Jupyterlab has to be accessed remotely via its IP address from other nodes. If you don’t have an own setup with a NVIDIA GPU, check out Saturn Cloud for a free GPU-powered Jupyter solution.
JUPYTERLAB DOCKER INSTALL
Both the Desktop image and Server Install image can be used, here we go with a minimal installation of the Desktop image which includes a browser and neccessary packages but no office packages. The most recent Ubuntu version 22.04 LTS is not at the moment of updating this guide, as some GPU-libraries require CUDA 11.6 which is not yet stable for Ubuntu 22.04. Canonical announced that from version 19 on, they come with a better support for Kubernetes and AI/ML developer experience, compared to 18.04 LTS, so we suggest 20.04. This setup was tested for Ubuntu 18.04 LTS, 19.10 and 20.04 LTS. (Optional) Deployment in a Docker Swarm 8. Install Docker, Docker-compose and NVIDIA Docker 5. (Optional) Validation of the CUDA Installation on the host system 4. Installation of CUDA and NVIDIA drivers 3.
Some time :) The installation consists of the following steps:ġ. A computer including a NVIDIA GPU (a desktop PC is recommended) 2. The corresponding code repository is iot-salzburg/GPU-Jupyter and the images are provided on Dockerhub. GPU utilization within a Terminal in Jupyterlab that runs within a Docker container.
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