PyTorch is an open source machine learning library for Python, based on Torch, used for applications such as natural language processing.
A GPU-enabled worker node must be requested in order to enable GPU acceleration. See Using GPUs on ShARC for more information.
As PyTorch and all its dependencies are written in Python, it can be installed locally in your home directory. The use of Anaconda (Python) is recommended as it is able to create a virtual environment in your home directory, allowing the installation of new Python packages without admin permission.
This software and documentation is maintained by the RSES group and GPUComputing@Sheffield. For feature requests or if you encounter any problems, please raise an issue on the GPU Computing repository.
Conda is used to create a virtual python enviroment for installing your local version of PyTorch.
Torch requires more than 2GB of RAM for installation so you must use the
-l rmem=8G flag to request more memory,
8G means 8 GB of RAM.
#To request 8GB of ram for the session qrshx -l rmem=8G #OR To request a GPU node with 8GB RAM qrshx -l rmem=8G -l gpu=1
Then PyTorch can be installed by the following
#Load the conda module module load apps/python/conda #*Only needed if we're using GPU* Load the CUDA and cuDNN module module load libs/cudnn/126.96.36.199/binary-cuda-9.0.176 #Create an conda virtual environment called 'pytorch' conda create -n pytorch python=3.6 #Activate the 'pytorch' environment source activate pytorch #Install PyTorch pip install torch torchvision
Every Session Afterwards and in Your Job Scripts
Every time you use a new session or within your job scripts, the modules must be loaded and conda must be activated again. Use the following command to activate the Conda environment with PyTorch installed:
#Load the conda module module load apps/python/conda #*Only needed if we're using GPU* Load the CUDA and cuDNN module module load libs/cudnn/188.8.131.52/binary-cuda-9.0.176 #Activate the 'pytorch' environment source activate pytorch
Taken from the official getting started page.
To ensure that PyTorch was installed correctly, we can verify the installation by running sample PyTorch code. Here we will construct a randomly initialized tensor.
import torch x = torch.rand(5, 3) print(x)
The output should be something similar to:
tensor([[0.3380, 0.3845, 0.3217], [0.8337, 0.9050, 0.2650], [0.2979, 0.7141, 0.9069], [0.1449, 0.1132, 0.1375], [0.4675, 0.3947, 0.1426]])
Additionally, to check if your GPU driver and CUDA is enabled and accessible by PyTorch, run the following commands to return whether or not the CUDA driver is enabled:
import torch torch.cuda.is_available()