Ray Serve supports serving deployments with different (possibly conflicting) Python dependencies. For example, you can simultaneously serve one deployment that uses legacy Tensorflow 1 and another that uses Tensorflow 2.
This is supported on Mac OS and Linux using Ray's {ref}runtime-environments feature.
As with all other Ray actor options, pass the runtime environment in via ray_actor_options in
your deployment. Be sure to first run pip install "ray[default]" to ensure the
Runtime Environments feature is installed.
Example:
:::{tip} Avoid dynamically installing packages that install from source: these can be slow and use up all resources while installing, leading to problems with the Ray cluster. Consider precompiling such packages in a private repository or Docker image. :::
The dependencies required in the deployment may be different than the dependencies installed in the driver program (the one running Serve API calls). In this case, you should use a delayed import within the class to avoid importing unavailable packages in the driver. This applies even when not using runtime environments.
Example: