skills.rest
TabithaFanny
TabithaFannyCOMMUNITY
Explore all skills by this author →
GitHub

run-experiment

Deploy and execute machine learning experiments on local, remote, Vast.ai, or Modal GPU environments.

Updated May 29, 2026
Search Tags:#data science#machine learning#cloud computing#experiment deployment#GPU computing
One-click install
npx skills add https://github.com/TabithaFanny/ThesisX --skill run-experiment-tabithafanny
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: run-experiment
Source: https://github.com/TabithaFanny/ThesisX/tree/main/skills_imported/aris/skills/run-experiment
Command: npx skills add https://github.com/TabithaFanny/ThesisX --skill run-experiment-tabithafanny

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires Bash, Read, Grep, Glob, Edit, Write, Agent, Skill(serverless-modal), and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the deployment and execution of machine learning experiments across local, remote, Vast.ai, and Modal serverless GPU environments, reducing the manual setup time and streamlining the workflow.

Core Features & Use Cases

  • Multi-Environment Support: Run experiments on local GPUs, remote servers, Vast.ai instances, or Modal's serverless GPU platform.
  • Environment Detection: Automatically identifies the appropriate environment for running the experiment based on project configuration files.
  • Pre-flight Checks: Validates GPU availability and code synchronization before deployment.
  • Automated Deployment: Deploys the experiment code and manages resources across different environments.
  • Monitoring and Reporting: Provides feedback on the experiment status, cost, and execution details.

Quick Start

To run a machine learning experiment, activate the skill with the command 'run experiment [experiment-description]'.

Frequently Asked Questions about run-experiment

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

FAQPage Schema
How do I automate machine learning experiment deployment across different GPU environments?▼

Automating machine learning experiment deployment is handled by detecting the environment, running pre-flight checks for GPU availability, and deploying code to local, remote, or cloud-based servers like Vast.ai and Modal.

Can I run experiments on Modal serverless GPUs and remote servers using SSH?▼

Yes, you can run experiments on Modal serverless GPUs and remote servers. The system requires SSH access for remote servers and valid access to cloud services to manage automated deployment and execution.

What pre-flight checks are needed before deploying code to a remote GPU environment?▼

Pre-flight checks for remote GPU environments validate GPU availability and ensure code synchronization before deployment. This prevents execution errors and confirms resources are ready for the machine learning workload.

How does environment detection work for cloud computing experiment execution?▼

Environment detection for cloud computing experiment execution works by reading project configuration files to automatically identify and select the appropriate local, remote, or serverless GPU environment for the workload.

Do I need specific machine learning frameworks installed to use automated experiment deployment?▼

Yes, appropriate machine learning frameworks and libraries are required. The automated experiment deployment process relies on these existing dependencies to execute code correctly across local, remote, and cloud-based GPU environments.

What is the best way to monitor execution status and costs for Vast.ai experiments?▼

Monitoring execution status and costs for Vast.ai experiments is built into the deployment workflow. The system provides continuous feedback on experiment status, execution details, and resource costs after automated deployment.

Related Skills

Explore More Software Engineering Skills→
czh-ee-2023czh-ee-2023

run-experiment

Deploy and execute machine learning experiments across local and remote environments.

Community
Advanced
gyy0592gyy0592
7

gen-report-detailed

Generate detailed 13-section markdown experiment reports from flame_moonshot training logs and metrics.

Community
Advanced
mostafasudomostafasudo
3

setup-deploy

Detect deployment platforms and configure production URLs and health check endpoints.

Community
Intermediate
Leo1349Leo1349

run-experiment

Deploy machine learning experiments to local or remote GPU servers via SSH.

Community
Advanced