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Docker jupyterlab5/26/2023 sudo apt update & sudo apt upgrade -y & sudo apt dist-upgrade -y & sudo apt install -f & sudo apt autoremove -y sudo apt-get install build-essential gcc g++ make binutils net-tools sudo reboot Therefore, upgrade and install basic packages and dependencies. The preferred way to do so is to adapt the netplan configuration. Jupyterlab has to be accessed remotely via its IP address from other nodes. If you don’t have your own setup with an NVIDIA GPU, check out Saturn Cloud for a free GPU-powered Jupyter solution. 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 necessary packages but no office packages. Canonical announced that from version 19 on, they come with better support for Kubernetes and AI/ML developer experience, compared to 18.04 LTS, so we suggest 20.04. This guide 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. A computer including an NVIDIA GPU (a desktop PC or server) The installation consists of the following steps:ġ. 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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