Frequently Asked Questions (FAQ)

A collection of frequently asked questions with answers. If you have a question and have found an answer, send a PR to add it here!

How should I specify another version of Python?

One can specify a Python version in the environment.yml file of a repository or runtime.txt file if using requirements.txt instead of environment.yml.

What versions of Python (or R or Julia…) are supported?

Python

Repo2docker officially supports the following versions of Python (specified in your environment.yml or runtime.txt file):

  • 3.7 (added in 0.7, default in 0.8)
  • 3.6 (default in 0.7 and earlier)
  • 3.5
  • 2.7

Additional versions may work, as long as the base environment can be installed for your version of Python. The most likely source of incompatibility is if one of the packages in the base environment is not packaged for your Python, either because the version of the package is too new and your chosen Python is too old, or vice versa.

I Python 2.7 is specified, a separate environment for the kernel will be installed with Python 2. The notebook server will run in the default Python 3.6 environment.

Julia

All Julia versions since Julia 0.7.0 are supported via a Project.toml file, and this is the recommended way to install Julia environments. Julia versions 0.6.x and earlier are supported via a REQUIRE file.

R

Only R 3.4.4 is currently supported, which is installed via apt from the ubuntu bionic repository.

Why is my repository is failing to build with ResolvePackageNotFound ?

If you used conda env export to generate your environment.yml it will generate a list of packages and versions of packages that is pinned to platform specific versions. These very specific versions are not available in the linux docker image used by repo2docker. A typical error message will look like the following:

Step 39/44 : RUN conda env update -n root -f "environment.yml" && conda clean -tipsy && conda list -n root
---> Running in ebe9a67762e4
Solving environment: ...working... failed

ResolvePackageNotFound:
- jsonschema==2.6.0=py36hb385e00_0
- libedit==3.1.20181209=hb402a30_0
- tornado==5.1.1=py36h1de35cc_0
...

We recommend to use conda env export --no-builds -f environment.yml to export your environment and then edit the file by hand to remove platform specific packages like appnope.

See How to automatically create a environment.yml that works with repo2docker for a recipe on how to create strict exports of your environment that will work with repo2docker.

Can I add executable files to the user’s PATH?

Yes! With a postBuild - Run code after installing the environment file, you can place any files that should be called from the command line in the folder ~/.local/. This folder will be available in a user’s PATH, and can be run from the command line (or as a subsequent build step.)

How do I set environment variables?

To configure environment variables for all users of a repository use the start configuration file.

When running repo2docker locally you can use the -e or --env command-line flag for each variable that you want to define.

For example jupyter-repo2docker -e VAR1=val1 -e VAR2=val2 ...

Can I use repo2docker to bootstrap my own Dockerfile?

No, you can’t.

If you pass the --debug flag to repo2docker, it outputs the intermediate Dockerfile that is used to build the docker image. While it is tempting to copy this as a base for your own Dockerfile, that is not supported & in most cases will not work. The --debug output is just our intermediate generated Dockerfile, and is meant to be built in a very specific way. Hence the output of --debug can not be built with a normal docker build -t . or similar traditional docker command.

Check out the binder-examples GitHub organization for example repositories you can copy & modify for your own use!

Can I use repo2docker to edit a local host repository within a Docker environment?

Yes: use the --editable or -E flag (don’t confuse this with the -e flag for environment variables), and run repo2docker on a local repository:

repo2docker -E my-repository/

This builds a Docker container from the files in that repository (using, for example, a requirements.txt or install.R file), then runs that container, while connecting the working directory inside the container to the local repository outside the container. For example, in case there is a notebook file (.ipynb), this will open in a local webbrowser, and one can edit it and save it. The resulting notebook is updated in both the Docker container and the local repository. Once the container is exited, the changed file will still be in the local repository.

This allows for easy testing of the container while debugging some items, as well as using a fully customizable container to edit notebooks (among others).

Note

Editable mode is a convenience option that will bind the repository to the container working directory (usually $HOME). If you need to mount to a different location in the container, use the --volumes option instead. Similarly, for a fully customized user Dockerfile, this option is not guaranteed to work.

Why is my R shiny app not launching?

If you are trying to run an R shiny app using the /shiny/folder_containing_shiny url option, but the launch returns “The application exited during initialization.”, there might be something wrong with the specification of the app. One way of debugging the app in the container is by running the rstudio url, open either the ui or server file for the app, and run the app in the container rstudio. This way you can see the rstudio logs as it tries to initialise the shiny app. If you a missing a package or other dependency for the container, this will be obvious at this stage.