gemini-code-assist[bot] commented on code in PR #39164: URL: https://github.com/apache/beam/pull/39164#discussion_r3500252580
########## sdks/python/apache_beam/testing/benchmarks/cloudml/requirements.txt: ########## @@ -15,15 +15,12 @@ # limitations under the License. # -# Core TFT dependencies with version bounds. -# Note: To avoid pip ResolutionTooDeep errors, always install using the constraints file: -# pip install -c constraints.txt -r requirements.txt -dill>=0.3,<0.5 -tfx_bsl>=1.15,<1.17 -tensorflow-transform>=1.15,<1.17 -tensorflow>=2.15,<2.16 -numpy>=1.22.0,<2.0 -tensorflow-metadata>=1.15,<1.16 -pyarrow>=10,<11 -tensorflow-serving-api>=2.15,<2.16 -tf-keras>=2.15,<2.16 +dill==0.4.1 +tfx_bsl==1.21.0 +tensorflow-transform==1.21.0 +tensorflow-metadata==1.21.0 +tensorflow +numpy +pyarrow +tensorflow-serving-api +tf-keras Review Comment:  Leaving core dependencies like `tensorflow`, `numpy`, `pyarrow`, `tensorflow-serving-api`, and `tf-keras` completely unpinned or without version bounds can lead to non-reproducible environments and unexpected CI failures when new major/minor versions of these packages are released. Since `tfx-bsl`, `tensorflow-transform`, and `tensorflow-metadata` are pinned to `1.21.0`, it is highly recommended to specify compatible version ranges or exact pins for their underlying dependencies (e.g., matching the TensorFlow and NumPy versions compatible with TFX 1.21.0) to ensure stability. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
