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Installing PyTorch on Kestrel#

To install PyTorch for use with GPUs on Kestrel, the first step is to load the anaconda module on the GPU node using module load conda. Once the anaconda module has been loaded, create a new environment in which to install PyTorch, e.g.,

Creating and activating a new conda environment
conda create --prefix /projects/<your-project-name>/<your-user-name>/<conda-env-dir>/pt python=3.9
conda activate /projects/<your-project-name>/<your-user-name>/<conda-env-dir>/pt


If you are not familiar with using Anaconda environments please refer to the NREL HPC page on using Conda environments and also the Conda guide to managing environments.

Once the environment has been activated, you can install PyTorch using the standard approach found under the Get Started tab of the PyTorch website, e.g., using pip,

Installing PyTorch using pip

pip3 install torch torchvision torchaudio


Currently, we recommend installing software using the GPU nodes.

Running a PyTorch Batch Job on Kestrel#

Sample job script: Kestrel - Shared (partial) GPU node
#SBATCH --account=<your-account-name> 
#SBATCH --reservation=<friendly-users-reservation>
#SBATCH --partition=gpu-h100
#SBATCH --nodes=1
#SBATCH --gres=gpu:h100:1 
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=1
#SBATCH --time=02:00:00
#SBATCH --job-name=<your-job-name>

module load conda
conda activate /projects/<your-project-name>/<your-user-name>/<conda-env-dir>/pt

srun python <your-pytorch-code>.py

PyTorch Example#

Below we present a simple convolutional neural network example for getting started using PyTorch with Kestrel GPUs. The original, more detailed version of this example can be found in the pytorch tutorials repo here.

CIFAR10 example
import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

# Check if there are GPUs. If so, use the first one in the list
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

# Load data and normalize
transform = transforms.Compose(
    [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

batch_size = 4
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                    download=True, transform=transform)
trainloader =, batch_size=batch_size,
                                      shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                   download=True, transform=transform)
testloader =, batch_size=batch_size,
                                     shuffle=False, num_workers=2)

# Define the CNN
class Net(nn.Module):
    def __init__(self):
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = torch.flatten(x, 1) # flatten all dimensions except batch
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

net = Net()

# send the network to the device
# If you want to use data parallelism across multiple GPUs, uncomment if statement below
#if torch.cuda.device_count() > 1:
#    net = nn.DataParallel(net)

# Define loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

# Train the network
for epoch in range(2):  # loop over the dataset multiple times

    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # get the inputs; data is a list of [inputs, labels]
        # inputs, labels = data # setup without device
        inputs, labels = data[0].to(device), data[1].to(device)

        # zero the parameter gradients

        # forward + backward + optimize
        outputs = net(inputs)
        loss = criterion(outputs, labels)

        # print statistics
        running_loss += loss.item()
        if i % 2000 == 1999:    # print every 2000 mini-batches
            print(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss / 2000:.3f}')
            running_loss = 0.0

print('Finished Training')


Currently, this code will run on a single GPU, specifically the GPU denoted cuda:0. To use multiple GPUs via data parallelism, uncomment the two lines above the command. Furthermore, use of multiple GPUs require requesting multiple GPUs for the batch or interactive job.


To better observe the multi-GPU peformance of the above example, you can change the size of the CNN. For example, by increasing the size of the second argument in the definition of self.conv1 and the first argument in self.conv2, you can increase the size of the network and use more resources for training.