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Zero-Copy Model Loading with Ray and PyTorch for Efficient Deep Learning Inference

Zero-Copy Model Loading with Ray and PyTorch for Efficient Deep Learning Inference

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Benchmark implementation

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Benchmark implementation

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Classroom Contents

Zero-Copy Model Loading with Ray and PyTorch for Efficient Deep Learning Inference

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  1. 1 Intro
  2. 2 Model Serving 101
  3. 3 Loading PyTorch tensors without copying data
  4. 4 Model inference on Ray using stateless tasks
  5. 5 Summary: Model inference with zero-copy loading
  6. 6 A simple benchmark
  7. 7 Pre- and post-processing with Ray Serve
  8. 8 Benchmark implementation
  9. 9 Benchmark Results

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