PyTorch is an open-source machine learning library.
Using PyTorch on Blue Crab
Option 1: Use an environment
Two versions of PyTorch are available on Blue Crab without using a custom environment. To use either
1.4.0, you can load the system-installed Anaconda module and then select a shared pre-installed environment. We are preparing to upgrade to CUDA 10 in the near future. Until then, our software is compatible with CUDA 9.2. To load PyTorch
1.4.0, use the following commands.
ml anaconda ml cuda/9.2 conda activate pytorch-1.4
To load PyTorch
1.1.0, use the following commands.
ml anaconda ml cuda/9.2 conda activate torchvision
Other shared environments provided by the Anaconda module (
ml anaconda) can be listed with
conda env list. Users can confirm that their GPU is available with the following code.
# interactive session on high-availability GPU debug nodes interact -p debug -g 1 -c 6 -t 20 ml anaconda conda activate pytorch-1.4 python -c "import torch; torch.cuda.is_available()" # True
If you require additional Python packages alongside PyTorch, you are welcome to install them to
pip install --user, however please keep in mind that these versions may cause conflicts if you later switch Python versions (the PyTorch modules above use Python 3.7). For greater control over your software versions, you should build a custom environment using the instructions below.
Option 2: Use a custom environment
If you wish to install a custom version of PyTorch, you can make a conda environment with
pytorch::pytorch in your requirements file. Note that until we upgrade to CUDA 10, you should use
pytorch::pytorch=1.3=*cuda9.2* to select our highest available CUDA version. Before using the code you should also load the appropriate module with
ml cuda/9.2 to ensure the environment is correctly linked.
Option 3: Legacy versions in containers
Blue Crab also offers older versions of PyTorch (
0.4) in a Singularity container, however this version is somewhat outdated:
module spider torch module load pytorch/0.4.1-gpu-py3