---

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 jeremy@intentsolutions.io 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": {
                "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

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

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.