To simplify installation process, you can deploy a container (~virtual machine) with all dependencies pre-installed.

tl;dr dockerhub url

Install Docker

We recommend you to use either native docker (recommended for linux) or kitematic(recommended for windows).

• Installing kitematic, a simple interface to docker (all platforms)
• Pure docker: Guide for windows, linux, or macOS.
• If you want to use your GPU make sure you have nvidia-docker and NVidia driver + CUDA 10.2 installed

Below are the instructions for both approaches.

Kitematic

Find dmittov/practical_rl in the search menu. Download and launch the container.

Click on "web preview" screen in the top-right or go to settings, ports and find at which port your jupyter is located, usually 32***.

Native

docker run --rm -it -v /path/to/your/repo:/notebooks -p <local_port>:8888 dmittov/practical_rl:spring2020-cpu

For example, docker run --rm -it -v /home/myuser/Documents/practical_rl:/notebooks -p 8888:8888 dmittov/practical_rl:spring2020-cpu

Then you can access your jupyter in a browser at localhost:<local_port>/?token=<token_you_see_in_container_logs>, e.g. localhost:8888/?token=ad1a5a0aab43efb47a9a805388fcf508d0b5f84a16e4542b&token=ad1a5a0aab43efb47a9a805388fcf508d0b5f84a16e4542b

GPU

docker run --rm -it -v /path/to/your/repo:/notebooks -p <local_port>:8888 --gpus all dmittov/practical_rl:spring2020-cuda-10.2

Manual

Build container

docker build -t practical_rl --build-arg device=cpu .

to build GPU version

docker build -t practical_rl --build-arg device=gpu .

Run it

$ docker run --rm -it -v <local_dir>:/notebooks -p <local_port>:8888 practical_rl

to run GPU version

$ docker run --rm -it -v <local_dir>:/notebooks -p <local_port>:8888 --gpus all practical_rl

examples:

$ docker run --rm -it -v `pwd`:/notebooks -p 8888:8888 practical_rl

Copy the token from console and run http://localhost:8888/?token=