readme.md

A Pytorch deep learning singularity container

Aim

• Quickly set up medial imaging deep learning research environment on Linux(singularity container based)

• GPU acceleration (CUDA and cuDNN included)

• Supported frameworks and packages:

  • PyTorch
  • Scikit-learn
  • OpenCV
  • SimpleITK
  • Scikit-image
  • Anaconda packages: https://docs.anaconda.com/anaconda/packages/py3.6_linux-64/

pre-requisite

• your host system must has an NVIDIA GPU card and a driver installed(you don't need to install cuda and cudnn)

• install singularity on your host

  # ubuntu
  sudo apt-get install -y singularity-container

• pull singularity image from singularity hub

  singularity pull --name pytorch_gpu.simg shub://yinglilu/pytorch_gpu_singularity:1.3.0

usage

1. enter into singularity container, run command in the container

# enter into singularityh container: imagine it as SSH into (passwordless) another machine
# --nv: leverage the nvidia gpu card
singularity shell --nv /containers/pytorch_gpu.simg

You will get:

Singularity: Invoking an interactive shell within container...

Singularity pytorch_gpu.simg:~>

You can type command now, for instance:

python /path/to/<your_script.py>

After finishing your work, type

exit

to exit the container.

note: /path/to/

• Singularity will bind your host's $HOME to container's $HOME automatically. That's mean, if you do modification on your host's home directory, you can see the modifications in the container's home directory, and vice versa.

• If current working directory is in your home directory or bind path, Singularity will replicate your current working directory within the container.

  Therefore, /path/to/ can be a relative path or absolute path of your home or bind path.

2. run singularity container command directly

singularity exec --nv pytorch_gpu.simg python /path/to/<your_script.py>