Fix cloudML TFT install avoiding pip ResolutionTooDeep

[aIbrahiim]

Fixes: #38780

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[gemini-code-assist]

Summary of Changes

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This pull request resolves installation failures in the CloudML benchmark suite caused by complex dependency resolution during the pip installation process. By decoupling the installation of tensorflow-transform from the primary requirements file, the build process is stabilized, ensuring consistent environment preparation for dataflow tests.

Highlights

• Dependency Installation Fix: Modified the installTFTRequirements task to install tensorflow-transform independently using --no-deps, preventing pip ResolutionTooDeep errors during environment setup.
• Test Trigger Update: Incremented the modification counter in the CloudML benchmark trigger file to ensure the updated installation logic is validated by the test suite.

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[gemini-code-assist]

Code Review

This pull request triggers the CloudML Benchmarks Dataflow test suite and updates the installTFTRequirements task in common.gradle to install tensorflow-transform separately with --no-deps after filtering it out of the base requirements. Feedback suggests avoiding the hardcoded temporary file path /tmp/cloudml_tft_base_requirements.txt to prevent potential conflicts or race conditions, recommending the use of mktemp for a unique temporary file instead.

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[gemini-code-assist]

Using a hardcoded temporary file path like /tmp/cloudml_tft_base_requirements.txt can lead to conflicts, permission issues, or race conditions if multiple builds or tasks run concurrently on the same machine.

It is safer to use mktemp to create a unique temporary file and ensure it is cleaned up after the installation is complete.

      args '-c', ". ${envdir}/bin/activate && " +
          'tmp_file=$(mktemp) && ' +
          'grep -v "^tensorflow-transform" requirements.txt > "$tmp_file" && ' +
          'pip install -r "$tmp_file" && ' +
          'rm "$tmp_file" && ' +
          'pip install --no-deps tensorflow-transform==1.16.0'

[github-actions]

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R: @claudevdm for label python.

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