GPU Job Submission

Using CUDA

There are several versions of CUDA Toolkits available on the HPC, including. Use the module avail command to check for the latest software versions on the cluster.

$ module avail cuda

------------------------------- /shared/centos7/modulefiles -------------------------------
cuda/10.0    cuda/10.2          cuda/11.1    cuda/11.3    cuda/11.7    cuda/12.1    cuda/9.1
cuda/10.1    cuda/11.0(default) cuda/11.2    cuda/11.4    cuda/11.8    cuda/9.0     cuda/9.2

To see details on a specific CUDA toolkit version, use module show (e.g., module show cuda/11.4).

To add CUDA to your path, use module load (e.g., module load cuda/11.4 adds CUDA 11.4).

Note

Executing nvidia-smi (i.e., NVIDIA System Management Interface) on a GPU node displays the CUDA driver information and monitor the GPU device.

GPUs for Deep Learning

See also

Deep learning frameworks tend to cost storage that can quickly surpass Home Directory Storage Quota: follow best practices for Conda environments.

Select the tab with the desire deeplearning framework.

Important

Each tab assumes you are on a GPU node before with CUDA 11.8 and anaconda modules loaded as done above.

PyTorch

The following example demonstrates how to build PyTorch inside a conda virtual environment for CUDA version 12.1.

PyTorch’s installation steps for Python 3.10 and Cuda 12.1:

srun --partition=gpu --nodes=1 --gres=gpu:v100-sxm2:1 --cpus-per-task=2 --mem=10GB --time=02:00:00 --pty /bin/bash
module load anaconda3/2022.05 cuda/12.1
conda create --name pytorch_env -c conda-forge python=3.10 -y
source activate pytorch_env
conda install jupyterlab -y
pip3 install torch torchvision torchaudio

Now, let us check the installation:

python -c 'import torch; print(torch.cuda.is_available())'

If CUDA is detected by PyTorch, you should see the result, True.

If you want to use an older version of CUDA, here is the following example that demonstrates how to build PyTorch inside a conda virtual environment for CUDA version 11.8.

PyTorch’s installation steps for Python 3.10 and Cuda 11.8:

srun --partition=gpu --nodes=1 --gres=gpu:v100-sxm2:1 --cpus-per-task=2 --mem=10GB --time=02:00:00 --pty /bin/bash
module load anaconda3/2022.05 cuda/11.8
conda create --name pytorch_env -c conda-forge python=3.10 -y
source activate pytorch_env
conda install jupyterlab -y
pip install torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 --index-url https://download.pytorch.org/whl/cu118

Now, let us check the installation:

python -c 'import torch; print(torch.cuda.is_available())'

If CUDA is detected by PyTorch, you should see the result, True.

See also

PyTorch documentation for the most up-to-date instructions and for different CUDA versions.

TensorFlow

Here are steps for installing CUDA 12.1 with the latest version of TensorFlow (TF).

See also

Compatibility of CUDA and TensorFlow versions, and detailed installation instructions.

For the latest installation, use the TensorFlow pip package, which includes GPU support for CUDA-enabled devices:

Tensorflow’s installation steps for Python 3.9 and Cuda 12.1:

srun -p gpu --gres=gpu:v100-pcie:1 --pty /bin/bash
module load anaconda3/2022.05
module load cuda/12.1
conda create --name TF_env python=3.9 -y
source activate TF_env
pip install --upgrade pip
pip install tensorflow[and-cuda]
pip install jupyterlab

Verify the installation:

python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))" # True

Note

Ignore the Warning messages that get generated after executing the above commands.