Domino Data Lab

domino-model-serving

Deploy, invoke, and retire Domino model APIs and registered models via REST. Covers modelServing lifecycle, registered-models v1 vs v2 paths, MLflow tracking vs registry API, inference URLs vs management API, and GenAI endpoint vanity URLs. Use when automating model deployment, predictions, registry updates, or debugging stop/archive/delete behavior.

domino-trace-setup

Set up GenAI tracing for an LLM or agent application in Domino by checking MLflow 3.2.0 and Domino SDK requirements, then adding a tracing_setup.py helper, evaluators, a config.yaml, and @add_tracing / DominoRun wiring. Use when the user asks to add tracing, observability, or evaluation to an agent or LLM app running on Domino.

domino-ui-bootstrap

Bootstrap or retrofit a Vite + React 18 + TypeScript project so it uses the Domino design system (`@dominodatalab/extensions-tools`). Scoped to a **fully standalone SPA frontend for a Domino application or extension**, not snippets, library additions, or work inside an existing Domino monorepo package. Use whenever the user wants to create, scaffold, refactor, retrofit, or set up such an SPA (a React app, web app, frontend, UI, or extension) that should "look like Domino", use Domino components, follow the Domino design system, or integrate with the Domino platform, including when they mention DominoThemeProviderDecorator, extensions-tools, or base-components without saying "Domino". Also use to make an existing standalone React/Vite project "Domino-styled" or add Domino theming. Handles React 18 / react-router 5 pinning, Storybook MCP registration, theme provider wiring, and build verification.

domino-ui-design

Build Domino-styled web applications matching the Domino Design System. Use when creating or styling Domino apps (Dash, Streamlit, Flask, FastAPI+HTML), generating UI components, reviewing UX, or when the user mentions "Domino app", "Domino UI", "Domino dashboard", or "Domino design system". Covers the full stack — FastAPI backend, Ant Design 5.x frontend via CDN, Domino theme tokens (colors, typography, spacing), chart styling with Highcharts, Domino API authentication, proxy-safe routing, UX writing, error handling, empty states, form design, table patterns, and layout best practices. Also use for UX reviews of existing Domino app screenshots.

domino-environments

Create and customize Domino Compute Environments - Docker containers defining tools, packages, and configurations. Covers Dockerfile customization, package installation, IDE configuration, DSE (Domino Standard Environments), and troubleshooting build failures. Use when installing dependencies, customizing environments, or fixing environment issues.

domino-experiment-tracking

Track traditional ML experiments in Domino using the MLflow-based Experiment Manager. Covers experiment setup, auto-logging for sklearn/TensorFlow/PyTorch, manual logging, artifact storage, run comparison, and model registration. Use when training ML models, logging metrics and parameters, comparing model runs, or registering models.

domino-flows

Orchestrate multi-step ML workflows using Domino Flows (built on Flyte). Define DAGs with typed inputs/outputs, heterogeneous environments, automatic lineage, and reproducibility. Use when building data pipelines, multi-stage training workflows, or processes requiring orchestration and monitoring.

domino-genai-tracing

Trace and evaluate GenAI applications including LLM calls, agents, RAG pipelines, and multi-step AI systems in Domino. Uses the Domino SDK (@add_tracing decorator, DominoRun context) with MLflow 3.2.0. Captures token usage, latency, cost, tool calls, and errors. Supports LLM-as-judge evaluators and custom metrics. Use when building agents, debugging LLM applications, or needing audit trails for GenAI systems.

domino-jobs

Create, run, and manage Domino Jobs - batch executions for scripts, training, and data processing. Covers job configuration, hardware tiers, scheduled jobs (cron), monitoring status, viewing logs, and API-driven execution. Use when running batch workloads, scheduling recurring tasks, or automating training pipelines.

domino-launchers

Create Domino Launchers - parameterized web forms for self-service job execution. Enable business users to run analyses, generate reports, and trigger batch predictions without coding. Covers parameter types, email notifications, result delivery, and access control. Use when building self-service data products or enabling non-technical users.

domino-model-endpoints

Deploy and monitor model API endpoints in Domino. Covers creating prediction endpoints, version management, Grafana dashboards for latency/errors/resources, alerting, and GPU inference with NVIDIA Triton. Use when deploying models as APIs, monitoring production endpoints, or debugging endpoint issues.

domino-model-monitoring

Monitor deployed models in Domino including drift detection, model quality tracking, and alerting. Covers data drift analysis, prediction capture, baseline comparison, alert configuration, and remediation workflows. Use when monitoring production models, detecting drift, or setting up model health alerts.

domino-modeling-assistant

Enable AI-assisted model development within Domino by writing needed model and training code and using MCP (Model Context Protocol) servers to execute domino jobs. AI coding assistants like Cursor and GitHub Copilot can execute commands as Domino jobs, maintaining security, governance, and reproducibility. Use when setting up AI code assistants to work with Domino, configuring MCP servers, or enabling vibe modeling workflows.

netapp-volumes

Work with Domino Volumes for NetApp ONTAP - enterprise-grade, multi-terabyte storage with near-instant snapshots. Covers volume creation, snapshot versioning with commit messages, cross-project sharing, mount paths (/mnt/netapp-volumes/ or /domino/netapp-volumes/), and the NetApp Volumes REST API. Use when managing large-scale data storage, needing fast no-copy snapshots, or integrating existing NetApp ONTAP infrastructure with Domino.

domino-projects

Work with Domino Projects including Git integration, DFS vs Git-based projects, collaboration, and version control. Covers project creation, Git provider setup (GitHub, GitLab, Bitbucket), branch management, collaborator permissions, and project settings. Use when creating projects, setting up Git repos, or managing team collaboration.

domino-python-sdk

Programmatically interact with Domino using python-domino SDK and REST APIs. Covers authentication, running jobs, managing projects, file operations, model deployment, and automation. Use when automating Domino workflows, integrating with CI/CD, or building custom tooling around Domino.

tags-and-properties

Manage Domino tags and properties via the Taxonomy API. Covers tags/namespaces (create, list, update, delete; tag entities — project, model, dataset, app, project_template, netapp_volume; query by tag; autocomplete; merge; CSV import/export) AND properties (typed metadata fields — text, number, date, boolean, url, user, organization, select, multi_select — with per-entity values, groups, and versioned entities like model_version/app_version). Use when organizing entities with tags, building hierarchical namespaces, defining typed metadata fields, setting property values on projects/models/apps, finding entities by tag, bulk-tagging during onboarding, or migrating taxonomy across environments.

domino-workspaces

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.