dominodatalab
NewFull Domino Data Lab platform support — workspaces, jobs, model deployment, experiment tracking, GenAI tracing, Spark/Ray/Dask, and app deployment for data science teams
Summary
This skill provides full support for the Domino Data Lab platform, enabling users to manage workspaces, jobs, model deployments, experiment tracking, GenAI tracing, Spark/Ray/Dask clusters, and app deployments directly from Claude Code.
- It streamlines data science workflows by allowing developers to interact with Domino resources without leaving the terminal.
Install & Usage
/plugin marketplace add <org/repo>Add the configuration to /plugin install dominodatalab@<marketplace>
/pluginUse Cases
Usage Examples
/dominodatalab create workspace --name my-analysis --environment 'Python 3.9' --hardware-tier 'large-gpu'
Submit a job to run train.py on a Spark cluster with 4 executors and 2GB memory each.
Deploy the model from project 'fraud-detection' version 2 as an API endpoint with 2 replicas.
Security Audits
Frequently Asked Questions
What is dominodatalab?
This skill provides full support for the Domino Data Lab platform, enabling users to manage workspaces, jobs, model deployments, experiment tracking, GenAI tracing, Spark/Ray/Dask clusters, and app deployments directly from Claude Code. It streamlines data science workflows by allowing developers to interact with Domino resources without leaving the terminal.
How to install dominodatalab?
To install dominodatalab: add a marketplace (/plugin marketplace add <org/repo>), then add the config to /plugin install dominodatalab@<marketplace>. Finally, /plugin in Claude Code.
What is dominodatalab best for?
dominodatalab is a plugin categorized under Development. It is designed for: deployment. Created by Domino Data Lab.
What can I use dominodatalab for?
dominodatalab is useful for: Launch a new workspace with a specific compute environment and hardware tier for exploratory data analysis.; Submit a batch job to run a Python script on a Spark cluster and monitor its progress.; Deploy a trained model as a REST API endpoint with autoscaling and versioning.; Track and compare multiple experiment runs with hyperparameters and metrics using Domino's experiment tracking.; Set up a Ray cluster for distributed training and submit a task to it.; Deploy a Streamlit app to Domino's app hosting service and configure access permissions..