From 421b5b64dc10a0b4ec933810eb2a4260494652fc Mon Sep 17 00:00:00 2001 From: sdesai345 Date: Mon, 4 Nov 2024 12:43:27 -0800 Subject: [PATCH 01/20] Create 2024-11-11-e2e-flyte-data-science-solution-on-aks.md --- ...-e2e-flyte-data-science-solution-on-aks.md | 105 ++++++++++++++++++ 1 file changed, 105 insertions(+) create mode 100644 blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md diff --git a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md new file mode 100644 index 00000000..bb7d1dbc --- /dev/null +++ b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md @@ -0,0 +1,105 @@ +--- +title: "Deploy and 'take Flyte' with an end-to-end data science solution on AKS" +description: "Learn about the open-source Flyte tools and how to deploy them securely for your data science tasks on Azure Kubernetes Service (AKS)" +date: 2024-11-11 +author: Sachi Desai +categories: ai +--- + +## Background + +Data is often at the heart of application design and development - it fuels user-centric design, provides insights for feature enhancements, and represents the value of an application as a whole. In that case, shouldn’t we use data science tools and workflows that are flexible and scalable on a platform like Kubernetes, for a range of application types? + +In collaboration with [David Espejo](https://www.linkedin.com/in/davidmirror/) and [Shalabh Chaudhri](https://www.linkedin.com/in/shalabhchaudhri/) from [Union.ai](https://www.union.ai/), we’ll dive into an example using one of these tools built on Kubernetes itself, called Flyte. As a data science orchestration platform, Flyte can help you manage and scale out exploratory data analysis (EDA) and machine learning jobs through a simple user interface. + +## First off, what is a Flyte cluster? + +A Flyte cluster provides you an API endpoint to register, compile, and execute ML workflows on Kubernetes, and the main Flyte components (user plane, data plane, and control plane) are implemented as Pods. +Adopters of Flyte include organizations running large-scale data operations and ML operations, including social media platforms, music/video streaming services, and bioinformatics companies. + +With a fully-configured Flyte cluster, you can: +* Process and visualize large, dynamic data sets and ensure up-to-date information retrieval +* Deploy ML workflows +* Track parallel and sequential Flyte tasks in your ML pipeline +* …and more + +Starting with this [reference implementation](https://www.union.ai/blog-post/flyte-on-azure-a-reference-implementation), you'll be able to take a basic Azure resource group and build an end-to-end data science solution on a working Flyte cluster. + +> [!NOTE] +> This example deploys Flyte as an open-source tool on your cluster, and it is not a managed feature on Azure Kubernetes Service today. + +## Let's get started + +Before you begin, take a look at the [prerequisite](https://github.com/unionai-oss/deploy-flyte/blob/main/environments/azure/flyte-core/README.md#prerequisites) steps on Azure, including: + +* Azure subscription with at least [Contributor role over all your resources](https://learn.microsoft.com/en-us/azure/role-based-access-control/built-in-roles/privileged#contributor) using Azure RBAC +* Azure CLI version 2.0 or later installed and configured +* Terraform version 1.3.7 or later installed +* Helm version 3.15.4 or later installed +* Kubernetes command-line client, [kubectl](https://kubernetes.io/docs/tasks/tools/install-kubectl/), installed and configured + +## Which Flyte backend components are installed, and what do they do? + +In your end-to-end solution, each of the following Flyte backend components will run on its own pod: + +| Flyte component | Description | +| -- | -- | +| Data catalog | Service that simplifies data indexing and allows you to query data artifacts based on metadata and/or tags | +| Flyte pod webhook | Deployment that creates the Webhook Pod called by ApiServer, when a simple Flyte task launches a Pod | +| Flyte admin | Main Flyte API that processes client requests, see [API specification](https://docs.flyte.org/en/latest/api/flyteidl/docs/service/service.html#ref-flyteidl-service-admin-proto) | +| Flyte console | Web user interface for the Flyte platform, hosted in the same Flyte cluster as Admin API | +| Flyte propeller | Core engine that executes workflows within the Flyte data plane | +| Flyte scheduler | Cloud-agnostic native scheduler for fixed-rate and cron-based schedules, defined at the init time for your workflow and activated/deactivated using FlyteAdmin API | +| Sync resources | Type of agent that enables request/response services (e.g. APIs) to return outputs | + +Once you've applied and generated the reference Flyte Terraform, you'll receive an endpoint to your Flyte cluster and verify a similar output to the status below: + +```bash +kubectl get pods -n flyte + +NAME READY STATUS RESTARTS AGE +datacatalog-6864645db6-99msb 1/1 Running 0 6m45s +flyte-pod-webhook-848d7db899-8wltj 1/1 Running 0 6m45s +flyteadmin-6cc67b49b4-cmt7j 1/1 Running 0 6m45s +flyteconsole-68f677797f-p4s98 1/1 Running 0 6m45s +flytepropeller-b88f7bf6d-lqc8s 1/1 Running 0 6m45s +flytescheduler-844db4658c-hfrhv 1/1 Running 0 6m45s +syncresources-767d7fc77b-5mj6n 1/1 Running 0 6m45s +``` + +## Which AKS features are built into my Flyte solution? + +Storage: Cost-effective general-purpose v2 Storage Account with Hierarchical Namespace enabled to support the blob storage resource, and a default storage container to store metadata and raw data. +Your metadata might consist of task inputs/outputs, data serialization format (protocol buffers), etc, while raw data will be unprocessed data and large objects that execution pods read from/write to the storage container. + +Compute: You’ll start with a Standard_D2_v2 CPU node pool of size 1, with [cluster autoscaler](https://learn.microsoft.com/en-us/azure/aks/cluster-autoscaler?tabs=azure-cli) enabled on your AKS cluster to meet workload demands. As your workflow or application submits an increasing number of Flyte tasks, cluster autoscaler will watch for pending pods and scale up your node pool size (in this case, to a maximum of 10 nodes) to minimize downtime due to resource constraints. + +> [!TIP] +> To create a GPU-enabled node pool using your `aks.tf` [configuration file](https://github.com/unionai-oss/deploy-flyte/blob/main/environments/azure/flyte-core/aks.tf), update the `gpu_node_pool_count` and `gpu_machine_type` to your desired node count and instance type, respectively, in the `locals` array. If you specify an `accelerator` type, make sure to select a [supported option](https://github.com/flyteorg/flytekit/blob/daeff3f5f0f36a1a9a1f86c5e024d1b76cdfd5cb/flytekit/extras/accelerators.py#L132-L160) for flytekit. + +Identity management: Workload identity is enabled for authentication using Azure AD tokens - the `flytepropeller`, `flyteadmin`, and `datacatalog` backend components use one user-assigned MI, while the Flyte task execution pods use a different user-assigned MI. + +Networking and security: TLDR: [insert diagram below] + +Automatic upgrades: [TBD] + +Container image management: Azure Container Registry (ACR) is created out-of-box with permissions for you to: +* Push all workflow output images to this private ACR, +* Pull your custom images through task execution pods from the ACR (otherwise, a default container image is used for each Flyte workflow execution) + +Monitoring: [Container Insights](https://learn.microsoft.com/en-us/azure/azure-monitor/containers/container-insights-analyze) is configured out-of-box, with system namespace logs and Flyte user workload logs ingested through Azure Monitor pipeline, to help you: +* Monitor workflow execution details from FlyteAdmin +* Troubleshoot deployment issues faster through Azure Portal +* Query historical logs using Log Analytics + +[insert screenshot of success FlyteAdmin logs retrieval through Log Analytics] + + +[insert image of successful Flyte deployment] + + +## What next? + +* See Union.ai's blog post about [Flyte on Azure](https://www.union.ai/blog-post/flyte-on-azure-a-reference-implementation) +* Check out more ML and feature engineering [tutorials with Flyte](https://docs.flyte.org/en/latest/flytesnacks/tutorials/index.html) +* Learn about [best practices for your MLOps pipelines](https://learn.microsoft.com/en-us/azure/aks/best-practices-ml-ops) on Azure Kubernetes Service From fc218fdccf3fb62de109d35bee110b604d3adbfd Mon Sep 17 00:00:00 2001 From: sdesai345 Date: Tue, 5 Nov 2024 11:00:42 -0800 Subject: [PATCH 02/20] Create README.md --- .../images/deploy-data-science-solution-with-flyte/README.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 blog/assets/images/deploy-data-science-solution-with-flyte/README.md diff --git a/blog/assets/images/deploy-data-science-solution-with-flyte/README.md b/blog/assets/images/deploy-data-science-solution-with-flyte/README.md new file mode 100644 index 00000000..6066cd9f --- /dev/null +++ b/blog/assets/images/deploy-data-science-solution-with-flyte/README.md @@ -0,0 +1 @@ +This folder contains images to be published in the open-source Flyte on AKS sample deployment blog post. 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zm~!16TFb*xwr>66%8P3|kUOErcFt=Yw}EV%>uopZ7CAcuSF=$XF!|cXC9hBIAAQyg zK^n6)FAptRuRnzyKAM#JFupe!lcuMU4g-yO@h1Y8c?1=F^pXk)RNlkEaZ=F3R5F}h zj%W4MypmcBI5|$1f0*rh>_&3wb`<>bzSi4adbs9F#&0R147UHbl1H;S@KRh+2kM1x zE$<7n0Ust66maeC?j{2=(&`y6G^$lm+2jmicJ_^oib}cpp{1>D%JMQi){rolAloq; z`yj|OV^PoH$L5l^`=*X-%3_<7Rnt>wm_#e*;vwIQ;F|yVGY2ktIGFn?**3VL?fA$3?7s|;`$#8_ zwK@Ils~w3c2ajG)WIhD{C<^Qd@H{>@05kbt+w^}@l>c_NS_!nti^Uik9WzJ-axSrE z9$vN#bv^}*%E`$&J>DfyP1ckK4+5DmfWElH+uiW(303^n36={ Date: Wed, 6 Nov 2024 10:35:42 -0800 Subject: [PATCH 04/20] Update blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md --- .../_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md index bb7d1dbc..51f3c065 100644 --- a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md +++ b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md @@ -92,7 +92,7 @@ Monitoring: [Container Insights](https://learn.microsoft.com/en-us/azure/azure-m * Troubleshoot deployment issues faster through Azure Portal * Query historical logs using Log Analytics -[insert screenshot of success FlyteAdmin logs retrieval through Log Analytics] +![image](AKS/assets/images/deploy-data-science-solution-with-flyte/flyte-admin-logs-view-log-analytics.png) [insert image of successful Flyte deployment] From c3f5757af29dbf1e5a7848fc82355f920b4f1110 Mon Sep 17 00:00:00 2001 From: sdesai345 Date: Thu, 7 Nov 2024 07:08:47 -0800 Subject: [PATCH 05/20] Update blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md --- .../_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md index 51f3c065..6d7e983f 100644 --- a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md +++ b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md @@ -77,7 +77,7 @@ Compute: You’ll start with a Standard_D2_v2 CPU node pool of size 1, with [clu > [!TIP] > To create a GPU-enabled node pool using your `aks.tf` [configuration file](https://github.com/unionai-oss/deploy-flyte/blob/main/environments/azure/flyte-core/aks.tf), update the `gpu_node_pool_count` and `gpu_machine_type` to your desired node count and instance type, respectively, in the `locals` array. If you specify an `accelerator` type, make sure to select a [supported option](https://github.com/flyteorg/flytekit/blob/daeff3f5f0f36a1a9a1f86c5e024d1b76cdfd5cb/flytekit/extras/accelerators.py#L132-L160) for flytekit. -Identity management: Workload identity is enabled for authentication using Azure AD tokens - the `flytepropeller`, `flyteadmin`, and `datacatalog` backend components use one user-assigned MI, while the Flyte task execution pods use a different user-assigned MI. +Identity management: [Workload Identity](https://learn.microsoft.com/en-us/azure/aks/workload-identity-overview?tabs=dotnet) is enabled for authentication using Azure AD tokens - the `flytepropeller`, `flyteadmin`, and `datacatalog` backend components use one user-assigned MI, while the Flyte task execution pods use a separate user-assigned MI. Networking and security: TLDR: [insert diagram below] From bee37ac966699c1ec79d526666fc3fcbd5572816 Mon Sep 17 00:00:00 2001 From: sdesai345 Date: Thu, 7 Nov 2024 07:08:55 -0800 Subject: [PATCH 06/20] Update blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md --- .../_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md index 6d7e983f..0eb6c894 100644 --- a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md +++ b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md @@ -95,7 +95,7 @@ Monitoring: [Container Insights](https://learn.microsoft.com/en-us/azure/azure-m ![image](AKS/assets/images/deploy-data-science-solution-with-flyte/flyte-admin-logs-view-log-analytics.png) -[insert image of successful Flyte deployment] +![image](AKS/assets/images/deploy-data-science-solution-with-flyte/successful-flyte-deployment-on-aks.png) ## What next? From 0a6013714674dae682702fa51d10370f36791e1a Mon Sep 17 00:00:00 2001 From: sdesai345 Date: Thu, 7 Nov 2024 07:09:07 -0800 Subject: [PATCH 07/20] Update blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md --- .../_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md index 0eb6c894..6029a8b8 100644 --- a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md +++ b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md @@ -81,7 +81,7 @@ Identity management: [Workload Identity](https://learn.microsoft.com/en-us/azure Networking and security: TLDR: [insert diagram below] -Automatic upgrades: [TBD] +Automatic cluster upgrades: AKS cluster [auto-upgrade](https://learn.microsoft.com/en-us/azure/aks/auto-upgrade-cluster?tabs=azure-cli#cluster-auto-upgrade-channels) is enabled by default to minimize workload downtime and stay up-to-date on the latest AKS patches. The reference Flyte Terraform plan sets `automatic_upgrade_channel = "stable"`, ensuring that the AKS cluster created will always remain in a [supported version](https://learn.microsoft.com/en-us/azure/aks/auto-upgrade-cluster?tabs=azure-cli#best-practices-for-cluster-auto-upgrade) (i.e. within the N-2 rule). Container image management: Azure Container Registry (ACR) is created out-of-box with permissions for you to: * Push all workflow output images to this private ACR, From 31be83b70b2152539f3affeae5632ff4d3a90be2 Mon Sep 17 00:00:00 2001 From: sdesai345 Date: Thu, 7 Nov 2024 07:11:50 -0800 Subject: [PATCH 08/20] Update blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md --- .../_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md index 6029a8b8..67ec5453 100644 --- a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md +++ b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md @@ -12,7 +12,7 @@ Data is often at the heart of application design and development - it fuels user In collaboration with [David Espejo](https://www.linkedin.com/in/davidmirror/) and [Shalabh Chaudhri](https://www.linkedin.com/in/shalabhchaudhri/) from [Union.ai](https://www.union.ai/), we’ll dive into an example using one of these tools built on Kubernetes itself, called Flyte. As a data science orchestration platform, Flyte can help you manage and scale out exploratory data analysis (EDA) and machine learning jobs through a simple user interface. -## First off, what is a Flyte cluster? +## What is a Flyte cluster, and who uses it? A Flyte cluster provides you an API endpoint to register, compile, and execute ML workflows on Kubernetes, and the main Flyte components (user plane, data plane, and control plane) are implemented as Pods. Adopters of Flyte include organizations running large-scale data operations and ML operations, including social media platforms, music/video streaming services, and bioinformatics companies. From 70e13d6ff021991f8ce665f4552a4646b22a24d3 Mon Sep 17 00:00:00 2001 From: sdesai345 Date: Thu, 7 Nov 2024 07:12:00 -0800 Subject: [PATCH 09/20] Update blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md --- blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md | 1 + 1 file changed, 1 insertion(+) diff --git a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md index 67ec5453..ee0f9ae8 100644 --- a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md +++ b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md @@ -15,6 +15,7 @@ In collaboration with [David Espejo](https://www.linkedin.com/in/davidmirror/) a ## What is a Flyte cluster, and who uses it? A Flyte cluster provides you an API endpoint to register, compile, and execute ML workflows on Kubernetes, and the main Flyte components (user plane, data plane, and control plane) are implemented as Pods. + Adopters of Flyte include organizations running large-scale data operations and ML operations, including social media platforms, music/video streaming services, and bioinformatics companies. With a fully-configured Flyte cluster, you can: From 268061fa8e12fd35075f2faab6a55503c0f4868d Mon Sep 17 00:00:00 2001 From: sdesai345 Date: Thu, 7 Nov 2024 18:24:21 -0800 Subject: [PATCH 10/20] Update blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md Co-authored-by: Erin Schaffer <69220686+schaffererin@users.noreply.github.com> --- .../_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md index ee0f9ae8..9ac7473c 100644 --- a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md +++ b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md @@ -8,7 +8,7 @@ categories: ai ## Background -Data is often at the heart of application design and development - it fuels user-centric design, provides insights for feature enhancements, and represents the value of an application as a whole. In that case, shouldn’t we use data science tools and workflows that are flexible and scalable on a platform like Kubernetes, for a range of application types? +Data is often at the heart of application design and development; it fuels user-centric design, provides insights for feature enhancements, and represents the value of an application as a whole. In that case, shouldn’t we use data science tools and workflows that are flexible and scalable on a platform like Kubernetes for a range of application types? In collaboration with [David Espejo](https://www.linkedin.com/in/davidmirror/) and [Shalabh Chaudhri](https://www.linkedin.com/in/shalabhchaudhri/) from [Union.ai](https://www.union.ai/), we’ll dive into an example using one of these tools built on Kubernetes itself, called Flyte. As a data science orchestration platform, Flyte can help you manage and scale out exploratory data analysis (EDA) and machine learning jobs through a simple user interface. From 5fc299d39063b4e7e99d857e9eb03b0571d67413 Mon Sep 17 00:00:00 2001 From: sdesai345 Date: Thu, 7 Nov 2024 18:25:04 -0800 Subject: [PATCH 11/20] Update blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md Co-authored-by: Erin Schaffer <69220686+schaffererin@users.noreply.github.com> --- .../_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md index 9ac7473c..d6268bad 100644 --- a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md +++ b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md @@ -10,7 +10,7 @@ categories: ai Data is often at the heart of application design and development; it fuels user-centric design, provides insights for feature enhancements, and represents the value of an application as a whole. In that case, shouldn’t we use data science tools and workflows that are flexible and scalable on a platform like Kubernetes for a range of application types? -In collaboration with [David Espejo](https://www.linkedin.com/in/davidmirror/) and [Shalabh Chaudhri](https://www.linkedin.com/in/shalabhchaudhri/) from [Union.ai](https://www.union.ai/), we’ll dive into an example using one of these tools built on Kubernetes itself, called Flyte. As a data science orchestration platform, Flyte can help you manage and scale out exploratory data analysis (EDA) and machine learning jobs through a simple user interface. +In collaboration with [David Espejo](https://www.linkedin.com/in/davidmirror/) and [Shalabh Chaudhri](https://www.linkedin.com/in/shalabhchaudhri/) from [Union.ai](https://www.union.ai/), we’ll dive into an example using one of these tools built on Kubernetes itself, called *Flyte*. As a data science orchestration platform, Flyte can help you manage and scale out exploratory data analysis (EDA) and machine learning jobs (ML) through a simple user interface. ## What is a Flyte cluster, and who uses it? From 2c285cc74654ca2e998fda553f7b7a00760c8aa8 Mon Sep 17 00:00:00 2001 From: sdesai345 Date: Thu, 7 Nov 2024 18:25:23 -0800 Subject: [PATCH 12/20] Update blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md Co-authored-by: Erin Schaffer <69220686+schaffererin@users.noreply.github.com> --- ...24-11-11-e2e-flyte-data-science-solution-on-aks.md | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md index d6268bad..0bd09dc0 100644 --- a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md +++ b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md @@ -18,11 +18,12 @@ A Flyte cluster provides you an API endpoint to register, compile, and execute M Adopters of Flyte include organizations running large-scale data operations and ML operations, including social media platforms, music/video streaming services, and bioinformatics companies. -With a fully-configured Flyte cluster, you can: -* Process and visualize large, dynamic data sets and ensure up-to-date information retrieval -* Deploy ML workflows -* Track parallel and sequential Flyte tasks in your ML pipeline -* …and more +With a fully configured Flyte cluster, you can: + +* Process and visualize large, dynamic data sets and ensure up-to-date information retrieval. +* Deploy ML workflows. +* Track parallel and sequential Flyte tasks in your ML pipeline. +* …and more! Starting with this [reference implementation](https://www.union.ai/blog-post/flyte-on-azure-a-reference-implementation), you'll be able to take a basic Azure resource group and build an end-to-end data science solution on a working Flyte cluster. From 2c4c312f75f41f8f34d74608d83345550f231ddb Mon Sep 17 00:00:00 2001 From: sdesai345 Date: Thu, 7 Nov 2024 18:25:35 -0800 Subject: [PATCH 13/20] Update blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md Co-authored-by: Erin Schaffer <69220686+schaffererin@users.noreply.github.com> --- .../_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md index 0bd09dc0..d8cc2ae8 100644 --- a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md +++ b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md @@ -32,7 +32,7 @@ Starting with this [reference implementation](https://www.union.ai/blog-post/fly ## Let's get started -Before you begin, take a look at the [prerequisite](https://github.com/unionai-oss/deploy-flyte/blob/main/environments/azure/flyte-core/README.md#prerequisites) steps on Azure, including: +Before you begin, take a look at the [prerequisites](https://github.com/unionai-oss/deploy-flyte/blob/main/environments/azure/flyte-core/README.md#prerequisites) on Azure, including: * Azure subscription with at least [Contributor role over all your resources](https://learn.microsoft.com/en-us/azure/role-based-access-control/built-in-roles/privileged#contributor) using Azure RBAC * Azure CLI version 2.0 or later installed and configured From 3f869a4d6aff990d715a60040d2c9fdbcf2c209b Mon Sep 17 00:00:00 2001 From: sdesai345 Date: Thu, 7 Nov 2024 18:25:49 -0800 Subject: [PATCH 14/20] Update blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md Co-authored-by: Erin Schaffer <69220686+schaffererin@users.noreply.github.com> --- .../_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md index d8cc2ae8..a33f18d4 100644 --- a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md +++ b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md @@ -28,7 +28,7 @@ With a fully configured Flyte cluster, you can: Starting with this [reference implementation](https://www.union.ai/blog-post/flyte-on-azure-a-reference-implementation), you'll be able to take a basic Azure resource group and build an end-to-end data science solution on a working Flyte cluster. > [!NOTE] -> This example deploys Flyte as an open-source tool on your cluster, and it is not a managed feature on Azure Kubernetes Service today. +> This example deploys Flyte as an open-source tool on your cluster. Currently, it's not a managed feature on Azure Kubernetes Service (AKS). ## Let's get started From f362cc0cb2bf635c2fa9193843902150c01fcda6 Mon Sep 17 00:00:00 2001 From: sdesai345 Date: Thu, 7 Nov 2024 18:26:58 -0800 Subject: [PATCH 15/20] Update blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md Co-authored-by: Erin Schaffer <69220686+schaffererin@users.noreply.github.com> --- ...-e2e-flyte-data-science-solution-on-aks.md | 43 +++++++++++++------ 1 file changed, 30 insertions(+), 13 deletions(-) diff --git a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md index a33f18d4..e097e93c 100644 --- a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md +++ b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md @@ -71,28 +71,45 @@ syncresources-767d7fc77b-5mj6n 1/1 Running 0 6m45s ## Which AKS features are built into my Flyte solution? -Storage: Cost-effective general-purpose v2 Storage Account with Hierarchical Namespace enabled to support the blob storage resource, and a default storage container to store metadata and raw data. -Your metadata might consist of task inputs/outputs, data serialization format (protocol buffers), etc, while raw data will be unprocessed data and large objects that execution pods read from/write to the storage container. +### Storage -Compute: You’ll start with a Standard_D2_v2 CPU node pool of size 1, with [cluster autoscaler](https://learn.microsoft.com/en-us/azure/aks/cluster-autoscaler?tabs=azure-cli) enabled on your AKS cluster to meet workload demands. As your workflow or application submits an increasing number of Flyte tasks, cluster autoscaler will watch for pending pods and scale up your node pool size (in this case, to a maximum of 10 nodes) to minimize downtime due to resource constraints. +Cost-effective general-purpose v2 Storage Account with Hierarchical Namespace enabled to support the blob storage resource, and a default storage container to store metadata and raw data. + +Your metadata might consist of task inputs/outputs, data serialization format (protocol buffers), etc., while raw data will be unprocessed data and large objects that execution pods read from/write to the storage container. + +### Compute + +You’ll start with a Standard_D2_v2 CPU node pool of size 1, with [cluster autoscaler](https://learn.microsoft.com/azure/aks/cluster-autoscaler) enabled on your AKS cluster to meet workload demands. As your workflow or application submits an increasing number of Flyte tasks, cluster autoscaler will watch for pending pods and scale up your node pool size (in this case, to a maximum of 10 nodes) to minimize downtime due to resource constraints. > [!TIP] > To create a GPU-enabled node pool using your `aks.tf` [configuration file](https://github.com/unionai-oss/deploy-flyte/blob/main/environments/azure/flyte-core/aks.tf), update the `gpu_node_pool_count` and `gpu_machine_type` to your desired node count and instance type, respectively, in the `locals` array. If you specify an `accelerator` type, make sure to select a [supported option](https://github.com/flyteorg/flytekit/blob/daeff3f5f0f36a1a9a1f86c5e024d1b76cdfd5cb/flytekit/extras/accelerators.py#L132-L160) for flytekit. -Identity management: [Workload Identity](https://learn.microsoft.com/en-us/azure/aks/workload-identity-overview?tabs=dotnet) is enabled for authentication using Azure AD tokens - the `flytepropeller`, `flyteadmin`, and `datacatalog` backend components use one user-assigned MI, while the Flyte task execution pods use a separate user-assigned MI. +### Identity management + +[Workload Identity](https://learn.microsoft.com/azure/aks/workload-identity-overview) is enabled for authentication using Azure AD tokens - the `flytepropeller`, `flyteadmin`, and `datacatalog` backend components use one user-assigned MI, while the Flyte task execution pods use a separate user-assigned MI. + +### Networking and security + +TLDR: [insert diagram below] + +### Automatic cluster upgrades + +AKS cluster [auto-upgrade](https://learn.microsoft.com/azure/aks/auto-upgrade-cluster#cluster-auto-upgrade-channels) is enabled by default to minimize workload downtime and stay up-to-date on the latest AKS patches. The reference Flyte Terraform plan sets `automatic_upgrade_channel = "stable"`, ensuring that the AKS cluster created will always remain in a [supported version](https://learn.microsoft.com/azure/aks/auto-upgrade-cluster#best-practices-for-cluster-auto-upgrade) (i.e. within the N-2 rule). + +### Container image management + +Azure Container Registry (ACR) is created out-of-box with permissions for you to: -Networking and security: TLDR: [insert diagram below] +* Push all workflow output images to this private ACR. +* Pull your custom images through task execution pods from the ACR (otherwise, a default container image is used for each Flyte workflow execution). -Automatic cluster upgrades: AKS cluster [auto-upgrade](https://learn.microsoft.com/en-us/azure/aks/auto-upgrade-cluster?tabs=azure-cli#cluster-auto-upgrade-channels) is enabled by default to minimize workload downtime and stay up-to-date on the latest AKS patches. The reference Flyte Terraform plan sets `automatic_upgrade_channel = "stable"`, ensuring that the AKS cluster created will always remain in a [supported version](https://learn.microsoft.com/en-us/azure/aks/auto-upgrade-cluster?tabs=azure-cli#best-practices-for-cluster-auto-upgrade) (i.e. within the N-2 rule). +### Monitoring -Container image management: Azure Container Registry (ACR) is created out-of-box with permissions for you to: -* Push all workflow output images to this private ACR, -* Pull your custom images through task execution pods from the ACR (otherwise, a default container image is used for each Flyte workflow execution) +[Container Insights](https://learn.microsoft.com/azure/azure-monitor/containers/container-insights-analyze) is configured out-of-box, with system namespace logs and Flyte user workload logs ingested through Azure Monitor pipeline, to help you: -Monitoring: [Container Insights](https://learn.microsoft.com/en-us/azure/azure-monitor/containers/container-insights-analyze) is configured out-of-box, with system namespace logs and Flyte user workload logs ingested through Azure Monitor pipeline, to help you: -* Monitor workflow execution details from FlyteAdmin -* Troubleshoot deployment issues faster through Azure Portal -* Query historical logs using Log Analytics +* Monitor workflow execution details from FlyteAdmin. +* Troubleshoot deployment issues faster through Azure Portal. +* Query historical logs using Log Analytics. ![image](AKS/assets/images/deploy-data-science-solution-with-flyte/flyte-admin-logs-view-log-analytics.png) From bf37b992c7b28d321ea8cd5fd7775c37ca476a01 Mon Sep 17 00:00:00 2001 From: sdesai345 Date: Thu, 7 Nov 2024 18:27:13 -0800 Subject: [PATCH 16/20] Update blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md Co-authored-by: Erin Schaffer <69220686+schaffererin@users.noreply.github.com> --- .../2024-11-11-e2e-flyte-data-science-solution-on-aks.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md index e097e93c..c06315fd 100644 --- a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md +++ b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md @@ -119,6 +119,6 @@ Azure Container Registry (ACR) is created out-of-box with permissions for you to ## What next? -* See Union.ai's blog post about [Flyte on Azure](https://www.union.ai/blog-post/flyte-on-azure-a-reference-implementation) -* Check out more ML and feature engineering [tutorials with Flyte](https://docs.flyte.org/en/latest/flytesnacks/tutorials/index.html) -* Learn about [best practices for your MLOps pipelines](https://learn.microsoft.com/en-us/azure/aks/best-practices-ml-ops) on Azure Kubernetes Service +* See Union.ai's blog post about [Flyte on Azure](https://www.union.ai/blog-post/flyte-on-azure-a-reference-implementation). +* Check out more ML and feature engineering [tutorials with Flyte](https://docs.flyte.org/en/latest/flytesnacks/tutorials/index.html). +* Learn about [best practices for your MLOps pipelines](https://learn.microsoft.com/azure/aks/best-practices-ml-ops) on Azure Kubernetes Service (AKS). From debac91e3e7400c4ca837b8a823f7fbbafea8d02 Mon Sep 17 00:00:00 2001 From: sdesai345 Date: Thu, 7 Nov 2024 18:27:32 -0800 Subject: [PATCH 17/20] Update blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md Co-authored-by: Erin Schaffer <69220686+schaffererin@users.noreply.github.com> --- ...11-11-e2e-flyte-data-science-solution-on-aks.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md index c06315fd..e7ecc435 100644 --- a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md +++ b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md @@ -46,13 +46,13 @@ In your end-to-end solution, each of the following Flyte backend components will | Flyte component | Description | | -- | -- | -| Data catalog | Service that simplifies data indexing and allows you to query data artifacts based on metadata and/or tags | -| Flyte pod webhook | Deployment that creates the Webhook Pod called by ApiServer, when a simple Flyte task launches a Pod | -| Flyte admin | Main Flyte API that processes client requests, see [API specification](https://docs.flyte.org/en/latest/api/flyteidl/docs/service/service.html#ref-flyteidl-service-admin-proto) | -| Flyte console | Web user interface for the Flyte platform, hosted in the same Flyte cluster as Admin API | -| Flyte propeller | Core engine that executes workflows within the Flyte data plane | -| Flyte scheduler | Cloud-agnostic native scheduler for fixed-rate and cron-based schedules, defined at the init time for your workflow and activated/deactivated using FlyteAdmin API | -| Sync resources | Type of agent that enables request/response services (e.g. APIs) to return outputs | +| Data catalog | Service that simplifies data indexing and allows you to query data artifacts based on metadata and/or tags. | +| Flyte pod webhook | Deployment that creates the Webhook Pod called by API Server, when a simple Flyte task launches a Pod. | +| Flyte admin | Main Flyte API that processes client requests, see [API specification](https://docs.flyte.org/en/latest/api/flyteidl/docs/service/service.html#ref-flyteidl-service-admin-proto). | +| Flyte console | Web user interface for the Flyte platform, hosted in the same Flyte cluster as Admin API. | +| Flyte propeller | Core engine that executes workflows within the Flyte data plane .| +| Flyte scheduler | Cloud-agnostic native scheduler for fixed-rate and cron-based schedules, defined at the init time for your workflow and activated/deactivated using FlyteAdmin API. | +| Sync resources | Type of agent that enables request/response services (e.g. APIs) to return outputs. | Once you've applied and generated the reference Flyte Terraform, you'll receive an endpoint to your Flyte cluster and verify a similar output to the status below: From df29911e92267d3fdd850fe8617cef27f9868f50 Mon Sep 17 00:00:00 2001 From: sdesai345 Date: Thu, 7 Nov 2024 18:27:43 -0800 Subject: [PATCH 18/20] Update blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md Co-authored-by: Erin Schaffer <69220686+schaffererin@users.noreply.github.com> --- .../_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md index e7ecc435..65f5b048 100644 --- a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md +++ b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md @@ -40,7 +40,7 @@ Before you begin, take a look at the [prerequisites](https://github.com/unionai- * Helm version 3.15.4 or later installed * Kubernetes command-line client, [kubectl](https://kubernetes.io/docs/tasks/tools/install-kubectl/), installed and configured -## Which Flyte backend components are installed, and what do they do? +## Which Flyte back-end components are installed, and what do they do? In your end-to-end solution, each of the following Flyte backend components will run on its own pod: From 8ae66cfedf0097ff9432e5042dd72bc1ba0095ea Mon Sep 17 00:00:00 2001 From: sdesai345 Date: Thu, 7 Nov 2024 18:27:54 -0800 Subject: [PATCH 19/20] Update blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md Co-authored-by: Erin Schaffer <69220686+schaffererin@users.noreply.github.com> --- ...024-11-11-e2e-flyte-data-science-solution-on-aks.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md index 65f5b048..17fe2b28 100644 --- a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md +++ b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md @@ -34,11 +34,11 @@ Starting with this [reference implementation](https://www.union.ai/blog-post/fly Before you begin, take a look at the [prerequisites](https://github.com/unionai-oss/deploy-flyte/blob/main/environments/azure/flyte-core/README.md#prerequisites) on Azure, including: -* Azure subscription with at least [Contributor role over all your resources](https://learn.microsoft.com/en-us/azure/role-based-access-control/built-in-roles/privileged#contributor) using Azure RBAC -* Azure CLI version 2.0 or later installed and configured -* Terraform version 1.3.7 or later installed -* Helm version 3.15.4 or later installed -* Kubernetes command-line client, [kubectl](https://kubernetes.io/docs/tasks/tools/install-kubectl/), installed and configured +* Azure subscription with at least [Contributor role over all your resources](https://learn.microsoft.com/azure/role-based-access-control/built-in-roles/privileged#contributor) using Azure RBAC. +* Azure CLI version 2.0 or later installed and configured. +* Terraform version 1.3.7 or later installed. +* Helm version 3.15.4 or later installed. +* Kubernetes command-line client, [kubectl](https://kubernetes.io/docs/tasks/tools/install-kubectl/), installed and configured. ## Which Flyte back-end components are installed, and what do they do? From b6259ed3fbbff784eca80f4562a4b534ad6dc759 Mon Sep 17 00:00:00 2001 From: sdesai345 Date: Thu, 7 Nov 2024 18:36:26 -0800 Subject: [PATCH 20/20] Update blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md --- .../2024-11-11-e2e-flyte-data-science-solution-on-aks.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md index 17fe2b28..0b0f7fb6 100644 --- a/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md +++ b/blog/_posts/2024-11-11-e2e-flyte-data-science-solution-on-aks.md @@ -122,3 +122,7 @@ Azure Container Registry (ACR) is created out-of-box with permissions for you to * See Union.ai's blog post about [Flyte on Azure](https://www.union.ai/blog-post/flyte-on-azure-a-reference-implementation). * Check out more ML and feature engineering [tutorials with Flyte](https://docs.flyte.org/en/latest/flytesnacks/tutorials/index.html). * Learn about [best practices for your MLOps pipelines](https://learn.microsoft.com/azure/aks/best-practices-ml-ops) on Azure Kubernetes Service (AKS). + +## Acknowledgements + +Shoutout to [Erin Schaffer](https://www.linkedin.com/in/erin-schaffer-65800215b/) for her contributions and feedback on this blog!

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