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run-experiment

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

Updated Aug 23, 2026
Search Tags:#automation#gpu#deploy#remote#ml#experiments
One-click install
npx skills add https://github.com/nxZhai/Nicy-Extension-Skills --skill run-experiment-nxzhai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: run-experiment
Source: https://github.com/nxZhai/Nicy-Extension-Skills/tree/main/skills-codex/run-experiment
Command: npx skills add https://github.com/nxZhai/Nicy-Extension-Skills --skill run-experiment-nxzhai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Deploy and run ML experiments on local or remote GPU servers, integrating environment checks and deployment workflows to speed up iteration.

Core Features & Use Cases

  • Environment-aware deployment: detects whether to run locally or on SSH hosts and configures GPUs accordingly.
  • Pre-flight validation: verifies GPU availability and basic prerequisites before launching training jobs.
  • Code synchronization: supports rsync or git-based deployment to sync code to the target environment.
  • Optional experiment tracking: integrates with Weights & Biases (wandb) if configured.
  • Remote execution: starts training in a managed screen/session on remote servers or locally, with logging and status checks.

Quick Start

Deploy an ML training script to a remote GPU server with the required arguments and monitor until completion.

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?▼

Deploy and run ML experiments on a remote GPU server by automating end-to-end execution, which includes environment detection, pre-flight GPU validation, code synchronization, and launching training jobs in managed screen sessions with logging.

Can I use rsync or git to synchronize code to a remote GPU environment?▼

Yes, you can synchronize code to a remote GPU environment using either rsync or git-based deployment, allowing flexible code syncing to target heterogeneous hardware before launching training jobs.

Does this workflow support Weights & Biases integration for experiment tracking?▼

Yes, Weights & Biases integration for experiment tracking is optionally supported, allowing you to monitor ML training metrics if configured within your local or remote GPU deployment environment.

How do pre-flight GPU checks work before launching remote training jobs?▼

Pre-flight GPU checks verify GPU availability and basic prerequisites on local or remote SSH hosts before launching training jobs, ensuring the target hardware is ready for ML experiment execution.

What is the best way to automate ML training execution across local and remote machines?▼

Automate ML training execution across local and remote machines using an environment-aware deployment workflow that detects the host, configures GPUs accordingly, and starts training in a managed screen session with notifications.

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