Installation¶

You will find below the steps for installing ClinicaDL on Linux or Mac. Please do not hesitate to contact us on the forum or GitHub if you encounter any issues.

Prepare your Python environment¶

You will need a Python environment to run ClinicaDL. We advise you to use Miniconda. Miniconda allows you to install, run, and update Python packages and their dependencies. It can also create environments to isolate your libraries. To install Miniconda, open a new terminal and type the following commands:

If you are on Linux:

curl https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -o /tmp/miniconda-installer.sh
bash /tmp/miniconda-installer.sh

If you are on Mac:

curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -o /tmp/miniconda-installer.sh
bash /tmp/miniconda-installer.sh

Install ClinicaDL¶

The latest release of ClinicaDL can be installed using pip as follows:

conda create --name clinicadlEnv python=3.7
conda activate clinicadlEnv
pip install clinicadl

Run the ClinicaDL environment¶

Activation of the ClinicaDL environment¶

Now that you have created the ClinicaDL environment, you can activate it:

conda activate clinicadlEnv

Success

Congratulations, you have installed ClinicaDL! At this point, you can try the basic clinicadl -h command and get the help screen:

(clinicadlEnv)$ clinicadl -h
Usage: clinicadl [OPTIONS] COMMAND [ARGS]...

  ClinicaDL command line.

  For more information please read the doc: https://clinicadl.readthedocs.io/en/latest/
  Source code is available on GitHub: https://github.com/aramis-lab/clinicaDL .

  Do not hesitate to create an issue to report a bug or suggest an improvement.

Options:
  --version      Show the version and exit.
  -v, --verbose  Increase logging verbosity.  [x>=0]
  -h, --help     Show this message and exit.

Commands:
  extract        Extract Pytorch tensors from nifti images.
  generate       Generation of synthetic dataset.
  interpret      Interpretation of trained models using saliency map method.
  predict        Infer the outputs of a trained model on a test set.
  quality-check  Performs quality check procedure for t1-linear or t1-volume...
  random-search  Hyperparameter exploration using random search.
  resume         Resume training job in specified maps.
  train          Train a deep learning model on your neuroimaging dataset.
  tsvtool        Manipulation of TSV files to prepare and manage input data.

Deactivation of the ClinicaDL environment¶

At the end of your session, you can deactivate your Conda environment:

conda deactivate