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",
            "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.