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 instances.
Create a GPU instance¶
You can either use a local machine with GPU attached or a GPU VM on the cloud. For example in Google Cloud you can follow this doc to create the GPU VM.
Environment Setup¶
Make sure you have cuda driver installed on the host.
Docker¶
Pytorch/XLA currently publish prebuilt docker images and wheels with cuda11.8/12.1 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.
sudo docker pull us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.8_cuda_12.1
# Installing the NVIDIA Container Toolkit per https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html
# For example
curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
&& curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
sudo apt-get update
sudo apt-get install -y nvidia-container-toolkit
# Configuring the NVIDIA Container Toolkit
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker
sudo docker run --shm-size=16g --net=host --gpus all -it -d us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.8_cuda_12.1 bin/bash
sudo docker exec -it $(sudo docker ps | awk 'NR==2 { print $1 }') /bin/bash
Note that you need to restart the docker to make gpu devices visible in the docker container. After logging into the docker, you can use nvidia-smi to verify the device is setup correctly.
(pytorch) root@20ab2c7a2d06:/# nvidia-smi
Thu Dec 8 06:24:29 2022
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 510.47.03 Driver Version: 510.47.03 CUDA Version: 11.6 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 Tesla V100-SXM2... Off | 00000000:00:04.0 Off | 0 |
| N/A 36C P0 38W / 300W | 0MiB / 16384MiB | 1% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
Check environment variable¶
Make sure PATH and LD_LIBRARY_PATH environment variables account for cuda. Please do a echo $PATH and echo $LD_LIBRARY_PATH to verify. If not, please follow link to do so. Example:
echo "export PATH=\$PATH:/usr/local/cuda-12.1/bin" >> ~/.bashrc
echo "export LD_LIBRARY_PATH=\$LD_LIBRARY_PATH:/usr/local/cuda-12.1/lib64" >> ~/.bashrc
source ~/.bashrc
Wheel¶
**NOTE:** The wheel file is compatible only with x86_64 linux based architecutre. To check the architecture of your linux system, execute the following command:
uname -a
pip3 install torch==2.4.0
# GPU whl for python 3.10 + cuda 12.1
pip3 install https://storage.googleapis.com/pytorch-xla-releases/wheels/cuda/12.1/torch_xla-2.4.0-cp310-cp310-manylinux_2_28_x86_64.whl
Wheels for other Python version and CUDA version can be found here.
Run some simple models¶
In order to run below examples, you need to clone the pytorch/xla repository.
MP_ImageNet Example¶
This example uses ImageNet. It is included in what we already cloned in our Docker container.
(pytorch) root@20ab2c7a2d06:/# export GPU_NUM_DEVICES=1 PJRT_DEVICE=CUDA
(pytorch) root@20ab2c7a2d06:/# git clone --recursive https://github.com/pytorch/xla.git
(pytorch) root@20ab2c7a2d06:/# python xla/test/test_train_mp_imagenet.py --fake_data
==> Preparing data..
Epoch 1 train begin 06:12:38
| Training Device=xla:0/0 Epoch=1 Step=0 Loss=6.89059 Rate=2.82 GlobalRate=2.82 Time=06:13:23
| Training Device=xla:0/0 Epoch=1 Step=20 Loss=6.79297 Rate=117.16 GlobalRate=45.84 Time=06:13:36
| Training Device=xla:0/0 Epoch=1 Step=40 Loss=6.43628 Rate=281.16 GlobalRate=80.49 Time=06:13:43
| Training Device=xla:0/0 Epoch=1 Step=60 Loss=5.83108 Rate=346.88 GlobalRate=108.82 Time=06:13:49
| Training Device=xla:0/0 Epoch=1 Step=80 Loss=4.99023 Rate=373.62 GlobalRate=132.43 Time=06:13:56
| Training Device=xla:0/0 Epoch=1 Step=100 Loss=3.92699 Rate=384.33 GlobalRate=152.40 Time=06:14:02
| Training Device=xla:0/0 Epoch=1 Step=120 Loss=2.68816 Rate=388.35 GlobalRate=169.49 Time=06:14:09
ResNet Example¶
This example uses ResNet.
(pytorch) root@20ab2c7a2d06:/# python3 /xla/examples/train_resnet_base.py
1:35PM UTC on Jun 08, 2024
epoch: 1, step: 0, loss: 6.887794017791748, rate: 8.746502586051985
epoch: 1, step: 10, loss: 6.877807140350342, rate: 238.4789458412044
epoch: 1, step: 20, loss: 6.867819786071777, rate: 329.86095958663503
epoch: 1, step: 30, loss: 6.857839584350586, rate: 367.3038003653586
epoch: 1, step: 40, loss: 6.847847938537598, rate: 381.53141087190835
epoch: 1, step: 50, loss: 6.837860584259033, rate: 387.80462249591113
...
epoch: 1, step: 260, loss: 6.628140926361084, rate: 391.135639565343
epoch: 1, step: 270, loss: 6.618192195892334, rate: 391.6901797745233
epoch: 1, step: 280, loss: 6.608224391937256, rate: 391.1602680460045
epoch: 1, step: 290, loss: 6.598264217376709, rate: 391.6731498290759
Epoch 1 train end 1:36PM UTC
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 and imagenet example. Note that we also used a modified version of optimizers to avoid the additional sync between device and host.
Develop PyTorch/XLA on a GPU instance (build PyTorch/XLA from source with GPU support)¶
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_12.1
# Installing the NVIDIA Container Toolkit per https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html
# For example
curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
&& curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
sudo apt-get update
sudo apt-get install -y nvidia-container-toolkit
# Configuring the NVIDIA Container Toolkit
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker
sudo docker run --shm-size=16g --net=host --gpus all -it -d us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/development:3.8_cuda_12.1
sudo docker exec -it $(sudo docker ps | awk 'NR==2 { print $1 }') /bin/bash
Build PyTorch and PyTorch/XLA from source.
Make sure PATH and LD_LIBRARY_PATH environment variables account for cuda. See the above for more info.
git clone https://github.com/pytorch/pytorch.git
cd pytorch
USE_CUDA=1 python setup.py install
USE_CUDA=1 python setup.py bdist_wheel # Required for hermetic Python in PyTorch/XLA build setup.
git clone https://github.com/pytorch/xla.git
cd xla
XLA_CUDA=1 python setup.py install
Verify if PyTorch and PyTorch/XLA have been installed successfully.
If you can run the tests in the section Run some simple models successfully, then PyTorch and PyTorch/XLA should have been installed successfully.
