NVIDIA Deep Learning Frameworks¶

Here is how easy one can use an NVIDIA environment for deep learning with all the following tools preset. A screenshot of that page is shown below.

web screenshot

First - pull the container (6.5GB).

singularity pull docker://nvcr.io/nvidia/pytorch:22.03-py3

Get an interactive shell.

singularity shell --nv ~/external_1TB/tmp/pytorch_22.03-py3.sif

Singularity> python3
Python 3.8.12 | packaged by conda-forge | (default, Jan 30 2022, 23:42:07)
[GCC 9.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.

>>> import torch
# Check torch version
>>> print(torch.__version__)
1.12.0a0+2c916ef

# Check if CUDA is available
>>> print(torch.cuda.is_available())
True

# Check which GPU architectures are supported
>>> print(torch.cuda.get_arch_list())
['sm_52', 'sm_60', 'sm_61', 'sm_70', 'sm_75', 'sm_80', 'sm_86', 'compute_86']

# test torch
>>> torch.zeros(1).to('cuda')
tensor([0.], device='cuda:0')

From the container shell, check what else is available...

Singularity> nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2022 NVIDIA Corporation
Built on Thu_Feb_10_18:23:41_PST_2022
Cuda compilation tools, release 11.6, V11.6.112
Build cuda_11.6.r11.6/compiler.30978841_0

# Check what conda packages are already there
Singularity> conda list -v

# Start a jupyter-lab (keep in mind the hostname)
Singularity> jupyter-lab
...
[I 13:35:46.270 LabApp] [jupyter_nbextensions_configurator] enabled 0.4.1
[I 13:35:46.611 LabApp] jupyter_tensorboard extension loaded.
[I 13:35:46.615 LabApp] JupyterLab extension loaded from /opt/conda/lib/python3.8/site-packages/jupyterlab
[I 13:35:46.615 LabApp] JupyterLab application directory is /opt/conda/share/jupyter/lab
[I 13:35:46.616 LabApp] [Jupytext Server Extension] NotebookApp.contents_manager_class is (a subclass of) jupytext.TextFileContentsManager already - OK
[I 13:35:46.616 LabApp] Serving notebooks from local directory: /home/pmitev
[I 13:35:46.616 LabApp] Jupyter Notebook 6.4.8 is running at:
[I 13:35:46.616 LabApp] http://hostname:8888/?token=d6e865a937e527ff5bbccfb3f150480b76566f47eb3808b1
[I 13:35:46.616 LabApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
...

You can use this container to add more packages.

Bootstrap: docker
From: nvcr.io/nvidia/pytorch:22.03-py3
...

Just keep in mind that "upgrading" the build-in torch package might install a package that is compatible with less GPU architectures and it might not work anymore on your hardware.

Singularity> python3 -c "import torch; print(torch.__version__); print(torch.cuda.is_available()); print(torch.cuda.get_arch_list()); torch.zeros(1).to('cuda')"

1.10.0+cu102
True
['sm_37', 'sm_50', 'sm_60', 'sm_70']
NVIDIA A100-PCIE-40GB with CUDA capability sm_80 is not compatible with the current PyTorch installation.
The current PyTorch install supports CUDA capabilities sm_37 sm_50 sm_60 sm_70.