⚠️ This repo has been deprecated. Please use the Deep Learning Ultra container instead.

Deprecated

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For updated code goto: https://github.com/salinaaaaaa/Deep-Learning-Ultra

NVIDIA GPU/Tensor Core Accelerator for PyTorch, PyTorch Geometric, TF2, Tensorboard + OpenCV A complete computer vision container for deep learning that includes Jupyter notebooks with built-in code hinting, Miniconda, CUDA 11.8, TensorRT inference accelerator for Tensor cores, CuPy (GPU drop in replacement for Numpy), PyTorch, PyTorch Geometric for geomteric learning and/or Graph Neural Networks, TendorFlow 2, Tensorboard, and OpenCV (complied for CUDA) for accelerated workloads on NVIDIA Tensor cores and GPUs. Roadmap: Adding Dask for GPU based image preprosccing and pipelines, as well as model mgm't, and model serving and monitoring.

• There are working notebook examples on how to wire up, both Torch and TF2 to Tensorboard in /app folder.

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Features

• Miniconda: Accelerated Python, version 3.11
• CuPy: GPU accelerated drop in replacement for Numpy
• OpenCV, latest version which is made to compile for CUDA GPUs in the container. Depending upon your GPU you may have to change -DCUDA_ARCH_BIN=7.5 in the OpenCV flags within the Dockerfile, and rebuild the image.
• PyTorch 2.0 with Torchvision for GPU, latest version
• PyTorch geometric for GNN's
• Captum to explain Torch models
• Tensorflow 2 with Keras
• Tensorboard for both Torch and TF2
• NVIDIA TensorRT inference accelerator for Tensor core access and CUDA 11 for GPUs
• Repo includes two working notebook examples on how to wire up Torch and TF2 to TensorBoard, located in /app folder

Built in code hinting in Jupyter Notebook

Press tab to see what methods you have access to by clicking tab.

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Before you begin (This might be optional)

Link to nvidia-docker2 install: Tutorial

You must install nvidia-docker2 and all it's deps first, assuming that is done, run:

sudo apt-get install nvidia-docker2

sudo pkill -SIGHUP dockerd

sudo systemctl daemon-reload

sudo systemctl restart docker

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How to run this container:

Step 1

docker build -t <container name> . < note the . after

If you get an authorized user from the docker pull cmd inside the container, try:

$ docker logout

...and then run it or pull again. As it is public repo you shouldn't need to login.

Step 2

Run the image, mount the volumes for Jupyter and app folder for your fav IDE, and finally the expose ports 8888 for Jupyter Notebook:

docker run --rm -it --gpus all --user $(id -u):$(id -g) --group-add container_user --group-add sudo -v "${PWD}:/app" -p 8888:8888 -p 6006:6006 <container name>

:P If on Windows 10:

winpty docker run --rm -it --gpus all -v "/c/path/to/your/directory:/app" -p 8888:8888 -p 6006:6006 <container name>

Disclaimer: You should be able to utilize the runtime argument on Docker 19+ as long as it is installed and configured in the daemon configuration file:

Install nvidia-docker2 package https://github.com/nvidia/nvidia-docker/wiki/Installation-(version-2.0)#ubuntu-distributions-1

Step 3: Check to make sure GPU drivers and CUDA is running

• Open another ssh tab, and exec into the container and check if your GPU is registering in the container and CUDA is working:

• Get the container id:

docker ps

• Exec into container:

docker exec -u root -t -i <container id> /bin/bash

• Check if NVIDIA GPU DRIVERS have container access:

nvidia-smi

• Check if CUDA is working:

nvcc -V

Initialize Tensorboard

• Exec into the container as stated above, and run the following:

tensorboard --logdir=//app --bind_all

• You will recieve output that looks somnething like this:

TensorBoard 2.1.0 at http://af5d7fc520cb:6006/

Just replace af5d7fc520cb with the word localhost and launch in the browser, then you will see:

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Known conflicts with nvidia-docker and Ubuntu

AppArmor on Ubuntu has sec issues, so remove docker from it on your local box, (it does not hurt security on your computer):

sudo aa-remove-unknown

You may have to do this to get the NVIDIA container runtime to work

Install the the nvidia-conatiner-runtime package, install and set-up config is here: https://github.com/NVIDIA/nvidia-container-runtime.

sudo apt-get install nvidia-container-runtime

sudo vim /etc/docker/daemon.json

Then , in this daemon.json file, add this content:

{
  "default-runtime": "nvidia"
  "runtimes": {
    "nvidia": {
      "path": "/usr/bin/nvidia-container-runtime",
       "runtimeArgs": []
     }
  }
}

sudo systemctl daemon-reload

sudo systemctl restart docker

Other misc troubleshooting

Method 2: Install the container runtime: https://github.com/NVIDIA/nvidia-container-runtime#ubuntu-distributions

Modify the config file: https://github.com/NVIDIA/nvidia-container-runtime#daemon-configuration-file

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