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[RLlib] Bump tf version in ML docker to tf==2.5.0; add tfp to ML-docker. #18544

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merged 7 commits into from
Sep 15, 2021

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sven1977
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@sven1977 sven1977 commented Sep 13, 2021

  • Bump tf version in ML docker to tf==2.5.0; add tfp to ML-docker.
  • remove tfp from tune requirements.
  • Add check for installed tfp to ML Docker.
  • Enhance error messages for RLlib algos using tfp (SAC and CQL) if tfp not installed.

Why are these changes needed?

Related issue number

Checks

  • I've run scripts/format.sh to lint the changes in this PR.
  • I've included any doc changes needed for https://docs.ray.io/en/master/.
  • I've made sure the tests are passing. Note that there might be a few flaky tests, see the recent failures at https://flakey-tests.ray.io/
  • Testing Strategy
    • Unit tests
    • Release tests
    • This PR is not tested :(

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@gjoliver gjoliver left a comment

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woohoo nice!
I am still gonna try that "ln -s" hack for now, so hopefully these tests turn green before the product team releases another image.

@@ -29,5 +29,8 @@ RUN sudo apt-get update \
&& sudo rm requirements_tune.txt && sudo rm requirements_rllib.txt \
&& sudo apt-get clean

# Make sure tfp is installed correctly and matches tf version.
RUN python -c "import tensorflow_probability"
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actually, I don't know if checks are usually included as part of the image build process?
I think a lot of things happen when you import tf for the first time? and those will be baked into the prod image now.
although, this is really nice. we should have some ways to test an image once it's built.

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actually, I notice Kai asked for it in a conversation.
maybe we can add a comment and call it a day for now.
we should probably merge this sooner.

@sven1977 sven1977 merged commit 8a00154 into ray-project:master Sep 15, 2021
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3 participants