coreweave-deploy-integration

# CoreWeave Deploy Integration ## Overview Deploy GPU-accelerated inference services on CoreWeave Kubernetes (CKS). This skill covers containerizing inference workloads with NVIDIA CUDA base images, configuring GPU resource limits and node affinity for A100/H100 scheduling, setting up health checks that validate GPU availability and model loading, and executing rolling updates that respect GPU node draining. CoreWeave's scheduler requires explicit GPU resource requests to place pods on the correct hardware tier. ## Docker Configuration ```dockerfile FROM nvidia/cuda:12.4.0-runtime-ubuntu22.04 AS base RUN apt-get update && apt-get install -y --no-install-recommends \ python3 python3-pip curl && rm -rf /var/lib/apt/lists/* WORKDIR /app COPY requirements.txt ./ RUN pip3 install --no-cache-dir -r requirements.txt FROM base RUN groupadd -r app && useradd -r -g app app COPY --chown=app:app src/ ./src/ COPY --chown=app:app models/ ./models/ USER app EXPOSE 8080 HEALTHCHECK --interval=30s --timeout=10s --retries=3 \ CMD curl -f http://localhost:8080/health || exit 1 CMD ["python3", "src/server.py"] ``` ## Environment Variables ```bash export COREWEAVE_API_KEY="cw_xxxxxxxxxxxx" export COREWEAVE_NAMESPACE="tenant-my-org" export MODEL_NAME="meta-llama/Llama-3.1-8B-Instruct" export GPU_TYPE="A100_PCIE_80GB" export GPU_COUNT="1" export LOG_LEVEL="info" export PORT="8080" ``` ## Health Check Endpoint ```typescript import express from 'express'; import { execSync } from 'chil...