Imported from jeremylongshore/tons-of-skills-marketplace (skills/.curated/coreweave-sdk-patterns/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill coreweave-sdk-patterns. Copyright stays with the author (MIT).

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": {
                "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

# 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

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

Prerequisites

• A namespace-scoped Kubernetes credential and endpoint from the approved environment.
• An image, GPU class, and resource budget reviewed for the target workload.
• A secret-manager reference for private registry or model access; never pass tokens into generated YAML or application logs.

Output

• A reusable client or deployment manifest pattern with explicit GPU resources and affinity constraints.
• A readiness-aware request path that distinguishes unavailable services from a valid application response.
• A generated manifest that can be reviewed, versioned, and rolled back before apply.

Examples

Generate a manifest, inspect it for the expected namespace and GPU resource limit, then apply it first in staging:

manifest = generate_inference_deployment('summarizer', 'registry.example/summarizer:v1')
open('summarizer.yaml', 'w').write(manifest)

kubectl -n inference-staging apply --dry-run=server -f summarizer.yaml
kubectl -n inference-staging apply -f summarizer.yaml
kubectl -n inference-staging rollout status deployment/summarizer --timeout=10m

If validation or rollout fails, retain the reviewed manifest and redacted events; do not broaden the client credential or bypass the admission policy.

Resources

• CoreWeave GPU Instances
• Kubernetes Python Client

Next Steps

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