martial

This looks to me like nvcc did not find the proper support for your hardware. What is your GPU?

I have made some mods to the reverse proxy to use one of the local IPs past the point of entry within the network (via tailscale) and it works again.

I have recently upgraded to 6.12.1 I used to have Tailscale running and a reverse proxy (NPM) set for the dashboard with a certificate to the TailScale IP over a DNS at CloudFlare. I cannot access the UI from the tailscale-enabled url anymore. I can access other services over their tailscale url just fine, just the main url is not working, so my dashboard is only accessible over the local network. I see that in the "Management Access" page I can see "Local access URLs" and I can not edit those values. I wonder if the webservice is refusing any url that is not in that list. Any idea on how to fix this?

Unfortunately, not easily. The base container is always going to be the same size, it is the size of building and installing the independent components. My solution to avoid the size limitation for docker on Unraid has been to not use the docker.img which has a limited disk space and instead use a directory which reside (preferred) on a cache disk to have all the containers, and therefore all the free space on that directory (disk) will be available for your containers. This link ought to help with that process. Thanks for this question. Hope this helps

It is a good question. The base container is "FROM nvidia:cuda-" to get the GPU optimizations for Nvidia devices. I do not own a Coral TPU, so I have not tried to see if I could build the container for this hardware, but if you are interested in trying, the source for the container build is public (see the first post).

Thanks for this information, would you be able to post the steps involved and I will add them to the FAQ?

Short FAQ --- Changing the default password In a running instance of the container, after login in, Start a "New" "Terminal" in the currently running NoteBook. Grant your user permission to modify the file where the password is stored using sudo chown jupyter.users /home/jupyter/.jupyter/jupyter_notebook_config.json (to prevent a possible permission error --not needed with update done on Container Image released on 20220402) From the shell prompt, type jupyter notebook password and entering your desired new password. In the Unraid Docker UI, Stop the container, then Start it again. When accessing the WebUI your new password will let you in. --- Shared Memory Please note that ML frameworks use shared memory. The amount of shared memory provided to your container can be modified by adding --shm-size=2G (adapt to what you are comfortable giving the container) in the "Extra Parameters:" section. --- Get versions information for OpenCV and TensorFlow Once the Notebook is started, you can create a "New" "Python3" notebook and use import tensorflow as tf import cv2 print(cv2.__version__) print(tf.__version__) --- List of pre-installed pip3 packages and their versions (including PyTorch) In a "New" "Terminal" (we recommend switching to a bash prompt), you can run /tmp/tf_info.sh ---Obtain more complete build details Check the container components version you are using (CUDA --if applicable--, TensorFlow, OpenCV) and compare it against the list of available ones on the DockerHub page (https://hub.docker.com/r/datamachines/jupyter_to-unraid or https://hub.docker.com/r/datamachines/jupyter_cto-unraid) and search the tags for the right match. For CUDA 11.3.1, Tensorflow 2.8.0, OpenCV 4.5.5, you will find 11.3.1_2.8.0_4.5.5-20220318 this value is the same value as the one of the base container those are built "FROM" and be seen in https://hub.docker.com/r/datamachines/tensorflow_opencv and https://hub.docker.com/r/datamachines/cudnn_tensorflow_opencv There you will see tables linking you to further details about those builds. --- Check available GPU resources (GPU version only) To confirm the GPU is accessible from within the container, from a "Terminal" run nvidia-smi to see the GPU resources available to you. If nothing appears, make sure the Nvidia driver is installed and configured and that the "Extra Parameters" of your container's settings contains --gpus all (added in the default template) --- Persisting user's installs/changes If you install software or pip packages, those will not persist a container restart. For further details, see https://github.com/datamachines/cuda_tensorflow_opencv/issues/30#issuecomment-1120455956 The following is written for the "Jupyter-TensorFlow_OpenCV" container, so please adapt as required for its "Jupyter-CuDNN_TensorFlow_OpenCV" counterpart. Persisting your "Custom" installation requires you to have the container still running and access to a Terminal on your Unraid server. - [terminal] Do a docker ps to confirm "Jupyter-TensorFlow_OpenCV" is running. - [terminal] run docker commit Jupyter-TensorFlow_OpenCV jupyter_to-custom:latest to create a new image named "jupyter_to-custom:latest" based on your changes - [terminal] confirm it is showing in a docker images listing - [WebUI] the "Docker" Tab's "Advanced View" will now show you an "Orphan Image", so it is a matter of creating a new template for this image in the /boot/config/plugins/dockerMan directory - [terminal] cd /boot/config/plugins/dockerMan/templates-user followed by cp my-Jupyter-TensorFlow_OpenCV.xml my-Jupyter-TensorFlow_OpenCV-Custom.xml - [WebUI] in the "Docker" tab, "Add Container" and use the newly available "Jupyter-TensorFlow_OpenCV-Custom" one. Edit the "Name" to be Jupyter-TensorFlow_OpenCV-Custom; this is important because if you have a "Jupter-TensorFlow-OpenCV" version already installed, it will overwrite that template when you apply this one otherwise (ie the filename use this "Name"). Change the "Repository" to be the image recently created: jupyter_to-custom:latest. Remove the content of the "Docker Hub URL". Change the "WebUI" port to be one that is not already in use. When clicking "Apply" you should get "The command finished successfully!", otherwise check the settings again. - [WebUI] in the "Docker" tab, you should now have a "Jupyter-TensorFlow_OpenCV-Custom" available to use. A "not available" will be displayed in the "VERSION" column; this is normal: the container can not be updated automatically.

December 2023 update: Please note that a new version under a different location and repository is available. Please search for Jupyter-CTPO and Jupyter-TPO, added to the CA in early December 2023 Unraid support thread for Jupyter-TensorFlow_OpenCV and Jupyter-CuDNN_TensorFlow_OpenCV (short FAQ in next message) For Jupyter-CuDNN_TensorFlow_OpenCV: 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 dmc The system is run as the jupyter user (has sudo privileges) and /dmc is where you can place your weights and other files to support your development. Jupyter-TensorFlow_OpenCV (> 4GB download) Unraid compatible Jupyter Notebook (Python kernel) container with CPU-ready Tensorflow, OpenCV, Pandas, PyTorch, ... Based on https://hub.docker.com/r/datamachines/tensorflow_opencv which is built as part of https://github.com/datamachines/cuda_tensorflow_opencv Jupyter-CuDNN_TensorFlow_OpenCV (> 16GB download) Unraid compatible Jupyter Notebook (Python kernel) container with GPU-optimized Tensorflow and OpenCV, and installations of Pandas, PyTorch, ... 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). Based on https://hub.docker.com/r/datamachines/cudnn_tensorflow_opencv which is built as part of https://github.com/datamachines/cuda_tensorflow_opencv 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. In the next entry, you will find a small FAQ covering the following: - Changing the default password - Shared Memory - Get versions information for OpenCV and TensorFlow - List of pre-installed pip3 packages and their versions (including PyTorch) - Obtain more complete build details - Check available GPU resources (GPU version only) - Persisting user's installs/changes Changelog: - 20220815: new release: (CUDA 11.3.1) TensorFlow 2.9.1, OpenCV 4.6.0 and PyTorch 1.12.1 - 20220530: new release: (CUDA 11.3.1) TensorFlow 2.9.1, OpenCV 4.5.5 and PyTorch 1.11 (PyTorch is now built from source) - 20220525: new release: (CUDA 11.3.1) TensorFlow 2.9.1, OpenCV 4.5.5 - 20220521: new release: (CUDA: 11.3.1) TensorFlow 2.9.0 and OpenCV 4.5.5 - 20220510: new release: (CUDA: 11.3.1) TensorFlow 2.8.0 and OpenCV 4.5.5 with updated base containers, including Nvidia's new package signing key - 20220509: Added FAQ table of content - 20220422: Multiple GPUs note - 20220403: Updated unraid template - 20220402: Container updated to fix issue preventing change of default password (same components) - 20220331: Initial release: latest is (CUDA: 11.3.1) TensorFlow 2.8.0 and OpenCV 4.5.5

Tailscale recently announced an HTTPS capability, see https://tailscale.com/kb/1153/enabling-https/ As I saw this new capability, it got me wondering: since this container provides the tailscale command line, I am wondering about the complexity of using it to add generation of certificates to support something like a HTTPS upgrading reverse proxy to access other Docker applications installed on an Unraid Server.