Skill UI

This guide details how to set up and execute large-scale, distributed GPU training jobs on the CoreWeave platform. It covers both single-node multi-GPU setups and multi-node training utilizing PyTorch DDP and Kubernetes resources (A100/H100). This workflow is ideal for fine-tuning large language models (LLMs) or running complex deep learning models that require high-performance, shared-storage compute clusters.

This skill guides the secure handling and lifecycle management of large-scale data artifacts, including training datasets, model weights, and checkpoint snapshots, within GPU cloud environments. It provides best practices for data pipelines, covering secure import via Kubernetes PVCs, compliant export, and stringent data validation, ensuring adherence to encryption standards (AES-256) and regulatory requirements (PII masking, RBAC).

This guide provides a deep dive into migrating machine learning workloads, including inference services and training pipelines, from major hyperscalers (AWS, GCP, Azure) to the CoreWeave GPU cloud. It covers cost comparisons, containerization, Kubernetes YAML adaptation, and a phased deployment strategy, helping users ensure a smooth, cost-effective, and optimized transition.

This guide provides expert strategies for optimizing GPU inference performance on CoreWeave infrastructure. It covers GPU selection based on workload (LLM, image generation, training), advanced techniques like continuous batching (using vLLM), autoscaling setup (HPA), and benchmarking data. Use this to maximize GPU utilization, minimize latency, and optimize large-scale AI model deployment.

A comprehensive checklist for ensuring GPU workloads, such as inference services or model training pipelines, are fully prepared for production deployment on CoreWeave. It covers critical MLOps and DevOps aspects including autoscaling, resource limits, security policies, persistent storage validation, monitoring setup, and rollback procedures.

A comprehensive solution for monitoring CoreWeave cluster events and GPU workload status using webhooks. It tracks critical lifecycle events such as pod readiness, job completion, volume attachment, and node health. Essential for building automated scaling, alerting, and recovery pipelines for large-scale GPU-accelerated inference and training workloads.

A comprehensive tool for creating high-quality training datasets for custom object detection and segmentation models. It supports multiple advanced annotation methods including BBox drawing, SAM2 pixel-perfect segmentation, and DINOv3 visual grounding. Features include AI auto-detection, object tracking, and standardized COCO format export, streamlining the entire data pipeline from capture to training.

A comprehensive skill designed to query and download large-scale public cancer imaging data from the NCI Imaging Data Commons (IDC). Utilizing the idc-index package, users can access diverse modalities, including radiology (CT, MR, PET) and pathology datasets. The tool supports complex metadata querying, subset selection by cancer type or anatomical site, and visualization, making it essential for AI model training and scientific medical research. No authentication is required for data access.

This comprehensive workflow manages the end-to-end life cycle of sales readiness training on the MindTickle platform. Users can create complex courses containing mixed media (videos, documents, quizzes). It allows assigning courses to teams with due dates, tracking learner progress and quiz scores via analytics API, and updating content modules as business needs evolve.

This guide provides a minimal working example demonstrating core MindTickle API functionality. It shows how to programmatically create structured training modules (including videos and quizzes), assign them to specific teams or users with deadlines, and track user progress and overall completion rates using analytics endpoints. It is ideal for integrating Learning Management System (LMS) functionality into internal corporate tools and automating onboarding processes.

This guide provides comprehensive, step-by-step instructions for installing and securely configuring the MindTickle SDK and API credentials. It covers prerequisites, environment variable setup, and includes verification snippets for both Node.js/TypeScript and Python environments. Use this when initiating a new MindTickle system integration or developing an application that requires API access to sales, training, or performance data.

This tool provides a robust local development workflow (dev loop) for integrating with the MindTickle sales enablement and readiness API. It simulates critical data points—such as training modules, user progress, and coaching data—using mock services. This allows developers to build and test complex sales readiness dashboards and features rapidly and reliably, by toggling between a mock mode for fast iteration and a sandbox mode for real-world validation against the MindTickle platform.