Add readme How to add a new ML benchmark pipeline

[Amar3tto]

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[gemini-code-assist]

Summary of Changes

Hello @Amar3tto, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This PR updates the ML inference benchmarks README to include a detailed guide on how to add new ML benchmark pipelines. It also updates existing links and adds descriptions for new PyTorch and VLLM benchmarks.

Highlights

• Intent: This pull request aims to provide comprehensive documentation on how to add new Machine Learning (ML) benchmark pipelines to the project. It clarifies the process for developers looking to contribute new performance tests for ML inference.
• Changes: The primary change is an update to the sdks/python/apache_beam/testing/benchmarks/inference/README.md file. This update includes:

1. Updated Links: Several outdated links related to performance dashboards and benchmark definitions have been corrected and updated.
2. New Benchmark Descriptions: Added detailed descriptions for two new ML benchmarks: 'PyTorch Sentiment Analysis DistilBERT base' and 'VLLM Gemma 2b Batch Performance on Tesla T4', including model, accelerator, host details, and links to their implementations.
3. New Guide: 'How to add a new ML benchmark pipeline': A detailed, step-by-step guide (9 steps) has been added. This guide covers the entire process from creating pipeline and benchmark implementations, configuring GitHub Actions, testing, verifying metrics in BigQuery, updating the website, to creating Looker content for performance visualization.

• Reviewer Activity: No specific reviewer activity has been recorded or provided in the context for this pull request.

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[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 36.21%. Comparing base (c8df4da) to head (f973a4e).
⚠️ Report is 3 commits behind head on master.

Additional details and impacted files

@@              Coverage Diff              @@
##             master   #36437       +/-   ##
=============================================
- Coverage     55.03%   36.21%   -18.82%     
  Complexity     1666     1666               
=============================================
  Files          1058     1058               
  Lines        165254   165291       +37     
  Branches       1190     1190               
=============================================
- Hits          90941    59866    -31075     
- Misses        72138   103250    +31112     
  Partials       2175     2175               

Flag	Coverage Δ
python	40.53% <ø> (-40.41%)	⬇️

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[liferoad]

maybe share a PR example here?

[Amar3tto]

Done

[liferoad]

/gemini review

[gemini-code-assist]

Code Review

Thank you for this detailed guide on adding new ML benchmark pipelines. It's a great addition to the documentation. I have a few suggestions to improve clarity and correctness, mostly related to making the instructions easier to follow for new contributors.

[github-actions]

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R: @damccorm for label python.

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