docs/content/codespaces/developing-in-codespaces/getting-started-with-github-codespaces-for-machine-learning.md

title, shortTitle, intro, versions, type, topics

title	shortTitle	intro	versions	type	topics
Getting started with GitHub Codespaces for machine learning	Machine learning	Learn about working on machine learning projects with {% data variables.product.prodname_github_codespaces %} and its out-of-the-box tools.	fpt ghec * *	tutorial	Codespaces Developer

Introduction

This guide introduces you to machine learning with {% data variables.product.prodname_github_codespaces %}. You’ll build a simple image classifier, learn about some of the tools that come preinstalled in {% data variables.product.prodname_github_codespaces %}, configure your development environment for NVIDIA CUDA, and use {% data variables.product.prodname_cli %} to open your codespace in JupyterLab.

Building a simple image classifier

We'll use a Jupyter notebook to build a simple image classifier.

Jupyter notebooks are sets of cells that you can execute one after another. The notebook we'll use includes a number of cells that build an image classifier using PyTorch. Each cell is a different phase of that process: download a dataset, set up a neural network, train a model, and then test that model.

We'll run all of the cells, in sequence, to perform all phases of building the image classifier. When we do this Jupyter saves the output back into the notebook so that you can examine the results.

Creating a repository and a codespace

1. Go to the github/codespaces-getting-started-ml template repository and click Use this template. {% data reusables.codespaces.open-codespace-from-template-repo %}

   By default, a codespace for this repository opens in a web-based version of {% data variables.product.prodname_vscode %}.

Opening the image classifier notebook

The default container image that's used by {% data variables.product.prodname_github_codespaces %} includes a set of machine learning libraries that are preinstalled in your codespace. For example, Numpy, pandas, SciPy, Matplotlib, seaborn, scikit-learn, TensorFlow, Keras, PyTorch, Requests, and Plotly. For more information about the default image, see "Introduction to dev containers" and the devcontainers/images repository.

1. In the {% data variables.product.prodname_vscode_shortname %} editor, close any "Get Started" tabs that are displayed.
2. Open the image-classifier.ipynb notebook file.

Building the image classifier

The image classifier notebook contains all the code you need to download a dataset, train a neural network, and evaluate its performance.

1. Click Run All to execute all of the notebook’s cells.

   

2. Scroll down to view the output of each cell.

   

Configuring NVIDIA CUDA for your codespace

Some software, such as TensorFlow, requires you to install NVIDIA CUDA to use your codespace’s GPU. Where this is the case, you can create your own custom configuration, by using a devcontainer.json file, and specify that CUDA should be installed. For more information on creating a custom configuration, see "Introduction to dev containers."

{% note %}

Note: For full details of the script that's run when you add the nvidia-cuda feature, see the devcontainers/features repository.

{% endnote %}

1. Within a codespace, open the .devcontainer/devcontainer.json file in the editor.

2. Add a top-level features object with the following contents:

     "features": {
       "ghcr.io/devcontainers/features/nvidia-cuda:1": { 
         "installCudnn": true
       }
     }
   

   For more information about the features object, see the development containers specification.

   If you are using the devcontainer.json file from the image classifier repository you created for this tutorial, your devcontainer.json file will now look like this:

   {
     "customizations": {
       "vscode": {
         "extensions": [
           "ms-python.python",
           "ms-toolsai.jupyter"
         ]
       }
     },
     "features": {
       "ghcr.io/devcontainers/features/nvidia-cuda:1": { 
         "installCudnn": true
       }
     }
   }
   

3. Save the change. {% data reusables.codespaces.rebuild-command %} The codespace container will be rebuilt. This will take several minutes. When the rebuild is complete the codespace is automatically reopened.

4. Commit the change to the repository so that CUDA will be installed in any new codespaces you create from this repository in future.

Opening your codespace in JupyterLab

You can open your codespace in JupyterLab from the "Your codespaces" page at github.com/codespaces, or by using {% data variables.product.prodname_cli %}. For more information, see "Opening an existing codespace."

{% data reusables.codespaces.jupyterlab-installed-in-codespace %}