Can’t add cache for my dependencies in GitHub Actions using Python?

Don’t worry, you’re not alone! Caching dependencies in GitHub Actions can be a bit tricky, but with this article, you’ll be caching like a pro in no time. Let’s dive in and explore the world of caching in GitHub Actions using Python!

Why caching dependencies is important

Caching dependencies can significantly reduce the build time of your GitHub Actions workflows. By storing dependencies in a cache, you can avoid downloading and installing them every time your workflow runs, resulting in faster build times and improved overall performance.

The problem: Cache not working for Python dependencies

You’ve tried using the `actions/cache` action to cache your Python dependencies, but it’s just not working. You’ve specified the correct cache key, stored the cache, and restored it, but your workflow is still downloading and installing dependencies every time it runs.

The issue lies in the way Python handles package installations. By default, pip installs packages in the `site-packages` directory, which is not cached by the `actions/cache` action. To cache Python dependencies, you need to take a different approach.

Step 1: Create a cache key

The first step in caching your Python dependencies is to create a cache key. The cache key is a unique identifier that specifies the cache to store and retrieve. In this case, you’ll use a cache key that includes the Python version and the dependencies required by your project.

python-version-${{ matrix.python-version }}-dependencies-cache

Step 2: Install dependencies using pip

Next, you’ll install your Python dependencies using pip. You’ll use the `pip install` command to install the required packages, and the `–no-deps` flag to prevent pip from installing dependencies recursively.

pip install --no-deps -r requirements.txt

Step 3: Cache the dependencies

Now that you’ve installed your dependencies, it’s time to cache them. You’ll use the `actions/cache` action to store the dependencies in the cache.

      - name: Cache dependencies
        uses: actions/cache@v2
        id: cache
        key: ${{ steps.cache-key.outputs.value }}
        path: ~/.cache/pip

Step 4: Restore the cache

In the next step, you’ll restore the cache in your workflow. This will retrieve the cached dependencies and make them available for your workflow.

      - name: Restore dependencies
        uses: actions/cache@v2
        id: cache
        key: ${{ steps.cache-key.outputs.value }}
        path: ~/.cache/pip

Caching example: poetry

Let’s take a look at an example of caching dependencies using Poetry. Poetry is a popular Python package manager that provides a simple and efficient way to manage dependencies.

name: Build and Test

on:
  push:
    branches:
      - main

jobs:
  build-and-test:
    runs-on: ubuntu-latest
    strategy:
      matrix:
        python-version: ['3.9']

    steps:
      - name: Checkout code
        uses: actions/checkout@v2

      - name: Create cache key
        run: |
          echo "python-version-${{ matrix.python-version }}-dependencies-cache" >> $GITHUB_ENV
          echo "cache-key=$(< $GITHUB_ENV)" >> $GITHUB_ENV

      - name: Install poetry
        run: |
          pip install poetry

      - name: Install dependencies
        run: |
          poetry install --no-deps

      - name: Cache dependencies
        uses: actions/cache@v2
        id: cache
        key: ${{ steps.cache-key.outputs.value }}
        path: ~/.cache/poetry

      - name: Restore dependencies
        uses: actions/cache@v2
        id: cache
        key: ${{ steps.cache-key.outputs.value }}
        path: ~/.cache/poetry

      - name: Run tests
        run: |
          poetry run pytest

Using the cache in subsequent runs

In the example above, the cache is stored and restored using the `actions/cache` action. In subsequent runs, the cache will be restored, and the dependencies will be available for use.

This means that you won’t need to download and install the dependencies every time your workflow runs, resulting in faster build times and improved overall performance.

Caching example: pip

Let’s take a look at an example of caching dependencies using pip. pip is the package installer for Python, and it provides a simple and efficient way to manage dependencies.

name: Build and Test

on:
  push:
    branches:
      - main

jobs:
  build-and-test:
    runs-on: ubuntu-latest
    strategy:
      matrix:
        python-version: ['3.9']

    steps:
      - name: Checkout code
        uses: actions/checkout@v2

      - name: Create cache key
        run: |
          echo "python-version-${{ matrix.python-version }}-dependencies-cache" >> $GITHUB_ENV
          echo "cache-key=$(< $GITHUB_ENV)" >> $GITHUB_ENV

      - name: Install dependencies
        run: |
          pip install --no-deps -r requirements.txt

      - name: Cache dependencies
        uses: actions/cache@v2
        id: cache
        key: ${{ steps.cache-key.outputs.value }}
        path: ~/.cache/pip

      - name: Restore dependencies
        uses: actions/cache@v2
        id: cache
        key: ${{ steps.cache-key.outputs.value }}
        path: ~/.cache/pip

      - name: Run tests
        run: |
          pytest

Using the cache in subsequent runs

In the example above, the cache is stored and restored using the `actions/cache` action. In subsequent runs, the cache will be restored, and the dependencies will be available for use.

This means that you won’t need to download and install the dependencies every time your workflow runs, resulting in faster build times and improved overall performance.

Troubleshooting common issues

If you’re having trouble caching your dependencies, here are some common issues to check:

• Cache key not specified: Make sure you’ve specified a cache key in your workflow file. The cache key should include the Python version and the dependencies required by your project.

• Cache not restored: Verify that you’ve restored the cache in your workflow file. The cache should be restored before installing dependencies.

• Dependencies not installed: Check that you’ve installed the required dependencies in your workflow file. You can use pip or Poetry to install dependencies.

Conclusion

Caching dependencies in GitHub Actions using Python is a powerful way to improve the performance of your workflows. By storing dependencies in a cache, you can avoid downloading and installing them every time your workflow runs, resulting in faster build times and improved overall performance.

In this article, we’ve explored the world of caching in GitHub Actions using Python. We’ve covered the importance of caching dependencies, the problem of caching Python dependencies, and provided step-by-step instructions for caching dependencies using Poetry and pip.

By following the examples and guidelines provided in this article, you should be able to cache your Python dependencies in GitHub Actions and improve the performance of your workflows.

GitHub Actions Cache	Description
Cache Key	A unique identifier that specifies the cache to store and retrieve.
Cache Path	The path where the cache is stored.
actions/cache	The GitHub Actions cache action that stores and restores the cache.

Remember, caching dependencies in GitHub Actions using Python is a powerful way to improve the performance of your workflows. By following the guidelines and examples provided in this article, you can cache your Python dependencies and improve the overall performance of your workflows.

Happy caching!

Frequently Asked Question

Get answers to the most common questions about adding cache for dependencies in GitHub Actions using Python