GPU Resource Management

Overview

Efficiently scheduling and utilizing GPU resources for ML workloads. This guide covers practical implementation for production ML systems.

Why This Matters in MLOps

Setup

bash
Install required tools
pip install kubernetes mlflow pandas numpy scikit-learn
Or with Docker
docker pull python:3.11-slim

Core Implementation

python
import os
import json
import logging
from datetime import datetime
from pathlib import Path
logger = logging.getLogger(__name__)
class GPUResourceManagement:
    """
    GPU Resource Management implementation.
    
    Handles: infrastructure
    Tool: kubernetes
    """
    
    def __init__(self, config: dict = None):
        self.config = config or self._default_config()
        self._setup()
    
    def _default_config(self) -> dict:
        return {
            "tool": "kubernetes",
            "environment": os.getenv("ENVIRONMENT", "development"),
            "log_level": "INFO",
        }
    
    def _setup(self):
        """Initialize kubernetes connection and resources."""
        logging.basicConfig(level=self.config.get("log_level", "INFO"))
        logger.info(f"Initialized GPU Resource Management with config: {self.config}")
    
    def run(self, **kwargs) -> dict:
        """Execute infrastructure."""
        start = datetime.utcnow()
        
        try:
            result = self._execute(**kwargs)
            
            elapsed = (datetime.utcnow() - start).total_seconds()
            logger.info(f"GPU Resource Management completed in {elapsed:.2f}s")
            
            return {
                "status": "success",
                "result": result,
                "elapsed_seconds": elapsed
            }
        
        except Exception as e:
            logger.error(f"GPU Resource Management failed: {e}")
            return {
                "status": "failed", 
                "error": str(e)
            }
    
    def _execute(self, **kwargs) -> dict:
        """Core infrastructure logic. Override to customize."""
        return {"completed": True, "tool": "kubernetes"}
Configuration
config = {
    "tool": "kubernetes",
    "tracking_uri": os.getenv("MLFLOW_TRACKING_URI", "http://localhost:5000"),
    "artifact_root": "./artifacts",
}
Initialize
processor = GPUResourceManagement(config)
result = processor.run()
print(json.dumps(result, indent=2))

KUBERNETES Integration

python
Specific kubernetes integration for infrastructure
import subprocess
def setup_kubernetes():
    """Configure kubernetes for infrastructure."""
    
    # Initialize project
    print(f"Setting up kubernetes for infrastructure...")
    
    # Example configuration
    config = {
        "project": "my-ml-project",
        "tool": "kubernetes",
        "specialty": "infrastructure",
        "version": "1.0.0"
    }
    
    # Save configuration
    Path(".kubernetes").mkdir(exist_ok=True)
    with open(f".kubernetes/config.json", "w") as f:
        json.dump(config, f, indent=2)
    
    print(f"kubernetes configured for infrastructure")
    return config
config = setup_kubernetes()

Monitoring and Alerting

python
from dataclasses import dataclass
import time
class MLOpsMonitor:
    """Monitor infrastructure metrics."""
    
    def __init__(self):
        self.metrics: list[MetricSnapshot] = []
        self.thresholds = {
            "error_rate": 0.05,
            "latency_p99_ms": 1000,
            "data_drift_score": 0.3
        }
    
    def record(self, metric: str, value: float, labels: dict = None):
        snapshot = MetricSnapshot(
            timestamp=time.time(),
            metric_name=metric,
            value=value,
            labels=labels or {}
        )
        self.metrics.append(snapshot)
        self._check_threshold(metric, value)
    
    def _check_threshold(self, metric: str, value: float):
        threshold = self.thresholds.get(metric)
        if threshold and value > threshold:
            logger.warning(f"ALERT: {metric}={value:.3f} exceeds threshold {threshold}")
monitor = MLOpsMonitor()

CI/CD Integration

yaml
.github/workflows/ml-pipeline.yml
name: ML Pipeline
on:
  push:
    paths: ['src/', 'data/']
jobs:
  train-and-evaluate:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      
      - name: Setup Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.11'
      
      - name: Install dependencies
        run: pip install -r requirements.txt
      
      - name: Run infrastructure
        run: python -m src.gpu_resource_management
        env:
          MLFLOW_TRACKING_URI: ${{ secrets.MLFLOW_URI }}
      
      - name: Check model quality
        run: python -m src.validate_model

Best Practices

Resources