Skill UI

This runbook provides structured steps for responding to critical production incidents on the CoreWeave platform. Use it when dealing with GPU workload failures, inference service outages, or general Kubernetes resource issues. It guides users through checking pod status, node health, and diagnosing common model loading errors to ensure rapid service recovery.

This guide provides step-by-step instructions to configure access to the CoreWeave Kubernetes Service (CKS). It covers downloading the kubeconfig file, setting API tokens, and verifying GPU node access using standard `kubectl` commands. This is essential for deploying AI/ML workloads on bare-metal GPU infrastructure.

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 details setting up and managing CoreWeave GPU cloud infrastructure across Development, Staging, and Production environments. It ensures strict resource separation using dedicated Kubernetes namespaces for GPU quotas and scaling policies. Use this when deploying complex ML/AI workloads requiring robust environment isolation and controlled promotion workflows.

This skill provides comprehensive observability for CoreWeave's GPU-intensive workloads running on Kubernetes. It monitors critical operational metrics, including GPU utilization, memory usage, inference latency (p99), and job completion rates. Use it for setting up DCGM metrics dashboards, configuring proactive alerts for underutilization or Out-Of-Memory (OOM) conditions, and ensuring high reliability for AI/ML deployments.

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.

This skill provides methods for managing GPU quota limits and rate restrictions for CoreWeave cloud services. It includes using `kubectl` to check resource quotas and demonstrates implementing robust request queuing mechanisms using Python's `asyncio` library, ensuring stable and efficient resource allocation for high-concurrency inference endpoints.

This reference architecture provides a comprehensive blueprint for deploying Machine Learning models on CoreWeave's GPU cloud infrastructure. It details the structure for multi-model serving (e.g., vLLM, TGI), scalable Kubernetes deployments, shared persistent storage (PVC), and autoscaling mechanisms using KServe/Knative. Use this when designing robust MLOps pipelines, planning high-performance multi-model inference services, or establishing standardized best practices for GPU cloud deployment.

This SDK provides production-ready Python patterns for managing GPU workloads on CoreWeave. It includes helpers for defining specific GPU affinity, setting resource limits (A100, H100), and generating complete Kubernetes Deployment YAML. Use this library for building robust inference clients, managing GPU deployments programmatically, or automating complex AI workflows.

Provides comprehensive guidelines for securing CoreWeave cloud deployments on Kubernetes. Covers critical security areas including credential management, implementing Role-Based Access Control (RBAC), enforcing network isolation policies, and handling sensitive secrets for ML model access. Essential for hardening GPU workloads and protecting data in cloud environments.

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.