Session Basics

What is a Session?

Sessions on RenkuLab are web-based user interfaces (like JupyterLab and RStudio) that you can launch to develop and run your code and data workflows. They’re commonly used for exploratory analysis because you can try out short blocks of code before combining everything into a (reproducible) workflow.

You can run JupyterLab or RStudio within a project independently from RenkuLab, but RenkuLab offers the following advantages:

  • Environments hosted in the cloud with a configurable amount of resources (memory, CPU, and sometimes GPU).

  • Environments are defined using Docker, so they can be shared and reproducibly re-created.

  • Auto-saving of work back to RenkuLab, so you can recover when your environment is shut down (this happens automatically after 24 hours of inactivity).

  • A git client pre-configured with your credentials to easily push your changes back to the server.

  • The functionality provided by the renku-python command-line interface (CLI) is automatically available.

What’s in my Session?

  • Your project, which is cloned into the environment on startup.

  • Your data files (if the option Automatically fetch LFS data is selected) that are stored in git LFS*.

  • All the software required to launch the environment and common tools for working with code (git, git LFS, vim, etc.).

  • Any dependencies you specified via conda (environment.yml), using language-specific dependency-management facilities (requirements.txt, install.R, etc.) or installed in the Dockerfile. An exception to this is if project has a pinned Docker image.

  • The renku command-line interface renku-python.

  • The amount of CPUs, memory, and (possibly) GPUs that you configured before launch.

For adding or changing software installed into your project’s session, check out Customizing sessions

Which Session will launch?

The template you choose when you create a project on RenkuLab (or locally call renku init on your project) determines the kind of session that is available to launch. Once it is initialized, your project can easily be modified, for example to install additional libraries into the environment - see Customizing sessions. We provide templates for basic Python, R, and Julia projects. If you wish to use custom templates for your projects, you can build your own! Please refer to the templating documentation.

Starting a new Session

You can start a session from RenkuLab in 3 different ways:

  • Click on the project quick start button. That’s available as a play icon on the project cards, both on the landing and search pages. Or on the top right of the project header on project pages. This action will automatically identify the most recent contribution on the default branch and start a new session there. Should anything be wrong (E.G. missing Docker images, running CI jobs, etc.), you will be redirected to the “Start with options” page. If a session is already running, you will see a “Connect” button instead of the “Start” button.

  • Click on the “Start with options” link in the “Start/Connect” dropdown menu available on the top right of the project page header. Or on the “New session” button on the project sessions tab. This action will bring you to a lengthy “Start Session” page with many more options to configure your session before starting.

  • Use an “Autostart Session” link. The user experience is very similar to the quick start button. You can easily generate and share links that start on the latest version of any branch or specify references and environmental variables to tailor the session to specific needs.

Start with options

When starting a new session through the “Start with options” button from the project page header or the “New session” button on the project sessions tab, you will be able to completely control the parameters for the session that will start. In general, the default values should work well. But there are situations where using the extended options are indispensable. Examples include:

  • Picking a non-default branch.

  • Increasing resource limits (processing power, memory, or disk space) if you know it is necessary or if the session crashes when starting or complains that there is not enough space.

Here’s the rundown of the configuration options.




This is the default branch (typically master or main). You can switch if you are working on another branch.


Typically the most recent commit, but could be different if there is a running session or uncommitted work from a recent session. Selecting a different commit can be useful to see the state of the project at a specific point in time, to avoid a commit in which the image does not build, or to ignore autosaved work.

Docker Image

This provides information about the Docker image used by the Session. When it fails, you can try to rebuild it, or you can check the GitLab job logs. An image can also be pinned so that new commits will not require a new image each time.

Default environment

Default is /lab, it loads the JupyterLab interface. If you are working with R, you may want to use /rstudio for RStudio. Mind that the corresponding packages need to be installed in the image. If you’re using a python template, the rstudio endpoint will not work.

Number of CPUs

The number of CPUs available, or the quota. Resources are shared, so please select the lowest amount that will work for your use case. Usually, the default value works well.

Amount of Memory

The amount of RAM available. Resources are shared, so please select the lowest amount that will work for your use case. Usually, the default value works well.

Number of GPUs

The number of GPUs available. If you can’t select any number, no GPUs are available in RenkuLab deployment you are using. If you request any, you might need to wait for GPUs to free up in order to be able to launch an environment.

Automatically fetch LFS data

Default is off. All the lfs data will be automatically fetched in if turned on. This is convenient, but it may considerably slow down the start time if the project contains a lot of data. Refer to Data in Renku for further information

What if the Docker image is not available?

Sessions are backed by Docker images. When launching a new session, a container is created from the image that matches the selected branch and commit, or the pinned image if the project’s settings specify any.

A GitLab’s CI/CD pipeline automatically builds a new image using the project’s Dockerfile when any of the following happens:

  • Creating of a project.

  • Forking a project (in which the new build happens for the fork).

  • Pushing changes to the project.

The pipeline is defined in the project’s .gitlab-ci.yml file. If the project’s settings include a pinned image, there is no need to run the image_build job. The image needs to be accessible anyway to the user. This may not be the case if it is set private.

It may take a long time to build an image for various reasons, but if you’ve just created the project on RenkuLab from one of the templates, it generally takes less than a minute or two.

The Docker image is still building

If the Docker image has a “still building” message, you can either wait patiently, or watch it build by clicking the associated link to see the streaming log messages on GitLab. This can be useful if you’ve made changes to the Dockerfile or added lines to requirements.txt, environment.yml, or install.R, where something might have gone wrong.

The Docker image build failed

If this happens, it’s best to click the link to view the logs on GitLab so you can see what happened. Here are some common reasons for build failure:

Software installation failure

Problem: You added a new software library to requirements.txt, environment.yml, or install.R, but something was wrong with the installation (e.g. typo in the name, extra dependencies required for the library but unavailable).

How to fix this: You can use the GitLab editor or clone your project locally to fix the installation, possibly by adding the extra dependencies it asks for into the Dockerfile (the commented out section in the file explains how to do this). As an alternative, you can start a session from an earlier commit.

How to avoid this: First try installing into your running session, e.g. by running pip install -r requirements.txt in the terminal on JupyterLab. You might not have needed to install extra dependencies when installing on your local machine, but the operating system (OS) defined in the Dockerfile has minimal dependencies to keep it lightweight.

The build timed out

By default, image builds are configured to time out after an hour. If your build takes longer than that, you might want to check out the section on Customizing sessions sessions before increasing the timeout.

Your project could not be cloned

If you accidentally added 100s of MBs or GBs of data to your repo and didn’t specify that it should be stored in git LFS, it might take too long to clone. In this case, read the docs on how to rewrite history and move these files into git LFS.

Another potential cause is if the project has submodules that are private.

The Docker image is not available

RenkuLab uses its internal instance of GitLab to build and store an image in the registry each time you create a project, push changes, or use the RenkuLab UI to fork a project. Thus, the image might be unavailable if you manage to get into a state that skips any of these steps.

On forked projects, images for old commits are typically unavailable. It may be a problem when working on non-default branches too. When RenkuLab detects the image is missing, it will guide you to fix the problem (given you have enough permissions to run pipelines for the project). This works well for the latest commit in any branch, but it may be trickier for older commits.

As a workaround, the easiest way to trigger a new image build would be adding a new trivial commit through the GitLab instance, like editing the file.