Coder for Data Science
On-demand workspaces for data science
Data scientists want to solve business problems, not waste time creating and configuring environments. Give them the self-serve infrastructure they need to do their work.

Empower your data scientists with Coder

Self-serve workspaces

Coder workspaces for data science can be pre-configured with everything the team needs to work on a project, from IDEs, to a precise version of Python, and all of the required libraries. Your data science teams should never have to worry about whether they remembered to install a needed library or conflicts between versions.

Increase governance and reduce risk

Training and running machine learning models require data, often sensitive customer data. With Coder, this data remains isolated and secure in workspaces residing in your organization's secure infrastructure, not sitting on data scientists' personal devices, thereby reducing the risk of both accidental and malicious data leakage.

Crunch large data sets with ease

Analyzing millions and millions of data points to train your deep learning model takes a lot of compute power and memory. With Coder workspaces, those resources are provided by your cloud infrastructure, not the scientist’s local machine. Coder workspaces can also be configured with GPUs.

Decrease data latency

Accessing large data sets or even returning query results to a user’s local machine requires traversing the distance between the cloud provider’s servers and the organization’s network. Because Coder workspaces are located in the cloud alongside the data sources this latency is reduced to a few milliseconds.

Data platform agnostic

Coder doesn't care where your data is stored. Whether you use AWS, Google Cloud, Databricks, Snowflake, an on-site data warehouse, or some combination of all of the above, if your scientists are allowed access to the data they can do so through their Coder workspace.

Works with the tools data scientists use

Data scientists know that it often takes more than one tool to address a problem. Coder has built-in support for the IDEs most preferred by data scientists, including RStudio, Jupyter, VS Code, PyCharm, and other JetBrains IDEs. Also integrates with all the major Git providers including GitHub, GitLab, Bitbucket, and Azure DevOps.

Effective and easy context switching

Coder makes it easy to switch quickly between projects. Start a long-running analysis in one workspace, then spin up a new workspace and start working on a separate project while the first workspace is crunching away. Try doing that with even the beefiest workstation. Data scientists can even configure workspaces to not auto-shutdown to preserve long-running tasks like model training and runs.

Excited about Coder? Try it now
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Install Coder on Docker

  • Be up and running in minutes
  • Free 30-day trial for 5 users
Learn more about running Coder on Docker or contact us
$ docker run --rm -it -p 7080:7080 \
    -v /var/run/docker.sock:/var/run/docker.sock \
    -v ~/.coder:/var/run/coder \
    codercom/coder:1.28.2

Our commitment to open source

Learn more about our projects and our commitment to the open source community.

code-server: the heart of Coder

code-server is the primary open source project we maintain. It allows developers to use a browser to access remote dev environments running VS Code. Coder builds upon the success of code-server and adds features designed for enterprise teams including support for additional IDEs and advanced security features.

39M

Docker Pulls

51K

GitHub Stars