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

Modern ML systems require rigorous operations practices:

Reliability: Models must perform consistently in production

Reproducibility: Experiments must be tracked and reproducible

Scalability: Systems must handle growing data and traffic

Observability: You need visibility into what models are doing

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
@dataclass
class MetricSnapshot:
    timestamp: float
    metric_name: str
    value: float
    labels: dict
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

Version everything — models, data, configs, and code

Automate testing — catch regressions before production

Monitor continuously — don't wait for users to report issues

Document experiments — future you will thank present you

Use feature flags — control rollouts without code changes

Resources

MLflow documentation: https://mlflow.org/docs

DVC documentation: https://dvc.org/doc

Kubeflow documentation: https://www.kubeflow.org/docs

Made With ML MLOps course: https://madewithml.com