DarDevCDE · Platform layer · Step 2
Code anywhere. Collaborate everywhere.
Secure cloud development environments for engineering teams and data scientistswith residency-aware rollout when you need it.
ROOT@DARDEV-CDE-INSTANCE:~
$ dardev up --instance "ml-gpu-env"
> Provisioning vCPU-8 / RAM-32G...
> Mounting high-speed NVMe storage...
> Attaching NVIDIA T4 GPU profile...
Ready. Access UI at node-8.cde.internal:8888
Supported environments
JupyterHub
Data science workspaces with pre-configured ML libraries and GPU access.
VS Code Cloud
Browser-based IDE with your extensions, themes, and local VS Code workflow.
R Studio
Statistical computing clusters allocated per analyst for data modeling.
Custom Shell
Persistent, isolated terminal sessions tailored to your team workflows.
Enterprise control. Developer freedom.
Centralized infrastructure security and standardizationwithout slowing developers down.
GitLab & Portainer integrated
Connect CDE instances to CI/CD pipelines for automated testing and deployment.
Intelligent auto-scaling
Heavy builds request more CPU automaticallyno manual intervention.
Common questions
Clear answers for teams evaluating DarDevCDE.
JupyterHub for data science, browser-based VS Code, R Studio for statistical computing, and custom persistent shell sessionsall provisioned on demand.
Yes. We scope residency and hosting region to your compliance requirements during rollout planning.
Stop managing laptops.
Move development environments to the cloud on the Control Plane.
Request CDE demo