Quickstart Guide

To start with a local host system, the MONAI Toolkit JupyterLab instance can be started with a ready-to-open website link with:

            

            
docker run --gpus all -it --rm --ipc=host --net=host nvcr.io/nvidia/clara/monai-toolkit:1.1
        

After the JupyterLab App is started, follow the onscreen instruction and open the URL in a web browser.

Note: By default, the container use the host system’s all GPU resources, networking and inter-process communication (IPC) namespace. Multiple notebooks require a large shared memory size for the container to run comprehensive workflows. For more information, please refer to changing the shared memory segment size

To run the MONAI Toolkit container with the bash shell, issue the command below to start the prebuilt container:

            

            
docker run  --gpus all -it --rm --ipc=host --net=host \
            nvcr.io/nvidia/clara/monai-toolkit:1.1 \
            /bin/bash
        

If you use Torch multiprocessing for multi-threaded data loaders, the default shared memory segment size that the container runs with may not be enough. To address this, you can increase the shared memory size by using one of the following options:

1. Use the –ipc=host flag when running your container.

2. Use the –shm-size= flag followed by the desired size of the shared memory segment.

For example, to increase the shared memory segment size to 16GB, you can issue the following command:

            

            
docker run --gpus all -it --rm --ipc=host --net=host --shm-size=16g \
            nvcr.io/nvidia/clara/monai-toolkit:1.1 \
            /bin/bash
        

By increasing the shared memory size, you can avoid issues such as “OSError: [Errno 12] Cannot allocate memory” when using multi-threaded data loaders in Torch.

Users can access the JupyterLab instance by simply typing the host machine’s URL or IP address in the web browser with the port number (default: 8888). A token may be required to log in for the first time. Users can find the token in the system which hosts the MONAI Toolkit container by looking for the code after /?token= on the screen.

JupyterLab is started on port 8888 by default. If the user wants to assign another port, the JupyterLab instance can be started by setting the JUPYTER_PORT environment variable:

            

            
-e JUPYTER_PORT=8900
        

For example:

            

            
docker run  --gpus all -it --rm --ipc=host --net=host \
            -e JUPYTER_PORT=8900 \
            nvcr.io/nvidia/clara/monai-toolkit:1.1 \
            /opt/docker/runtoolkit.sh
        

Note: More details about running docker commands are explained in the Running A Container chapter in the NVIDIA Containers For Deep Learning Frameworks User’s Guide and specify the registry, repository, and tags.

To mount a custom data directory, the users can use -v to mount the drive(s) and override the default data directory environment variable MONAI_DATA_DIRECTORY used by many notebooks. For example:

            

            
docker run  --gpus all -it --rm --ipc=host --net=host \
            -v ~/workspace:/workspace \
            -e MONAI_DATA_DIRECTORY=/workspace/data \
            nvcr.io/nvidia/clara/monai-toolkit:1.1 \
            /opt/docker/runtoolkit.sh
        

/opt/docker/runtoolkit.sh provides an entry point for the user to configure Jupyter Lab with the default settings. However, it cannot cover all use scenarios and APIs of Jupyter. The user can run the Jupyter Lab command in the MONAI Toolkit container and configure the instance simply by:

            

            
docker run --gpus all -it --rm --ipc=host --net=host nvcr.io/nvidia/clara/monai-toolkit jupyter lab