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Update 2.3 readme (#6957)
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‎README.md

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@@ -14,14 +14,7 @@ TPUs](https://cloud.google.com/tpu/). You can try it right now, for free, on a
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single Cloud TPU VM with
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[Kaggle](https://www.kaggle.com/discussions/product-feedback/369338)!
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Take a look at one of our [Kaggle
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notebooks](https://github.com/pytorch/xla/tree/master/contrib/kaggle) to get
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started:
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* [Stable Diffusion with PyTorch/XLA
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2.0](https://github.com/pytorch/xla/blob/master/contrib/kaggle/pytorch-xla-2-0-on-kaggle.ipynb)
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* [Distributed PyTorch/XLA
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Basics](https://github.com/pytorch/xla/blob/master/contrib/kaggle/distributed-pytorch-xla-basics-with-pjrt.ipynb)
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Please find tutorials on our [GitHub page](https://github.com/pytorch/xla) for the latest release.
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## Installation
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## Available docker images and wheels
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### Python packages
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PyTorch/XLA releases starting with version r2.1 will be available on PyPI. You
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can now install the main build with `pip install torch_xla`. To also install the
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Cloud TPU plugin, install the optional `tpu` dependencies:
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```
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pip install torch_xla[tpu] -f https://storage.googleapis.com/libtpu-releases/index.html
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```
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GPU, XRT (legacy runtime), and nightly builds are available in our public GCS
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bucket.
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| Version | Cloud TPU/GPU VMs Wheel |
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| --- | ----------- |
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| 2.2 (Python 3.8) | `https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.2.0-cp38-cp38-manylinux_2_28_x86_64.whl` |
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| 2.2 (Python 3.10) | `https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.2.0-cp310-cp310-manylinux_2_28_x86_64.whl` |
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| 2.2 (CUDA 12.1 + Python 3.8) | `https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-2.2.0-cp38-cp38-manylinux_2_28_x86_64.whl` |
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| 2.2 (CUDA 12.1 + Python 3.10) | `https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-2.2.0-cp310-cp310-manylinux_2_28_x86_64.whl` |
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| nightly (Python 3.8) | `https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-nightly-cp38-cp38-linux_x86_64.whl` |
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| nightly (Python 3.10) | `https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-nightly-cp310-cp310-linux_x86_64.whl` |
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| nightly (CUDA 12.1 + Python 3.8) | `https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-nightly-cp38-cp38-linux_x86_64.whl` |
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<details>
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<summary>older versions</summary>
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| Version | Cloud TPU VMs Wheel |
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|---------|-------------------|
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| 2.1 (XRT + Python 3.10) | `https://storage.googleapis.com/pytorch-xla-releases/wheels/xrt/tpuvm/torch_xla-2.1.0%2Bxrt-cp310-cp310-manylinux_2_28_x86_64.whl` |
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| 2.1 (Python 3.8) | `https://storage.googleapis.com/tpu-pytorch/wheels/tpuvm/torch_xla-2.1-cp38-cp38-linux_x86_64.whl` |
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| 2.0 (Python 3.8) | `https://storage.googleapis.com/tpu-pytorch/wheels/tpuvm/torch_xla-2.0-cp38-cp38-linux_x86_64.whl` |
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| 1.13 | `https://storage.googleapis.com/tpu-pytorch/wheels/tpuvm/torch_xla-1.13-cp38-cp38-linux_x86_64.whl` |
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| 1.12 | `https://storage.googleapis.com/tpu-pytorch/wheels/tpuvm/torch_xla-1.12-cp38-cp38-linux_x86_64.whl` |
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| 1.11 | `https://storage.googleapis.com/tpu-pytorch/wheels/tpuvm/torch_xla-1.11-cp38-cp38-linux_x86_64.whl` |
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| 1.10 | `https://storage.googleapis.com/tpu-pytorch/wheels/tpuvm/torch_xla-1.10-cp38-cp38-linux_x86_64.whl` |
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<br/>
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Note: For TPU Pod customers using XRT (our legacy runtime), we have custom
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wheels for `torch` and `torch_xla` at
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`https://storage.googleapis.com/tpu-pytorch/wheels/xrt`.
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| Package | Cloud TPU VMs Wheel (XRT on Pod, Legacy Only) |
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| --- | ----------- |
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| torch_xla | `https://storage.googleapis.com/pytorch-xla-releases/wheels/xrt/tpuvm/torch_xla-2.1.0%2Bxrt-cp310-cp310-manylinux_2_28_x86_64.whl` |
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| torch | `https://storage.googleapis.com/pytorch-xla-releases/wheels/xrt/tpuvm/torch-2.1.0%2Bxrt-cp310-cp310-linux_x86_64.whl` |
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<br/>
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| Version | GPU Wheel + Python 3.8 |
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| --- | ----------- |
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| 2.1+ CUDA 11.8 | `https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/11.8/torch_xla-2.1.0-cp38-cp38-manylinux_2_28_x86_64.whl` |
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| 2.0 + CUDA 11.8 | `https://storage.googleapis.com/tpu-pytorch/wheels/cuda/118/torch_xla-2.0-cp38-cp38-linux_x86_64.whl` |
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| 2.0 + CUDA 11.7 | `https://storage.googleapis.com/tpu-pytorch/wheels/cuda/117/torch_xla-2.0-cp38-cp38-linux_x86_64.whl` |
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| 1.13 | `https://storage.googleapis.com/tpu-pytorch/wheels/cuda/112/torch_xla-1.13-cp38-cp38-linux_x86_64.whl` |
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| nightly + CUDA 12.0 >= 2023/06/27| `https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.0/torch_xla-nightly-cp38-cp38-linux_x86_64.whl` |
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| nightly + CUDA 11.8 <= 2023/04/25| `https://storage.googleapis.com/tpu-pytorch/wheels/cuda/118/torch_xla-nightly-cp38-cp38-linux_x86_64.whl` |
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| nightly + CUDA 11.8 >= 2023/04/25| `https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/11.8/torch_xla-nightly-cp38-cp38-linux_x86_64.whl` |
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<br/>
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| Version | GPU Wheel + Python 3.7 |
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| --- | ----------- |
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| 1.13 | `https://storage.googleapis.com/tpu-pytorch/wheels/cuda/112/torch_xla-1.13-cp37-cp37m-linux_x86_64.whl` |
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| 1.12 | `https://storage.googleapis.com/tpu-pytorch/wheels/cuda/112/torch_xla-1.12-cp37-cp37m-linux_x86_64.whl` |
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| 1.11 | `https://storage.googleapis.com/tpu-pytorch/wheels/cuda/112/torch_xla-1.11-cp37-cp37m-linux_x86_64.whl` |
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| nightly | `https://storage.googleapis.com/tpu-pytorch/wheels/cuda/112/torch_xla-nightly-cp37-cp37-linux_x86_64.whl` |
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<br/>
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| Version | Colab TPU Wheel |
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| --- | ----------- |
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| 2.0 | `https://storage.googleapis.com/tpu-pytorch/wheels/colab/torch_xla-2.0-cp310-cp310-linux_x86_64.whl` |
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You can also add `+yyyymmdd` after `torch_xla-nightly` to get the nightly wheel
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of a specified date. To get the companion pytorch and torchvision nightly wheel,
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replace the `torch_xla` with `torch` or `torchvision` on above wheel links.
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#### Installing libtpu (before PyTorch/XLA 2.0)
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For PyTorch/XLA release r2.0 and older and when developing PyTorch/XLA, install
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the `libtpu` pip package with the following command:
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```
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pip3 install torch_xla[tpuvm]
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```
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This is only required on Cloud TPU VMs.
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</details>
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### Docker
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| Version | Cloud TPU VMs Docker |
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| --- | ----------- |
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| 2.2 | `us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.2.0_3.10_tpuvm` |
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| 2.1 | `us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.1.0_3.10_tpuvm` |
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| 2.0 | `gcr.io/tpu-pytorch/xla:r2.0_3.8_tpuvm` |
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| 1.13 | `gcr.io/tpu-pytorch/xla:r1.13_3.8_tpuvm` |
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| nightly python | `us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.10_tpuvm` |
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<br/>
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| Version | GPU CUDA 12.1 Docker |
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| --- | ----------- |
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| 2.2 | `us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.2.0_3.10_cuda_12.1` |
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| 2.1 | `us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.1.0_3.10_cuda_12.1` |
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| nightly | `us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.8_cuda_12.1` |
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| nightly at date | `us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.8_cuda_12.1_YYYYMMDD` |
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<br/>
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| Version | GPU CUDA 11.8 + Docker |
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| --- | ----------- |
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| 2.1 | `us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:r2.1.0_3.10_cuda_11.8` |
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| 2.0 | `gcr.io/tpu-pytorch/xla:r2.0_3.8_cuda_11.8` |
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| nightly | `us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.8_cuda_11.8` |
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| nightly at date | `us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.8_cuda_11.8_YYYYMMDD` |
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<br/>
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<details>
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<summary>older versions</summary>
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| Version | GPU CUDA 11.7 + Docker |
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| --- | ----------- |
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| 2.0 | `gcr.io/tpu-pytorch/xla:r2.0_3.8_cuda_11.7` |
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<br/>
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| Version | GPU CUDA 11.2 + Docker |
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| --- | ----------- |
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| 1.13 | `gcr.io/tpu-pytorch/xla:r1.13_3.8_cuda_11.2` |
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<br/>
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| Version | GPU CUDA 11.2 + Docker |
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| --- | ----------- |
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| 1.13 | `gcr.io/tpu-pytorch/xla:r1.13_3.7_cuda_11.2` |
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| 1.12 | `gcr.io/tpu-pytorch/xla:r1.12_3.7_cuda_11.2` |
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</details>
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To run on [compute instances with
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GPUs](https://cloud.google.com/compute/docs/gpus/create-vm-with-gpus).
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For all builds and all versions of `torch-xla`, see our main [GitHub
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README](https://github.com/pytorch/xla).
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## Troubleshooting
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