovms_config.sh](https://raw.githubusercontent.com/microsoft/jumpstart-agora-apps/contoso_manufacturing/deployment/configs/ovms_config.sh)*. | Model | Scenario | Links | | ----- | -------- | ----- | @@ -48,17 +48,17 @@ The Edge AI inference flow can be divided into two configuration steps and three After the configuration is complete, the inference flow can be initiated. The **Web AI Inference and UI** application retrieves data, such as video frames from an RTSP source or images, and applies any necessary preprocessing, such as resizing an image to a specific size with a specific RGB/BGR format. The data is then sent to the model server for inference, and any required post processing is applied to store or display the results to the user. -The steps involved in the inference/procressing can be described as follows: +The steps involved in the *inference/procressing* can be described as follows: -1. **Video Streamer Capture:** this is the code of the **Web AI Inference and UI** pod that handles the capture of the RTSP feeds. Depending on the AI scenario, this code will grab the appropriate video and using **OpenCV** will handle the video frames for processing and AI inference. For more information on the different videos available, please refer to the *[rtsp-simulator.yaml](https://github.com/microsoft/jumpstart-agora-apps/tree/manufacturing/contoso_manufacturing/operations/charts/rtsp-simulator)* file. +1. **Video Streamer Capture:** this is the code of the **Web AI Inference and UI** pod that handles the capture of the RTSP feeds. Depending on the AI scenario, this code will grab the appropriate video and using **OpenCV** will handle the video frames for processing and AI inference. For more information on the different videos available, please refer to the *[rtsp-simulator.yaml](https://github.com/microsoft/jumpstart-agora-apps/tree/contoso_manufacturing/operations/charts/rtsp-simulator)* file. -1. **Pre-process and AI inference:** this is the code of the **Web AI Inference and UI** pod that handles the **preprocessing** of the image and sends the processed input to the AI inference server. Each model has it's own Python class ([Yolo8](https://github.com/microsoft/jumpstart-agora-apps/blob/main/contoso_manufacturing/developer/webapp-decode/yolov8.py), [Welding](https://github.com/microsoft/jumpstart-agora-apps/blob/main/contoso_manufacturing/developer/webapp-decode/welding.py) and [Pose Estimator](https://github.com/microsoft/jumpstart-agora-apps/blob/main/contoso_manufacturing/developer/webapp-decode/pose_estimator.py)) that implements the **pre-process**, **post-process** and **run** methods according to the model requirements. Once the input data, generally a **torch** is created, it's then sent to the AI inference server using the [ovmsclient library](https://pypi.org/project/ovmsclient/). +2. **Pre-process and AI inference:** this is the code of the **Web AI Inference and UI** pod that handles the **preprocessing** of the image and sends the processed input to the AI inference server. Each model has it's own Python class ([Yolo8](https://github.com/microsoft/jumpstart-agora-apps/blob/main/contoso_manufacturing/developer/webapp-decode/yolov8.py), [Welding](https://github.com/microsoft/jumpstart-agora-apps/blob/main/contoso_manufacturing/developer/webapp-decode/welding.py) and [Pose Estimator](https://github.com/microsoft/jumpstart-agora-apps/blob/main/contoso_manufacturing/developer/webapp-decode/pose_estimator.py)) that implements the **pre-process**, **post-process** and **run** methods according to the model requirements. Once the input data, generally a **torch** is created, it's then sent to the AI inference server using the [ovmsclient library](https://pypi.org/project/ovmsclient/). The AI inference server, in our case the [OpenVINO™ Model Server](https://docs.openvino.ai/2023.3/ovms_what_is_openvino_model_server.html), hosts models and makes them accessible to software components over standard network protocols: a client sends a request to the model server, which performs model inference and sends a response back to the client. - The OVMS model server is configured as part of the helm deployment, using the **OVMS Operator**. The deployment installs the OVMS Operator, sets up the storage for AI models, and configures the OVMS pods and services. For more information about the setup, check [OVMS Helm](https://github.com/microsoft/jumpstart-agora-apps/tree/manufacturing/contoso_manufacturing/operations/charts/ovms). For more information about OVMS Operator, check [OpenVINO™ Model Server with Kubernetes](https://docs.openvino.ai/archive/2021.4/ovms_docs_kubernetes.html). The OVMS model server is typically deployed as a set of pods. Each pod contains one or more instances of the OVMS software, along with any necessary dependencies. The pods are managed by a Kubernetes controller, which is responsible for ensuring that the desired number of pods are running at all times. + The OVMS model server is configured as part of the helm deployment, using the **OVMS Operator**. The deployment installs the OVMS Operator, sets up the storage for AI models, and configures the OVMS pods and services. For more information about the setup, check [OVMS Helm](https://github.com/microsoft/jumpstart-agora-apps/tree/contoso_manufacturing/operations/charts/ovms). For more information about OVMS Operator, check [OpenVINO™ Model Server with Kubernetes](https://docs.openvino.ai/archive/2021.4/ovms_docs_kubernetes.html). The OVMS model server is typically deployed as a set of pods. Each pod contains one or more instances of the OVMS software, along with any necessary dependencies. The pods are managed by a Kubernetes controller, which is responsible for ensuring that the desired number of pods are running at all times. -2. **Post-process and UI rendering:** this is the code in the **Web AI Inference and UI** pod is responsible for handling post-processing and final UI rendering for the user. Depending on the model, once the OVMS provides the inference response, post-processing is applied to the image, such as adding labels, bounding boxes, or human skeleton graphs. Once the visual data is added to the frame, it's then served to the UI frontend using a **Flask App** method. For more information on the Flask App, please refer to the [Web UI - app.py](https://github.com/microsoft/jumpstart-agora-apps/blob/main/contoso_manufacturing/developer/webapp-decode/app.py) +3. **Post-process and UI rendering:** this is the code in the **Web AI Inference and UI** pod is responsible for handling post-processing and final UI rendering for the user. Depending on the model, once the OVMS provides the inference response, post-processing is applied to the image, such as adding labels, bounding boxes, or human skeleton graphs. Once the visual data is added to the frame, it's then served to the UI frontend using a **Flask App** method. For more information on the Flask App, please refer to the [Web UI - app.py](https://github.com/microsoft/jumpstart-agora-apps/blob/main/contoso_manufacturing/developer/webapp-decode/app.py) ## OpenVINO™ Model Server diff --git a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/ai_inferencing/img/ai_flow.png b/docs/azure_jumpstart_ag/contoso_motors/ai_inferencing/img/ai_flow.png similarity index 100% rename from docs/azure_jumpstart_ag/manufacturing/contoso_motors/ai_inferencing/img/ai_flow.png rename to docs/azure_jumpstart_ag/contoso_motors/ai_inferencing/img/ai_flow.png diff --git a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/ai_inferencing/img/ovms.png b/docs/azure_jumpstart_ag/contoso_motors/ai_inferencing/img/ovms.png similarity index 100% rename from docs/azure_jumpstart_ag/manufacturing/contoso_motors/ai_inferencing/img/ovms.png rename to docs/azure_jumpstart_ag/contoso_motors/ai_inferencing/img/ovms.png diff --git 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a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/arc_monitoring_servers/img/vminsights_map.png b/docs/azure_jumpstart_ag/contoso_motors/arc_monitoring_servers/img/vminsights_map.png similarity index 100% rename from docs/azure_jumpstart_ag/manufacturing/contoso_motors/arc_monitoring_servers/img/vminsights_map.png rename to docs/azure_jumpstart_ag/contoso_motors/arc_monitoring_servers/img/vminsights_map.png diff --git a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/arc_monitoring_servers/img/vminsights_performance.png b/docs/azure_jumpstart_ag/contoso_motors/arc_monitoring_servers/img/vminsights_performance.png similarity index 100% rename from docs/azure_jumpstart_ag/manufacturing/contoso_motors/arc_monitoring_servers/img/vminsights_performance.png rename to docs/azure_jumpstart_ag/contoso_motors/arc_monitoring_servers/img/vminsights_performance.png diff --git a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/cleanup/_index.md b/docs/azure_jumpstart_ag/contoso_motors/cleanup/_index.md similarity index 80% rename from docs/azure_jumpstart_ag/manufacturing/contoso_motors/cleanup/_index.md rename to docs/azure_jumpstart_ag/contoso_motors/cleanup/_index.md index d30c696b..cae4d866 100644 --- a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/cleanup/_index.md +++ b/docs/azure_jumpstart_ag/contoso_motors/cleanup/_index.md @@ -21,4 +21,4 @@ To clean up your deployment, simply delete the resource group using Azure CLI or ## Next steps -If you still having issues with the deployment, please refer to the [Troubleshooting](../troubleshooting//) section. Otherwise, if you have additional questions or feedback, please refer to the [FAQ](../../../faq/) section. +If you still having issues with the deployment, please refer to the [Troubleshooting](../troubleshooting/) section. Otherwise, if you have additional questions or feedback, please refer to the [FAQ](../../../faq/) section. diff --git a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/cleanup/img/az_group_delete.png b/docs/azure_jumpstart_ag/contoso_motors/cleanup/img/az_group_delete.png similarity index 100% rename from docs/azure_jumpstart_ag/manufacturing/contoso_motors/cleanup/img/az_group_delete.png rename to docs/azure_jumpstart_ag/contoso_motors/cleanup/img/az_group_delete.png diff --git a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/cleanup/img/portal_delete.png b/docs/azure_jumpstart_ag/contoso_motors/cleanup/img/portal_delete.png similarity index 100% rename from docs/azure_jumpstart_ag/manufacturing/contoso_motors/cleanup/img/portal_delete.png rename to docs/azure_jumpstart_ag/contoso_motors/cleanup/img/portal_delete.png diff --git a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/data_opc/_index.md b/docs/azure_jumpstart_ag/contoso_motors/data_opc/_index.md similarity index 91% rename from docs/azure_jumpstart_ag/manufacturing/contoso_motors/data_opc/_index.md rename to docs/azure_jumpstart_ag/contoso_motors/data_opc/_index.md index b40a8423..e26c19be 100644 --- a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/data_opc/_index.md +++ b/docs/azure_jumpstart_ag/contoso_motors/data_opc/_index.md @@ -54,27 +54,27 @@ Grafana, a leading open-source platform for monitoring and observability, taps i ![Screenshot showing the simulated data](./img/mqtt_events_assembly_batteries.png) -### Influxdb Dashboard Reports +### InfluxDB Dashboard Reports -Contoso supports Influxdb dashboard reports for the manufacturing analytics and monitoring at the edge. These reports are created in Influxdb to allow staff who are working locally at the manufacturing plant to view dashboards reports. These reports are generated based on live data received from the sensors directly into Influxdb database. +Contoso supports InfluxDB dashboard reports for the manufacturing analytics and monitoring at the edge. These reports are created in InfluxDB to allow staff who are working locally at the manufacturing plant to view dashboards reports. These reports are generated based on live data received from the sensors directly into InfluxDB database. These reports are readily available to access through Edge browser. Follow the steps below to access these reports on the Client VM. -- On the Client VM (_Ag-VM-Client_), open Edge browser, expand Influxdb favorite collection and chose Detroit or Monterrey plants to view dashboard reports. +- On the Client VM (_Ag-VM-Client_), open Edge browser, expand InfluxDB favorite collection and chose Detroit or Monterrey plants to view dashboard reports. - ![Screenshot showing how to open Influxdb dashboard reports](./img/influxdb_open_dashboard_reports.png) + ![Screenshot showing how to open InfluxDB dashboard reports](./img/influxdb_open_dashboard_reports.png) - Login with the _admin_ username and password provided when you created the deployment. - ![Screenshot showing how to login to Influxdb dashboard reports](./img/influxdb_dashboard_reports_login.png) + ![Screenshot showing how to login to InfluxDB dashboard reports](./img/influxdb_dashboard_reports_login.png) - Click on Dashboards icon as shown in the screen below to view list of available dashboard reports. There are 3 dashboard reports created to show car assembly line, battery line, and welding energy consumption reports. - ![Screenshot showing how to open Influxdb dashboard reports](./img/influxdb_list_dashboard_reports.png) + ![Screenshot showing how to open InfluxDB dashboard reports](./img/influxdb_list_dashboard_reports.png) - ![Screenshot showing how to open Influxdb dashboard reports](./img/influxdb_dashboard_reports.png) + ![Screenshot showing how to open InfluxDB dashboard reports](./img/influxdb_dashboard_reports.png) -- Click on _Constoso Motors - Assembly Car Line_ dashboard report to view. Contoso staff can start monitoring the car assembly production line. The most critical KPI featured is the Assembly Robot Efficiency. The Efficiency indicator is color-coded for at-a-glance status updates: +- Click on _Contoso Motors - Assembly Car Line_ dashboard report to view. Contoso staff can start monitoring the car assembly production line. The most critical KPI featured is the Assembly Robot Efficiency. The Efficiency indicator is color-coded for at-a-glance status updates: | Color | Indication | | ------- | ------------------------------------------------------------------ | @@ -86,11 +86,11 @@ These reports are readily available to access through Edge browser. Follow the s - Additionally, the dashboard breaks down the components of Assembly Car Line report; _Availability_, _Cars produced_, and _Staff on shift_, to provide a detailed analysis. A key focus is on _Availability_, with constant monitoring of downtime to identify and classify lost time reasons, such as equipment malfunctions. -- Click on _Constoso Motors - Battery Line_ dashboard report to view. Contoso staff can start monitoring the car battery assembly line KPIs featured in the dashboard report. +- Click on _Contoso Motors - Battery Line_ dashboard report to view. Contoso staff can start monitoring the car battery assembly line KPIs featured in the dashboard report. ![Screenshot showing battery assembly line dashboard reports](./img/influxdb_dashboard_assembly_battery_line.png) -- Click on _Constoso Motors - Welding Energy Consumption_ dashboard report to view. Contoso staff can start monitoring the welding enerty consumption KPIs featured in the dashboard report. +- Click on _Contoso Motors - Welding Energy Consumption_ dashboard report to view. Contoso staff can start monitoring the welding enerty consumption KPIs featured in the dashboard report. ![Screenshot showing welding energy consumption dashboard reports](./img/influxdb_dashboard_welding_energy_consumption.png) diff --git a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/data_opc/img/adx_confirm_dashboard_report_name.png b/docs/azure_jumpstart_ag/contoso_motors/data_opc/img/adx_confirm_dashboard_report_name.png similarity index 100% rename from docs/azure_jumpstart_ag/manufacturing/contoso_motors/data_opc/img/adx_confirm_dashboard_report_name.png rename to docs/azure_jumpstart_ag/contoso_motors/data_opc/img/adx_confirm_dashboard_report_name.png diff --git a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/data_opc/img/adx_contoso_motors_production_metrics.png b/docs/azure_jumpstart_ag/contoso_motors/data_opc/img/adx_contoso_motors_production_metrics.png similarity index 100% rename from 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docs/azure_jumpstart_ag/manufacturing/contoso_motors/data_opc/img/sample_data_generation.png rename to docs/azure_jumpstart_ag/contoso_motors/data_opc/img/sample_data_generation.png diff --git a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/deployment/_index.md b/docs/azure_jumpstart_ag/contoso_motors/deployment/_index.md similarity index 96% rename from docs/azure_jumpstart_ag/manufacturing/contoso_motors/deployment/_index.md rename to docs/azure_jumpstart_ag/contoso_motors/deployment/_index.md index d759e5f7..28d25e3b 100644 --- a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/deployment/_index.md +++ b/docs/azure_jumpstart_ag/contoso_motors/deployment/_index.md @@ -11,7 +11,7 @@ linkTitle: Deployment guide Jumpstart Agora provides a simple deployment process using Azure Bicep and PowerShell that minimizes user interaction. This automation automatically configures the Contoso Motors scenario environment, including the infrastructure, the Contoso-Motors applications, CI/CD artifacts, and observability components. The diagram below details the high-level architecture that is deployed and configured as part of the automation. -![Architecture diagram](./img/architecture_diagram.png) +![Screenshot showing Contoso Motors virtual edge environment architecture diagram](./img/architecture_diagram.png) Deploying the "Contoso Motors" scenario consists of the following steps (once prerequisites are met). @@ -24,11 +24,11 @@ Deploying the "Contoso Motors" scenario consists of the following steps (once pr Once automation is complete, users can immediately start enjoying the Contoso Motors experience. -![Deployment flow architecture diagram](./img/deployment_workflow.png) +![Screenshot showing deployment flow architecture diagram](./img/deployment_workflow.png) ## Prerequisites -- [Install or update Azure CLI to version 2.53.0 or above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the following command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 or above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the following command to check your current installed version. ```shell az --version @@ -148,7 +148,7 @@ Once automation is complete, users can immediately start enjoying the Contoso Mo az bicep upgrade ``` -- Edit the [main.parameters.json](https://github.com/microsoft/azure_arc/blob/main/azure_jumpstart_ag/manufacturing/bicep/main.parameters.json) template parameters file and supply some values for your environment. +- Edit the [main.parameters.json](https://github.com/microsoft/azure_arc/blob/main/azure_jumpstart_ag/contoso_motors/bicep/main.parameters.json) template parameters file and supply some values for your environment. - _`spnClientId`_ - Your Azure service principal application id - _`spnClientSecret`_ - Your Azure service principal secret - _`spnObjectId`_ - Your Azure service principal id @@ -189,7 +189,7 @@ Once automation is complete, users can immediately start enjoying the Contoso Mo $customLocationRPOID=(az ad sp list --filter "displayname eq 'Custom Locations RP'" --query "[?appDisplayName=='Custom Locations RP'].id" -o tsv) ``` -- Now you will deploy the Bicep file. Navigate to the local cloned [deployment folder](https://github.com/microsoft/azure_arc/tree/main/azure_jumpstart_ag/manufacturing/bicep) and run the below command: +- Now you will deploy the Bicep file. Navigate to the local cloned [deployment folder](https://github.com/microsoft/azure_arc/tree/main/azure_jumpstart_ag/contoso_motors/bicep) and run the below command: ```shell az login diff --git a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/deployment/img/architecture_diagram.png b/docs/azure_jumpstart_ag/contoso_motors/deployment/img/architecture_diagram.png similarity index 100% rename from docs/azure_jumpstart_ag/manufacturing/contoso_motors/deployment/img/architecture_diagram.png rename to docs/azure_jumpstart_ag/contoso_motors/deployment/img/architecture_diagram.png diff --git a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/deployment/img/automation.png b/docs/azure_jumpstart_ag/contoso_motors/deployment/img/automation.png similarity index 100% rename from docs/azure_jumpstart_ag/manufacturing/contoso_motors/deployment/img/automation.png rename to docs/azure_jumpstart_ag/contoso_motors/deployment/img/automation.png diff --git 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a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/k8s_infra_observability/_index.md +++ b/docs/azure_jumpstart_ag/contoso_motors/k8s_infra_observability/_index.md @@ -1,8 +1,8 @@ --- type: docs weight: 6 -title: Infrastructure observability for Kubernetes and Arc-enabled Kubernetes -linkTitle: Infrastructure observability for Kubernetes and Arc-enabled Kubernetes +title: Kubernetes infrastructure observability +linkTitle: Kubernetes infrastructure observability summary: | Infrastructure observability plays a crucial role in the success of Contoso Motors' cloud to edge strategy. By implementing infrastructure observability, Contoso gains comprehensive monitoring and visualization capabilities for their Kubernetes and Arc-enabled Kubernetes environments. This empowers them to proactively monitor the health and performance of their infrastructure, identify potential issues, and make data-driven decisions to optimize their operations. With infrastructure observability, Contoso can ensure that their cloud and edge infrastructure remain reliable, efficient, and resilient, enabling them to deliver exceptional customer experiences. serviceOrPlatform: INFRASTRUCTURE @@ -13,7 +13,7 @@ technologyStack: - AKS EDGE ESSENTIALS --- -# Infrastructure observability for Kubernetes and Arc-enabled Kubernetes +# Kubernetes infrastructure observability ## Overview @@ -39,11 +39,7 @@ Grafana's dashboards in Contoso's implementation provide a visually appealing an - **Node Exporter Full**: The Kubernetes Nodes Dashboard in Grafana provides a detailed view of node-level metrics and resources in your Kubernetes cluster. It enables you to monitor CPU and RAM usage, track pods running on each node, and identify any resource anomalies or performance issues. The dashboard also offers system-level metrics such as system load, context switches, and file descriptors, allowing for troubleshooting and optimization. Additionally, it provides insights into storage capacity, volumes, and I/O operations on the nodes, aiding in the effective management of storage resources. -> **Grafana Dashboard Credits:** -> The Kubernetes dashboards included in this guide are based on the work of publicly available dashboards. We would like to express our appreciation for authors' efforts in creating this insightful dashboard that enhances our monitoring capabilities for Kubernetes. -> The original dashboards can be found at: -> - [Node Exporter Full](https://grafana.com/grafana/dashboards/1860-node-exporter-full) -> - [Grafana Dashboards Kubernetes](https://github.com/dotdc/grafana-dashboards-kubernetes) +> **Note:** The Kubernetes dashboards included in this guide are based on the work of publicly available dashboards. We would like to express our appreciation for authors' efforts in creating this insightful dashboard that enhances our monitoring capabilities for Kubernetes. The original dashboards can be found at _[Node Exporter Full](https://grafana.com/grafana/dashboards/1860-node-exporter-full)_ and _[Grafana Dashboards Kubernetes](https://github.com/dotdc/grafana-dashboards-kubernetes)_. ## Access the dashboards diff --git a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/k8s_infra_observability/img/grafana.png b/docs/azure_jumpstart_ag/contoso_motors/k8s_infra_observability/img/grafana.png similarity index 100% rename from docs/azure_jumpstart_ag/manufacturing/contoso_motors/k8s_infra_observability/img/grafana.png rename to docs/azure_jumpstart_ag/contoso_motors/k8s_infra_observability/img/grafana.png diff --git a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/k8s_infra_observability/img/grafana_detroit_global01.png b/docs/azure_jumpstart_ag/contoso_motors/k8s_infra_observability/img/grafana_detroit_global01.png similarity index 100% rename from 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docs/azure_jumpstart_ag/contoso_motors/troubleshooting/img/az_vm_list_usage.png diff --git a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/troubleshooting/img/logs_folder.png b/docs/azure_jumpstart_ag/contoso_motors/troubleshooting/img/logs_folder.png similarity index 100% rename from docs/azure_jumpstart_ag/manufacturing/contoso_motors/troubleshooting/img/logs_folder.png rename to docs/azure_jumpstart_ag/contoso_motors/troubleshooting/img/logs_folder.png diff --git a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/welding_defect/_index.md b/docs/azure_jumpstart_ag/contoso_motors/welding_defect/_index.md similarity index 84% rename from docs/azure_jumpstart_ag/manufacturing/contoso_motors/welding_defect/_index.md rename to docs/azure_jumpstart_ag/contoso_motors/welding_defect/_index.md index fc5352cd..5ca5f8d8 100644 --- a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/welding_defect/_index.md +++ b/docs/azure_jumpstart_ag/contoso_motors/welding_defect/_index.md @@ -30,28 +30,28 @@ This diagram shows the welding defect inference flow, which consists of five mai 1. **Select Site UI:** The user selects a working station from the interactive UI. Each station corresponds to a specific AI flow. In particular, when the user selects the welding working station (highlighted in the image above), the welding detection flow is triggered. -1. **RTSP video simulation:** The welding defect flow requires a particular video of a welding to apply the AI inference. In this scenario, due to the lack of a real video camera, an RTSP simulated feed is used. The simulated feed is designed to closely mimic the behavior of a real welding scenario, providing a reliable video for the AI inference process. +2. **RTSP video simulation:** The welding defect flow requires a particular video of a welding to apply the AI inference. In this scenario, due to the lack of a real video camera, an RTSP simulated feed is used. The simulated feed is designed to closely mimic the behavior of a real welding scenario, providing a reliable video for the AI inference process. -1. **Frame capturing:** This step involves using **OpenCV** to establish a connection with the RTSP video feed and retrieve the welding video frames. Each frame is then passed to the appropriate welding AI inference class, which applies the required preprocessing and post process to the frame. The welding AI inference class is implemented in *[welding.py](https://github.com/microsoft/jumpstart-agora-apps/blob/main/contoso_manufacturing/developer/webapp-decode/welding.py)* and is designed to handle the specific requirements of the welding defect detection process. +3. **Frame capturing:** This step involves using **OpenCV** to establish a connection with the RTSP video feed and retrieve the welding video frames. Each frame is then passed to the appropriate welding AI inference class, which applies the required preprocessing and post process to the frame. The welding AI inference class is implemented in *[welding.py](https://github.com/microsoft/jumpstart-agora-apps/blob/main/contoso_manufacturing/developer/webapp-decode/welding.py)* and is designed to handle the specific requirements of the welding defect detection process. -1. **Frame pre-processing/inferencing:** This step involves applying various techniques to enhance, transform and normalize the captured frames. For this welding model, the image preprocessing consists of the following transformations: - 1. Convert the color space of the input image from BGR to RGB. - 2. Resize the input image to the specified width and height. - 3. Convert the data type of the input image to float32 - 4. Transpose the dimensions of the input image from (height, width, channels) to (channels, height, width). - 5. Add a new dimension to the input image at the beginning of the array to create a "batch" of images. - 6. Flip the order of the color channels from RGB to BGR. +4. **Frame pre-processing/inferencing:** This step involves applying various techniques to enhance, transform and normalize the captured frames. For this welding model, the image preprocessing consists of the following transformations: + - Convert the color space of the input image from BGR to RGB. + - Resize the input image to the specified width and height. + - Convert the data type of the input image to float32 + - Transpose the dimensions of the input image from (height, width, channels) to (channels, height, width). + - Add a new dimension to the input image at the beginning of the array to create a "batch" of images. + - Flip the order of the color channels from RGB to BGR. After the pre-processing step is completed, the final frame data is sent to the OpenVINO™ model server for inference. This is achieved using gRPC and the [ovmsclient](https://pypi.org/project/ovmsclient/) library, which provides a convenient and efficient way to communicate with the server. The server uses the OpenVINO™ toolkit to perform the inference process, which involves running the input data through a trained machine learning model to generate predictions or classifications. Once the inference is complete, the results are returned to the client for further processing or display. -1. **Frame post-processing/rednering:** this is the final step and involves parsing the inference reposnse and apply the required post-process. For this welding model, the post-process involves the following transformations: +5. **Frame post-processing/rednering:** this is the final step and involves parsing the inference reposnse and apply the required post-process. For this welding model, the post-process involves the following transformations: - 1. Apply the `softmax` function to the output of the model to convert it into a probability distribution. - 2. Flatten the probability distribution into a 1D array. - 3. Find the index of the highest probability value in the array. - 4. Find the highest probability value in the array. - 5. Map the index of the highest probability value to a corresponding class label. - 6. Draw the label containing the predicted class and its corresponding probability on the input image + - Apply the `softmax` function to the output of the model to convert it into a probability distribution. + - Flatten the probability distribution into a 1D array. + - Find the index of the highest probability value in the array. + - Find the highest probability value in the array. + - Map the index of the highest probability value to a corresponding class label. + - Draw the label containing the predicted class and its corresponding probability on the input image Once the image is processed, is then served to the main application to render it to the user. Final image contains the result of the welding inference (**no weld**, **normal weld**, **porosity**) and the probability of the result. diff --git a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/welding_defect/img/control-center-menu.png b/docs/azure_jumpstart_ag/contoso_motors/welding_defect/img/control-center-menu.png similarity index 100% rename from docs/azure_jumpstart_ag/manufacturing/contoso_motors/welding_defect/img/control-center-menu.png rename to docs/azure_jumpstart_ag/contoso_motors/welding_defect/img/control-center-menu.png diff --git a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/welding_defect/img/control-center-site.png b/docs/azure_jumpstart_ag/contoso_motors/welding_defect/img/control-center-site.png similarity index 100% rename from docs/azure_jumpstart_ag/manufacturing/contoso_motors/welding_defect/img/control-center-site.png rename to docs/azure_jumpstart_ag/contoso_motors/welding_defect/img/control-center-site.png diff --git a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/welding_defect/img/control-center-welding.png b/docs/azure_jumpstart_ag/contoso_motors/welding_defect/img/control-center-welding.png similarity index 100% rename from docs/azure_jumpstart_ag/manufacturing/contoso_motors/welding_defect/img/control-center-welding.png rename to docs/azure_jumpstart_ag/contoso_motors/welding_defect/img/control-center-welding.png diff --git a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/welding_defect/img/flow.png b/docs/azure_jumpstart_ag/contoso_motors/welding_defect/img/flow.png similarity index 100% rename from docs/azure_jumpstart_ag/manufacturing/contoso_motors/welding_defect/img/flow.png rename to docs/azure_jumpstart_ag/contoso_motors/welding_defect/img/flow.png diff --git a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/welding_defect/img/welding-defect.png b/docs/azure_jumpstart_ag/contoso_motors/welding_defect/img/welding-defect.png similarity index 100% rename from docs/azure_jumpstart_ag/manufacturing/contoso_motors/welding_defect/img/welding-defect.png rename to docs/azure_jumpstart_ag/contoso_motors/welding_defect/img/welding-defect.png diff --git a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/welding_defect/img/welding_ui.png b/docs/azure_jumpstart_ag/contoso_motors/welding_defect/img/welding_ui.png similarity index 100% rename from docs/azure_jumpstart_ag/manufacturing/contoso_motors/welding_defect/img/welding_ui.png rename to docs/azure_jumpstart_ag/contoso_motors/welding_defect/img/welding_ui.png diff --git a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/workers_safety/_index.md b/docs/azure_jumpstart_ag/contoso_motors/workers_safety/_index.md similarity index 85% rename from docs/azure_jumpstart_ag/manufacturing/contoso_motors/workers_safety/_index.md rename to docs/azure_jumpstart_ag/contoso_motors/workers_safety/_index.md index 5fb14b64..5b2da040 100644 --- a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/workers_safety/_index.md +++ b/docs/azure_jumpstart_ag/contoso_motors/workers_safety/_index.md @@ -28,25 +28,25 @@ This diagram shows the workers safety inference flow, which consists of five mai 1. **Select Site UI:** The user selects a working station from the interactive UI. Each station corresponds to a specific AI flow. In particular, when the user selects the two workers walking station (highlighted in the image above), the workers safety flow is triggered. -1. **RTSP video simulation:** The workers safety flow requires a particular video of two workers walking with helmets to apply the AI inference. In this scenario, due to the lack of a real video camera, an RTSP simulated feed is used. The simulated feed is designed to closely mimic the behavior of two workers walking on site with the appropaite safety gear, providing a reliable video for the AI inference process. +2. **RTSP video simulation:** The workers safety flow requires a particular video of two workers walking with helmets to apply the AI inference. In this scenario, due to the lack of a real video camera, an RTSP simulated feed is used. The simulated feed is designed to closely mimic the behavior of two workers walking on site with the appropaite safety gear, providing a reliable video for the AI inference process. -1. **Frame capturing:** This step involves using **OpenCV** to establish a connection with the RTSP video feed and retrieve the welding video frames. Each frame is then passed to the appropriate worker safety AI inference class, which applies the required preprocessing and post process to the frame. The worker safety AI inference class is implemented in [yolov8.py](https://github.com/microsoft/jumpstart-agora-apps/blob/main/contoso_manufacturing/developer/webapp-decode/welding.py) and is designed to handle the specific requirements of the worker safety detection process. +3. **Frame capturing:** This step involves using **OpenCV** to establish a connection with the RTSP video feed and retrieve the welding video frames. Each frame is then passed to the appropriate worker safety AI inference class, which applies the required preprocessing and post process to the frame. The worker safety AI inference class is implemented in [yolov8.py](https://github.com/microsoft/jumpstart-agora-apps/blob/main/contoso_manufacturing/developer/webapp-decode/welding.py) and is designed to handle the specific requirements of the worker safety detection process. -1. **Frame pre-processing/inferencing:** This step involves applying various techniques to enhance, transform and normalize the captured frames. For this worker safety model, the image preprocessing consists of the following transformations: - 1. Resize the input image to the specified width and height. - 2. Convert the color space of the input image from BGR to RGB. - 3. Convert the data type of the input image to float32 - 4. Transpose the dimensions of the input image from (height, width, channels) to (channels, height, width). - 5. Add a new dimension to the input image at the beginning of the array to create a "batch" of images. +4. **Frame pre-processing/inferencing:** This step involves applying various techniques to enhance, transform and normalize the captured frames. For this worker safety model, the image preprocessing consists of the following transformations: + - Resize the input image to the specified width and height. + - Convert the color space of the input image from BGR to RGB. + - Convert the data type of the input image to float32 + - Transpose the dimensions of the input image from (height, width, channels) to (channels, height, width). + - Add a new dimension to the input image at the beginning of the array to create a "batch" of images. After the pre-processing step is completed, the final frame data is sent to the OpenVINO™ model server for inference. This is achieved using gRPC and the [ovmsclient](https://pypi.org/project/ovmsclient/) library, which provides a convenient and efficient way to communicate with the server. The server uses the OpenVINO™ toolkit to perform the inference process, which involves running the input data through a trained machine learning model to generate predictions or classifications. Once the inference is complete, the results are returned to the client for further processing or display. -1. **Frame post-processing/rednering:** this is the final step and involves parsing the inference reposnse and apply the required post-process. For this welding model, the post-process involves the following transformations: +5. **Frame post-processing/rednering:** this is the final step and involves parsing the inference reposnse and apply the required post-process. For this welding model, the post-process involves the following transformations: - 1. Use the OVMS inference result and hte original frame to calculate the bounding box coordinates, scores, and class IDs for each detection in the output tensor. - 1. Apply a non-maximum suppression function to filter out overlapping bounding boxes. - 1. Draw each selected detection on the input image and creates a table of the detection information. - 1. Return the frame with the detections and labels + - Use the OVMS inference result and hte original frame to calculate the bounding box coordinates, scores, and class IDs for each detection in the output tensor. + - Apply a non-maximum suppression function to filter out overlapping bounding boxes. + - Draw each selected detection on the input image and creates a table of the detection information. + - Return the frame with the detections and labels Once the image is processed, is then served to the main application to render it to the user. Final image contains the result of the worker safety inference (**helmet**, **head** and **person**) and the probability of the result. diff --git a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/workers_safety/img/control-center-helmet-detection.png b/docs/azure_jumpstart_ag/contoso_motors/workers_safety/img/control-center-helmet-detection.png similarity index 100% rename from docs/azure_jumpstart_ag/manufacturing/contoso_motors/workers_safety/img/control-center-helmet-detection.png rename to docs/azure_jumpstart_ag/contoso_motors/workers_safety/img/control-center-helmet-detection.png diff --git a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/workers_safety/img/control-center-menu.png b/docs/azure_jumpstart_ag/contoso_motors/workers_safety/img/control-center-menu.png similarity index 100% rename from docs/azure_jumpstart_ag/manufacturing/contoso_motors/workers_safety/img/control-center-menu.png rename to docs/azure_jumpstart_ag/contoso_motors/workers_safety/img/control-center-menu.png diff --git a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/workers_safety/img/control-center-site.png b/docs/azure_jumpstart_ag/contoso_motors/workers_safety/img/control-center-site.png similarity index 100% rename from docs/azure_jumpstart_ag/manufacturing/contoso_motors/workers_safety/img/control-center-site.png rename to docs/azure_jumpstart_ag/contoso_motors/workers_safety/img/control-center-site.png diff --git a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/workers_safety/img/control-center-workers-safety.png b/docs/azure_jumpstart_ag/contoso_motors/workers_safety/img/control-center-workers-safety.png similarity index 100% rename from docs/azure_jumpstart_ag/manufacturing/contoso_motors/workers_safety/img/control-center-workers-safety.png rename to docs/azure_jumpstart_ag/contoso_motors/workers_safety/img/control-center-workers-safety.png diff --git a/docs/azure_jumpstart_ag/manufacturing/contoso_motors/workers_safety/img/flow.png b/docs/azure_jumpstart_ag/contoso_motors/workers_safety/img/flow.png similarity index 100% rename from docs/azure_jumpstart_ag/manufacturing/contoso_motors/workers_safety/img/flow.png rename to docs/azure_jumpstart_ag/contoso_motors/workers_safety/img/flow.png diff --git a/docs/azure_jumpstart_ag/retail/contoso_supermarket/_index.md b/docs/azure_jumpstart_ag/contoso_supermarket/_index.md similarity index 98% rename from docs/azure_jumpstart_ag/retail/contoso_supermarket/_index.md rename to docs/azure_jumpstart_ag/contoso_supermarket/_index.md index 7848d6cc..8d98387a 100644 --- a/docs/azure_jumpstart_ag/retail/contoso_supermarket/_index.md +++ b/docs/azure_jumpstart_ag/contoso_supermarket/_index.md @@ -1,12 +1,12 @@ --- type: docs linkTitle: "Contoso Supermarket" -weight: 1 +weight: 4 description: >- Learn how Contoso Supermarket, a leading global retailer implement cloud to edge strategy while leveraging Azure Arc, IoT services, AKS hybrid, AI, and software distribution and data pipelines. --- -## Contoso Supermarket overview +## Overview Contoso Supermarket is a leading global retailer at the forefront of the digital revolution. With a strong presence in multiple countries, Contoso Supermarket is dedicated to providing exceptional customer experiences while embracing the latest technologies. Their cloud to edge strategy, combined with a commitment to digital transformation, has positioned them as an industry leader. @@ -29,7 +29,7 @@ To support their digital transformation aspirations, Contoso Supermarket stores - **Checkout Queue Monitoring** - Enhancing customer experience by monitoring and managing checkout queues in real-time to minimize waiting times and improve operational efficiency. - **Freezer Monitoring for Food Safety** - Ensuring food safety by monitoring and maintaining optimal conditions in freezers to prevent spoilage and maintain product quality. -![Applications and technology stack architecture diagram](./img/architecture_diagram.png) +![Screenshot showing Contoso Supermarket applications and technology stack architecture diagram](./img/architecture_diagram.png) ## Getting started diff --git a/docs/azure_jumpstart_ag/retail/contoso_supermarket/ai/_index.md b/docs/azure_jumpstart_ag/contoso_supermarket/ai/_index.md similarity index 99% rename from docs/azure_jumpstart_ag/retail/contoso_supermarket/ai/_index.md rename to docs/azure_jumpstart_ag/contoso_supermarket/ai/_index.md index 57c9d85c..9f8ea187 100644 --- a/docs/azure_jumpstart_ag/retail/contoso_supermarket/ai/_index.md +++ b/docs/azure_jumpstart_ag/contoso_supermarket/ai/_index.md @@ -120,7 +120,7 @@ Contoso's DevOps team has received a request from the _Chicago_ store managers t - We need to have "Docker Desktop" running in the background. Open it from the Start menu and have it minimized. - ![Screenshot showing opening Docker Dekstop from the start menu](./img/start_docker_desktop.png) + ![Screenshot showing opening Docker Desktop from the start menu](./img/start_docker_desktop.png) - Open VSCode from the desktop shortcut. diff --git a/docs/azure_jumpstart_ag/retail/contoso_supermarket/ai/img/ag_aks_clusters.png b/docs/azure_jumpstart_ag/contoso_supermarket/ai/img/ag_aks_clusters.png similarity index 100% rename from docs/azure_jumpstart_ag/retail/contoso_supermarket/ai/img/ag_aks_clusters.png rename to docs/azure_jumpstart_ag/contoso_supermarket/ai/img/ag_aks_clusters.png diff --git a/docs/azure_jumpstart_ag/retail/contoso_supermarket/ai/img/ai_diagram.png b/docs/azure_jumpstart_ag/contoso_supermarket/ai/img/ai_diagram.png similarity index 100% rename from docs/azure_jumpstart_ag/retail/contoso_supermarket/ai/img/ai_diagram.png rename to docs/azure_jumpstart_ag/contoso_supermarket/ai/img/ai_diagram.png diff --git 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b/docs/azure_jumpstart_ag/contoso_supermarket/ci_cd/_index.md @@ -99,7 +99,7 @@ The development process will start from the local _dev_ cluster, where as a deve - We need to have "Docker Desktop" running in the background. Open it from the Start menu and have it minimized. - ![Screenshot showing opening Docker Dekstop from the start menu](./img/start_docker_desktop.png) + ![Screenshot showing opening Docker Desktop from the start menu](./img/start_docker_desktop.png) - Open VSCode from the desktop shortcut. diff --git a/docs/azure_jumpstart_ag/retail/contoso_supermarket/ci_cd/img/ag_aks_clusters.png b/docs/azure_jumpstart_ag/contoso_supermarket/ci_cd/img/ag_aks_clusters.png similarity index 100% rename from docs/azure_jumpstart_ag/retail/contoso_supermarket/ci_cd/img/ag_aks_clusters.png rename to docs/azure_jumpstart_ag/contoso_supermarket/ci_cd/img/ag_aks_clusters.png diff --git a/docs/azure_jumpstart_ag/retail/contoso_supermarket/ci_cd/img/ci_cd_workflow.png b/docs/azure_jumpstart_ag/contoso_supermarket/ci_cd/img/ci_cd_workflow.png similarity index 100% rename from docs/azure_jumpstart_ag/retail/contoso_supermarket/ci_cd/img/ci_cd_workflow.png rename to 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b/docs/azure_jumpstart_ag/contoso_supermarket/deployment/_index.md @@ -11,7 +11,7 @@ linkTitle: Deployment guide Jumpstart Agora provides a simple deployment process using Azure Bicep and PowerShell that minimizes user interaction. This automation automatically configures the Contoso Supermarket scenario environment, including the infrastructure, the Contoso-Supermarket applications, CI/CD artifacts, and observability components. The diagram below details the high-level architecture that is deployed and configured as part of the automation. -![Architecture diagram](./img/architecture_diagram.png) +![Screenshot showing Contoso Supermarket applications and technology stack architecture diagram](./img/architecture_diagram.png) Deploying the "Contoso Supermarket" scenario consists of the following steps (once prerequisites are met). @@ -24,7 +24,7 @@ Deploying the "Contoso Supermarket" scenario consists of the following steps (on Once automation is complete, users can immediately start enjoying the Contoso Supermarket experience. -![Deployment flow architecture diagram](./img/deployment_workflow.png) +![Screenshot showing deployment flow architecture diagram](./img/deployment_workflow.png) ## Prerequisites @@ -86,7 +86,7 @@ Once automation is complete, users can immediately start enjoying the Contoso Su > **Note:** The token shown in the above screenshot is a placeholder value for example purposes only and not a working token. -- [Install or update Azure CLI to version 2.53.0 or above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the following command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 or above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the following command to check your current installed version. ```shell az --version @@ -200,7 +200,7 @@ Once automation is complete, users can immediately start enjoying the Contoso Su az bicep upgrade ``` -- Edit the [main.parameters.json](https://github.com/microsoft/azure_arc/blob/main/azure_jumpstart_ag/retail/bicep/main.parameters.json) template parameters file and supply some values for your environment. +- Edit the [main.parameters.json](https://github.com/microsoft/azure_arc/blob/main/azure_jumpstart_ag/contoso_supermarket/bicep/main.parameters.json) template parameters file and supply some values for your environment. - _`sshRSAPublicKey`_ - Your SSH public key - _`spnClientId`_ - Your Azure service principal id - _`spnClientSecret`_ - Your Azure service principal secret @@ -213,7 +213,7 @@ Once automation is complete, users can immediately start enjoying the Contoso Su ![Screenshot showing example parameters](./img/parameters_bicep.png) -- Now you will deploy the Bicep file. Navigate to the local cloned [deployment folder](https://github.com/microsoft/azure_arc/tree/main/azure_jumpstart_ag/retail/bicep) and run the below command: +- Now you will deploy the Bicep file. Navigate to the local cloned [deployment folder](https://github.com/microsoft/azure_arc/tree/main/azure_jumpstart_ag/contoso_supermarket/bicep) and run the below command: ```shell az login diff --git a/docs/azure_jumpstart_ag/retail/contoso_supermarket/deployment/img/architecture_diagram.png b/docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/architecture_diagram.png similarity index 100% rename from docs/azure_jumpstart_ag/retail/contoso_supermarket/deployment/img/architecture_diagram.png rename to docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/architecture_diagram.png diff --git a/docs/azure_jumpstart_ag/retail/contoso_supermarket/deployment/img/automation.png b/docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/automation.png similarity index 100% rename from docs/azure_jumpstart_ag/retail/contoso_supermarket/deployment/img/automation.png rename to docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/automation.png diff --git 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docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/azd_init.png diff --git a/docs/azure_jumpstart_ag/retail/contoso_supermarket/deployment/img/azd_up.png b/docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/azd_up.png similarity index 100% rename from docs/azure_jumpstart_ag/retail/contoso_supermarket/deployment/img/azd_up.png rename to docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/azd_up.png diff --git a/docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/bastion_connect.png b/docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/bastion_connect.png new file mode 100644 index 00000000..c7718b3f Binary files /dev/null and b/docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/bastion_connect.png differ diff --git a/docs/azure_jumpstart_ag/retail/contoso_supermarket/deployment/img/connect_jit.png b/docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/connect_jit.png similarity index 100% rename from docs/azure_jumpstart_ag/retail/contoso_supermarket/deployment/img/connect_jit.png rename to docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/connect_jit.png diff --git a/docs/azure_jumpstart_ag/retail/contoso_supermarket/deployment/img/contoso_supermarket_complete.png b/docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/contoso_supermarket_complete.png similarity index 100% rename from docs/azure_jumpstart_ag/retail/contoso_supermarket/deployment/img/contoso_supermarket_complete.png rename to docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/contoso_supermarket_complete.png diff --git a/docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/create_spn_powershell.png b/docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/create_spn_powershell.png new file mode 100644 index 00000000..5b77a156 Binary files /dev/null and b/docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/create_spn_powershell.png differ diff --git a/docs/azure_jumpstart_ag/retail/contoso_supermarket/deployment/img/deployed_resources.png b/docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/deployed_resources.png similarity index 100% rename from docs/azure_jumpstart_ag/retail/contoso_supermarket/deployment/img/deployed_resources.png rename to docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/deployed_resources.png diff --git a/docs/azure_jumpstart_ag/retail/contoso_supermarket/deployment/img/deployment_workflow.png b/docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/deployment_workflow.png similarity index 100% rename from docs/azure_jumpstart_ag/retail/contoso_supermarket/deployment/img/deployment_workflow.png rename to docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/deployment_workflow.png diff --git a/docs/azure_jumpstart_ag/retail/contoso_supermarket/deployment/img/enable_jit.png b/docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/enable_jit.png similarity index 100% rename from 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b/docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/fork_repo2.png similarity index 100% rename from docs/azure_jumpstart_ag/retail/contoso_supermarket/deployment/img/fork_repo2.png rename to docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/fork_repo2.png diff --git a/docs/azure_jumpstart_ag/retail/contoso_supermarket/deployment/img/github_PAT0.png b/docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/github_PAT0.png similarity index 100% rename from docs/azure_jumpstart_ag/retail/contoso_supermarket/deployment/img/github_PAT0.png rename to docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/github_PAT0.png diff --git a/docs/azure_jumpstart_ag/retail/contoso_supermarket/deployment/img/github_PAT1.png b/docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/github_PAT1.png similarity index 100% rename from docs/azure_jumpstart_ag/retail/contoso_supermarket/deployment/img/github_PAT1.png rename to 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similarity index 100% rename from docs/azure_jumpstart_ag/retail/contoso_supermarket/deployment/img/github_PAT2.png rename to docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/github_PAT2.png diff --git a/docs/azure_jumpstart_ag/retail/contoso_supermarket/deployment/img/github_PAT3.png b/docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/github_PAT3.png similarity index 100% rename from docs/azure_jumpstart_ag/retail/contoso_supermarket/deployment/img/github_PAT3.png rename to docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/github_PAT3.png diff --git a/docs/azure_jumpstart_ag/retail/contoso_supermarket/deployment/img/github_PAT4.png b/docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/github_PAT4.png similarity index 100% rename from docs/azure_jumpstart_ag/retail/contoso_supermarket/deployment/img/github_PAT4.png rename to docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/github_PAT4.png diff --git a/docs/azure_jumpstart_ag/retail/contoso_supermarket/deployment/img/github_PAT5.png b/docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/github_PAT5.png similarity index 100% rename from docs/azure_jumpstart_ag/retail/contoso_supermarket/deployment/img/github_PAT5.png rename to docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/github_PAT5.png diff --git a/docs/azure_jumpstart_ag/retail/contoso_supermarket/deployment/img/github_PAT6.png b/docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/github_PAT6.png similarity index 100% rename from docs/azure_jumpstart_ag/retail/contoso_supermarket/deployment/img/github_PAT6.png rename to docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/github_PAT6.png diff --git a/docs/azure_jumpstart_ag/retail/contoso_supermarket/deployment/img/github_PAT7.png b/docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/github_PAT7.png similarity index 100% rename from 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a/docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/nsg_add_rdp_rule.png b/docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/nsg_add_rdp_rule.png new file mode 100644 index 00000000..d86e27c8 Binary files /dev/null and b/docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/nsg_add_rdp_rule.png differ diff --git a/docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/nsg_add_rule.png b/docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/nsg_add_rule.png new file mode 100644 index 00000000..2353d3b7 Binary files /dev/null and b/docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/nsg_add_rule.png differ diff --git a/docs/azure_jumpstart_ag/retail/contoso_supermarket/deployment/img/nsg_rdp_all_rules.png b/docs/azure_jumpstart_ag/contoso_supermarket/deployment/img/nsg_rdp_all_rules.png similarity index 100% rename from docs/azure_jumpstart_ag/retail/contoso_supermarket/deployment/img/nsg_rdp_all_rules.png rename to 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diff --git a/docs/azure_jumpstart_ag/retail/contoso_supermarket/freezer_monitor/img/adx_view_dashboards.png b/docs/azure_jumpstart_ag/contoso_supermarket/freezer_monitor/img/adx_view_dashboards.png similarity index 100% rename from docs/azure_jumpstart_ag/retail/contoso_supermarket/freezer_monitor/img/adx_view_dashboards.png rename to docs/azure_jumpstart_ag/contoso_supermarket/freezer_monitor/img/adx_view_dashboards.png diff --git a/docs/azure_jumpstart_ag/retail/contoso_supermarket/freezer_monitor/img/azure_portal_iot_hub.png b/docs/azure_jumpstart_ag/contoso_supermarket/freezer_monitor/img/azure_portal_iot_hub.png similarity index 100% rename from docs/azure_jumpstart_ag/retail/contoso_supermarket/freezer_monitor/img/azure_portal_iot_hub.png rename to docs/azure_jumpstart_ag/contoso_supermarket/freezer_monitor/img/azure_portal_iot_hub.png diff --git a/docs/azure_jumpstart_ag/retail/contoso_supermarket/freezer_monitor/img/azure_portal_iot_hub_queries.png 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docs/azure_jumpstart_ag/contoso_supermarket/img/architecture_diagram.png diff --git a/docs/azure_jumpstart_ag/retail/contoso_supermarket/k8s_infra_observability/_index.md b/docs/azure_jumpstart_ag/contoso_supermarket/k8s_infra_observability/_index.md similarity index 95% rename from docs/azure_jumpstart_ag/retail/contoso_supermarket/k8s_infra_observability/_index.md rename to docs/azure_jumpstart_ag/contoso_supermarket/k8s_infra_observability/_index.md index f5fc81a7..93420e94 100644 --- a/docs/azure_jumpstart_ag/retail/contoso_supermarket/k8s_infra_observability/_index.md +++ b/docs/azure_jumpstart_ag/contoso_supermarket/k8s_infra_observability/_index.md @@ -41,11 +41,7 @@ Grafana's dashboards in Contoso's implementation provide a visually appealing an - **Freezer Monitoring**: The Freezer Monitoring dashboard in Grafana provides the current temperature of the freezers and the temperature history for the last 24 hours. You can also set up alerts to notify you when the temperature exceeds a certain threshold. This allows you to proactively monitor the freezer's health and performance and take corrective actions to prevent any issues. This would be covered in more detail in the ["Data pipeline and reporting across cloud and edge for sensor telemetry" guide.](../freezer_monitor/) -> **Grafana Dashboard Credits:** -> The Kubernetes dashboards included in this guide are based on the work of publicly available dashboards. We would like to express our appreciation for Author's efforts in creating this insightful dashboard that enhances our monitoring capabilities for Kubernetes. -> The original dashboards can be found at: -> - [Node Exporter Full](https://grafana.com/grafana/dashboards/1860-node-exporter-full) -> - [Grafana Dashboards Kubernetes](https://github.com/dotdc/grafana-dashboards-kubernetes) +> **Note:** The Kubernetes dashboards included in this guide are based on the work of publicly available dashboards. We would like to express our appreciation for authors' efforts in creating this insightful dashboard that enhances our monitoring capabilities for Kubernetes. The original dashboards can be found at _[Node Exporter Full](https://grafana.com/grafana/dashboards/1860-node-exporter-full)_ and _[Grafana Dashboards Kubernetes](https://github.com/dotdc/grafana-dashboards-kubernetes)_. ## Access the dashboards diff --git a/docs/azure_jumpstart_ag/retail/contoso_supermarket/k8s_infra_observability/img/grafana_dev.png b/docs/azure_jumpstart_ag/contoso_supermarket/k8s_infra_observability/img/grafana_dev.png similarity index 100% rename from docs/azure_jumpstart_ag/retail/contoso_supermarket/k8s_infra_observability/img/grafana_dev.png rename to docs/azure_jumpstart_ag/contoso_supermarket/k8s_infra_observability/img/grafana_dev.png diff --git a/docs/azure_jumpstart_ag/retail/contoso_supermarket/k8s_infra_observability/img/grafana_dev_dashboard_global.png b/docs/azure_jumpstart_ag/contoso_supermarket/k8s_infra_observability/img/grafana_dev_dashboard_global.png similarity index 100% rename from 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b/docs/azure_jumpstart_ag/faq/_index.md index bd252cff..10a5ef5b 100644 --- a/docs/azure_jumpstart_ag/faq/_index.md +++ b/docs/azure_jumpstart_ag/faq/_index.md @@ -1,7 +1,7 @@ --- type: docs linkTitle: "Agora FAQ" -weight: 3 +weight: 5 --- # Jumpstart Agora Frequently Asked Questions (FAQ) @@ -14,7 +14,7 @@ Jumpstart Agora is a marketplace of various “cloud-to-edge” industry scenari ## What industry scenarios are available in Jumpstart Agora? -Agora offers two industry scenarios, [Contoso Motors](/azure_jumpstart_ag/manufacturing/contoso_motors) and [Contoso Supermarket](/azure_jumpstart_ag/retail/contoso_supermarket). Each industry scenario includes everything needed to deploy, configure, and use a real-world including CI/CD, observability, security, and more. +Agora offers three industry scenarios, [Contoso Motors](/azure_jumpstart_ag/manufacturing/contoso_motors), [Contoso Supermarket](/azure_jumpstart_ag/retail/contoso_supermarket) and [Contoso Hypermarket](/azure_jumpstart_ag/manufacturing/contoso_hypermarket). Each industry scenario includes everything needed to deploy, configure, and use a real-world including CI/CD, observability, security, and more. ## What is required to deploy Agora? @@ -26,6 +26,7 @@ Agora can be deployed to the following regions: | Agora scenario | Region support | | ------- | ----------- | +| Contoso Hypermarket | eastus, eastus2, westus2, westus3, westeurope | | Contoso Motors | eastus, eastus2, westus2, westus3, northeurope | | Contoso Supermarket | eastus, eastus2, westus, westus2, northeurope, west europe | @@ -33,6 +34,10 @@ Agora can be deployed to the following regions: Agora incurs normal Azure consumption charges for various Azure resources such as virtual machines and storage. Each industry scenario in Agora may use a different combination of Azure resources and therefore costs vary depending on the industry scenario used. You can view example estimates of Agora costs in the link below. +- Contoso Hypermarket cost estimate has multiple deployment options: + - [Contoso Hypermarket standard deployment cost estimate](https://aka.ms/AgoraContosoHypermarketCostEstimate) + - [Contoso Hypermarket with GPU-enabled SKUs option 1 cost estimate](https://aka.ms/AgoraContosoHypermarketCostEstimateGPU1) + - [Contoso Hypermarket with GPU-enabled SKUs option 2 cost estimate](https://aka.ms/AgoraContosoHypermarketCostEstimateGPU2) - [Contoso Motors cost estimate](https://aka.ms/AgoraContosoMotorsCostEstimate) - [Contoso Supermarket cost estimate](https://aka.ms/AgoraContosoSupermarketCostEstimate) @@ -40,7 +45,40 @@ Agora incurs normal Azure consumption charges for various Azure resources such a Agora has a dedicated pages for troubleshooting that you can review for common issues. +- [Troubleshoot Contoso Hypermarket](/azure_jumpstart_ag/retail/contoso_hypermarket/troubleshooting) - [Troubleshoot Contoso Motors](/azure_jumpstart_ag/manufacturing/contoso_motors/troubleshooting) - [Troubleshoot Contoso Supermarket](/azure_jumpstart_ag/retail/contoso_supermarket/troubleshooting) -If you're still stuck, please [submit an issue](https://aka.ms/JumpstartIssue) on our GitHub repository and the Jumpstart team will try to assist as soon as we can.. +## What AI models are used in Jumpstart Agora? + +Jumpstart Agora uses a combination of the following AI models: + +- **Azure OpenAI** for general AI tasks. +- **Microsoft _Phi-3-Mini-4K-Instruct_ model**, a specialized language model for advanced natural language processing. +- **Intel OpenVINO models**, which are designed for optimized deployment of AI models on Intel hardware. + +## What is the license for the Microsoft Phi-3-Mini-4K-Instruct model?** + +The **_Phi-3-Mini-4K-Instruct_** model by Microsoft is licensed under the [MIT License](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE). You can review the full details of the license in the `LICENSE` file located in the Jumpstart Agora repository. + +## What license applies to Intel OpenVINO models? + +The **Intel OpenVINO** models are distributed under the **Apache License 2.0**. You can view the full terms of the license in the [Intel OpenVINO License file](https://github.com/openvinotoolkit/openvino/blob/master/LICENSE). + +## Can I use these models in my own projects? + +Yes, as long as you comply with the respective licenses. For the Microsoft _Phi-3-Mini-4K-Instruct_ model, you must adhere to the terms of the MIT License. For Intel OpenVINO, the Apache License 2.0 governs the usage. Make sure to review each model’s license to understand the permissions and restrictions. + +## Can I modify and distribute the models used in Jumpstart Agora? + +- **Microsoft _Phi-3-Mini-4K-Instruct_ model**: You are permitted to modify and distribute the model under the MIT License, as long as you include the appropriate copyright notice and disclaimers. +- **Intel OpenVINO models**: You are allowed to modify and distribute Intel OpenVINO models under the terms of the Apache License 2.0, with the conditions outlined in the license. + +## Is there any support or documentation available for using these models? + +- For **Azure OpenAI** and **_Phi-3-Mini-4K-Instruct_** model, refer to the [Azure OpenAI documentation](https://learn.microsoft.com/azure/cognitive-services/openai/) and the official [Hugging Face repository for the _Phi-3-Mini-4K-Instruct_ model](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct). +- For **Intel OpenVINO**, you can access detailed documentation and support from the [OpenVINO GitHub repository](https://github.com/openvinotoolkit/openvino) and the official Intel documentation. + +## What if I am still required assistance? + +If you're still stuck, please [submit an issue](https://aka.ms/JumpstartIssue) on our GitHub repository and the Jumpstart team will try to assist as soon as we can. diff --git a/docs/azure_jumpstart_ag/getting_started/_index.md b/docs/azure_jumpstart_ag/getting_started/_index.md index f6fb9dd1..520a684a 100644 --- a/docs/azure_jumpstart_ag/getting_started/_index.md +++ b/docs/azure_jumpstart_ag/getting_started/_index.md @@ -27,10 +27,12 @@ Our mission is to create a rich marketplace of applications that can leverage Hy Each Agora scenario is a full-stack deployment, from the infrastructure layer all the way to the line of business applications. To get started with Jumpstart Agora, each scenario has a dedicated set of guides that will walk you through the deployment process. The guides are designed to be as simple as possible but also keep the detailed-oriented spirit of the Jumpstart. -| __Scenario__ | __Industry__ | __Version__ | -|---------------------------------------------------------------------------------------------|---------------|-----------------------| -| [Contoso Supermarket](./../retail/contoso_supermarket/) | Retail | 1.0 | -| [Contoso Motors](./../manufacturing/contoso_motors/) | Manufacturing | 1.0 | +| __Scenario__ | __Industry__ | +|---------------------------------------------------------|---------------| +| [Contoso Hypermarket](./../contoso_hypermarket/) | Retail and Manufacturing | +| [Contoso Supermarket](./../contoso_supermarket/) | Retail | +| [Contoso Motors](./../contoso_motors/) | Manufacturing | + ### Jumpstart Agora use cases diff --git a/docs/azure_jumpstart_ag/manufacturing/_index.md b/docs/azure_jumpstart_ag/manufacturing/_index.md deleted file mode 100644 index db32a661..00000000 --- a/docs/azure_jumpstart_ag/manufacturing/_index.md +++ /dev/null @@ -1,5 +0,0 @@ ---- -type: docs -linkTitle: "Manufacturing" -weight: 3 ---- \ No newline at end of file diff --git a/docs/azure_jumpstart_ag/retail/_index.md b/docs/azure_jumpstart_ag/retail/_index.md deleted file mode 100644 index 3a2a0c72..00000000 --- a/docs/azure_jumpstart_ag/retail/_index.md +++ /dev/null @@ -1,5 +0,0 @@ ---- -type: docs -linkTitle: "Retail" -weight: 2 ---- \ No newline at end of file diff --git a/docs/azure_jumpstart_arcbox/DataOps/_index.md b/docs/azure_jumpstart_arcbox/DataOps/_index.md index 3e493e80..8362526b 100644 --- a/docs/azure_jumpstart_arcbox/DataOps/_index.md +++ b/docs/azure_jumpstart_arcbox/DataOps/_index.md @@ -78,7 +78,7 @@ ArcBox uses an advanced automation flow to deploy and configure all necessary re ## Prerequisites -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_jumpstart_arcbox/DevOps/_index.md b/docs/azure_jumpstart_arcbox/DevOps/_index.md index 7df2503a..f5fa647f 100644 --- a/docs/azure_jumpstart_arcbox/DevOps/_index.md +++ b/docs/azure_jumpstart_arcbox/DevOps/_index.md @@ -100,7 +100,7 @@ ArcBox uses an advanced automation flow to deploy and configure all necessary re ## Prerequisites -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_jumpstart_arcbox/ITPro/_index.md b/docs/azure_jumpstart_arcbox/ITPro/_index.md index 40de688d..79697da9 100644 --- a/docs/azure_jumpstart_arcbox/ITPro/_index.md +++ b/docs/azure_jumpstart_arcbox/ITPro/_index.md @@ -64,7 +64,7 @@ ArcBox uses an advanced automation flow to deploy and configure all necessary re ## Prerequisites -- [Install or update Azure CLI to version 2.53.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 and above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version diff --git a/docs/azure_jumpstart_hcibox/AKS/_index.md b/docs/azure_jumpstart_hcibox/AKS/_index.md index 6378208e..3c3c9008 100644 --- a/docs/azure_jumpstart_hcibox/AKS/_index.md +++ b/docs/azure_jumpstart_hcibox/AKS/_index.md @@ -1,14 +1,14 @@ --- type: docs -linkTitle: "AKS on Azure Stack HCI" +linkTitle: "AKS on Azure Local" weight: 8 --- -# Azure Kubernetes Service on your Azure Stack HCI HCIBox +# Azure Kubernetes Service on your Azure Local HCIBox -Azure Stack HCI can provide host infrastructure for [Azure Kubernetes Service clusters](https://learn.microsoft.com/azure-stack/aks-hci/). HCIBox comes pre-configured with Azure Kubernetes Service on HCI (AKS-HCI). To see this in action, navigate to the resource group where you deployed HCIBox and follow these steps. +Azure Local can provide host infrastructure for [Azure Kubernetes Service clusters](https://learn.microsoft.com/azure-stack/aks-hci/). HCIBox comes pre-configured with Azure Kubernetes Service on Azure Local. To see this in action, navigate to the resource group where you deployed HCIBox and follow these steps. -## Explore AKS on Azure Stack HCI +## Explore AKS on Azure Local HCIBox is preconfigured with a network subnet dedicated to AKS deployment. Subnet details are as follows: @@ -21,7 +21,7 @@ HCIBox is preconfigured with a network subnet dedicated to AKS deployment. Subne Before creating an AKS workload cluster, you must create a local virtual network object. HCIBox includes a script that will complete this task using the pre-configured network. The script will then create a new AKS workload cluster. -- Open your HCIBox resource group and click on the _jumpstart_ custom location resource, then click "Arc-enabled services." Here you should see the hybridaksextension service available on the cluster. This is installed by default on Azure Stack HCI clusters, and is required for creating AKS workload clusters on HCI. +- Open your HCIBox resource group and click on the _jumpstart_ custom location resource, then click "Arc-enabled services." Here you should see the hybridaksextension service available on the cluster. This is installed by default on Azure Local instance(s), and is required for creating AKS workload clusters on Azure Local. ![Screenshot showing aks extension](./custom_location_resources.png) @@ -78,4 +78,4 @@ Access to the AKS cluster is [managed through Azure RBAC](https://learn.microsof ## Next steps -Azure Kubernetes Service on Azure Stack HCI provides many more capabilities not directly covered here. Review the documentation to continue your journey with [AKS on HCI](https://learn.microsoft.com/azure-stack/aks-hci/). +Azure Kubernetes Service on Azure Local provides many more capabilities not directly covered here. Review the documentation to continue your journey with [AKS on Azure Local](https://learn.microsoft.com/azure-stack/aks-hci/). diff --git a/docs/azure_jumpstart_hcibox/RB/_index.md b/docs/azure_jumpstart_hcibox/RB/_index.md index ab05f3d2..0f9bc2a3 100644 --- a/docs/azure_jumpstart_hcibox/RB/_index.md +++ b/docs/azure_jumpstart_hcibox/RB/_index.md @@ -5,15 +5,15 @@ weight: 7 --- # Virtual machine provisioning with Azure Arc in your HCIBox -Azure Stack HCI supports [VM provisioning the Azure portal](https://learn.microsoft.com/azure-stack/hci/manage/manage-arc-virtual-machines). Like all Azure Stack HCI clusters, the HCIBox cluster comes preconfigured with the components needed for VM management through Azure portal. Follow this guide to configure a basic VM from a marketplace image. +Azure Local supports [VM provisioning the Azure portal](https://learn.microsoft.com/azure-stack/hci/manage/manage-arc-virtual-machines). Like all Azure Local instances, the HCIBox instance comes preconfigured with the components needed for VM management through Azure portal. Follow this guide to configure a basic VM from a marketplace image. ## Create Virtual Machine images from Azure marketplace -Before you can create virtual machines on your HCI cluster from Azure portal, you must create some VM images that can be used as a base. These images can be imported from Azure marketplace or provided directly by the user. In this use case, you will create an image from Azure marketplace. +Before you can create virtual machines on your Azure Local instance from Azure portal, you must create some VM images that can be used as a base. These images can be imported from Azure marketplace or provided directly by the user. In this use case, you will create an image from Azure marketplace. -- Navigate to your cluster resource inside the HCIBox resource group and click it. +- Navigate to your instance resource inside the HCIBox resource group and click it. - ![Screenshot showing cluster resource](./hcicluster_rg.png) + ![Screenshot showing instance resource](./hcicluster_rg.png) - Click on "VM Images" in the menu and then click the "Add VM image" dropdown and select "From Azure Marketplace." @@ -23,13 +23,13 @@ Before you can create virtual machines on your HCI cluster from Azure portal, yo ![Screenshot showing create VM image details](./create_vm_detail_win11.png) -- It will take some time for the VM image to download to your cluster from Azure marketplace. You can monitor progress by visiting the VM Image resource in your resource group and reviewing the resource properties. +- It will take some time for the VM image to download to your instance from Azure marketplace. You can monitor progress by visiting the VM Image resource in your resource group and reviewing the resource properties. ![Screenshot of VM image properties](./monitor_vm_image_progress.png) -- Monitor the image as needed until is it finished downloading. While you wait, proceed to the next section to create the logical network on the cluster. +- Monitor the image as needed until is it finished downloading. While you wait, proceed to the next section to create the logical network on the instance. -## Create a logical network on your HCI cluster +## Create a logical network on your Azure Local instance HCIBox networking includes a 192.168.200.0/24 subnet tagged to VLAN200. This network is designed for use with Arc-enabled VMs on HCIBox. To use this preconfigured network, you must create a logical network resource that maps to this subnet. @@ -54,7 +54,7 @@ HCIBox networking includes a 192.168.200.0/24 subnet tagged to VLAN200. This net ![Screenshot showing VM image complete](./monitor_vm_image_available.png) -- Open your Azure Stack HCI cluster resource and then open the Virtual machines blade, then click the "Create virtual machine" button. +- Open your Azure Local instance resource and then open the Virtual machines blade, then click the "Create virtual machine" button. ![Screenshot showing create VM overview](./create_vm.png) @@ -72,10 +72,10 @@ HCIBox networking includes a 192.168.200.0/24 subnet tagged to VLAN200. This net ![Screenshot showing final create VM step](./vm_image_review_create.png) -- Open the Azure Stack HCI VM resource and see connectivity to Arc and other details. +- Open the Azure Local VM resource and see connectivity to Arc and other details. ![Screenshot showing VM resource](./vm_resource_detail.png) ## Next steps -Review the [Azure Stack HCI VM management](https://learn.microsoft.com/azure-stack/hci/manage/azure-arc-enabled-virtual-machines#what-is-azure-arc-resource-bridge) documentation for additional information. +Review the [Azure Local VM management](https://learn.microsoft.com/azure-stack/hci/manage/azure-arc-enabled-virtual-machines#what-is-azure-arc-resource-bridge) documentation for additional information. diff --git a/docs/azure_jumpstart_hcibox/cloud_deployment/_index.md b/docs/azure_jumpstart_hcibox/cloud_deployment/_index.md index d49b7af1..f36e6708 100644 --- a/docs/azure_jumpstart_hcibox/cloud_deployment/_index.md +++ b/docs/azure_jumpstart_hcibox/cloud_deployment/_index.md @@ -16,12 +16,12 @@ Once your bicep deployment is complete with Azure CLI or Azure Developer CLI, yo ## Connecting to the HCIBox Client virtual machine -> **Note:** As the subnet the HCI VMs resides on is on the second nested layer, it is necessary to connect to the AzSMGMT machine to be able to connect to those VMs: -> The HCI VM subnet is also not routable into the Azure Virtual Network, hence it won't be possible to connect to virtual machines on the HCI cluster using Azure Bastion. +> **Note:** As the subnet the Azure Local VMs resides on is on the second nested layer, it is necessary to connect to the AzSMGMT machine to be able to connect to those VMs: +> The Azure Local VM subnet is also not routable into the Azure Virtual Network, hence it won't be possible to connect to virtual machines on the Azure Local instance using Azure Bastion. > -> If you are having difficulties connecting into a Stack HCI VM: +> If you are having difficulties connecting into a Azure Local VM: > You can run `mstsc /v:192.168.1.11` from HCIBox-Client to connect to the AzSMGMT nested VM. -> From there, run `mstsc /v:192.168.200.x` to connect to the HCI VMs (replace x with the IP of your deployed VM). +> From there, run `mstsc /v:192.168.200.x` to connect to the Azure Local VMs (replace x with the IP of your deployed VM). Various options are available to connect to _HCIBox-Client_ VM, depending on the parameters you supplied during deployment. @@ -74,23 +74,23 @@ If you already have [Microsoft Defender for Cloud](https://learn.microsoft.com/a ![Screenshot showing _HCIBox-Client_](./automation.png) -- In Azure portal, validate that both HCI nodes(AzSHOST1 and AzSHOST2) have been created as Arc-enabled servers. +- In Azure portal, validate that both Azure Local machines (AzSHOST1 and AzSHOST2) have been created as Arc-enabled servers. -- Verify that both of the Arc-enabled servers have successfully installed the three HCI extensions: AzureEdgeTelemetryAndDiagnostics, AzureEdgeLifecycleManager, and AzureEdgeDeviceManagement +- Verify that both of the Arc-enabled servers have successfully installed the three Azure Local extensions: AzureEdgeTelemetryAndDiagnostics, AzureEdgeLifecycleManager, and AzureEdgeDeviceManagement ![Screenshot showing extensions successfully installed](./extensions_installed.png) -- If everything looks good, and you did not choose to configure the `autoDeployClusterResource` parameter with the value `true` in your deployment, proceed to the next section to validate and deploy your cluster. If you did enable this parameter, you may proceed to the section _Deployment complete_ at the end of this page. +- If everything looks good, and you did not choose to configure the `autoDeployClusterResource` parameter with the value `true` in your deployment, proceed to the next section to validate and deploy your instance. If you did enable this parameter, you may proceed to the section _Deployment complete_ at the end of this page. Visit [troubleshooting](/azure_jumpstart_hcibox/troubleshooting/) if needed for deployment issues. -## Azure portal Azure Stack HCI cluster validation and deployment +## Azure portal Azure Local instance validation and deployment -Azure Stack HCI uses a two-step process to create and register clusters in Azure using an ARM template. +Azure Local uses a two-step process to create and register instances in Azure using an ARM template. - 1. **Validate** - an ARM template is deployed with a "validate" flag. This begins the final cluster validation step and takes around 20 minutes. - 2. **Deploy** - the same ARM template is redeployed with the "deploy" flag. This deploys the cluster and Arc infrastructure and registers the cluster. This step takes around 2-3 hours. + 1. **Validate** - an ARM template is deployed with a "validate" flag. This begins the final instance validation step and takes around 20 minutes. + 2. **Deploy** - the same ARM template is redeployed with the "deploy" flag. This deploys the instance and Arc infrastructure and registers the instance. This step takes around 2-3 hours. -### Validate cluster in Azure portal +### Validate instance in Azure portal - Before submitting the ARM deployment, you need to add your user account as a Key Vault Administrator on the HCIBox resource group. Navigate to the resource group then click "Access Control (IAM)" and then "Add role assignment". Select the "Key Vault Administrator role" then click on to the next screen to select your user account and assign your user the role. @@ -98,7 +98,7 @@ Azure Stack HCI uses a two-step process to create and register clusters in Azure - Repeat this process to add your user account as a "Storage Account Contributor" on the HCIBox resource group. -- Now you will use the generated ARM template to validate the HCI cluster in Azure portal. Open File Explorer on _HCIBox-Client_ and navigate to the _C:\HCIBox_ folder. Right-click on the folder and open it in VSCode. +- Now you will use the generated ARM template to validate the Azure Local instance in Azure portal. Open File Explorer on _HCIBox-Client_ and navigate to the _C:\HCIBox_ folder. Right-click on the folder and open it in VSCode. - Open and review the hci.json and hci.parameters.json files in VSCode. Verify that the hci.parameters.json file looks correct without "_-staging_" placeholder parameter values. @@ -118,24 +118,24 @@ Azure Stack HCI uses a two-step process to create and register clusters in Azure ![Screenshot showing edit parameters option](./edit_parameters.png) -- Click through again on "Create" and then "Review and Create" to start the validation phase of cluster deployment. +- Click through again on "Create" and then "Review and Create" to start the validation phase of instance deployment. ![Screenshot showing starting validation phase](./validation_in_progress.png) - Monitor validation as needed until complete. -## Deploy cluster in Azure portal +## Deploy instance in Azure portal -- When validation is complete navigate to the cluster resource in your HCIBox resource group. The banner should indicate that your cluster is validated but not yet deployed. Click the "Deploy now" link. +- When validation is complete navigate to the instance resource in your HCIBox resource group. The banner should indicate that your instance is validated but not yet deployed. Click the "Deploy now" link. - ![Screenshot showing validated cluster resource in Azure portal](./validated_cluster_resource.png) + ![Screenshot showing validated instance resource in Azure portal](./validated_cluster_resource.png) -- Click through to submit the deployment. The cluster may take some time to deploy. If you navigate elsewhere in the Azure Portal, you can return to monitor progress on the _Deployments_ tab of the cluster resource. Click Refresh to get the latest status on deployment. +- Click through to submit the deployment. The instance may take some time to deploy. If you navigate elsewhere in the Azure Portal, you can return to monitor progress on the _Deployments_ tab of the instance. Click Refresh to get the latest status on deployment. - ![Screenshot showing progress of deploying cluster](./cluster_deployment_progress.png) + ![Screenshot showing progress of deploying instance](./cluster_deployment_progress.png) ## Deployment complete -- Once the HCIBox cluster is deployed it's time to start exploring various HCIBox features. Head on to the [Using HCIBox](/azure_jumpstart_hcibox/using_hcibox) guide for the next steps. +- Once the HCIBox instance is deployed it's time to start exploring various HCIBox features. Head on to the [Using HCIBox](/azure_jumpstart_hcibox/using_hcibox) guide for the next steps. - ![screenshot showing deployed cluster](./cluster_detail.png) + ![screenshot showing deployed instance](./cluster_detail.png) diff --git a/docs/azure_jumpstart_hcibox/deployment_az/_index.md b/docs/azure_jumpstart_hcibox/deployment_az/_index.md index 6215cb5c..a18c14df 100644 --- a/docs/azure_jumpstart_hcibox/deployment_az/_index.md +++ b/docs/azure_jumpstart_hcibox/deployment_az/_index.md @@ -19,7 +19,7 @@ Azure CLI can be used to deploy HCIBox into your Azure subscription. Azure CLI i git clone https://github.com/microsoft/azure_arc.git ``` -- [Install or update Azure CLI to version 2.56.0 or above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. +- [Install or update Azure CLI to version 2.65.0 or above](https://learn.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest). Use the below command to check your current installed version. ```shell az --version @@ -100,13 +100,13 @@ Azure CLI can be used to deploy HCIBox into your Azure subscription. Azure CLI i az bicep upgrade ``` -- Retrieve the object id of your directory's Azure Stack HCI resource provider. +- Retrieve the object id of your directory's Azure Local resource provider. ```shell az ad sp list --display-name "Microsoft.AzureStackHCI Resource Provider" ``` - ![Screenshot showing retrieving StackHCI resource provider id](./hci_rp_id.png) + ![Screenshot showing retrieving Azure Local resource provider id](./hci_rp_id.png) > **Note:** Please avoid using the $ symbol in the `windowsAdminPassword`. Using this symbol can cause the LogonScript to fail. @@ -115,13 +115,13 @@ Azure CLI can be used to deploy HCIBox into your Azure subscription. Azure CLI i - _`spnClientId`_ - Your Azure service principal id - _`spnClientSecret`_ - Your Azure service principal secret - _`spnTenantId`_ - Your Azure tenant id - - _`spnProviderId`_ - Your Azure Stack HCI resource provider id, retrieved in an earlier step + - _`spnProviderId`_ - Your Azure Local resource provider id, retrieved in an earlier step - _`windowsAdminUsername`_ - Client Windows VM Administrator username - _`windowsAdminPassword`_ - Client Windows VM Password. Password must have 3 of the following: 1 lower case character, 1 upper case character, 1 number, and 1 special character. The value must be between 12 and 123 characters long. - _`logAnalyticsWorkspaceName`_ - Unique name for the HCIBox Log Analytics workspace - _`deployBastion`_ - Option to deploy Azure Bastion which used to connect to the _HCIBox-Client_ VM instead of normal RDP. - - _`autoDeployClusterResource`_ - Option to enable automatic deployment of the Azure Arc-enabled HCI cluster after the client VM deployment and automation script execution is complete. - - _`autoUpgradeClusterResource`_ - Option to enable automatic upgrade of the Azure Arc-enabled HCI cluster after the cluster deployment is complete (only applicable if autoDeployClusterResource is set to `true`). + - _`autoDeployClusterResource`_ - Option to enable automatic deployment of the Azure Arc-enabled Azure Local instance after the client VM deployment and automation script execution is complete. + - _`autoUpgradeClusterResource`_ - Option to enable automatic upgrade of the Azure Arc-enabled Azure Local instance after the instance deployment is complete (only applicable if autoDeployClusterResource is set to `true`). ![Screenshot showing example parameters](./parameters_bicep.png) diff --git a/docs/azure_jumpstart_hcibox/getting_started/_index.md b/docs/azure_jumpstart_hcibox/getting_started/_index.md index 9c5cbbc4..58c13f05 100644 --- a/docs/azure_jumpstart_hcibox/getting_started/_index.md +++ b/docs/azure_jumpstart_hcibox/getting_started/_index.md @@ -4,18 +4,18 @@ linkTitle: "Getting Started" isGettingStarted: false weight: 2 --- -# Evaluating Azure Stack HCI with HCIBox +# Evaluating Azure Local with HCIBox ## Getting started -HCIBox is a turnkey solution that provides a complete sandbox for exploring [Azure Stack HCI](https://learn.microsoft.com/azure-stack/hci/whats-new) capabilities and hybrid cloud integration in a virtualized environment. HCIBox is designed to be completely self-contained within a single Azure subscription and resource group, which will make it easy for a user to get hands-on with Azure Stack HCI and [Azure Arc](https://learn.microsoft.com/azure/azure-arc/overview) technology without the need for physical hardware. +HCIBox is a turnkey solution that provides a complete sandbox for exploring [Azure Local](https://learn.microsoft.com/azure-stack/hci/whats-new) capabilities and hybrid cloud integration in a virtualized environment. HCIBox is designed to be completely self-contained within a single Azure subscription and resource group, which will make it easy for a user to get hands-on with Azure Local and [Azure Arc](https://learn.microsoft.com/azure/azure-arc/overview) technology without the need for physical hardware. - > **Note:** [Azure Stack HCI 23H2](https://learn.microsoft.com/azure-stack/hci/whats-new) is now generally available. 23H2 simplifies configuration and deployment of HCI clusters and related workloads like [VM management](https://learn.microsoft.com/azure-stack/hci/manage/azure-arc-vm-management-overview) for VM self-service management in Azure portal. HCIBox has also been updated and now offers clusters built on the new 23H2 OS, and prior Azure Stack HCI releases are no longer part of HCIBox or supported by the Jumpstart team. If you have used earlier versions of HCIBox you should read this guide thoroughly to understand the new HCIBox deployment process. + > **Note:** [Azure Stack HCI OS, version 23H2](https://learn.microsoft.com/azure-stack/hci/whats-new) is now generally available. 23H2 simplifies configuration and deployment of Azure Local instances and related workloads like [VM management](https://learn.microsoft.com/azure-stack/hci/manage/azure-arc-vm-management-overview) for VM self-service management in Azure portal. HCIBox has also been updated and now offers instances built on the new 23H2 OS, and prior Azure Local releases are no longer part of HCIBox or supported by the Jumpstart team. If you have used earlier versions of HCIBox you should read this guide thoroughly to understand the new HCIBox deployment process. Jumpstart HCIBox logo ## Use cases -- Sandbox environment for getting hands-on with Azure Stack HCI and Azure Arc technologies +- Sandbox environment for getting hands-on with Azure Local and Azure Arc technologies - Accelerator for Proof-of-concepts or pilots - Training tool for skills development - Demo environment for customer presentations or events @@ -23,21 +23,21 @@ HCIBox is a turnkey solution that provides a complete sandbox for exploring [Azu ![Screenshot showing HCIBox architecture diagram](./arch.png) -## Azure Stack HCI capabilities available in HCIBox +## Azure Local capabilities available in HCIBox -### 2-node Azure Stack HCI cluster +### 2-node Azure Local instance -HCIBox automatically creates and configures a two-node Azure Stack HCI cluster using nested virtualization with Hyper-V running on an Azure Virtual Machine. This Hyper-V host creates three guest virtual machines: two Azure Stack HCI nodes (_AzSHost1_, _AzSHost2_), and one nested Hyper-V host (_AzSMGMT_). _AzSMGMT_ itself hosts two guest VMs: an [Active Directory domain controller](https://learn.microsoft.com/windows-server/identity/ad-ds/get-started/virtual-dc/active-directory-domain-services-overview), and a [Routing and Remote Access Server](https://learn.microsoft.com/windows-server/remote/remote-access/remote-access) acting as a virtual router. +HCIBox automatically creates and configures a two-node Azure Local instance using nested virtualization with Hyper-V running on an Azure Virtual Machine. This Hyper-V host creates three guest virtual machines: two Azure Local machines (_AzSHost1_, _AzSHost2_), and one nested Hyper-V host (_AzSMGMT_). _AzSMGMT_ itself hosts two guest VMs: an [Active Directory domain controller](https://learn.microsoft.com/windows-server/identity/ad-ds/get-started/virtual-dc/active-directory-domain-services-overview), and a [Routing and Remote Access Server](https://learn.microsoft.com/windows-server/remote/remote-access/remote-access) acting as a virtual router. ![Screenshot showing HCIBox nested virtualization](./nested_virtualization.png) ### Virtual machine management -HCIBox comes with [guest VM management in Azure portal](https://learn.microsoft.com/azure-stack/hci/manage/azure-arc-vm-management-overview). The HCIBox documentation will walk you through how to use this feature, including configuring VM images from Azure marketplace and creating VMs on your cluster. +HCIBox comes with [guest VM management in Azure portal](https://learn.microsoft.com/azure-stack/hci/manage/azure-arc-vm-management-overview). The HCIBox documentation will walk you through how to use this feature, including configuring VM images from Azure marketplace and creating VMs on your instance. ### Azure Kubernetes Service on Azure Stack HCI -Azure Stack HCI includes [Azure Kubernetes Services on Azure Stack HCI (AKS hybrid)](https://learn.microsoft.com/azure/aks/hybrid/) as part of the default configuration. A user script is provided that can be used to create a workload cluster. +Azure Stack HCI includes [Azure Kubernetes Services on Azure Local (AKS hybrid)](https://learn.microsoft.com/azure/aks/hybrid/) as part of the default configuration. A user script is provided that can be used to create a workload instance. ## HCIBox Azure Consumption Costs @@ -50,9 +50,9 @@ HCIBox supports [Bicep](https://learn.microsoft.com/azure/azure-resource-manager Deploying HCIBox is a multi-step process that includes: 1) Deploy Azure infrastructure - 2) Automation scripts configure virtual HCI cluster and generate ARM template - 3) User or automation deploys ARM template (HCI cluster validate phase) - 4) User or automation re-deploys ARM template (HCI cluster deploy phase) + 2) Automation scripts configure virtual Azure Local instance and generate ARM template + 3) User or automation deploys ARM template (Azure Local instance validate phase) + 4) User or automation re-deploys ARM template (Azure Local instance deploy phase) ![Screenshot showing deployment flow diagram for Bicep-based deployments](./deployment_flow.png) diff --git a/docs/azure_jumpstart_hcibox/troubleshooting/_index.md b/docs/azure_jumpstart_hcibox/troubleshooting/_index.md index 27bdc751..a81e84e8 100644 --- a/docs/azure_jumpstart_hcibox/troubleshooting/_index.md +++ b/docs/azure_jumpstart_hcibox/troubleshooting/_index.md @@ -9,7 +9,7 @@ weight: 9 Occasionally deployments of HCIBox may fail at various stages. Common reasons for failed deployments include: -- Invalid service principal id, service principal secret, or service principal Azure tenant ID provided in _main.parameters.json_ file. This can cause failures when running automation that requires logging into Azure, such as the scripts that register the HCI cluster, deploy AKS-HCI, or configure Arc Resource Bridge. +- Invalid service principal id, service principal secret, or service principal Azure tenant ID provided in _main.parameters.json_ file. This can cause failures when running automation that requires logging into Azure, such as the scripts that register the Azure Local instance, deploy AKS Hybrid, or configure Arc Resource Bridge. - Not enough vCPU quota available in your target Azure region - check vCPU quota and ensure you have at least 32 available. See the [prerequisites](/azure_jumpstart_hcibox/getting_started/#prerequisites) section for more details. - Corruption when downloading HCIBox VHD files can interrupt deployments. HCIBox should automatically halt if this occurs. Re-running the PowerShell script at _C:\HCIBox\HCIBoxLogonScript.ps1_ can often repair this issue. @@ -22,7 +22,7 @@ Occasionally, you may need to review log output from scripts that run on the _HC | Log file | Description | | --------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------- | | _C:\HCIBox\Logs\Bootstrap.log_ | Output from the initial bootstrapping script that runs on _HCIBox-Client_. | -| _C:\HCIBox\Logs\New-HCIBoxCluster.log_ | Output of _New-HCIBoxCluster.ps1_ which configures the Hyper-V host and builds the HCI cluster, management VMs, and other configurations. | +| _C:\HCIBox\Logs\New-HCIBoxCluster.log_ | Output of _New-HCIBoxCluster.ps1_ which configures the Hyper-V host and builds the Azure Local instance, management VMs, and other configurations. | | _C:\HCIBox\Logs\Generate-ARM-Template.log_ | Log output of the script that builds the _hci.json_ and _hci.parameters.json_ file | | _C:\HCIBox\Logs\HCIBoxLogonScript.log_ | Log output from the orchestrator script that manages the install | | _C:\HCIBox\Logs\Tools.log_ | Log output from tools installation during bootstrap | diff --git a/docs/azure_jumpstart_hcibox/using_hcibox/_index.md b/docs/azure_jumpstart_hcibox/using_hcibox/_index.md index 6b927b5d..3d538d7b 100644 --- a/docs/azure_jumpstart_hcibox/using_hcibox/_index.md +++ b/docs/azure_jumpstart_hcibox/using_hcibox/_index.md @@ -12,15 +12,15 @@ HCIBox has many features that can be explored through the Azure portal or from i ## Nested virtualization -HCIBox simulates a 2-node physical deployment of Azure Stack HCI by using [nested virtualization on Hyper-V](https://learn.microsoft.com/virtualization/hyper-v-on-windows/user-guide/nested-virtualization). To ensure you have the best experience with HCIBox, take a moment to review the details below to help you understand the various nested VMs that make up the solution. +HCIBox simulates a 2-node physical deployment of Azure Local by using [nested virtualization on Hyper-V](https://learn.microsoft.com/virtualization/hyper-v-on-windows/user-guide/nested-virtualization). To ensure you have the best experience with HCIBox, take a moment to review the details below to help you understand the various nested VMs that make up the solution. ![Screenshot showing HCIBox nested virtualization stack diagram](./nested_virtualization_arch.png) | Computer Name | Role | Domain Joined | Parent Host | OS | | ---------------- | ----------------------------------- | ------------- | --------------- | ------------------- | | _HCIBox-Client_ | Primary host | No | Azure | Windows Server 2022 | -| _AzSHOST1_ | HCI node | Yes | _HCIBox-Client_ | Azure Stack HCI | -| _AzSHOST2_ | HCI node | Yes | _HCIBox-Client_ | Azure Stack HCI | +| _AzSHOST1_ | HCI node | Yes | _HCIBox-Client_ | Azure Local | +| _AzSHOST2_ | HCI node | Yes | _HCIBox-Client_ | Azure Local | | _AzSMGMT_ | Nested hypervisor | No | _HCIBox-Client_ | Windows Server 2022 | | _JumpstartDC_ | Domain controller | Yes (DC) | _AzSMGMT_ | Windows Server 2022 | | _Vm-Router_ | Remote Access Server | No | _AzSMGMT_ | Windows Server 2022 | @@ -31,11 +31,11 @@ Once you are logged into the _HCIBox-Client_ VM using the local admin credential > **Note:** The password for this account is set as the same password you supplied during deployment for the local account. Many HCIBox operations will use the domain account wherever credentials are required. -## Monitoring Azure Stack HCI +## Monitoring Azure Local -Azure Stack HCI integrates with [Azure Monitor](https://learn.microsoft.com/azure-stack/hci/manage/monitor-hci-single) to support monitoring HCI cluster insights through the Azure portal. Follow these steps to configure monitoring on your HCIBox cluster. +Azure Local integrates with [Azure Monitor](https://learn.microsoft.com/azure-stack/hci/manage/monitor-hci-single) to support monitoring Azure Local instance insights through the Azure portal. Follow these steps to configure monitoring on your HCIBox instance. -- Open the Overview blade of your cluster and see that platform monitoring is available by default. +- Open the Overview blade of your instance and see that platform monitoring is available by default. ![Screenshot showing platform monitoring in Az monitor](./monitor_platform.png) @@ -43,20 +43,20 @@ Azure Stack HCI integrates with [Azure Monitor](https://learn.microsoft.com/azur ![Screenshot showing capabilities tab](./create_dcr.png) -- It will take time for the logs data to flow through to Insights. Once data is available, click on the Insights blade of the _HCIBox-Cluster_ resource to view the Insights workbook and explore logs from your cluster. +- It will take time for the logs data to flow through to Insights. Once data is available, click on the Insights blade of the _HCIBox-Cluster_ resource to view the Insights workbook and explore logs from your instance. ![Screenshot showing logs configured](./hci_insights.png) -## Upgrading Azure Stack HCI +## Upgrading Azure Local HCIBox is regularly updated with the latest available version of the installation medium. The product group regularly publish updates which contains security fixes and feature improvements, which may be released after the latest HCIBox update. -Due to this, it might be required to trigger an update after the initial deployment of the HCI cluster. +Due to this, it might be required to trigger an update after the initial deployment of the Azure Local instance. -If you chose to enable the parameter `autoUpgradeClusterResource` to enable automatic upgrade of the HCI cluster after the deployment is complete, you should already be on the latest version available. +If you chose to enable the parameter `autoUpgradeClusterResource` to enable automatic upgrade of the Azure Local instance after the deployment is complete, you should already be on the latest version available. If not, we recommend you to check whether there are any updates available. - ![Screenshot showing cluster updates](./cluster_updates.png) + ![Screenshot showing instance updates](./cluster_updates.png) If the _Status_-column does not indicate _Up to date_, you can trigger the update process by clicking _One-time update_. @@ -64,15 +64,15 @@ See the [product documentation](https://learn.microsoft.com/azure-stack/hci/upda ## Virtual machine management through Azure portal -Azure Stack HCI supports [VM management through the Azure portal](https://learn.microsoft.com/azure-stack/hci/manage/azure-arc-enabled-virtual-machines). Open the [HCIBox VM provisioning documentation](/azure_jumpstart_hcibox/RB/) to get started. +Azure Local supports [VM management through the Azure portal](https://learn.microsoft.com/azure-stack/hci/manage/azure-arc-enabled-virtual-machines). Open the [HCIBox VM provisioning documentation](/azure_jumpstart_hcibox/RB/) to get started. ![Screenshot showing VM provisioning blade](./vm_provisioning.png) ## Azure Kubernetes Service (AKS) -HCIBox comes pre-configured with [Azure Kubernetes Service on Azure Stack HCI](https://learn.microsoft.com/azure-stack/aks-hci/). Open the [HCIBox AKS-HCI documentation](/azure_jumpstart_hcibox/AKS/) to explore currently available features. +HCIBox comes pre-configured with [Azure Kubernetes Service on Azure Local](https://learn.microsoft.com/azure-stack/aks-hci/). Open the [HCIBox AKS-HCI documentation](/azure_jumpstart_hcibox/AKS/) to explore currently available features. -![Screenshot showing AKS on Azure Stack HCI](./cluster_detail.png) +![Screenshot showing AKS on Azure Local](./cluster_detail.png) ## Advanced Configurations diff --git a/docs/badges/Boxing_Champ.png b/docs/badges/Boxing_Champ.png new file mode 100644 index 00000000..6bdb3b58 Binary files /dev/null and b/docs/badges/Boxing_Champ.png differ diff --git a/docs/badges/Code_Whisperer.png b/docs/badges/Code_Whisperer.png new file mode 100644 index 00000000..c334a03e Binary files /dev/null and b/docs/badges/Code_Whisperer.png differ diff --git a/docs/badges/Dream_Builder.png b/docs/badges/Dream_Builder.png new file mode 100644 index 00000000..b1b17c85 Binary files /dev/null and b/docs/badges/Dream_Builder.png differ diff --git a/docs/badges/Jumpstart_Wizard.png b/docs/badges/Jumpstart_Wizard.png new file mode 100644 index 00000000..3c7b41d3 Binary files /dev/null and b/docs/badges/Jumpstart_Wizard.png differ diff --git a/docs/badges/Movie_Star.png b/docs/badges/Movie_Star.png new file mode 100644 index 00000000..da15673d Binary files /dev/null and b/docs/badges/Movie_Star.png differ diff --git a/docs/badges/Sandbox_player.png b/docs/badges/Sandbox_player.png new file mode 100644 index 00000000..93ac76cc Binary files /dev/null and b/docs/badges/Sandbox_player.png differ diff --git a/docs/badges/Scenario_Sorcerer_level_1.png b/docs/badges/Scenario_Sorcerer_level_1.png new file mode 100644 index 00000000..af80d23b Binary files /dev/null and b/docs/badges/Scenario_Sorcerer_level_1.png differ diff --git a/docs/badges/Scenario_Sorcerer_level_2.png b/docs/badges/Scenario_Sorcerer_level_2.png new file mode 100644 index 00000000..b78c60bc Binary files /dev/null and b/docs/badges/Scenario_Sorcerer_level_2.png differ diff --git a/docs/badges/Scenario_Sorcerer_level_3.png b/docs/badges/Scenario_Sorcerer_level_3.png new file mode 100644 index 00000000..eac07ff3 Binary files /dev/null and b/docs/badges/Scenario_Sorcerer_level_3.png differ diff --git a/docs/badges/The_Giver_level_1.png b/docs/badges/The_Giver_level_1.png new file mode 100644 index 00000000..0a1b74a8 Binary files /dev/null and b/docs/badges/The_Giver_level_1.png differ diff --git a/docs/badges/The_Giver_level_2.png b/docs/badges/The_Giver_level_2.png new file mode 100644 index 00000000..47248849 Binary files /dev/null and b/docs/badges/The_Giver_level_2.png differ diff --git a/docs/badges/The_Giver_level_3.png b/docs/badges/The_Giver_level_3.png new file mode 100644 index 00000000..4c412886 Binary files /dev/null and b/docs/badges/The_Giver_level_3.png differ diff --git a/docs/badges/Treasure_Hunter.png b/docs/badges/Treasure_Hunter.png new file mode 100644 index 00000000..548a54de Binary files /dev/null and b/docs/badges/Treasure_Hunter.png differ diff --git a/docs/badges/Words_of_Wisdom_level_1.png b/docs/badges/Words_of_Wisdom_level_1.png new file mode 100644 index 00000000..a7258303 Binary files /dev/null and b/docs/badges/Words_of_Wisdom_level_1.png differ diff --git a/docs/badges/Words_of_Wisdom_level_2.png b/docs/badges/Words_of_Wisdom_level_2.png new file mode 100644 index 00000000..8e41c9f2 Binary files /dev/null and b/docs/badges/Words_of_Wisdom_level_2.png differ diff --git a/docs/badges/Words_of_Wisdom_level_3.png b/docs/badges/Words_of_Wisdom_level_3.png new file mode 100644 index 00000000..f5675d08 Binary files /dev/null and b/docs/badges/Words_of_Wisdom_level_3.png differ diff --git a/docs/badges/_index.md b/docs/badges/_index.md new file mode 100644 index 00000000..6ca4c5fe --- /dev/null +++ b/docs/badges/_index.md @@ -0,0 +1,59 @@ +--- +type: docs +title: "Jumpstart Community Badges" +linkTitle: "Jumpstart Community Badges" +weight: 7 +--- + +## Jumpstart Community Badges + +**Welcome to Jumpstart Community Badges!** + +Jumpstart Community Badges are a recognition system designed to celebrate and reward active participation and contributions within the Jumpstart community. They help foster engagement and acknowledge members’ efforts in various activities and initiatives. + +### The Jumpstart Badges + +| Badge | Name | Description | How to earn it | Tiers | +| ------------- | ------------- | ------------- | -----------------| ------------- | +| ![Scenario_Sorcerer_level_1](./Scenario_Sorcerer_level_1.png) | Scenario Sorcerer | Jumpstart Scenario contributor | A contributor of Jumpstart Scenarios. | ⭐ 1 Jumpstart Scenario contribution
⭐⭐ 3 Jumpstart Scenario contributions
⭐⭐⭐ 5+ Jumpstart Scenario contributions | +| ![The_Giver_level_1](./The_Giver_level_1.png) | The Giver | Jumpstart Drops contributor | A contributor of Jumpstart Drops. | ⭐ 1 Jumpstart Drop contribution
⭐⭐ 3 Jumpstart Drop contributions
⭐⭐⭐ 5+ Jumpstart Drop contributions | +| ![Words_of_Wisdom_level_1](./Words_of_Wisdom_level_1.png) | Words of Wisdom | Jumpstart advocacy champion | A contributor who created a Jumpstart community advocacy asset (blog, video, webinar, presentation, etc.). | ⭐ 1 Jumpstart community contribution
⭐⭐ 3 Jumpstart community contributions
⭐⭐⭐ 5+ Jumpstart community contributions | +| ![Sandbox_player](./Sandbox_player.png) | Sandbox Player | Jumpstart ArcBox contributor | A contributor of Jumpstart ArcBox code and/or documentation. | One-time badge | +| ![Boxing_Champ](./Boxing_Champ.png) | Boxing Champ | Jumpstart HCIBox contributor | A contributor of Jumpstart HCIBox code and/or documentation. | One-time badge | +| ![Dream_Builder](./Dream_Builder.png) | Dream Builder | Jumpstart Agora contributor | A contributor of Jumpstart Agora code and/or documentation. | One-time badge | +| ![Jumpstart_Wizard](./Jumpstart_Wizard.png) | Jumpstart Wizard | Jumpstart problem solver | A contributor who received an "Accepted answer" on Jumpstart issue and/or Jumpstart GitHub Discussions and/or attended research activities. | One-time badge | +| ![Treasure_Hunter](./Treasure_Hunter.png) | Treasure Hunter | Jumpstart diagrams collaborator | A contributor who worked with the team on an existing or new published architecture diagram. | One-time badge | +| ![Code_Whisperer](./Code_Whisperer.png) | Code Whisperer | Jumpstart maintainer | A Jumpstart core maintainer and an official team member. | One-time badge | +| ![Movie_Star](./Movie_Star.png) | Movie Star | Jumpstart Lightning guest | A guest on Jumpstart Lightning. | One-time badge | + + +### Apply for a Jumpstart Community Badge + +You can apply for a Jumpstart Community Badge using this form: https://aka.ms/JumpstartBadgesForm + + +### Frequently Asked Questions (FAQ) + +#### What are Jumpstart Community Badges? +Jumpstart Community Badges are a recognition system designed to celebrate and reward active participation and contributions within the Jumpstart community. + +#### How can I earn a Jumpstart Community Badge? +You can earn badges by participating in various community contributions and activities, such as contributing code, contributing to discussions, writing documentation, creating diagrams, and helping other members. You can find more information about the different badges and how to earn them in the table above. You can then apply for a badge on https://aka.ms/JumpstartBadges. + +#### Where can I see the badges I’ve earned? +You can view your earned badges on your Credly profile page and add them also to your social media profiles such as LinkedIn. + +#### Are there different types of badges? +Yes, there are various types of badges that recognize different kinds of contributions and achievements, ranging from contributor to advocacy roles. + +#### Can I lose a badge once I’ve earned it? +Generally, once you earn a badge, it remains on your profile. + +#### How do badges benefit me? +Badges serve as a visible acknowledgment of your contributions, enhancing your reputation within the community. + +#### Can I suggest new badges? +Yes, community members are encouraged to suggest new badges. You can submit your ideas through the feedback section on the Jumpstart platform. + +#### Who can I contact if I have questions about badges? +If you have any questions or need assistance, you can reach out to the community support team via the contact options provided on the Jumpstart platform. diff --git a/docs/contribution_guidelines/_index.md b/docs/contribution_guidelines/_index.md index ca55445f..543eb195 100644 --- a/docs/contribution_guidelines/_index.md +++ b/docs/contribution_guidelines/_index.md @@ -13,6 +13,10 @@ We extend our heartfelt gratitude for considering a contribution to Arc Jumpstar Use these guidelines as a roadmap for crafting a successful, polished contribution that aligns with our standards. For newcomers, we encourage you to explore the raw code of existing scenarios, which offer valuable insights into the approved scenario format and encompass all the guidelines outlined here. If you have questions, our team of Arc Jumpstart project maintainers is available to help. +> **Disclaimer:** Some content in this repository may have been created with the assistance of AI tools. To maintain quality, we follow [Microsoft’s principles for AI-generated content](https://learn.microsoft.com/principles-for-ai-generated-content) and have a thorough review process in place. Please refer to our [AI disclaimer](https://github.com/Azure/arc_jumpstart_docs/blob/main/AI_DISCLAIMER.MD) for more information. + +We welcome contributions and encourage all contributors to be mindful of these guidelines when expanding or editing AI-assisted documentation. + ## Code reviews The Arc Jumpstart is a mix of various automation scripts and techniques, code styles, and documentation. diff --git a/docs/drops_contribution_guidelines/_index.md b/docs/drops_contribution_guidelines/_index.md index 099f87f0..4633bd6c 100644 --- a/docs/drops_contribution_guidelines/_index.md +++ b/docs/drops_contribution_guidelines/_index.md @@ -22,64 +22,64 @@ The contribution process consists of the following steps: - **Include Code in Pull Request:** Include the code/artifacts in the pull request and host all the content as part of the [Arc Jumpstart Drops repository](https://github.com/Azure/arc_jumpstart_drops). - **Reference to Author's Repository:** Keep the code in the author's repository and provide a reference URL under the _"Source"_ in the Drop definition file (check [Drops schema](https://github.com/Azure/arc_jumpstart_drops/blob/main/SCHEMA.md)). Ensure that the repository is publicly available. -1. **Artifacts creation and uploading:** Develop and validate the source code of your Jumpstart Drop and the JSON schema file. Before submitting your pull request, ensure to check the following: +2. **Artifacts creation and uploading:** Develop and validate the source code of your Jumpstart Drop and the JSON schema file. Before submitting your pull request, ensure to check the following: 1. Test your Drop end-to-end in a new, unmodified environment to ensure it works as expected. - 1. Choose a descriptive and actionable title that accurately represents your Drop's purpose. Remember that users will see this title when browsing the Drops gallery and should have a good idea of your Drop just by seeing the Drop's card. - 1. Provide clear documentation that includes screenshots or videos demonstrating the steps involved and potential outcomes. - 1. Have someone else review your code before submitting it. Don't include any credentials as part of your submission. - 1. Ensure checking the spelling, grammar, and wording of your submission. To help validation, use [Vale](https://vale.sh/), already configured as part of the project. Ensure to use `vale sync` before starting linting, to get the latest configurations. + 2. Choose a descriptive and actionable title that accurately represents your Drop's purpose. Remember that users will see this title when browsing the Drops gallery and should have a good idea of your Drop just by seeing the Drop's card. + 3. Provide clear documentation that includes screenshots or videos demonstrating the steps involved and potential outcomes. + 4. Have someone else review your code before submitting it. Don't include any credentials as part of your submission. + 5. Ensure checking the spelling, grammar, and wording of your submission. To help validation, use [Vale](https://vale.sh/), already configured as part of the project. Ensure to use `vale sync` before starting linting, to get the latest configurations. The final submission should contain the following files: - - **Source Code**: Include all the code files you've created, along with any necessary documentation (README, images, videos, etc.). Ensure these files follow the correct structure defined in the [folder structure](#folder-structure) section. + - **Source Code**: Include all the code files you've created, along with any necessary documentation (README, images, videos, etc.). Ensure these files follow the correct structure defined in the [folder structure](#folder-structure) section. - - **Drop Definition**: Provide a JSON file with all the required fields as described in the [Drops Schema](https://github.com/Azure/arc_jumpstart_drops/blob/main/SCHEMA.md) definition. This file will be used by the Arc Jumpstart Drops page to create a Drop card with all the necessary information, as you can see in the following image. + - **Drop Definition**: Provide a JSON file with all the required fields as described in the [Drops Schema](https://github.com/Azure/arc_jumpstart_drops/blob/main/SCHEMA.md) definition. This file will be used by the Arc Jumpstart Drops page to create a Drop card with all the necessary information, as you can see in the following image. ![Drop card](./drop_definition.png) 1. Drop card created for each *JSON* definition schema file placed under the [drops](https://github.com/Azure/arc_jumpstart_drops/tree/main/drops) folder, with a unique *Title* and *Authors*. The name should be unique, have a proper description, and use **snake_case**. - 1. Description of the Drop - 1. Filter bar to filter Drops based on the metadata of the Drop, like tags, products, creation date, and topics. - 1. Tags and action buttons: _Download_ and _Share_. + 2. Description of the Drop + 3. Filter bar to filter Drops based on the metadata of the Drop, like tags, products, creation date, and topics. + 4. Tags and action buttons: _Download_ and _Share_. -1. **Create Pull Request to Canary**: Submit your pull request (PR) to the Canary branch of [Arc Jumpstart Drops](https://github.com/Azure/arc_jumpstart_drops). +3. **Create Pull Request to Canary**: Submit your pull request (PR) to the Canary branch of [Arc Jumpstart Drops](https://github.com/Azure/arc_jumpstart_drops). - For example, the following Drop submission aims to contribute the code to the [arc_jumsptart_drops](https://github.com/Azure/arc_jumpstart_drops) repository, hence as part of the pull request you can see the `win11_iot_ram_deduction.json` file under the `drops` folder, and then the artifacts under the `script_automation/win11_iot_ram_deduction` folder. Also, you can see that the *Source* is pointing to the [arc_jumsptart_drops](https://github.com/Azure/arc_jumpstart_drops) repository. + For example, the following Drop submission aims to contribute the code to the [arc_jumpstart_drops](https://github.com/Azure/arc_jumpstart_drops) repository, hence as part of the pull request you can see the `win11_iot_ram_deduction.json` file under the `drops` folder, and then the artifacts under the `script_automation/win11_iot_ram_deduction` folder. Also, you can see that the *Source* is pointing to the [arc_jumpstart_drops](https://github.com/Azure/arc_jumpstart_drops) repository. ![Pull request example](./drop_submission.png) -1. **GitHub Checks**: As part of the validation process, there are multiple [GitHub Actions](https://github.com/Azure/arc_jumpstart_drops/actions) that run during the pull request review process to ensure: +4. **GitHub Checks**: As part of the validation process, there are multiple [GitHub Actions](https://github.com/Azure/arc_jumpstart_drops/actions) that run during the pull request review process to ensure: - Drop JSON schema definition and folder structure. - [MIT licensing](https://github.com/Azure/arc_jumpstart_drops?tab=MIT-1-ov-file#readme) and Microsoft CLA license check. - CodeQL for vulnerabilities. - - [Vale.sh](https://vale.sh/) linter for documentation grammar and styling. + - [Vale.sh](https://vale.sh/) linter for documentation grammar and styling. When creating a pull request, you'll see your checks running for ~2-3 minutes. Carefully review the result of the checks, and fix any errors found on your submission. ![Pull request checks](./checks.png) -1. **Manual review from Jumpstart Core maintainers**: Reviewers will be added based on the products and type of Drop submitted. The PR can only be merged if all GitHub checks pass and at least 2 reviewers approve the submission. +5. **Manual review from Jumpstart Core maintainers**: Reviewers will be added based on the products and type of Drop submitted. The PR can only be merged if all GitHub checks pass and at least 2 reviewers approve the submission. -1. **Merge to Canary**: If everything is correct, the PR will be merged to the **canary** branch, and the new Drop will be part of the approved Drops curated list. +6. **Merge to Canary**: If everything is correct, the PR will be merged to the **canary** branch, and the new Drop will be part of the approved Drops curated list. -1. **Validation with Preview Site**: Validate the changes using the [Preview Azure Arc Jumpstart site](https://preview.arcjumpstart.com/azure_jumpstart_drops). Ensure to check that your Drop is rendering correctly. Take some time to review the Drop card, and once finished, review the right-sidebar with all the context from your _README.md_ or _Index.md_. +7. **Validation with Preview Site**: Validate the changes using the [Preview Azure Arc Jumpstart site](https://preview.arcjumpstart.com/azure_jumpstart_drops). Ensure to check that your Drop is rendering correctly. Take some time to review the Drop card, and once finished, review the right-sidebar with all the context from your _README.md_ or _Index.md_. ![Preview site](./preview_site.png) -1. **Merge to Main**: Finally, once the **Canary** branch is merged to **Main** and published to Production, the Drop will be available as part of [Azure Arc Jumpstart Drops](https://arcjumpstart.com/azure_jumpstart_drops). +8. **Merge to Main**: Finally, once the **Canary** branch is merged to **Main** and published to Production, the Drop will be available as part of [Azure Arc Jumpstart Drops](https://arcjumpstart.com/azure_jumpstart_drops). ## Drop Index -When you select a Drop for more information, a right-sidebar will appear, displaying a more detailed view of the Drop. The content of the right bar is based on the *_README.md_ or _Index.md_ file of the Drop's source code, which is used to render different sections. To ensure that the right bar displays all the relevant information, headers (H2, H3, or H4) must be included in the _README.md_ or _Index.md_ file. +When you select a Drop for more information, a right-sidebar will appear, displaying a more detailed view of the Drop. The content of the right bar is based on the *_README.md_ or _Index.md_ file of the Drop's source code, which is used to render different sections. To ensure that the right bar displays all the relevant information, headers (H2, H3, or H4) must be included in the _README.md_ or _Index.md_ file. ![Right bar rendering](./right_bar.png) When accessing the right-sidebar, you'll be presented with the following sections: 1. **Metadata header**: This section will display important information from the Drop JSON schema file, allowing you to quickly and easily access key details about your Drop data. -1. **Section selector**: A dropdown menu will enable you to choose from different sections of the Drop Index file, making it easy to navigate through the Drop documentation. -1. **Selected content**: Once you've chosen a section, the content will be displayed, allowing you to view the information you need without having to navigate away from the page. -1. **Full-screen mode**: If you need more space to work with, you can switch to full-screen mode with the click of a button, giving you a larger view of your data and making it easier to work with. +2. **Section selector**: A dropdown menu will enable you to choose from different sections of the Drop Index file, making it easy to navigate through the Drop documentation. +3. **Selected content**: Once you've chosen a section, the content will be displayed, allowing you to view the information you need without having to navigate away from the page. +4. **Full-screen mode**: If you need more space to work with, you can switch to full-screen mode with the click of a button, giving you a larger view of your data and making it easier to work with. To help contributors write effective documentation for their Drop Index page, we recommend including the following sections: @@ -87,7 +87,7 @@ To help contributors write effective documentation for their Drop Index page, we - **Prerequisites**: This section should list all the prerequisites that need to be configured before running the Drop. For each prerequisite, we encourage authors to be descriptive about the specific version (if needed) and provide appropriate documentation links on how to configure it. - **Getting Started**: This section should provide detailed instructions on how to run the Drop, including a quick start approach and coverage of the different configurations and parameters available. This section assumes that the prerequisites are already satisfied. If there's a way to run the Drop with one command, we recommend including it and explaining the different variables/parameters that could be used. - **Resources**: This section should list resources that users can use to find helpful information regarding the Drop and the products/prerequisites used. -- **Others**: This section will render the content for all other headers that don't match the above. +- **Others**: This section will render the content for all other headers that don't match the above. It's important to note that while the sections described above are recommended for an optimal user experience, contributors are free to use their own layouts and sections. If no matching sections are found, all content will be rendered in a single tab labeled **Overview**. @@ -103,8 +103,8 @@ The folder structure guidelines for submitting Drop source code as part of a Pul 1. Create a root folder with the name of the Drop using **snake_case**. For example, *azure_arc_management_and_monitor_workbook*. -1. Inside the root folder, include a file named *_index.md* that serves as a README file for the Drop. This file should provide a brief introduction and overview of the Drop, its objectives, prerequisites, deployment steps, and any additional resources or references. +2. Inside the root folder, include a file named *_index.md* that serves as a README file for the Drop. This file should provide a brief introduction and overview of the Drop, its objectives, prerequisites, deployment steps, and any additional resources or references. -1. Create a new folder named *artifacts* inside the root folder. This folder should contain all the files and scripts necessary to run the Drop. +3. Create a new folder named *artifacts* inside the root folder. This folder should contain all the files and scripts necessary to run the Drop. -1. Optionally, create a new folder named *media* inside the artifacts folder. This folder can contain any screenshots or architecture diagrams that showcase the Drop. +4. Optionally, create a new folder named *media* inside the artifacts folder. This folder can contain any screenshots or architecture diagrams that showcase the Drop.