ML pipelines: RunInference - OSS Image Classification

[Amar3tto]

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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 pull request integrates a new PyTorch-based image classification pipeline into Apache Beam's ML examples. The pipeline is designed for streaming data, leveraging Pub/Sub for input and BigQuery for output, and features an EfficientNet-B0 model. A key aspect is its adaptive batch sizing mechanism, which dynamically adjusts inference batch sizes to maximize GPU efficiency. The PR also includes comprehensive performance benchmarks and documentation to track the pipeline's operational characteristics.

Highlights

• New Image Classification Pipeline: Introduces a PyTorch EfficientNet-B0 streaming image classification example pipeline.
• Adaptive Batch Sizing: Implements adaptive batch sizing for RunInference to optimize GPU utilization.
• Pub/Sub and BigQuery Integration: Reads image URIs from Pub/Sub, processes them, and writes classification results to BigQuery with exactly-once semantics.
• Performance Benchmarking: Adds new benchmark tests and documentation for the pipeline's performance metrics.

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• Ignored by pattern: .github/workflows/** (2)
  • .github/workflows/beam_Inference_Python_Benchmarks_Dataflow.yml
  • .github/workflows/load-tests-pipeline-options/beam_Inference_Python_Benchmarks_Dataflow_Pytorch_Image_Classification_Rightfit.txt

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[Amar3tto]

Moved to #37186