osscar-jupyter-docker

A docker solution for OSSCAR web applications.

Requirements

• Docker version 17 or later

Setup development environment

We setup the development environment in a Docker container with the following command.

• make init

This command gets the resources for training and testing, and then prepares the Docker image for the experiments. After creating the Docker image, you run the following command.

• make create-container

The above command creates a Docker container from the Docker image which we create with make init, and then login to the Docker container. Now we made the development environment. For create and evaluate the model, you run the following command.

Development with Docker container

This section shows how we develop with the created Docker container.

Edit source code

Most of the source codes of this project, osscar-jupyter-docker are stored in the osscar_jupyter_docker directory. Generated Docker container mounts the project directory to /work of the container and therefore when you can edit the files in the host environment with your favorite editor such as Vim, Emacs, Atom or PyCharm. The changes in host environment are reflected in the Docker container environment.

Update dependencies

When we need to add libraries in Dockerfile or requirements.txt which are added to working environment in the Docker container, we need to drop the current Docker container and image, and then create them again with the latest setting. To remove the Docker the container and image, run make clean-docker and then make init-docker command to create the Docker container with the latest setting.

Login Docker container

Only the first time you need to create a Docker container, from the image created in make init command. make create-container creates and launch the osscar_jupyter_docker container. After creating the container, you just need run make start-container.

Logout from Docker container

When you logout from shell in Docker container, please run exit in the console.

Run linter

When you check the code quality, please run make lint

Run test

When you run test in tests directory, please run make test

Sync data source to local data directory

When you want to download data in remote data sources such as Amazon S3 or NFS, sync-from-remote target downloads them.

Sync local data to remote source

When you modify the data in local environment, sync-to-remote target uploads the local files stored in data to specified data sources such as S3 or NFS directories.

Show profile of Docker container

When you see the status of Docker container, please run make profile in host machine.

Use Jupyter Notebook

To launch Jupyter Notebook, please run make jupyter in the Docker container. After launch the Jupyter Notebook, you can access the Jupyter Notebook service in http://localhost:8999.

Run formatter

When you format project's codes, please run make format. More details of black inhttps://github.com/psf/black

Credits

This package was created with Cookiecutter and the cookiecutter-docker-science project template.