-
Notifications
You must be signed in to change notification settings - Fork 2
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge pull request #475 from threefoldtech/development_ai_ml
added ai-ml guide
- Loading branch information
Showing
4 changed files
with
130 additions
and
2 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
125 changes: 125 additions & 0 deletions
125
src/documentation/system_administrators/advanced/ai_ml_workloads.md
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,125 @@ | ||
| <h1> AI & ML Workloads </h1> | ||
|
|
||
| <h2> Table of Contents </h2> | ||
|
|
||
| - [Introduction](#introduction) | ||
| - [Prerequisites](#prerequisites) | ||
| - [Prepare the System](#prepare-the-system) | ||
| - [Install the GPU Driver](#install-the-gpu-driver) | ||
| - [Set a Python Virtual Environment](#set-a-python-virtual-environment) | ||
| - [Install PyTorch and Test Cuda](#install-pytorch-and-test-cuda) | ||
| - [Set and Access Jupyter Notebook](#set-and-access-jupyter-notebook) | ||
| - [Run AI/ML Workloads](#run-aiml-workloads) | ||
|
|
||
| *** | ||
|
|
||
| ## Introduction | ||
|
|
||
| We present a basic method to deploy artificial intelligence (AI) and machine learning (ML) on the TFGrid. For this, we make use of dedicated nodes and GPU support. | ||
|
|
||
| In the first part, we show the steps to install the Nvidia driver of a GPU card on a full VM Ubuntu 22.04 running on the TFGrid. | ||
|
|
||
| In the second part, we show how to use PyTorch to run AI/ML tasks. | ||
|
|
||
| ## Prerequisites | ||
|
|
||
| You need to reserve a [dedicated GPU node](../../dashboard/deploy/dedicated_machines.md) on the ThreeFold Grid. | ||
|
|
||
| ## Prepare the System | ||
|
|
||
| - Update the system | ||
| ``` | ||
| dpkg --add-architecture i386 | ||
| apt-get update | ||
| apt-get dist-upgrade | ||
| reboot | ||
| ``` | ||
| - Check the GPU info | ||
| ``` | ||
| lspci | grep VGA | ||
| lshw -c video | ||
| ``` | ||
| ## Install the GPU Driver | ||
| - Download the latest Nvidia driver | ||
| - Check which driver is recommended | ||
| ``` | ||
| apt install ubuntu-drivers-common | ||
| ubuntu-drivers devices | ||
| ``` | ||
| - Install the recommended driver (e.g. with 535) | ||
| ``` | ||
| apt install nvidia-driver-535 | ||
| ``` | ||
| - Reboot and reconnect to the VM | ||
| - Check the GPU status | ||
| ``` | ||
| nvidia-smi | ||
| ``` | ||
| Now that the GPU node is set, let's work on setting PyTorch to run AI/ML workloads. | ||
| ## Set a Python Virtual Environment | ||
| Before installing Python package with pip, you should create a virtual environment. | ||
| - Install the prerequisites | ||
| ``` | ||
| apt update | ||
| apt install python3-pip python3-dev | ||
| pip3 install --upgrade pip | ||
| pip3 install virtualenv | ||
| ``` | ||
| - Create a virtual environment | ||
| ``` | ||
| mkdir ~/python_project | ||
| cd ~/python_project | ||
| virtualenv python_project_env | ||
| source python_project_env/bin/activate | ||
| ``` | ||
| ## Install PyTorch and Test Cuda | ||
| Once you've created and activated a virtual environment for Pyhton, you can install different Python packages. | ||
| - Install PyTorch and upgrade Numpy | ||
| ``` | ||
| pip3 install torch | ||
| pip3 install numpy --upgrade | ||
| ``` | ||
| Before going further, you can check if Cuda is properly installed on your machine. | ||
| - Check that Cuda is available on Python with PyTorch by using the following lines: | ||
| ``` | ||
| import torch | ||
| torch.cuda.is_available() | ||
| torch.cuda.device_count() # the output should be 1 | ||
| torch.cuda.current_device() # the output should be 0 | ||
| torch.cuda.device(0) | ||
| torch.cuda.get_device_name(0) | ||
| ``` | ||
| ## Set and Access Jupyter Notebook | ||
| You can run Jupyter Notebook on the remote VM and access it on your local browser. | ||
| - Install Jupyter Notebook | ||
| ``` | ||
| pip3 install notebook | ||
| ``` | ||
| - Run Jupyter Notebook in no-browser mode and take note of the URL and the token | ||
| ``` | ||
| jupyter notebook --no-browser --port=8080 --ip=0.0.0.0 | ||
| ``` | ||
| - On your local machine, copy and paste on a browser the given URL but make sure to change `127.0.0.1` with the WireGuard IP (here it is `10.20.4.2`) and to set the correct token. | ||
| ``` | ||
| http://10.20.4.2:8080/tree?token=<insert_token> | ||
| ``` | ||
| ## Run AI/ML Workloads | ||
| After following the steps above, you should now be able to run Python codes that will make use of your GPU node to compute AI and ML workloads. | ||
| Feel free to explore different ways to use this feature. For example, the [HuggingFace course](https://huggingface.co/learn/nlp-course/chapter1/1) on natural language processing is a good introduction to machine learning. |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters