Resolving Dependency Issues in Bazel: How to Load Multiple Python Libraries Efficiently

Discover how to handle multiple Python dependencies in Bazel effectively. Learn the correct way to load and manage requirements in your BUILD file to avoid import errors.
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Resolving Dependency Issues in Bazel: How to Load Multiple Python Libraries Efficiently

When working with Bazel in Python projects, you might encounter challenges with dependency management, particularly when your project has multiple requirement files. This post dives deep into a common issue where developers struggle to load multiple dependencies effectively in their Bazel build environment, especially when attempting to import Python libraries from different requirement files.

The Problem: Importing Multiple Dependencies

Imagine you have a Python repository that contains two requirement files:

Your WORKSPACE file attempts to import these libraries using the pip_install method:

[[See Video to Reveal this Text or Code Snippet]]

However, when you try to build your project, you get an error stating:

[[See Video to Reveal this Text or Code Snippet]]

This error indicates that Bazel cannot find the necessary BUILD file for the requested dependencies. So, how can you resolve this?

The Solution: Properly Loading Dependencies

Step 1: Load Dependencies with Different Names

To manage multiple dependencies in the same BUILD file without conflicts, you can load the requirements with unique names:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Reference Dependencies Explicitly

Once loaded under different names, you can reference these requirements directly in your py_library definition. Adjust your BUILD file as follows:

[[See Video to Reveal this Text or Code Snippet]]

Important Note: Dependencies Hierarchy

However, it's essential to recognize the inherent dependencies between libraries. In your specific case, scipy depends on numpy, which means if both are imported as different packages, you might end up with multiple copies of numpy.

If both libraries are the same version, you may not encounter any issues.

If they are different versions, you might experience unpredictable behavior in your application.

Suggested Simplification

To avoid complications, it’s recommended that you only explicitly load scipy and rely on its dependency resolution for numpy. You can simplify your deps in the py_library to just include scipy:

[[See Video to Reveal this Text or Code Snippet]]

By doing this, Bazel will ensure that only one version of numpy is used, the one that comes as a dependency of scipy.

Conclusion

Managing multiple dependencies in Bazel can be tricky, but with the right approach, you can seamlessly integrate them into your Python projects. By loading your requirements carefully and understanding their interdependencies, you can avoid the common pitfalls that lead to import errors.

If you encounter issues in your Bazel environment, remember to verify your BUILD and WORKSPACE files for correctness and potential dependency conflicts. Stick to best practices, and your Python builds will run smoothly!