PyTorch (Python)

Train neural networks and other models

Directions for setting up PyTorch with Python depend on if you’re using a CPU or GPU

PyTorch using GPUs

Saturn Cloud has a built in GPU image for PyTorch that has all the required libraries to get started using PyTorch on a GPU. When creating a new resource, select the saturn-pytorch image. Once the resource starts, your PyTorch code should be ready to run. This will also work well with Dask, and is how the Saturn Cloud PyTorch examples run.

If you want to create your own image, you will need to install the GPU version of PyTorch. Conda is the easiest way to access these. Look for “cuda” in the name, as this indicates GPU support.

$ conda search pytorch
#> pytorch                        1.5.1 py3.7_cpu_0                      pytorch
#> pytorch                        1.5.1 py3.7_cuda10.1.243_cudnn7.6.3_0  pytorch
#> pytorch                        1.5.1 py3.7_cuda10.2.89_cudnn7.6.5_0   pytorch
#> pytorch                        1.5.1 py3.7_cuda9.2.148_cudnn7.6.3_0   pytorch
#> pytorch                        1.5.1 py3.8_cpu_0                      pytorch
#> pytorch                        1.5.1 py3.8_cuda10.1.243_cudnn7.6.3_0  pytorch
#> pytorch                        1.5.1 py3.8_cuda10.2.89_cudnn7.6.5_0   pytorch

You have to consider the CUDA version and the Python version here. This example shows selecting CUDA 10.1.

  - pytorch
  - defaults
  - pytorch=1.5.1=py3.7_cuda10.1.243_cudnn7.6.3_0

PyTorch using CPUs

If you want to use PyTorch but on a CPU resource (which may be cheaper depending on which Saturn Cloud plan you are using), you can manually set up PyTorch yourself by creating a resource with the following settings:

  • Hardware: CPU
  • Image: saturn
  • Extra Packages (Conda): Add the following: pytorch torchvision torchaudio cpuonly -c pytorch