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Version: 24.1

Overview

Data Studios is a unified platform where you can host a combination of container images and compute environments for interactive analysis using your preferred tools, like JupyterLab and RStudio Notebooks, Visual Studio Code IDEs, or Xpra remote desktops. Each data studio session is an individual interactive environment that encapsulates the live environment for dynamic data analysis.

On Seqera Cloud, the free tier permits only one running data studio session at a time. To run simultaneous sessions, contact Seqera for a Seqera Cloud Pro license.

Data Studios is currently in public preview and is available from Seqera Platform v24.1. Contact Seqera support if you experience any problems during the deployment process. Data Studios in Enterprise is not enabled by default. You can enable Data Studios in the environment variables configuration.

Requirements​

Before you get started, you need the following:

  • Valid credentials to access your cloud storage data resources.
  • At least the Maintain role set of permissions.
  • A compute environment with sufficient resources. This is highly dependent on the volume of data you wish to process, but we recommended at least 2 CPUs allocated with 8192 MB of memory. See AWS Batch for more information about compute environment configuration.
  • Data Explorer is enabled.

Currently, Data Studios only supports AWS Batch compute environments that do not have Fargate enabled.

Container image templates​

Data Studios provides four container image templates: JupyterLab, RStudio Server, Visual Studio Code, and Xpra. The image templates install a very limited number of packages when the session container is built. You can install additional packages as needed during a session.

You can also create your own container image templates. For more information, see Custom environments.

JupyterLab 4.2.5

The default user is the root account. The following conda-forge packages are available by default:

  • python=3.13.0
  • pip=24.2
  • jedi-language-server=0.41.4
  • jupyterlab=4.2.5
  • jupyter-collaboration=1.2.0
  • jupyterlab-git=0.50.1
  • jupytext=1.16.4
  • jupyter-dash=0.4.2
  • ipywidgets=7.8.4
  • pandas[all]=2.2.3
  • scikit-learn=1.5.2
  • statsmodels=0.14.4
  • itables=2.2.2
  • seaborn[stats]=0.13.2
  • altair=5.4.1
  • plotly=5.24.1
  • r-ggplot2=3.5.1
  • nb_black=1.0.7
  • qgrid=1.3.1

To install additional Python packages, execute !pip install <packagename> commands in your notebook environment. Additional system-level packages can be installed in a terminal window using apt install <packagename>.

RStudio Server 4.4.1

The default user is the root account. To install R packages, execute install.packages("<packagename>") commands in your notebook environment. Additional system-level packages can be installed in a terminal window using apt install <packagename>.

Visual Studio Code 1.93.1

Visual Studio Code is an integrated development environment (IDE) that supports many programming languages. The default user is the root account. To install extensions, select Extensions. Additional system-level packages can be installed in a terminal window using apt install <packagename>.

Xpra 6.2.0

Xpra, known as screen for X, allows you to run X11 programs by giving you remote access to individual graphical applications. The container template image also installs NVIDIA Linux x64 (AMD64/EM64T) drivers for Ubuntu 22.04 for running GPU-enabled applications. To use these GPU drivers, your compute environment must specify GPU instance families.

The default user is the root account. The image is based on ubuntu:jammy. Additional system-level packages can be installed in a terminal window using apt install <package_name>.

Session statuses​

Data studios have the following possible statuses:

  • building: When a custom environment is building the template image for a new data studio session. The Wave service performs the build action. For more information on this status, see Inspect custom container template build status.

  • build-failed: When a custom environment build has failed. This is a non-recoverable error. Logs are provided to assist with troubleshooting. For more information on this status, see Inspect custom container template build status.

  • starting: The data studio is initializing.

  • running: When a data studio session is running, you can connect to it, copy the data studio URL, or stop it. In addition, the session can continue to process requests/run computations in the absence of an ongoing connection.

  • stopping: The recently-running session is in the process of being stopped.

  • stopped: When a session is stopped, the associated compute resources are deallocated. You can start or delete the data studio when it's in this state.

  • errored: This state most often indicates that there has been an error starting the data studio session but it is in a stopped state. There might be errors reported by the session itself but these will be overwritten with a running status if the data studio session is still running.

    If you encounter an error with the public preview release of Data Studios, contact Seqera support.

Session checkpoints​

When you start a session, it automatically creates a checkpoint. A checkpoint saves changes that you make to the root filesystem and stores it in the compute environment's pipeline work directory in the .studios/checkpoints folder with a unique name. The checkpoint is updated every five minutes.

When you stop and start a data studio session, or start a new data studio session from a previously created checkpoint, changes such as installed software packages and configuration files are restored and made available in the data studio session. Changes made to mounted data are not included in a checkpoint.

Checkpoints can be renamed and the name has to be unique per data studio. Spaces in checkpoint names are converted to underscores automatically.

Checkpoint files in the compute environment work directory may be shared by multiple data studios. Each checkpoint file is cleaned up asynchronously after the last data studio referencing the checkpoint is deleted.

The cleanup process is a best effort and not guaranteed. Seqera attempts to remove the checkpoint, but it can fail if, for example, the compute environment credentials used do not have sufficient permissions to delete objects from storage buckets.