ML pipelines: RunInference - OSS Image Object detection

[Amar3tto]

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Summary of Changes

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This pull request significantly enhances Apache Beam's machine learning capabilities by integrating a new PyTorch-based image object detection pipeline. The pipeline leverages the RunInference transform for efficient batched GPU inference with open-source TorchVision models, processing images from cloud storage and outputting structured detection results to BigQuery. This addition is complemented by a new performance benchmark and corresponding documentation, ensuring that the pipeline's efficiency and resource usage can be consistently monitored and evaluated.

Highlights

• New PyTorch Object Detection Example: Introduced a new example pipeline for PyTorch image object detection using Apache Beam's RunInference, capable of processing image URIs from GCS, performing batched GPU inference with TorchVision models, and writing results to BigQuery.
• Dedicated Performance Benchmark: Added a new benchmark test (PytorchImageObjectDetectionBenchmarkTest) to measure and track the performance of the PyTorch image object detection pipeline, focusing on stable GPU inference workloads.
• Updated Documentation and Dependencies: Included new Python dependencies for PyTorch object detection and updated the project's website with a dedicated performance page for the new benchmark, including placeholders for Looker Studio metrics.

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Ignored Files

• 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_Detection.txt

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