Installation

Supported OS

Currently, ivadomed supports GPU/CPU on Linux and Windows, and CPU only on macOS and Windows Subsystem for Linux.

Step 1: Setup dedicated python environment

You can setup ivadomed using either Conda or Venv:
Setup Python Venv Virtual Environment.
ivadomed requires Python >= 3.7 and <3.10.

First, make sure that a compatible version of Python 3 is installed on your system by running:
python3 --version
python --version
If your system’s Python is not 3.7, 3.8, or 3.9 (or if you don’t have Python 3 installed at all), please install Python before continuing.

Once you have a supported version of Python installed, run the following command:
# Replacing ``3.X`` with the Python version number that you installed):
python3.X -m venv ivadomed_env
Note

If you use Debian or Ubuntu, you may be prompted to install the python3-venv module when creating the virtual environment. This is expected, so please follow the instructions provided by Python. For other operating systems, venv will be installed by default.
python -m venv ivadomed_env
Activate the new virtual environment (default named ivadomed_env)
source ivadomed_env/bin/activate
cd ivadomed_env/Scripts/
activate
Create new conda environment using environment.yml file
conda env create --name ivadomed_env
Activate the new conda environment
conda activate ivadomed_env
There are numerous constraints and limited package availabilities with ComputeCanada cluster environment.

It is best to attempt venv based installations and follow up with ComputeCanada technical support as MANY specially compiled packages (e.g. numpy) are exclusively available for Compute Canada HPC environment.

If you are using Compute Canada, you can load modules as mentioned here and also here.

Step 2: Install `ivadomed`

Install ivadomed and its requirements from PyPI:
pip install --upgrade pip

pip install ivadomed
Bleeding-edge developments are available on the project’s master branch on Github. Install ivadomed from source:
git clone https://github.com/ivadomed/ivadomed.git

cd ivadomed

pip install -e .

Step 3: Install `torch` and `torchvision` with CPU or GPU Support

ivadomed requires CUDA11 to execute properly. If you have a nvidia GPU, try to look up its Cuda Compute Score here, which needs to be > 3.5 to support CUDA11. Then, make sure to upgrade to nvidia driver to be at least v450+ or newer.

You can use nvidia-smi in both Linux and Windows to check for driver CUDA Version listed at the top right of the output console. On Linux, simply type in nvidia-smi in any console to see the output. On windows, you will need to locate the nvidia-smi.exe tool by following the instructions on this page.

If you have a compatible NVIDIA GPU that supports CUDA11, and you have a recent enough driver installed, then run the following command:
pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 --find-links https://download.pytorch.org/whl/torch_stable.html
If you plan to run ivadomed on CPU only, install PyTorch per instructions provided below for your specific operating system:
pip install torch==1.8.0+cpu torchvision==0.9.0+cpu --find-links https://download.pytorch.org/whl/torch_stable.html
pip install torch==1.8.0 torchvision==0.9.0 --find-links https://download.pytorch.org/whl/torch_stable.html
Run this only if you have already downloaded/cloned the repo with access to the requirement_gpu.txt file, then run the following command while at the repository root level:
pip install -r requirements_gpu.txt

Developer-only Installation Steps

The additional steps below are only necessary for contributors to the ivadomed project.

The pre-commit package is used to enforce a size limit on committed files. The requirements_dev.txt also contain additional dependencies related to documentation building and testing.

After you’ve installed ivadomed, install the pre-commit hooks by running:
pip install -r requirements_dev.txt
pre-commit install