A modified version of zimovnov/coursera-aml-docker: https://github.com/ZEMUSHKA/coursera-aml-docker, which now support GPU
Docker container with Jupyter Environment for Coursera "Advanced Machine Learning" specialization: https://www.coursera.org/specializations/aml
For Mac: https://docs.docker.com/docker-for-mac/install/
For Windows (64bit Windows 10 Pro, Enterprise and Education): https://docs.docker.com/docker-for-windows/install/#what-to-know-before-you-install
For Windows (older versions): https://docs.docker.com/toolbox/toolbox_install_windows/
For Linux: https://docs.docker.com/engine/installation/
make sure you have fulfill the same prerequisites as the Nvidia Docker: https://github.com/NVIDIA/nvidia-docker/wiki/Installation-(version-2.0)#prerequisites
You will also have to install NVIDIA GPU driver, CUDA toolkit and CuDNN (requires registration with NVIDIA) in your container in order for TensorFlow to work with your GPU: https://www.tensorflow.org/versions/r1.2/install/install_linux#nvidia_requirements_to_run_tensorflow_with_gpu_support
First run docker pull zimovnov/coursera-aml-docker to pull the latest version of image.
Run using docker run -it -p 127.0.0.1:8080:8080 --name coursera-aml-1 zimovnov/coursera-aml-docker.
This command downloads the prepared image from a public hub and starts a Jupyter for you.
Let this command continue running in the terminal while you work with Jupyter.
You can now navigate to http://localhost:8080 in your browser to see Jupyter.
This "stop and start" scenario is useful when you want to take a break and turn off your host machine.
Save your work inside the container, then run docker stop coursera-aml-1 in different terminal window
to stop a running container. You will be able to start it later.
Run docker start -a coursera-aml-1 to run previously stopped container and attach to its stdout.
You can continue to work where you left off.
You might want to make a checkpoint of your work so that you can return to it later. Think of it as a backup or commit in version control system.
You will first have to stop the container following instructions above.
Now you need to save the container state so that you can return to it later:
docker commit coursera-aml-1 coursera-aml-snap-1.
You can make sure that it's saved by running docker images.
If you want to continue working from a particular checkpoint, you should run a new container from your
saved image by executing docker run -it -p 127.0.0.1:8080:8080 --name coursera-aml-2 coursera-aml-snap-1.
Notice that we incremented index in the container name, because we created a new container.
Check out https://github.com/Cheukting/GCP-GPU-Jupyter for using Terraform to launch this docker app to GCP