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
runtime.txt file if using
requirements.txt instead of
What versions of Python (or R or Julia…) are supported?#
3.7 (added in 0.7, default in 0.8)
3.6 (default in 0.7 and earlier)
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 3.7 environment.
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.
The default version of R is currently R 3.6.1. You can select the version of R you want to use by specifying it in the runtime.txt file.
We support R versions 3.4, 3.5 and 3.6.
Why is my repository failing to build with
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
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
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
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
flag for each variable that you want to define.
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
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
-E flag (don’t confuse this with
-e flag for environment variables), and run repo2docker on a
repo2docker -E my-repository/
This builds a Docker container from the files in that repository
(using, for example, a
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 (
this will open in a local web browser, 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).
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
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.
Why does repo2docker need to exist? Why not use tool like source2image?#
The Jupyter community believes strongly in building on top of pre-existing tools whenever possible (this is why repo2docker buildpacks largely build off of patterns that already exist in the data analytics community). We try to perform due-diligence and search for other communities to leverage and help, but sometimes it makes the most sense to build our own new tool. In the case of repo2docker, we spent time integrating with a pre-existing tool called source2image. This is an excellent open tool for containerization, but we ultimately decided that it did not fit the use-case we wanted to address. For more information, here is a short blog post about the decision and the reasoning behind it.