PyTorch/XLA is a Python package that uses the XLA deep learning compiler to connect the PyTorch deep learning framework and Cloud TPUs. You can try it right now, for free, on a single Cloud TPU with Google Colab, and use it in production and on Cloud TPU Pods with Google Cloud. Take a look at one of our Colab notebooks to quickly try different PyTorch networks running on Cloud TPUs and learn how to use Cloud TPUs as PyTorch devices. We are also introducing new TPU VMs for more transparent and easier access to the TPU hardware. This is our recommedned way of running PyTorch/XLA on Cloud TPU. Please check out our Cloud TPU VM User Guide. Cloud TPU VM is currently on general availability and provides direct access to the TPU host. The recommended setup for running distributed training on TPU Pods uses the pairing of Compute VM Instance Groups and TPU Pods. Each of the Compute VM in the instance group drives 8 cores on the TPU Pod.

Features

  • PyTorch/XLA can use the bfloat16 datatype when running on TPUs
  • XLA tensors can be moved from the CPU to an XLA device and from an XLA device to the CPU
  • A utility API is provided to save data by taking care of previously moving it to CPU
  • Our CircleCI runs PyTorch native python tests for every change and every day
  • Consume prebuilt compute VM Images
  • Create your instance group

Project Samples

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License

MIT License

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Additional Project Details

Programming Language

C++

Related Categories

C++ Machine Learning Software, C++ Deep Learning Frameworks

Registered

2022-08-11