PyTorch on ARC
PyTorch is a Python package that provides two high-level features:
- Tensor computation (like NumPy) with strong GPU acceleration
- Deep neural networks built on a tape-based autograd system
You can reuse your favorite Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.
Installing PyTorch
You will need a working local Conda install in your home directory first. If you do not have it yet, please follow these instructions to have it installed.
Once you have your own Conda, activate it with
$ source ~/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.")
# -------------------------------------------------------
Test script 2
torch-gpu-test2.py:
#! /usr/bin/env python
# -------------------------------------------------------
import os
import sys
import socket
import torch
# -------------------------------------------------------
dev = os.environ['CUDA_VISIBLE_DEVICES']
host = socket.gethostname()
tdev = torch.cuda.current_device()
tavail = torch.cuda.is_available()
tcount = torch.cuda.device_count()
tname = torch.cuda.get_device_name()
print("Host: %s\nENV Devices: %s\nCudaDev: %s\nCUDA is available: %s\nDevice count: %d\nDevice: %s" % \
(host, dev, tdev, tavail, tcount, tname))
print(os.popen("/usr/bin/nvidia-smi -L").read().strip())
print(os.popen("env | grep CUDA").read().strip())
print("")
# -------------------------------------------------------
Using PyTorch on ARC
Requesting GPU Resources for PyTorch Jobs
For interactive use see this How-To: How to request an interactive GPU on ARC.
An example of the job script torch_job.slurm:
#! /bin/bash
# ====================================
#SBATCH --job-name=torch-test
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=4
#SBATCH --mem=16GB
#SBATCH --time=0-04:00:00
#SBATCH --gres=gpu:1
#SBATCH --partition=gpu-v100
# ====================================
source ~/software/init-conda
conda activate pytorch
python torch-gpu-test.py
Checkpointing
Refer to the checkpointing tutorial at https://pytorch.org/tutorials/recipes/recipes/saving_and_loading_a_general_checkpoint.html.
See also
- https://github.com/pytorch/pytorch - PyTorch project page on GitHub.
- https://docs.computecanada.ca/wiki/PyTorch - Compute Canada article is not directly applicable on ARC but contains a lot of good information.
- https://pytorch.org/tutorials/recipes/recipes/saving_and_loading_a_general_checkpoint.html - Checkpointing tutorial
Links
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