cheukting/coursera-aml-docker repository overview

⁠coursera-aml-docker

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

⁠Install Stable Docker Community Edition

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/⁠

⁠Instruction for running it locally

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

⁠Running container for the first time

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.

⁠Stopping and starting the container

This "stop and start" scenario is useful when you want to take a break and turn off your host machine.

⁠Stopping the container

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.

⁠Starting container after stopping

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.

⁠Container checkpoints

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.

⁠Saving container state

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.

⁠Creating new container from previous checkpoint

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.

⁠Running it using Google Cloud Platform

Check out https://github.com/Cheukting/GCP-GPU-Jupyter⁠ for using Terraform to launch this docker app to GCP