run-experiment

Name: run-experiment
Availability: InStock
Author: Chenghao

Automates ML experiment deployment and execution on local or remote GPU servers.

2|Updated Mar 19, 2026

Search Tags:#automation #gpu #deploy #remote #ml #experiments

Ready to run

npx skills add https://github.com/chenghaoYang/auto-coder-trainer --skill run-experiment-chenghaoyang

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: run-experiment
Source: https://github.com/chenghaoYang/auto-coder-trainer/tree/main/aris/skills/run-experiment
Command: npx skills add https://github.com/chenghaoYang/auto-coder-trainer --skill run-experiment-chenghaoyang

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Deploy and run ML experiments on local or remote GPU servers, simplifying deployment, orchestration, and monitoring for reproducible results.

Core Features & Use Cases

Environment detection and pre-flight GPU checks to confirm resources and readiness.
Flexible deployment workflows: remote screen-based execution or local launches with GPU binding.
Optional integrations and notifications (W&B, Feishu) to track progress and share results.

Quick Start

Describe your experiment and let the skill detect the environment, prepare the run, deploy on a GPU host, and start the training.

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 to a remote GPU server?▼

Automating ML experiment deployment involves environment detection, GPU validation, code synchronization via rsync or git, and remote execution using screen, followed by post-launch verification to ensure the run started successfully.

Can I run local GPU training with environment checks and GPU binding?▼

Yes, local GPU training is supported through local launches with GPU binding, preceded by environment detection and pre-flight GPU checks to confirm hardware resources and readiness before execution.

Does the deployment workflow support code synchronization using rsync and git?▼

Code synchronization is fully supported using either rsync or git, ensuring your remote GPU server receives the exact updated experiment code required for reproducible runtime execution.

How do I integrate Weights and Biases (WandB) into my remote experiment launch?▼

WandB integration is applied optionally during the deployment setup, configuring the experiment tracking environment on the remote or local GPU host prior to initiating the runtime execution.

What is the best way to orchestrate end-to-end ML experiment runs on remote GPUs?▼

The best way to orchestrate remote ML runs is automating end-to-end workflows that handle environment detection, code syncing, optional WandB setup, and screen-based remote deployment with final verification.

Why do I need pre-flight GPU checks before running my machine learning experiments?▼

Pre-flight GPU checks are needed to validate hardware availability and confirm resource readiness, preventing deployment failures caused by insufficient GPU memory or unallocated devices on the target host.