---
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: 1.0.0
license: MIT
author: Jeremy Longshore <jeremy@intentsolutions.io>
tags: [saas, gpu-cloud, kubernetes, inference, coreweave]
compatible-with: claude-code
---

# 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",
            "cpu": str(config.cpu_cores),
        },
        "requests": {
            "nvidia.com/gpu": str(config.gpu_count),
            "memory": f"{config.memory_gb // 2}Gi",
            "cpu": str(config.cpu_cores // 2),
        },
    }
```

### Inference Client Wrapper

```python
# inference_client.py
import requests
from typing import Optional

class CoreWeaveInferenceClient:
    def __init__(self, endpoint: str, timeout: int = 30):
        self.endpoint = endpoint.rstrip("/")
        self.timeout = timeout
        self.session = requests.Session()

    def generate(self, prompt: str, max_tokens: int = 256, **kwargs) -> str:
        resp = self.session.post(
            f"{self.endpoint}/v1/completions",
            json={"prompt": prompt, "max_tokens": max_tokens, **kwargs},
            timeout=self.timeout,
        )
        resp.raise_for_status()
        return resp.json()["choices"][0]["text"]

    def chat(self, messages: list[dict], **kwargs) -> str:
        resp = self.session.post(
            f"{self.endpoint}/v1/chat/completions",
            json={"messages": messages, **kwargs},
            timeout=self.timeout,
        )
        resp.raise_for_status()
        return resp.json()["choices"][0]["message"]["content"]

    def health(self) -> bool:
        try:
            resp = self.session.get(f"{self.endpoint}/health", timeout=5)
            return resp.status_code == 200
        except Exception:
            return False
```

### Deployment Template Generator

```python
import yaml

def generate_inference_deployment(
    name: str,
    image: str,
    gpu_type: str = "a100-80gb",
    replicas: int = 1,
    port: int = 8000,
) -> str:
    config = GPU_CATALOG[gpu_type]
    return yaml.dump({
        "apiVersion": "apps/v1",
        "kind": "Deployment",
        "metadata": {"name": name},
        "spec": {
            "replicas": replicas,
            "selector": {"matchLabels": {"app": name}},
            "template": {
                "metadata": {"labels": {"app": name}},
                "spec": {
                    "containers": [{
                        "name": name,
                        "image": image,
                        "ports": [{"containerPort": port}],
                        "resources": gpu_resources(config),
                    }],
                    "affinity": gpu_affinity_block(config.gpu_class),
                },
            },
        },
    })
```

## Error Handling

| Error | Cause | Solution |
|-------|-------|----------|
| GPU class not found | Typo in node label | Use exact values from `gpu.nvidia.com/class` |
| OOM on inference | Model too large for GPU | Use larger GPU or quantized model |
| Connection refused | Service not ready | Check pod readiness probe |

## Resources

- [CoreWeave GPU Instances](https://docs.coreweave.com/docs/platform/instances/gpu-instances)
- [Kubernetes Python Client](https://github.com/kubernetes-client/python)

## Next Steps

Apply patterns in `coreweave-core-workflow-a` for KServe inference deployments.