MONAIBootcamp2020

This repository hosts the notebooks for the 2020 MONAI Bootcamp event. The data required for the notebooks is available through the download mechanisms given in each notebook or through the organizers. All bootcamp participants can access the bootcamp Slack channel to ask for help with any issues.

Most of the notebooks in this repository would benefit considerably from having GPU support enabled. Therefore, it is recommended to run notebooks on Google Colab. Instructions to replicate the Python environment on your local machines are also provided (See Install Local Environment)

Run on Google Colab (Recommended)

Notebooks can be accessed in Colab by using the links below:

Day 1 Notebooks:

• Lab 1 Transforms
• Lab 2 End-to-End
• Lab 3 Datasets
• Lab 3 Networks
• Lab 3 Post Transforms

Day 2 Notebooks:

• MedNIST GAN Tutorial
• Cardiac Segmentation Challenge (Solution)

Further notes

1. Required Packages for Colab Execution

The Day 1 notebooks have the pip command for install MONAI, however this will have to be added to any subsequent notebook. Place this at the top of the first cell to install MONAI the first time a colab notebook is run:

%pip install -qU "monai[nibabel,ignite,torchvision]==0.3.0rc2"

2. Enabling GPU Support

To use GPU resources through Colab remember to change the runtime to GPU:

1. From the "Runtime" menu select "Change Runtime Type"
2. Choose "GPU" from the drop-down menu
3. Click "SAVE"

This will reset the notebook and probably ask you if you are a robot (these instructions assume you are not). Running

!nvidia-smi

in a cell will verify this has worked and show you what kind of hardware you have access to.

3. Google Drive Files and Resources

Google Drive files can be accessed by mounting your account in the notebook, this is a convenient way of accessing stored data. Add the following to a cell to mount your directory, you will be asked to authenticate through Google once you run it:

from google.colab import drive

drive.mount('/content/drive')
!ls -l "/content/drive/My Drive/Colab Notebooks"

Instal Local Environment

Instructions to setup the (local) Python development environment are reported below, either using venv or conda (for Anaconda Python distribution):

Set up environment using conda

If you are using Anaconda Python distribution, it is possible to re-create the entire virtual (conda) environment using the monai_bootcamp.yml (YAML) file:

conda env create -f monai_bootcamp.yml

This will create a new environment named monai-bootcamp, with all the required packages. To activate the environment:

conda activate monai-bootcamp

Set up environment using venv

The venv module provides support for creating lightweight “virtual environments” with their own site directories, optionally isolated from system site directories. Each virtual environment has its own Python binary (which matches the version of the binary that was used to create this environment) and can have its own independent set of installed Python packages in its site directories.

Note: The venv module is part of the Python Standard Library, so no further installation is required. Python 3.7+ is assumed.

The following 3 steps are required to setup a new virtual environment using venv:

1. Create the environment:

   python -m venv <PATH-TO-VENV-FOLDER>/monai-bootcamp

2. Activate the environment:

source <PATH-TO-VENV-FOLDER>/monai-bootcamp/bin/activate

3. Install the Required Package (using the requirements.txt file):

   pip install -r requirements.txt

Notes on Jupyter Notebook Kernel

Notes: The following instructions only applies to virtual environment created using venv

In order to enable the new venv environment within your default Jupyter server, a new Jupyter Kernel should be added.

In order to do so, the following command should be executed:

python -m ipykernel install --user --prefix <PATH-TO-VENV-FOLDER>/monai-bootcamp --display-name "Python 3 (MONAI Bootcamp 2020)"

This will add a new Python 3 (MONAI Bootcamp 2020) to the list of available Jupyter kernel. Please make sure to select or change this kernel to run the notebooks in this repository.

Further information here

Notes on GPU support on Local machine

If your local machine has GPU support, please follow the instructions on the official PyTorch documentation on how to install PyTorch with GPU support in your local environment, depending on your system configuration.