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Switching from GPUs to IPUs for Machine Learning Models
Version: latest
  • 1. Using IPUs for machine learning
  • 2. Poplar SDK
  • 3. Training on IPUs
    • 3.1. PyTorch models
    • 3.2. PyTorch Geometric models
    • 3.3. TensorFlow and Keras models
    • 3.4. ONNX models
  • 4. Fine-tuning on IPUs
  • 5. Performance profiling on IPUs
  • 6. Inference on IPUs
    • 6.1. PyTorch models
    • 6.2. PyTorch Geometric models
    • 6.3. Triton Inference Server
    • 6.4. TensorFlow Serving
    • 6.5. ONNX models
  • 7. Distributed systems
  • 8. Using IPUs in the Cloud
  • 9. Hugging Face models
  • 10. Out of memory errors
  • 11. CUDA code
  • 12. Tutorials and examples
  • 13. Performance benchmarks
  • 14. Useful resources
    • 14.1. GitHub repositories
    • 14.2. Documentation
    • 14.3. Other resources
    • 14.4. IPU-powered Jupyter notebooks
  • 15. Trademarks and copyright
Switching from GPUs to IPUs for Machine Learning Models

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15. Trademarks and copyright

Graphcloud®, Graphcore®, Poplar® and PopVision® are registered trademarks of Graphcore Ltd.

Bow™, Bow-2000™, Bow Pod™, Colossus™, In-Processor-Memory™, IPU-Core™, IPU-Exchange™, IPU-Fabric™, IPU-Link™, IPU-M2000™, IPU-Machine™, IPU-POD™, IPU-Tile™, PopART™, PopDist™, PopLibs™, PopRun™, PopTorch™, Streaming Memory™ and Virtual-IPU™ are trademarks of Graphcore Ltd.

All other trademarks are the property of their respective owners.

Copyright © 2023 Graphcore Ltd. All rights reserved.

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