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Automates Triton inference config guidance for ML deployment, covering serving, MLOps pipelines, monitoring, and production-ready configurations aligned with best practices.

This guide provides comprehensive patterns for optimizing Turborepo build pipelines within monorepos. It covers local and remote caching strategies, including integration with Vercel and self-hosted solutions. Use it when setting up new monorepos, improving CI/CD performance, or debugging complex build dependency issues.

This skill provides a comprehensive workflow for integrating TwinMind meeting transcription services into continuous integration/continuous deployment (CI/CD) pipelines. It automates the processes of meeting documentation, real-time transcription (using Ear-3), and structured action item extraction using advanced AI models like GPT-4 and Claude. Use this when setting up automated meeting intelligence operations or managing enterprise-level transcription workflows via API.

An expert guide for building high-performance, cross-platform games using Unity 6 LTS. This skill covers advanced C# programming (DOTS, Job System), modern rendering pipelines (URP/HDRP), comprehensive performance optimization (Profiling, Culling), advanced game architecture (ECS, MVC), and networked multiplayer implementation. Ideal for professional game developers tackling complex, large-scale projects.

uv is an extremely fast Python package installer and resolver written in Rust. This guide provides a comprehensive workflow for modern Python project management and dependency handling. Use this skill to set up new projects quickly, manage dependencies much faster than traditional tools like pip, resolve complex conflicts, and optimize CI/CD pipelines, especially when dealing with monorepos or needing reproducible builds.

This skill validates API responses against predefined schemas, including OpenAPI and JSON Schema, to ensure strict contract compliance and data integrity. Use it for implementing robust contract tests in middleware or CI/CD pipelines, detecting schema drift, and guaranteeing backward compatibility across API versions.

This tool rigorously validates API schemas against industry standards, including OpenAPI 3.0/3.1, JSON Schema, and GraphQL SDL. It performs structural analysis to detect incomplete schemas, inconsistent naming, undocumented endpoints, and critical breaking changes. Use it within your CI/CD pipeline to ensure API contracts are robust, compliant, and maintainable before release.

This guide details how to integrate Vast.ai's GPU provisioning capabilities into automated CI/CD pipelines, such as GitHub Actions. It enables developers to run GPU-accelerated tests, model benchmarks, and validations automatically. Key features include robust instance lifecycle management (provisioning, running, and cleanup) and advanced cost-control mechanisms (e.g., setting budget limits) to ensure reliable and efficient testing in a cloud environment.

A comprehensive guide demonstrating the full lifecycle of GPU resource utilization on Vast.ai. This includes searching for available hardware using CLI and REST APIs, provisioning a dedicated GPU instance (e.g., using PyTorch images), connecting via SSH, running deep learning workloads, and finally, securely destroying the instance to stop billing. Ideal for testing cloud integration or managing ML compute pipelines.

This deep dive guides users through migrating complex GPU workloads, such as deep learning training pipelines, from major hyperscalers (AWS, GCP, Azure) or other cloud platforms to Vast.ai. It covers critical steps including cost comparison analysis, adapting Docker images for new environments, securely handling cloud credentials via environment variables, and implementing robust validation and rollback procedures to ensure a smooth transition.

A comprehensive guide to optimizing deep learning workflows and maximizing efficiency on cloud GPU platforms like Vast.ai. Learn techniques for selecting optimal instances by performance-per-dollar, reducing startup latency, tuning data pipelines (DataLoader, pin_memory), and scaling multi-GPU training using PyTorch and Bash scripting.

This guide details the production-grade reference architecture for running complex GPU compute workflows on Vast.ai. It covers the foundational three-tier pattern—Orchestrator, GPU Workers, and Artifact Storage—providing blueprints for job queue management, fault-tolerant training pipelines, and robust checkpointing. Use this when designing scalable and reliable ML/AI training systems.