PyTorch on ARC

Intro to Torch

• Compute Canada article is not directly applicable on ARC but contains a lot of good information:

https://docs.computecanada.ca/wiki/PyTorch

Checkpointing

• https://pytorch.org/tutorials/recipes/recipes/saving_and_loading_a_general_checkpoint.html

Installing PyTorch

You will need a working local Conda install in your home directory first. If you do not have it yet, plaese follow these instructions to have it isntalled.

Once you have your own Conda, activate it with

$ ~/software/init-conda

We will install PyTorch into its own conda environment.

It is very important to create the environment with python and pytorch in the same command. This way conda can select the best pytorch and python combination.

$ conda create -n pytorch python pytorch-gpu torchvision

Once it is done, activate your pytorch environment:

$ conda activate pytorch

You can test with the torch-gpu-test.py script shown below. Copy and paste the text into a file and run if from the command line:

$ python torch-gpu-test.py

If you try this on the login node, it should tell you that GPUs are not available. It is normal, as the login node does not have any. You will need a GPU node to test the GPUs.

Once you know that your pytorch environment is working properly, you can add more packages to the environment using conda.

To deactivate the environment use the

$ conda deactivate 

command.

Test script

torch-gpu-test.py:

#! /usr/bin/env python 
# -------------------------------------------------------
import torch
# -------------------------------------------------------
print("Defining torch tensors:")
x = torch.Tensor(5, 3)
print(x)
y = torch.rand(5, 3)
print(y)

# -------------------------------------------------------
# let us run the following only if CUDA is available
if torch.cuda.is_available():
    print("CUDA is available.")
    x = x.cuda()
    y = y.cuda()
    print(x + y)
else:
    print("CUDA is NOT available.")

# -------------------------------------------------------

Using PyTorch on ARC

Interactive Job

1 GPU and 4 CPUs on the gpu-v100 partition for 1 hour (16 GB of RAM):

salloc -N1 -n1 -c4 --mem=16GB --gres=gpu:1 -p gpu-v100 -t 1:00:00 

1 GPU and 4 CPUs on the bigmem partition for 1 hour (256 GB of RAM):

$ salloc -N1 -n1 -c4 --mem=256gb --gres=gpu:1 -p bigmem -t 1:00:00

Use the nvidia-smi command to check the GPU:

$ nvidia-smi
Fri Jun  3 11:35:14 2022       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 470.57.02    Driver Version: 470.57.02    CUDA Version: 11.4     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA A100-PCI...  Off  | 00000000:17:00.0 Off |                    0 |
| N/A   39C    P0    42W / 250W |      0MiB / 40536MiB |     32%      Default |
|                               |                      |             Disabled |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+