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

1. Navigate to your project
2. Click Workspaces in the navigation
3. Click Launch Workspace
4. 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)
5. 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:

1. Enable SSH in workspace settings
2. Get connection details from running workspace
3. 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

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

Troubleshooting

Workspace Won't Start

• Check hardware tier availability
• Verify environment builds successfully
• Check resource quotas

Lost Work

• Check /mnt/ directories for persisted files
• Review Domino project files
• Check Git history if using version control

Slow Startup

• Use smaller base environments
• Enable package persistence
• Pre-install packages in environment Dockerfile

Documentation Reference

• Use Workspaces
• Launch a Workspace
• Start a Jupyter Workspace