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