Add gpu doc for how to build PyTorch/XLA from source with GPU support.

docs/gpu.md

@@ -1,15 +1,15 @@
 # How to run with PyTorch/XLA:GPU
 
-PyTorch/XLA enables PyTorch users to utilize the XLA compiler which supports accelerators including TPU, GPU, and CPU This doc will go over the basic steps to run PyTorch/XLA on a nvidia gpu instance
+PyTorch/XLA enables PyTorch users to utilize the XLA compiler which supports accelerators including TPU, GPU, and CPU. This doc will go over the basic steps to run PyTorch/XLA on a nvidia GPU instances.
 
 ## Create a GPU instance
-Pytorch/XLA currently publish prebuilt docker images and wheels with cuda11.7/8 and python 3.8. We recommend users to create a GPU instance with corresponding config. For a full list of docker images and wheels, please refer to [this doc](https://github.com/pytorch/xla/tree/jackcao/gpu_doc#-available-images-and-wheels).
+To create a GPU VM in Google Compute Engine, follow the [Google Cloud documentation](https://cloud.google.com/compute/docs/gpus/create-vm-with-gpus).
 
-## Environment Setup
 
-To create a GPU VM in Google Compute Engine, follow the [Google Cloud documentation](https://cloud.google.com/compute/docs/gpus/create-vm-with-gpus).
+## Environment Setup
 
 ### Docker
+Pytorch/XLA currently publish prebuilt docker images and wheels with cuda11.7/8 and python 3.8. We recommend users to create a docker container with corresponding config. For a full list of docker images and wheels, please refer to [this doc](https://github.com/pytorch/xla#available-docker-images-and-wheels).
 ```
 sudo docker pull us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.8_cuda_11.7
 sudo apt-get install -y apt-transport-https ca-certificates curl gnupg-agent    software-properties-common
@@ -74,3 +74,34 @@ Epoch 1 train begin 06:12:38
 ```
 ## AMP (AUTOMATIC MIXED PRECISION)
 AMP is very useful on GPU training and PyTorch/XLA reuse Cuda's AMP rule. You can checkout our [mnist example](https://github.com/pytorch/xla/blob/master/test/test_train_mp_mnist_amp.py) and [imagenet example](https://github.com/pytorch/xla/blob/master/test/test_train_mp_imagenet_amp.py). Note that we also used a modified version of [optimizers](https://github.com/pytorch/xla/tree/master/torch_xla/amp/syncfree) to avoid the additional sync between device and host.
+
+## Develop PyTorch/XLA on a GPU instance (build PyTorch/XLA from source with GPU support)
+
+1. Inside a GPU VM, create a docker container from a development docker image. For example:
+```
+sudo docker pull us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/development:3.8_cuda_11.8
+sudo apt-get install -y apt-transport-https ca-certificates curl gnupg-agent    software-properties-common
+distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
+curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
+curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
+sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
+sudo systemctl restart docker
+sudo docker run --gpus all -it -d us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/development:3.8_cuda_11.8
+sudo docker exec -it $(sudo docker ps | awk 'NR==2 { print $1 }') /bin/bash
+```
+
+2. Build PyTorch and PyTorch/XLA from source.
+```
+git clone https://github.com/pytorch/pytorch.git
+cd pytorch
+USE_CUDA=0 python setup.py install
+
+git clone https://github.com/pytorch/xla.git
+cd xla
+BAZEL_REMOTE_CACHE=0 XLA_CUDA=1 python setup.py install
+```
+
+3. Verify if PyTorch and PyTorch/XLA have been installed successfully.
+If you can run the test in the section
+[Run a simple model](#run-a-simple-model) successfully, then PyTorch and
+PyTorch/XLA should have been installed successfully.