Public | Automated Build

Last pushed: 6 months ago
Short Description
All dependencies for running fastai source code
Full Description

Docker for fast.ai Course 1

A Jupyter environment for fast.ai's Deep Learning MOOC at http://course.fast.ai.

Runs a Jupyter notebook on port 8888 with the default password used in the course ('dl_course').

Uses CPUs by default and NVIDIA GPUs when run with nvidia-docker.

The container comes with:

Usage

CPU Only

docker run -it -p 8888:8888 deeprig/fastai-course-1

With GPU

nvidia-docker run -it -p 8888:8888 deeprig/fastai-course-1

Data management

Docker containers are designed to be ephemeral, so if you need persistent data for Kaggle competitions you should download it on your local machine and mount the directory as a host volume when you run the container.

For example, if your data directory is at /Users/yourname/data, start your container with this command:

docker run -it -p 8888:8888 -v /Users/yourname/data:/home/docker/data deeprig/fastai-course-1

Your local data directory will now be visible in the container at /home/docker/data.

Installing packages

All packages should ideally be part of the Dockerfile. If something is missing, please open an issue or submit a PR to update the Dockerfile. If you need to install something as a workaround, follow the steps below:

  1. Get a shell into the running container with docker exec -it <container_name> /bin/bash
  2. sudo apt-get update && sudo apt-get install package_name

Running on AWS

I've build preconfigured AMIs for us-west-2 that include Docker and nvidia-docker (for p2 instances). You can spin up instances based on them using the commands below:

GPU instance

# spin up a p2.xlarge instance
docker-machine create \
  --driver amazonec2 \
  --amazonec2-region='us-west-2' \
  --amazonec2-root-size=50 \
  --amazonec2-ami='ami-e03a8480' \
  --amazonec2-instance-type='p2.xlarge' \
  fastai-p2

# open Jupyter port 8888
aws ec2 authorize-security-group-ingress --group-name docker-machine --port 8888 --protocol tcp --cidr 0.0.0.0/0

# open an SSH shell on the new machine
docker-machine ssh fastai-p2

# (on the remote machine fastai-p2) run Jupyter interactively
nvidia-docker run -it -p 8888:8888 deeprig/fastai-course-1

# (on your local machine) get the IP of the new machine:
docker-machine ip fastai-p2

Open http://[NEW_MACHINE_IP]:8888 in your browser to view notebooks.

CPU instance

# spin up a t2.xlarge instance
docker-machine create \
  --driver amazonec2 \
  --amazonec2-region='us-west-2' \
  --amazonec2-root-size=50 \
  --amazonec2-ami='ami-a073cdc0' \
  --amazonec2-instance-type='t2.xlarge' \
  fastai-t2

# open Jupyter port 8888
aws ec2 authorize-security-group-ingress --group-name docker-machine --port 8888 --protocol tcp --cidr 0.0.0.0/0

# open an SSH shell on the new machine
docker-machine ssh fastai-t2

# (on the remote machine fastai-t2) run Jupyter interactively
docker run -it -p 8888:8888 deeprig/fastai-course-1

# (on your local machine) get the IP of the new machine:
docker-machine ip fastai-t2

Open http://[NEW_MACHINE_IP]:8888 in your browser to view notebooks.

Docker Pull Command
Owner
jlrk
Source Repository