zemushka/coursera-aml-docker

Docker container with Jupyter Environment for Coursera "Advanced Machine Learning" specialization.

coursera-aml-docker

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/

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 -p 127.0.0.1:7007:7007 --name coursera-aml-2 coursera-aml-snap-1. Notice that we incremented index in the container name, because we created a new container.

Using GPU in your container (Linux hosts only)

You can use NVIDIA GPU in your container on Linux host machine.

• Setup docker following instructions from NVIDIA: https://github.com/NVIDIA/nvidia-docker/wiki/Installation-(version-2.0)#prerequisites
• In your container replace CPU TensorFlow version with the one that supports GPU:

pip3 uninstall tensorflow
pip3 install tensorflow-gpu==1.2.1

• 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 It can be hard to follow, so you might choose to stick to a CPU version, which is also fine for the purpose of this course.
• TensorFlow provides Docker files with TensorFlow on GPU, but they don't have all the additional dependencies we need, this is for advanced users: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/docker

coursera-aml-docker's Issues

TensorBoard not running from within container? (macOS)

Hey,

I can run the Jupyter notebook from within the Docker container, but in the 2nd week (v2) lessons, it says to go to run the tensorboard command in bash. I run this within the Jupyter terminal, and I can't figure out where to find the webpage...

empty jupyter tree

I followed the instruction, I downloaded the image and then when I ran:
docker run -it -p 127.0.0.1:8080:8080 --name coursera-aml-1 zimovnov/coursera-aml-docker.
the jupyter tree was empty.
Is it the complete and right procedure, or I forgot some steps?

Coursera Deep Learning

I am getting the following error message while running
'import download_utils' on Jupytor note book. But I could see the file "tqdm.py" in intro-to-dl folder. Unable to procede further. Any guidance will be highly appreciated. Thanks

ModuleNotFoundError Traceback (most recent call last)
in
----> 1 import download_utils

~\intro-to-dl\download_utils.py in
7 from functools import wraps
8 import traceback
----> 9 import tqdm_utils
10
11

~\intro-to-dl\tqdm_utils.py in
2 # -- coding: utf-8 --
3 from future import print_function
----> 4 import tqdm
5 tqdm.monitor_interval = 0 # workaround for tqdm/tqdm#481
6

ModuleNotFoundError: No module named 'tqdm'

conda_requirements are not satisfiable

There is a conflict in the conda requirements. Keras 2.0.6 needs tensorflow <= 1.0.1 but tensorflow=1.2.1 is specified.

amls) Quasar:~ subhachandra$ conda install --yes --file conda_requirements.txt
Fetching package metadata .............
Solving package specifications: .

UnsatisfiableError: The following specifications were found to be in conflict:

• keras ==2.0.6 -> tensorflow <=1.0.1 -> protobuf ==3.0.0b2
• tensorflow ==1.2.1
  Use "conda info " to see the dependencies for each package.

Clearer instructions for running on GPU?

Hello,

I've followed the instructions for running on GPU, done tons of research and I've gotten GPU support to work in other containers just not this one. Any assistance? (Week 4 takes a really long time, and I would like to see how it fares with my GPU)

Broken link in README

Link https://www.tensorflow.org/versions/r1.2/install/install_linux#nvidia_requirements_to_run_tensorflow_with_gpu_support mentioned in README is broken.

Starting localhost:8080 puts me in an empty repository

Greetings

I am in a Windows 10 Professional PC using docker.

When I try to mount the image, run the docker and open Jupyter; I found myself in an empty repository.

I am taking the course of NPL too, that course's repository works well doing the same steps.

What can I do to expand the workaround of the bug? Maybe a Docker diagnose will share more information.