run-experiment

Deploy and run ML experiments across local, remote, Vast.ai, and Modal environments.

Updated Apr 1, 2026

Search Tags:#automation #workflow #deployment #gpu #multi-environment #experiment #ml

Ready to run

npx skills add https://github.com/Wenwen555/ARIS-LVLM --skill run-experiment-wenwen555

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: run-experiment
Source: https://github.com/Wenwen555/ARIS-LVLM/tree/main/skills/run-experiment
Command: npx skills add https://github.com/Wenwen555/ARIS-LVLM --skill run-experiment-wenwen555

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Deploy and run ML experiments across local, remote, Vast.ai, or Modal serverless GPUs, removing manual setup and environment juggling.

Core Features & Use Cases

Environment-aware deployment: detects local, remote, Vast.ai, and Modal GPUs and configures the run accordingly.
End-to-end workflow: validates resources, syncs code, launches on the selected platform, and monitors progress.
Lifecycle management: supports resource lifecycle, notifications, and optional auto-destroy of cloud instances for cost-efficient experimentation.

Quick Start

Describe the experiment script, target environment, and resource needs to launch and monitor it.

Frequently Asked Questions about run-experiment

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

FAQPage Schema

How do I automate ML experiment deployment across multiple GPU environments?▼

Automating ML experiment deployment involves detecting local, remote, Vast.ai, and Modal environments to configure runs. The automation enforces pre-flight checks, syncs code, launches jobs on selected GPUs, and monitors progress.

Can I run training jobs on Vast.ai and Modal serverless GPUs without manual setup?▼

Yes, you can run training jobs on Vast.ai and Modal serverless GPUs without manual setup. The process detects the target environment, configures resources automatically, and handles code synchronization before launching.

How do I manage cloud GPU costs when running multiple ML experiments?▼

Managing cloud GPU costs requires optional cost-aware resource management during ML experiment execution. You can enforce auto-destroy of cloud instances and receive notifications to maintain cost-efficient experimentation.

What is the best way to monitor ML experiments across diverse platforms?▼

Monitoring ML experiments across diverse platforms is best handled through an end-to-end workflow. This workflow validates resources, syncs code, launches the job on the selected platform, and tracks progress throughout the lifecycle.

Does this workflow support local and remote multi-environment execution?▼

Yes, multi-environment execution supports both local and remote setups. The system applies environment detection to identify the platform and configures the deployment accordingly for running ML training jobs.

What pre-flight checks are needed before launching an ML experiment?▼

Pre-flight checks needed before launching an ML experiment include validating resources and ensuring code synchronization. These checks enforce environment readiness before deploying to local, remote, or cloud GPUs.