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ToXMon
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domino-environments

Create and customize Domino compute environments as Docker container images.

Updated Apr 11, 2026
Search Tags:#mlops#docker#dockerfile#gpu#environments#package-management#domino
One-click install
npx skills add https://github.com/ToXMon/tolu --skill domino-environments-toxmon
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: domino-environments
Source: https://github.com/ToXMon/tolu/tree/main/agent-zero-backup/workdir/memory-palace/skills/domino/domino-environments
Command: npx skills add https://github.com/ToXMon/tolu --skill domino-environments-toxmon

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provisioning, customizing, and troubleshooting Domino compute environments is complex and prone to configuration drift, package conflicts, and failed builds; this skill provides clear, actionable guidance to produce reproducible Docker-based environments for workspaces and jobs.

Core Features & Use Cases

  • Dockerfile customization: Guidance for adding system packages, pinned Python and R libraries, environment variables, and efficient layer management to reduce build times and improve reproducibility.
  • Domino Standard Environments & GPU support: How to start from DSEs, configure CUDA/TensorFlow/PyTorch GPU stacks, and verify GPU availability at runtime.
  • Package installation strategies & troubleshooting: When to install in the Dockerfile versus runtime, use of requirement files or pre/post-run scripts, and common failure diagnostics including permission issues, network timeouts, and missing packages.
  • Use case: Prepare a reproducible ML training environment with pinned dependencies and GPU drivers, test locally via docker build and docker run, then publish a revision in Domino for consistent job execution.

Quick Start

Create or update a Domino environment by writing Dockerfile instructions to pin packages and set environment variables, then trigger a build in the Domino UI and verify the environment revision boots and runs your test command.

Frequently Asked Questions about domino-environments

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I create a reproducible Domino compute environment with pinned Python dependencies?▼

To create a reproducible Domino compute environment, you customize a Dockerfile by pinning Python and R libraries, adding system packages, and setting environment variables. This ensures consistent runtime configurations for workspaces and jobs without configuration drift.

How do I configure GPU CUDA and TensorFlow stacks in a Domino Dockerfile?▼

You configure GPU stacks in a Domino Dockerfile by starting from Domino Standard Environments, then adding CUDA, TensorFlow, or PyTorch layers. The skill provides guidance on setting up GPU drivers and verifying GPU availability at runtime.

What is the best way to troubleshoot Domino environment build failures?▼

Troubleshooting Domino environment build failures involves diagnosing common issues like permission errors, network timeouts, and missing packages. You can test locally using docker build and docker run commands to isolate failures before triggering a build in the Domino UI.

When should I install packages in a Dockerfile versus using runtime scripts for Domino jobs?▼

You should pin package installations in the Dockerfile for reproducible base environments, while pre-run or post-run scripts handle dynamic runtime installations. This layer management strategy reduces build times and prevents package conflicts during CI.

Can I test a Domino environment locally before publishing a new revision?▼

Yes, you can test a Domino environment locally by running docker build and docker run commands to verify your Dockerfile instructions. After local verification, you publish a revision in Domino for consistent job execution across workspaces.

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