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Mar 9, 2023
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Fix typo in README.md #4748

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4 changes: 2 additions & 2 deletions README.md
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Expand Up @@ -60,7 +60,7 @@ run on XLA devices(TPU, GPU, CPU and...)

## Running on a Single Cloud TPU VM

Google Cloud offers 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](https://cloud.google.com/tpu/docs/pytorch-xla-ug-tpu-vm). To learn more about the Cloud TPU System Architecture, please check out [this doc](https://cloud.google.com/tpu/docs/system-architecture-tpu-vm#tpu_vms).
Google Cloud offers TPU VMs for more transparent and easier access to the TPU hardware. This is our **recommended way** of running PyTorch/XLA on Cloud TPU. Please check out our [Cloud TPU VM User Guide](https://cloud.google.com/tpu/docs/pytorch-xla-ug-tpu-vm). To learn more about the Cloud TPU System Architecture, please check out [this doc](https://cloud.google.com/tpu/docs/system-architecture-tpu-vm#tpu_vms).


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Expand Down Expand Up @@ -189,4 +189,4 @@ You can find additional useful reading materials in
* [Performance debugging on Cloud TPU VM](https://cloud.google.com/blog/topics/developers-practitioners/pytorchxla-performance-debugging-tpu-vm-part-1)
* [Lazy tensor intro](https://pytorch.org/blog/understanding-lazytensor-system-performance-with-pytorch-xla-on-cloud-tpu/)
* [Scaling deep learning workloads with PyTorch / XLA and Cloud TPU VM](https://cloud.google.com/blog/topics/developers-practitioners/scaling-deep-learning-workloads-pytorch-xla-and-cloud-tpu-vm)
* [Scaling PyTorch models on Cloud TPUs with FSDP](https://pytorch.org/blog/scaling-pytorch-models-on-cloud-tpus-with-fsdp/)
* [Scaling PyTorch models on Cloud TPUs with FSDP](https://pytorch.org/blog/scaling-pytorch-models-on-cloud-tpus-with-fsdp/)