https://github.com/salinaaaaaa/NVIDIA-GPU-Tensor-Core-Accelerator-PyTorch-OpenCV
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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Contains most of the features in NVIDIA Rapids.ai
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cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data.
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cuML is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects.
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cuGraph library is a collection of graph analytics that allow you to vizualize what is going on inside graph-based neural networks.
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Learn to vizualize what is going on inside graph-based neural networks with cuGraph, see here for more!
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NVIDIA TensorRT inference accelerator and CUDA 10
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PyTorch 1.0
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PyCUDA 2018.1.1
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CuPy:latest
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Tensorflow for GPU v1.13.1 & TensorBoard
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OpenCV v4.0.1 for GPU
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Ubuntu 18.04 so you can 'nix your way through the cmd line!
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cuDNN7.4.1.5 for deeep learning in CNN's
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Hot Reloading: code updates will automatically update in container from /apps folder.
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TensorBoard is on localhost:6006 and GPU enabled Jupyter is on localhost:8888.
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Python 3.6.7
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Only Pascal and Turing arch are supported
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:
docker build -t <container name> . < note the . after
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>
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Exec into the container and check if your GPU is registering in the container and CUDA is working:
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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
(It helps to use multiple tabs in cmd line, as you have to leave at least 1 tab open for TensorBoard@:6006)
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Demonstrates the functionality of TensorBoard dashboard
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Exec into container if you haven't, as shown above:
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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
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Demonstrate GPU vs CPU performance:
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Exec into the container if you haven't, and cd over to /tf/notebooks/apps/gpu_benchmarks and run:
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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
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
NVIDIA Rapids is a WIP, see status of pull reqs, here.