This container is no longer supported, and has been deprecated in favor of:

https://github.com/salinaaaaaa/NVIDIA-GPU-Tensor-Core-Accelerator-PyTorch-OpenCV

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Machine Vision Container: Docker, TensorFlow, TensorRT, PyTorch, Rapids.ai: CuGraph, CuML, and CuDF, CUDA 10, OpenCV, CuPy, and PyCUDA

GPU Acclerated computing container for computer vision applications, that are reproducible across environments.

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Features

• Contains most of the features in NVIDIA Rapids.ai

• cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data.

• cuML is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects.

• cuGraph library is a collection of graph analytics that allow you to vizualize what is going on inside graph-based neural networks.

• Learn to vizualize what is going on inside graph-based neural networks with cuGraph, see here for more!

• NVIDIA TensorRT inference accelerator and CUDA 10

• PyTorch 1.0

• PyCUDA 2018.1.1

• CuPy:latest

• Tensorflow for GPU v1.13.1 & TensorBoard

• OpenCV v4.0.1 for GPU

• Ubuntu 18.04 so you can 'nix your way through the cmd line!

• cuDNN7.4.1.5 for deeep learning in CNN's

• Hot Reloading: code updates will automatically update in container from /apps folder.

• TensorBoard is on localhost:6006 and GPU enabled Jupyter is on localhost:8888.

• Python 3.6.7

• Only Pascal and Turing arch are supported

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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

How to run this container:

Step 1

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

Step 2

Run the image, mount the volumes for Jupyter and app folder for your fav IDE, and finally the expose ports 8888 for TF1, and 6006 for TensorBoard.

docker run --rm -it --runtime=nvidia --user $(id -u):$(id -g) --group-add container_user --group-add sudo -v "${PWD}:/apps" -v $(pwd):/tf/notebooks -p 8888:8888 -p 0.0.0.0:6006:6006 <container name>

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

• 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

Step 4: How to launch TensorBoard

(It helps to use multiple tabs in cmd line, as you have to leave at least 1 tab open for TensorBoard@:6006)

• Demonstrates the functionality of TensorBoard dashboard

• Exec into container if you haven't, as shown above:

• Get the <container id>:

docker ps

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

• Then run in cmd line:

tensorboard --logdir=//tmp/tensorflow/mnist/logs

• Type in: cd / to get root.

Then cd into the folder that hot reloads code from your local folder/fav IDE at: /apps/apps/gpu_benchmarks and run:

python tensorboard.py

• Go to the browser and navigate to: localhost:6006

Step 5: Run tests to prove container based GPU perf

• Demonstrate GPU vs CPU performance:

• Exec into the container if you haven't, and cd over to /tf/notebooks/apps/gpu_benchmarks and run:

• CPU Perf:

python benchmark.py cpu 10000

• CPU perf should return something like this:

Shape: (10000, 10000) Device: /cpu:0 Time taken: 0:00:03.934996

• GPU perf:

python benchmark.py gpu 10000

• GPU perf should return something like this:

Shape: (10000, 10000) Device: /gpu:0 Time taken: 0:00:01.032577

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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

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NVIDIA Rapids is a WIP, see status of pull reqs, here.