run-experiment

Deploy and manage ML experiments on local or remote GPU resources.

1|Updated Mar 26, 2026

Search Tags:#gpu #conda #remote-server #wandb #ml-training #vast-ai #experiment-deployment

Ready to run

npx skills add https://github.com/Lingrongye/federated-learning --skill run-experiment-lingrongye

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Please help me install this Agent Skill.
Skill: run-experiment
Source: https://github.com/Lingrongye/federated-learning/tree/main/Auto-claude-code-research-in-sleep/skills/run-experiment
Command: npx skills add https://github.com/Lingrongye/federated-learning --skill run-experiment-lingrongye

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Deploy and manage ML experiments across local GPUs, remote SSH servers, or Vast.ai instances, reducing setup time and manual orchestration.

Core Features & Use Cases

Environment detection and pre-flight checks to ensure GPUs are available.
Flexible deployment targets (local, remote, vast.ai) with code synchronization and monitoring.
Optional W&B integration and multi-target orchestration for parallel experiments.

Quick Start

Provide a concise experiment description to initiate a local or remote GPU run.

Frequently Asked Questions about run-experiment

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

FAQPage Schema

How do I deploy and run ML experiments on a remote GPU server?▼

To deploy ML experiments on a remote GPU server, the skill handles environment detection, code synchronization, and deployment using configurations defined in a CLAUDE.md file. It automates pre-flight checks to ensure GPU availability before executing your training run.

Can I run ML training on Vast.ai instances automatically?▼

Yes, you can run ML training on Vast.ai instances automatically. The skill supports vast.ai workflows alongside local and remote SSH targets, managing code synchronization, deployment, and monitoring to streamline orchestration across these flexible compute environments.

Does this tool support W&B integration for monitoring ML training runs?▼

Yes, this tool supports optional W&B integration for monitoring ML training runs. It provides built-in tracking capabilities across its supported local, remote, and vast.ai deployment targets, allowing you to oversee experiment metrics and progress during execution.

What do I need to configure before running ML experiments on remote GPUs?▼

Before running ML experiments on remote GPUs, you need to configure a CLAUDE.md file with your remote environment details. This configuration provides the necessary deployment targets and connection parameters for the skill to execute pre-flight checks and synchronize code.

How does the skill handle GPU availability checks before deploying experiments?▼

The skill handles GPU availability by performing automated pre-flight checks and environment detection before deploying experiments. This safeguard ensures that local or remote GPU resources are actually present and ready, preventing execution errors before code synchronization begins.

Can I orchestrate parallel ML experiments across multiple deployment targets?▼

Yes, you can orchestrate parallel ML experiments across multiple deployment targets. The skill supports multi-target orchestration, allowing you to manage concurrent local, remote, and vast.ai workflows while maintaining deterministic execution and monitoring.