Fix CloudML TFT driver install avoiding pip ResolutionTooDeep

[aIbrahiim]

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[gemini-code-assist]

Summary of Changes

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This pull request addresses dependency resolution issues encountered during the setup of CloudML TFT benchmarks. By decoupling the installation process and explicitly managing driver requirements, the build configuration now avoids redundant dependency re-resolution that previously triggered 'ResolutionTooDeep' errors in the pip installer.

Highlights

• Dependency Management: Introduced a new requirements file 'cloudml_driver_requirements.txt' to explicitly manage dependencies for CloudML TFT benchmarks.
• Build Configuration: Updated the 'installTFTRequirements' task in 'common.gradle' to use the new requirements file and install 'tfx_bsl' alongside 'tensorflow-transform' with '--no-deps' to prevent 'ResolutionTooDeep' errors during pip installation.

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[gemini-code-assist]

Code Review

This pull request introduces a new requirements file cloudml_driver_requirements.txt for CloudML TFT benchmarks and updates the Gradle task installTFTRequirements to use this file, installing tfx_bsl and tensorflow-transform with --no-deps to avoid dependency resolution issues. Feedback was provided regarding an invalid version of dill (0.4.1) specified in the requirements file, which does not exist on PyPI and would cause installation failures.

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[gemini-code-assist]

The version dill==0.4.1 does not exist on PyPI (the latest stable releases are in the 0.3.x series, such as 0.3.8 or 0.3.9). Additionally, Apache Beam has strict constraints on dill (typically dill>=0.3.1.1,<0.3.9 or <0.3.10 depending on the Beam version). Specifying dill==0.4.1 will cause the pip install step to fail.

Please update this to a valid version compatible with Beam's constraints, such as dill>=0.3.1.1,<0.3.10.

dill>=0.3.1.1,<0.3.10

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