Can I use this with CI platforms other than GitHub Actions?

While the primary templates use GitHub Actions, the logic is based on the Vast.ai CLI and Python scripts, making it easily adaptable to GitLab CI, Jenkins, or CircleCI.

How does this skill help manage CI/CD costs?

It includes budget-aware search scripts that prioritize the cheapest reliable GPUs and enforces automatic instance destruction after tests finish to avoid idle charges.

What happens to the GPU instance if a test job fails?

The provided workflow patterns use 'if: always()' cleanup steps to ensure that the Vast.ai instance is destroyed regardless of whether the tests passed or failed.

What credentials are required to use this skill?

You need a valid VASTAI_API_KEY from your Vast.ai account, which should be stored as an encrypted secret in your GitHub repository or CI environment.

Vast.ai CI/CD Integration

Name: Vast.ai CI/CD Integration
Author: jeremylongshore

byjeremylongshore

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1,687

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배포 및 DevOps

Automates the provisioning and management of GPU instances within CI/CD pipelines for high-performance testing and model validation.

This skill enables developers to seamlessly integrate Vast.ai's cost-effective GPU marketplace into automated workflows, specifically focusing on GitHub Actions. It provides standardized patterns for searching for optimal GPU offers, provisioning instances with custom Docker images, executing GPU-accelerated test suites, and ensuring resources are destroyed after completion to maintain cost efficiency. This is ideal for teams running machine learning benchmarks, training validation, or hardware-intensive testing that requires transient access to powerful GPUs without the overhead of managing permanent infrastructure.

주요 기능

01Conditional mock mode for graceful test execution in non-GPU environments

021,687 GitHub stars

03Automated GPU instance provisioning and lifecycle management via GitHub Actions

04Cost-controlled search parameters to find the most affordable and reliable hardware

05Flexible test execution patterns using SSH or direct API calls within pipelines

06Integrated budget caps and automated cleanup logic to prevent unexpected billing

사용 사례

01Executing nightly performance benchmarks for LLMs or computer vision models

02Validating Docker image compatibility with NVIDIA drivers before production deployment

03Running automated PyTest suites on live GPU hardware for every Pull Request

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jeremylongshore/claude-code-plugins-plus-skills vastai-ci-integration

For use in Claude.ai and ChatGPT

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