Installation for Linux#

Warning

If you use Windows Subsystem for Linux (WSL), please refer to the Windows installation section.

Requirements#

Supported Operating Systems#

  • Debian >=9

  • Ubuntu >= 16.04

  • Fedora >= 19

  • RedHat/CentOS >= 7

Gnu Compiler Collection (gcc)#

You need to have gcc installed. We recommend installing it via your package manager.

For example on Debian/Ubuntu:

apt install gcc

On CentOS/RedHat:

yum -y install gcc

Installation Options#

Option 2: Install from GitHub (development)#

If you wish to benefit from the cutting-edge version of SCT, or if you wish to contribute to the code, we recommend you download the GitHub version.

  1. Retrieve the SCT code

    Clone the repository and hop inside:

    git clone https://github.com/spinalcordtoolbox/spinalcordtoolbox
    
    cd spinalcordtoolbox
    
  2. (Optional) Checkout the revision of interest, if different from master:

    git checkout ${revision_of_interest}
    
  3. Run the installer and follow the instructions

    ./install_sct
    

Option 3: Install with pip (experimental)#

SCT can be installed using pip, with some caveats:

  • The installation is done in-place, so the folder containing SCT must be kept around

  • In order to ensure coexistence with other packages, the dependency specifications are loosened, and it is possible that your package combination has not been tested with SCT.

    So in case of problems, try again with the reference installation, and report a bug indicating the dependency versions retrieved using sct_check_dependencies.

Procedure:

  1. Retrieve the SCT code to a safe place

    Clone the repository and hop inside:

    git clone https://github.com/spinalcordtoolbox/spinalcordtoolbox
    
    cd spinalcordtoolbox
    
  2. Checkout the revision of interest, if different from master:

    git checkout ${revision_of_interest}
    
  3. If numpy is not already on the system, install it, either using your distribution package manager or pip.

  4. Install sct using pip

    If running in a virtualenv:

    pip install -e .
    

    else:

    pip install --user -e .
    

Option 4: Install with Docker#

Docker is a portable (Linux, macOS, Windows) container platform.

In the context of SCT, it can be used:

  • To run SCT on Windows, until SCT can run natively there

  • For development testing of SCT, faster than running a full-fledged virtual machine

  • <your reason here>

Basic Installation (No GUI)#

First, install Docker. Be sure to install from your distribution’s repository.

Note

Docker Desktop for Linux is not recommended if you intend to use the GUI. Instead install the Docker Server Engine, which is separate to the Docker Desktop Engine. For example on Ubuntu/Debian, follow the instructions for installing Docker from the apt repository.

By default, Docker commands require the use of sudo for additional permissions. If you want to run Docker commands without needing to add sudo, please follow these instructions to create a Unix group called docker, then add users your user account to it.

Then, follow the example below to create an OS-specific SCT installation.

Docker Image: Ubuntu#
# Start from the Terminal
sudo docker pull ubuntu:22.04
# Launch interactive mode (command-line inside container)
sudo docker run -it ubuntu:22.04
# Now, inside Docker container, install dependencies
apt-get update
apt install -y git curl bzip2 libglib2.0-0 libgl1-mesa-glx libxrender1 libxkbcommon-x11-0 libdbus-1-3 gcc
# Note for above: libglib2.0-0, libgl1-mesa-glx, libxrender1, libxkbcommon-x11-0, libdbus-1-3 are required by PyQt
# Install SCT
git clone https://github.com/spinalcordtoolbox/spinalcordtoolbox.git sct
cd sct
./install_sct -y
source /root/.bashrc
# Test SCT
sct_testing
# Save the state of the container as a docker image.
# Back on the Host machine, open a new terminal and run:
sudo docker ps -a  # list all containers (to find out the container ID)
# specify the ID, and also choose a name to use for the docker image, such as "sct_v6.0"
sudo docker commit <CONTAINER_ID> <IMAGE_NAME>/ubuntu:ubuntu22.04

Enable GUI Scripts (Optional)#

In order to run scripts with GUI you need to allow X11 redirection. First, save your Docker image if you haven’t already done so:

  1. Open another Terminal

  2. List current docker images

    sudo docker ps -a
    
  3. If you haven’t already, save the container as a new image

    sudo docker commit <CONTAINER_ID> <IMAGE_NAME>/ubuntu:ubuntu22.04
    

Forward X11 server:

Note

The following instructions have been tested with Xorg and xWayland.

Set up may vary if you are using a different X11 server.

  1. Install xauth and xhost on the host machine, if not already installed:

    For example on Debian/Ubuntu:

    sudo apt install xauth x11-xserver-utils
    
  2. Permit docker access to the X11 Server

    If hosting container from the local machine:

    xhost +local:docker
    
  3. In your Terminal window, run:

    sudo docker run -it --rm --privileged -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix <IMAGE_NAME>/ubuntu:ubuntu22.04``
    
  4. You can test whether GUI scripts are available by running the following command in your Docker container:

    sct_check_dependencies
    

    You should see two green [OK] symbols at the bottom of the report for “PyQT” and “matplotlib” checks, which represent the GUI features provided by SCT.

Option 5: Hard-core Installation-less SCT usage#

This is completely unsupported.

Procedure:

  1. Retrieve the SCT code

  2. Install dependencies

    Example for Ubuntu 18.04:

    # The less obscure ones may be packaged in the distribution
    sudo apt install python3-{numpy,scipy,nibabel,matplotlib,h5py,mpi4py,keras,tqdm,sympy,requests,sklearn,skimage}
    # The more obscure ones would be on pip
    sudo apt install libmpich-dev
    pip3 install --user distribute2mpi nipy dipy
    

    Example for Debian 8 Jessie:

    # The less obscure ones may be packaged in the distribution
    sudo apt install python3-{numpy,scipy,matplotlib,h5py,mpi4py,requests}
    # The more obscure ones would be on pip
    sudo apt install libmpich-dev
    pip3 install --user distribute2mpi sympy tqdm Keras nibabel nipy dipy scikit-image sklearn
    
  3. Prepare the runtime environment

    # Create launcher-less scripts
    mkdir -p bin
    find scripts/ -executable | while read file; do ln -sf "../${file}" "bin/$(basename ${file//.py/})"; done
    PATH+=":$PWD/bin"
    
    # Download binary programs
    mkdir bins
    pushd bins
    sct_download_data -d binaries_linux
    popd
    PATH+=":$PWD/bins"
    
    # Download models & cie
    mkdir data; pushd data; for x in PAM50 optic_models pmj_models deepseg_sc_models deepseg_gm_models deepseg_lesion_models c2c3_disc_models deepreg_models ; do sct_download_data -d $x; done; popd
    
    # Add path to spinalcordtoolbox to PYTHONPATH
    export PYTHONPATH="$PWD:$PWD/scripts"