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Merge pull request #61448 from tensorflow:sushreebarsa-patch-2
PiperOrigin-RevId: 553365736
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TensorFlower Gardener
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@ -30,43 +30,44 @@ Before sending your pull requests, make sure you do the following:
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**2. Valid?**
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- If the PR passes all the quality checks then we go ahead and assign a
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reviewer.
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- If the PR didn't meet the validation criteria, we request for additional
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changes to be made to PR to pass quality checks and send it back or on a rare
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occassion we may reject it.
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- If the PR passes all the quality checks then we go ahead and assign a
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reviewer.
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- If the PR didn't meet the validation criteria, we request for additional
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changes to be made to PR to pass quality checks and send it back or on a
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rare occasion we may reject it.
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**3. Review**
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- For Valid PR, reviewer (person familiar with the code/functionality) checks if
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the PR looks good or needs additional changes.
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- If all looks good, reviewer would approve the PR.
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- If a change is needed, the contributor is requested to make suggested change.
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- You make the change and submit for the review again.
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- This cycle repeats itself till the PR gets approved.
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- Note: As a friendly reminder we may reach out to you if the PR is awaiting
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your response for more than 2 weeks.
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- For a valid PR, reviewer (person familiar with the code/functionality)
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checks if the PR looks good or needs additional changes.
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- If all looks good, the reviewer will approve the PR.
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- If a change is needed, the contributor is requested to make the suggested
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change.
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- You make the change and submit it for the review again.
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- This cycle repeats itself untill the PR gets approved.
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- Note: As a friendly reminder, we may reach out to you if the PR is awaiting
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your response for more than 2 weeks.
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**4. Approved**
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- Once the PR is approved, it gets `kokoro:force-run` label applied and it
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initiates CI/CD tests.
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- We can't move forward if these tests fail.
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- In such situations, we may request you to make further changes to your PR for
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the tests to pass.
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- Once the tests pass, we now bring all the code in the internal code base,
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using a job called "copybara".
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- Once the PR is approved, it gets `kokoro:force-run` label applied and it
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initiates CI/CD tests.
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- We can't move forward if these tests fail.
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- In such situations, we may request you to make further changes to your PR
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for the tests to pass.
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- Once the tests pass, we now bring all the code into the internal code base,
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using a job called "copybara".
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**5. Copy to Google Internal codebase and run internal CI**
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- Once the PR is in Google codebase, we make sure it integrates well with its
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dependencies and the rest of the system.
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- Rarely, but If the tests fail at this stage, we cannot merge the code.
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- If needed, we may come to you to make some changes. At times, it may not be
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you, it may be us who may have hit a snag. Please be patient while we work to
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fix this.
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- Once the internal tests pass, we go ahead and merge the code internally as
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well as externally on GitHub.
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- Once the PR is in the Google codebase, we make sure it integrates well with
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its dependencies and the rest of the system.
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- Rarely, If the tests fail at this stage, we cannot merge the code.
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- If needed, we may come to you to make some changes. At times, it may not be
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you, it may be us who may have hit a snag. Please be patient while we work
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to fix this.
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- Once the internal tests pass, we go ahead and merge the code internally as
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well as externally on GitHub.
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In a graphical form, the entire lifetime of a PR looks like
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@ -147,7 +148,7 @@ TensorFlow coding style.
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* As every PR requires several CPU/GPU hours of CI testing, we discourage
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submitting PRs to fix one typo, one warning,etc. We recommend fixing the
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same issue at the file level at least (e.g.: fix all typos in a file, fix
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all compiler warning in a file, etc.)
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all compiler warnings in a file, etc.)
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* Tests should follow the
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[testing best practices](https://www.tensorflow.org/community/contribute/tests)
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guide.
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@ -234,8 +235,8 @@ There are two ways to run TensorFlow unit tests.
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[Docker images](https://hub.docker.com/r/tensorflow/tensorflow/tags/), e.g.,
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`tensorflow/tensorflow:devel` and `tensorflow/tensorflow:devel-gpu` for
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development to avoid installing the packages directly on your system (in
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which case remember to change directory from `/root` to `/tensorflow` once
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you get into the running container so `bazel` can find the `tensorflow`
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which case remember to change the directory from `/root` to `/tensorflow`
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once you get into the running container so `bazel` can find the `tensorflow`
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workspace).
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Once you have the packages installed, you can run a specific unit test in
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@ -245,8 +246,8 @@ There are two ways to run TensorFlow unit tests.
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export flags="--config=opt -k"
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```
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If the tests are to be run on GPU, add CUDA paths to LD_LIBRARY_PATH and add
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the `cuda` option flag
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If the tests are to be run on the GPU, add CUDA paths to LD_LIBRARY_PATH and
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add the `cuda` option flag
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```bash
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export LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH"
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