skills.rest
hve4638
hve4638COMMUNITY
Explore all skills by this author →
GitHub

run-experiment

Deploy and run ML experiments on local or remote GPU servers.

Updated Mar 1, 2026
Search Tags:#deployment#gpu#ssh#ml#experiments#wandb
One-click install
npx skills add https://github.com/hve4638/hve-cc-marketplace --skill run-experiment-hve4638
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: run-experiment
Source: https://github.com/hve4638/hve-cc-marketplace/tree/main/aris/skills/skills-codex/run-experiment
Command: npx skills add https://github.com/hve4638/hve-cc-marketplace --skill run-experiment-hve4638

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Deploy and run ML experiments on local or remote GPU servers, simplifying orchestration, resource management, and deployment.

Core Features & Use Cases

  • Environment detection across local and remote GPUs, including SSH-based remote execution.
  • Pre-flight GPU checks and resource validation before launching training jobs.
  • Flexible code synchronization via rsync or git, plus optional Weights & Biases (W&B) integration.
  • Remote deployment using screen sessions for persistent training, with logging and status checks.
  • Local execution support with GPU binding and background run options.

Quick Start

Provide your project’s AGENTS.md, then run the run-experiment skill to deploy and monitor a training script on a chosen GPU server.

Frequently Asked Questions about run-experiment

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

FAQPage Schema
How do I run ML experiments on a remote GPU server via SSH?▼

The skill deploys and runs ML experiments on remote GPU servers by synchronizing code via rsync or git, then executing training through persistent SSH screen sessions with logging and status checks.

Can I use Weights and Biases integration for remote GPU training?▼

Yes, Weights and Biases (W&B) integration is supported optionally for remote GPU training. The skill allows you to enable W&B tracking when deploying and running your ML experiments on local or remote servers.

What is the best way to synchronize code to a GPU cluster for training?▼

Synchronizing code to a GPU cluster is handled through flexible rsync or git operations. The skill automates this code synchronization during deployment before launching your ML training jobs.

Does this skill require an AGENTS.md file for environment detection?▼

Yes, an AGENTS.md file is required for environment detection. Providing your project's AGENTS.md allows the skill to detect environments and perform pre-flight GPU validation before running ML experiments.

Can I deploy training scripts locally on a CUDA-enabled machine?▼

Yes, you can deploy and run training scripts locally on a CUDA-enabled machine. The skill supports local execution with GPU binding and background run options alongside remote GPU cluster deployment capabilities.

Related Skills

Explore More Software Engineering Skills→
llv22llv22
5

run-experiment

Launch and manage ML experiments on local or remote GPU servers.

Community
Intermediate
gztcodegztcode

weights-and-biases

Automate ML experiment tracking and collaboration with Weights & Biases.

Community
Advanced
pluginagentmarketplacepluginagentmarketplace
3

data-ai

Automate Linux data science environment setup with GPU drivers and ML frameworks.

Community
Advanced
AJBcodingAJBcoding
4

moai-domain-cloud

Automate design and validation of enterprise multi-cloud architectures across AWS, GCP, and Azure.

Community
Advanced