Fix pip ResolutionTooDeep

[aIbrahiim]

Fixes: #38780

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[aIbrahiim]

Run TFT Criteo Benchmarks

[gemini-code-assist]

Summary of Changes

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This pull request addresses recurring dependency resolution issues in the CloudML benchmark environment. By introducing a formal constraints file and updating dependency specifications in the requirements file, the build process is now more robust and less prone to complex resolution failures during package installation.

Highlights

• Dependency Resolution Fix: Introduced a new constraints file to pin package versions, preventing pip's 'ResolutionTooDeep' errors during environment setup.
• Requirements Update: Updated 'requirements.txt' to use version ranges for core dependencies, ensuring better compatibility and resolution.
• Build Script Improvement: Refactored the 'installTFTRequirements' Gradle task to utilize the new constraints file instead of manual dependency filtering.

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[gemini-code-assist]

Code Review

This pull request introduces a constraints file (constraints.txt) to pin dependency versions for CloudML benchmarks, helping to avoid pip ResolutionTooDeep errors. It also updates requirements.txt with adjusted version bounds and simplifies the installation step in common.gradle to use this constraints file. The review feedback suggests documenting the usage of the constraints file within requirements.txt and adding a pin for tensorflow-serving-api in constraints.txt for completeness.

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[gemini-code-assist]

To prevent developers or automated scripts from hitting ResolutionTooDeep errors when installing these requirements directly, it is highly recommended to document that the constraints file should be used.

# Core TFT dependencies with version bounds.\n# Note: To avoid pip ResolutionTooDeep errors, always install using the constraints file:\n# pip install -c constraints.txt -r requirements.txt

[damccorm]

Could you add this comment please?

[gemini-code-assist]

For completeness and to ensure consistent resolution of all TensorFlow ecosystem packages, consider pinning tensorflow-serving-api in the constraints file as well, since it is specified in requirements.txt.

# TensorFlow ecosystem\ntensorflow==2.15.1\ntensorflow-metadata==1.15.0\ntf-keras==2.15.1\ntensorflow-serving-api==2.15.1

[github-actions]

Assigning reviewers:

R: @damccorm for label python.

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[damccorm]

Thanks - this generally LGTM, just had one comment

[damccorm]

Could you add this comment please?