/py-faster-rcnn-docker

Docker image with py-faster-rcnn

Primary LanguageJupyter Notebook

1) Install docker

sudo apt-get install \
  apt-transport-https \
  ca-certificates \
  curl \
  software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo apt-key fingerprint 0EBFCD88
sudo apt-get update
sudo apt-get install docker-ce

To test docker run sudo docker run hello-world

Configure docker to run as non-privileged user

sudo groupadd docker
sudo usermod -aG docker $USER
docker run hello-world

2) Install nvidia-docker

This allows Docker to find your GPU(s).

Note: nvidia-docker requires NVIDIA drivers. Howevever, if you have CUDA installed, you already have the drivers.

wget -P /tmp https://github.com/NVIDIA/nvidia-docker/releases/download/v1.0.1/nvidia-docker_1.0.1-1_amd64.deb
sudo dpkg -i /tmp/nvidia-docker*.deb && rm /tmp/nvidia-docker*.deb

Test nvidia-docker run nvidia-docker run --rm nvidia/cuda nvidia-smi

3) Clone this repository

git clone https://github.com/ToddMorrill/py-faster-rcnn-docker.git

4) Build the Faster RCNN Docker image

Change into the directory with the Dockerfile and run nvidia docker build -t caffe/py-faster-rcnn .

5) Add _init_paths.py file to /path/to/your/workingdir on your local host

6) Run the new image as a Docker container

nvidia-docker run -d -v "/path/to/your/workingdir:/root/py-faster-rcnn/working-dir" -p 8888:8888 caffe/py-faster-rcnn sh -c "jupyter notebook"

7) Test that Faster RCNN is working by running the demo.ipynb script in the tools folder

  • Navigate to localhost:8888 to see Jupyter Notebook
  • Launch the tools/demo.ipynb (make sure your kernel says py-faster-r-cnn-env)

8) Begin your work in the working-dir

  • Note: anything in this folder will be synced with your host machine folder that you specified