domino-workspaces

name: domino-workspaces description: Work with Domino Workspaces - interactive development environments including Jupyter, JupyterLab, VS Code, and RStudio. Covers launching workspaces, configuring hardware tiers, environment selection, volume mounting, SSH access, and package installation. Use when setting up development environments, configuring workspace settings, or troubleshooting IDE issues.

Domino Workspaces Skill

Description

This skill helps users work with Domino Workspaces - interactive development environments for data science work including Jupyter notebooks, JupyterLab, VS Code, and RStudio.

Activation

Activate this skill when users want to:

Launch or configure a workspace in Domino
Work with Jupyter notebooks, VS Code, or RStudio
Configure workspace settings (hardware tier, environment, volumes)
Understand workspace persistence and file management
Use SSH to connect to remote workspaces
Install packages in workspaces

Workspace Types

Available IDEs

Domino provides these default workspace types:

Jupyter Notebook: Classic notebook interface
JupyterLab: Modern Jupyter interface with file browser
VS Code: Full-featured code editor with extensions
RStudio: IDE for R development
Custom IDEs: Can configure additional workspace types

Launching a Workspace

Via Domino UI

Navigate to your project
Click Workspaces in the navigation
Click Launch Workspace
Select:
- IDE: Choose Jupyter, VS Code, RStudio, etc.
- Hardware Tier: CPU/memory/GPU resources
- Compute Environment: Docker environment with tools
- Volume Size: Persistent storage (if needed)
Click Launch

Via Python SDK

from domino import Domino

domino = Domino("project-owner/project-name")

# Start a workspace
workspace = domino.workspace_start(
    environment_id="env-123",
    hardware_tier_name="small",
    workspace_type="JupyterLab"
)

print(f"Workspace ID: {workspace['workspaceId']}")

File Persistence

Persistent Directories

Work saved to these directories persists across workspace sessions:

/mnt/ - Project files (synced with Domino)
/mnt/data/ - Domino Datasets
/mnt/artifacts/ - Project artifacts
/mnt/imported/ - Imported data

Home Directory Persistence

Domino 6.1+ supports home directory persistence:

Installed packages persist across sessions
User configurations (.bashrc, .vimrc) are retained
Reduces workspace startup time

Package Installation

Temporary (Current Session)

# Python packages
pip install pandas numpy scikit-learn

# R packages
install.packages("tidyverse")

Persistent (Via Environment)

Add to environment's Dockerfile instructions:

RUN pip install pandas numpy scikit-learn

Or use requirements.txt in your project:

pandas>=2.0.0
numpy>=1.24.0
scikit-learn>=1.3.0

SSH Access to Workspaces

Domino 6.1+ allows SSH connections from local machines:

Enable SSH in workspace settings
Get connection details from running workspace
Connect via SSH:

ssh -i ~/.ssh/domino_key user@workspace-hostname

Benefits:

Use local IDE (VS Code Remote, PyCharm)
Access remote compute from local machine
Browse files locally while computing remotely

Git Integration in Workspaces

Using Git

# Clone a repo in workspace
git clone https://github.com/user/repo.git

# Standard git workflow
git add .
git commit -m "Update model"
git push origin main

Sync with Domino

Click Sync in the workspace UI to push changes back to Domino project.

Workspace Operations

Stop a Workspace

domino.workspace_stop(workspace_id)

Resume a Workspace

Stopped workspaces can be resumed to continue work.

View Workspace Logs

Access logs for debugging through the Domino UI or API.

Best Practices

Save frequently: Sync work to Domino regularly
Use appropriate hardware: Match resources to workload
Clean up: Stop workspaces when not in use to save resources
Version control: Use Git for code versioning
Environment management: Use Domino Environments for reproducibility