PyTorch is an open source machine learning library for Python, based on Torch. It is used for applications such as natural language processing.
About PyTorch on Bessemer¶
A GPU-enabled worker node must be requested in order to enable GPU acceleration. See Using GPUs on Bessemer 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 for the installation of new Python packages without needing 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.
Installation in Home Directory¶
Conda is used to create a virtual python environment for installing your local version of PyTorch.
Torch requires more than 2GB of RAM for installation
so you must use the
--mem=8G flag to request more memory.
8G means 8 GB of RAM.
# To request 8GB of RAM for the session srun --mem=8G --pty bash # OR To request and 8GB RAM _and_ a GPU srun --partition=gpu --qos=gpu --mem=8G --nodes=1 --gpus-per-node=1 --pty bash
Then PyTorch can be installed by the following
# Load the conda module module load Anaconda3/5.3.0 # (Only needed if we're using GPU) Load a cuDNN module # (which in this case implicitly loads CUDA 10.1.243) module load cuDNN/184.108.40.206-gcccuda-2019b # 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 Anaconda3/5.3.0 # *Only needed if we're using GPU* Load the CUDA and cuDNN module module load cuDNN/220.127.116.11-gcccuda-2019b # Activate the 'pytorch' environment source activate pytorch
Testing your PyTorch installation¶
To ensure that PyTorch was installed correctly, we can verify the installation by running sample PyTorch code e.g. an example from the official PyTorch getting started guide (replicated below).
Here we 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()