Unraid support thread for Jupyter-CTPO and Jupyter-TPO (short FAQ in next message)
Changelog:
- 20240421: Release with support for CUDA 12.3.2, TensorFlow 2.16.1, PyTorch 2.2.2 and OpenCV 4.9.0
- 20231120: Initial Release, with support for CUDA 11.8.0, TensorFlow 2.12.0, PyTorch 2.0.1 and OpenCV 4.7.0.
Project details: https://github.com/Infotrend-Inc/CTPO/
For Jupyter-CTPO: If you have multiple GPUs with some allocated to VMs, make sure to change --gpus all (see below)
The default password for the notebook is iti
The system is run as the jupyter user (has sudo privileges) and /iti is where you can place your weights and other files to support your development.
Jupyter-TPO (> 5GB download)
Unraid compatible Jupyter Lab (Python kernel) container with CPU-ready Tensorflow, PyTorch, OpenCV, etc.
Jupyter-CTPO (> 19GB download)
Unraid compatible Jupyter Lab (Python kernel) container with GPU-optimized Tensorflow, PyTorch, OpenCV, etc.
This GPU-bound container requires the Nvidia driver installed on your Unraid server with support for Docker. This driver needs to support the version of CUDA in use by this container. The template adds --gpus all to the way the docker container is started to get access to the GPU(s).
The Unraid Nvidia Plugin is available in the community apps store
If you have multiple GPUs in your system with some allocated to VMs, make sure to replace --gpus all with --runtime=nvidia and follow the steps below to set the NVIDIA_DRIVER_CAPABILITIES and NVIDIA_VISIBLE_DEVICES variables to only give the container access to selected GPUs.