coreweave-sdk-patterns

name	coreweave-sdk-patterns
description	Production-ready patterns for CoreWeave GPU workload management with kubectl and Python. Use when building inference clients, managing GPU deployments programmatically, or creating reusable CoreWeave deployment templates. Trigger with phrases like "coreweave patterns", "coreweave client", "coreweave Python", "coreweave deployment template".
allowed-tools	Read, Write, Edit
version	1.11.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","gpu-cloud","kubernetes","inference","coreweave"]
compatibility	Designed for Claude Code

CoreWeave SDK Patterns

Community-contributed. Not affiliated with, endorsed by, or sponsored by CoreWeave, Inc. CoreWeave is a registered trademark of CoreWeave, Inc.

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

# 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": {
                : [{
                    : [{
                        : ,
                        : ,
                        : [gpu_class],
                    }]
                }]
            }
        }
    }

 () -> :
     {
        : {
            : (config.gpu_count),
            : ,
            : (config.cpu_cores),
        },
        : {
            : (config.gpu_count),
            : ,
            : (config.cpu_cores // ),
        },
    }