Skill UI

A comprehensive guide for deploying Machine Learning training jobs and inference services on the Vast.ai GPU cloud. This skill covers the entire deployment lifecycle, including optimizing Docker images for performance, creating automated provisioning scripts using the Vast.ai CLI, implementing robust data transfer strategies (SCP, rsync), and performing post-deployment health checks.

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 guide establishes a fast, reproducible local development workflow for GPU workloads using Vast.ai. It covers key practices such as testing Docker images locally (in CPU mode), mocking external APIs like Vast.ai, and implementing robust unit and integration tests. This is essential for minimizing cloud GPU costs and accelerating the iteration cycle for complex AI/ML projects.

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.

Comprehensive guide for upgrading the Vast.ai CLI and Python SDK. This skill helps users manage API version migrations, detect breaking changes, and update associated Docker images (e.g., CUDA versions). It ensures continuous compatibility when working with cloud GPU computing resources.

This comprehensive tool automates the process of translating non-English official documents (images) for visa applications. It handles image preprocessing (conversion and rotation), performs robust OCR using multiple frameworks, translates the extracted text into professional English while maintaining document structure, and generates a certified bilingual PDF containing both the original image and the formatted translation.

This skill leverages AI to analyze your application and automatically generate highly optimized Dockerfiles. It incorporates industry best practices like multi-stage builds, minimal base images, and robust security configurations. Use it when containerizing applications or needing to optimize existing Docker configurations for production deployment and efficient orchestration.

Generate premium images, videos, and audio straight from the terminal using muapi.ai’s schema-driven workflows and 100+ models such as Flux, Midjourney, Kling, Veo, and Suno.

Automates posting AI-focused job ads on Xiaohongshu by generating geek-style cover and detail images, crafting compliant copy, and running Playwright to publish with minimal input.

MarkItDown converts PDFs, DOCX, PPTX, XLSX, HTML, JSON, XML, archives, YouTube links, images via OCR, and audio via transcription into clean Markdown, providing token-efficient text ready for LLMs and documentation workflows.

Manage microscopy data with the OMERO Python API: access projects/datasets/images, retrieve and analyze pixel data, handle ROIs and annotations, store results in tables, and automate batch workflows for high-content screening.

PathML is a Python toolkit for computational pathology workflows, loading 160+ slide formats, preprocessing H&E images, constructing spatial graphs, training HoVer-Net/HACTNet models, and analyzing multiplex imaging for ML-ready pathology data.