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run-experiment

Automate ML training experiment deployment across local, remote, Vast.ai, and Modal GPU environments.

2|Updated Aug 12, 2025
Search Tags:#remote-ssh#gpu-deployment#vast-ai#ml-experiment#training-orchestration#modal-serverless#wandb-integration
One-click install
npx skills add https://github.com/goupup-ai/miccai25 --skill run-experiment-goupup-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: run-experiment
Source: https://github.com/goupup-ai/miccai25/tree/main/ARIS/skills/run-experiment
Command: npx skills add https://github.com/goupup-ai/miccai25 --skill run-experiment-goupup-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Manually configuring and launching ML training experiments across different GPU environments (local, remote, cloud, serverless) requires repetitive environment setup, code syncing, and job management tasks that waste researcher and engineer time.

Core Features & Use Cases

  • Multi-Environment Deployment: Supports local GPUs, remote SSH servers, on-demand Vast.ai instances, and serverless Modal GPUs for flexible resource selection.
  • Automated Workflow: Handles environment detection, GPU availability checks, code synchronization, optional Weights & Biases logging integration, and job orchestration end-to-end.
  • Use Case: A medical imaging researcher can launch a vertebrae segmentation model training job on a cloud GPU without manually configuring the server or syncing code, simply by specifying the experiment details.

Quick Start

Use the run-experiment skill to launch the training job for the vertebrae segmentation model defined in train.py on an available GPU.

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 training experiments across remote SSH and serverless GPU environments without manual setup?▼

Running ML training experiments across environments is automated by detecting local, remote SSH, Vast.ai, and Modal GPUs, validating availability, syncing code, and orchestrating jobs to ensure reproducible launches without manual server configuration.

What's the best way to launch machine learning jobs on Vast.ai or Modal GPUs?▼

Launching jobs on Vast.ai or Modal GPUs is handled through automated environment detection and deployment, which validates GPU availability and orchestrates the training execution end-to-end, eliminating the need to manually provision cloud instances.

Can I integrate Weights & Biases logging into my automated GPU training workflow?▼

Weights & Biases logging is supported as an optional integration within the automated training workflow, allowing you to track experiment metrics and outputs automatically as your jobs execute across different GPU environments.

Do I need to manually sync code to remote servers before deploying ML experiments?▼

Manual code synchronization is not required before deploying ML experiments; the automation handles code syncing, environment detection, and job orchestration end-to-end, ensuring consistent deployment across local and remote GPU environments.

Does automated ML experiment deployment work with local GPUs as well as cloud instances?▼

Automated ML experiment deployment supports local GPUs, remote SSH servers, on-demand Vast.ai instances, and serverless Modal GPUs, providing flexible resource selection and consistent environment validation across all supported platforms.

Why does deploying ML experiments to different GPU environments require so much repetitive setup?▼

Deploying ML experiments to different GPU environments traditionally requires repetitive environment setup and code synchronization, but this is resolved by automating environment detection, GPU availability checks, and job orchestration for consistent, reproducible launches.

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