Installation¶

Supported OS¶

Currently, we only support MacOS and Linux operating systems. Windows users have the possibility to install and use ivadomed via Windows Subsystem for Linux (WSL). The steps below (about updating bashrc) are strongly recommended for MacOS users in the installation process but are optional for Linux users.

Open your bash/zsh script file with editor on your computer.

If you are using bash shell
vim ~/.bashrc
If you are using zsh shell
vim ~/.zshrc

Write in your .bashrc/.zshrc file with following line.

export HDF5_USE_FILE_LOCKING='FALSE'

Save this change and restart your terminal to apply the change.

There are fundamentally two different approaches to install IvadoMed:

Install via Conda

This is the easiest way for personal computers.
Install via Venv/VirtualEnv

This is compatible with ComputeCanada cluster environment.

Approach 1: Conda¶

Step 1: Clone the ivadomed repository.¶

git clone https://github.com/neuropoly/ivadomed.git
cd ivadomed

Step 2: Create new Conda Env called IvadoMedEnv (may take 5 to 15 minutes)¶

conda env create --file environment.yml

Step 3 : Activate environment and use¶

conda activate IvadoMedEnv

Step 4 : Install from source¶

pip install -e .

Approach 2: Venv¶

Step 1: Setup Python Virtual Environment.¶

ivadomed requires Python >= 3.6 and <3.9 (If you are using Compute Canada, you can load modules (e.g. python 3.9) as mentioned here and also here ). We recommend working under a virtual environment, which could be set as follows:

virtualenv venv-ivadomed
source venv-ivadomed/bin/activate

Warning

If the default Python version installed in your system does not fit the version requirements, you might need to specify a version of Python associated with your virtual environment:

virtualenv venv-ivadomed --python=python3.6

Step 2: Clone the ivadomed repository.¶

git clone https://github.com/ivadomed/ivadomed.git
cd ivadomed

Step 3: Install PyTorch 1.5 and TorchVision¶

If you have a compatible NVIDIA GPU that supports CUDA, run the following command:

pip install -r requirements_gpu.txt

If you do not have a compatible GPU, run the following installer to use ivadomed with CPU.

pip install -r requirements.txt

Step 4: Install from release (recommended)¶

Install ivadomed and its requirements from Pypi:

pip install --upgrade pip
pip install ivadomed

(Optional) Alternative Step 4 for Developers: Install from source¶

Bleeding-edge developments are available on the project’s master branch on Github. Installation procedure is the following at repository root:

cd ivadomed
pip install -e .

(Optional) Step 5 for Developers Install pre-commit hooks¶

We use pre-commit to enforce a limit on file size. After you’ve installed ivadomed, install the hooks:

pip install -r requirements_dev.txt
pre-commit install