coreweave-sdk-patterns

# CoreWeave SDK Patterns ## Overview CoreWeave is Kubernetes-native -- use kubectl, Kubernetes Python client, or Helm for programmatic management. These patterns cover GPU-aware deployment templates, inference client wrappers, and node affinity configurations. ## Instructions ### GPU Affinity Helper ```python # coreweave_helpers.py from dataclasses import dataclass @dataclass class GPUConfig: gpu_class: str # A100_PCIE_80GB, H100_SXM5, L40, etc. gpu_count: int = 1 memory_gb: int = 32 cpu_cores: int = 4 GPU_CATALOG = { "a100-80gb": GPUConfig("A100_PCIE_80GB", memory_gb=48, cpu_cores=8), "h100-80gb": GPUConfig("H100_SXM5", memory_gb=64, cpu_cores=12), "l40": GPUConfig("L40", memory_gb=24, cpu_cores=4), "a100-8x": GPUConfig("A100_NVLINK_A100_SXM4_80GB", gpu_count=8, memory_gb=256, cpu_cores=64), } def gpu_affinity_block(gpu_class: str) -> dict: return { "nodeAffinity": { "requiredDuringSchedulingIgnoredDuringExecution": { "nodeSelectorTerms": [{ "matchExpressions": [{ "key": "gpu.nvidia.com/class", "operator": "In", "values": [gpu_class], }] }] } } } def gpu_resources(config: GPUConfig) -> dict: return { "limits": { "nvidia.com/gpu": str(config.gpu_count), "memory": f"{config.memory_gb}Gi", ...