NeuroImaging in PyTorch

NITorch is currently in a very alpha state: its API is far from stable, it has been loosely tested, and there is not warranty that it can run on any OS or against any version of PyTorch. Up to now, it has only been used on linux, compiled from source against PyTorch 1.4, 1.5 and 1.6.

0. (If GPU support needed) Install cuda

   • You need the driver and the toolkit (compiler, headers and libraries).
   • Follow instructions from the nvidia website based on your OS and the cuda version supported by your device.
   • See also: section Troubleshooting.

1. Build conda environement

   • (If GPU support needed) Change the cudatoolkit version so that it matches that of your local cuda toolkit.
   • (Else) Remove cudatoolkit from the package list

conda env create --file ./conda/nitorch.yml
conda activate nitorch

2. Compile C++/CUDA library
   • install copies files in the python site-packages directory
   • develop softlinks files in the python site-packages directory, allowing them to be modified without requiring a new install step.

./setup.py [install|develop]

3. Use nitorch

import nitorch as ni

The demo folder contains various Jupyter notebooks that showcase some of NITorch's funtionality. Just follow the instructions in the notebook to get started, or follow the links below to run an installation-free copy instantly in Google Colab:

• Affine Registration
  
• Spatial Tools
  

1. Build conda environement

conda env create --file ./conda/nitorch.yml
conda activate nitorch

NITorch is a compiled package and is therefore specific to an OS, a Python version and (if CUDA is enabled) a CUDA version. Since we link against libtorch, I think it is also specific to a PyTorch version (this should be checked). You must therefore be careful about what packages are present in your environment. It would be good practice to name the created wheel files accordingly.

2. Build a wheel file

./setup.py bdist_wheel

This will create a wheel file in a dist/ directory:

.
├── dist
│   ├── nitorch-[*].whl

3. Install wheel file using pip

pip install nitorch-[*].whl

• Different versions of the CUDA toolkit support different compute capability versions (see: https://en.wikipedia.org/wiki/CUDA#GPUs_supported). You should install a version of the toolkit that is compatible with the compute capability of your device.
• The toolkit installer allows both the driver and the toolkit (compiler, headers, libraries) to be installed. The driver needs admin priviledges to be installed, but the toolkit does not. Here's a way to install the toolkit without admin priviledges (copied from here):

  ./cuda_<VERSION>_linux.run --silent --toolkit --toolkitpath=<INSTALLPATH> --defaultroot=<INSTALLPATH>
  

• If your CUDA toolkit is installed in a non-standard location (i.e., different from /usr/local/cuda), use the environement variable CUDA_HOME to help the setup script locate it:

  CUDA_HOME=<PATH_TO_CUDA> ./setup.py install
  

  However, note that nvcc should call the correct nvidia compiler. Therefore, setup your path accordingly:

  export PATH="$CUDA_HOME/bin:$PATH"
  

• The nvidia compiler (nvcc) calls a host compiler (gcc, clang, ...). If you wish to use a non-standard host compiler (e.g., you are using gcc-8 instead of the native gcc), things might be trickier. A solution could be to alias nvcc so that it uses the -ccbin option by default. In your ~/.bashrc, add:

  alias nvcc='nvcc -ccbin <PATH_TO_GCC_BIN>'
  

NITorch has been mostly written by Yael Balbastre and Mikael Brudfors, while post-docs in John Ashburner's group at the FIL (or Wellcome Centre for Human Neuroimaging as it is officially known). It is therefore conceptually related to SPM.

All contributions are welcome (though no nicely drafted guidelines exist, we'll try to get better).

NITorch is released under the MIT license.