Energy-Optimized Inference Scheduler

What It Does

Installation

Usage

python energy_optimized_inference.py --model my_model.pt --input data.npy --min_batch 16 --max_batch 64

Source Code

import argparse
import torch
import numpy as np
import psutil
import schedule
import time
from typing import Tuple

def monitor_energy_usage() -> float:
    """
    Monitors and returns the current energy usage of the system.
    """
    try:
        # Simulate energy monitoring using CPU utilization as a proxy
        return psutil.cpu_percent(interval=0.1)
    except Exception as e:
        print(f"Error monitoring energy usage: {e}")
        return 0.0

def optimize_batch_size(model: torch.nn.Module, data: np.ndarray, min_batch: int, max_batch: int) -> Tuple[int, np.ndarray]:
    """
    Optimizes batch size for energy-efficient inference.

    Args:
        model (torch.nn.Module): PyTorch model for inference.
        data (np.ndarray): Input data for inference.
        min_batch (int): Minimum batch size.
        max_batch (int): Maximum batch size.

    Returns:
        Tuple[int, np.ndarray]: Optimized batch size and inference results.
    """
    best_batch_size = min_batch
    best_energy_usage = float('inf')
    results = None

    for batch_size in range(min_batch, max_batch + 1):
        try:
            # Simulate batching
            batches = [data[i:i + batch_size] for i in range(0, len(data), batch_size)]
            energy_usage = 0

            for batch in batches:
                inputs = torch.tensor(batch, dtype=torch.float32)
                with torch.no_grad():
                    _ = model(inputs)
                energy_usage += monitor_energy_usage()

            avg_energy_usage = energy_usage / len(batches)

            if avg_energy_usage < best_energy_usage:
                best_energy_usage = avg_energy_usage
                best_batch_size = batch_size
                results = torch.tensor(data, dtype=torch.float32)

        except Exception as e:
            print(f"Error during batch size optimization: {e}")

    return best_batch_size, results

def schedule_inference(model_path: str, input_path: str, min_batch: int, max_batch: int):
    """
    Schedules inference tasks to optimize energy consumption.

    Args:
        model_path (str): Path to the PyTorch model file.
        input_path (str): Path to the input data file (numpy array).
        min_batch (int): Minimum batch size.
        max_batch (int): Maximum batch size.
    """
    try:
        model = torch.load(model_path)
        data = np.load(input_path)

        def inference_task():
            batch_size, results = optimize_batch_size(model, data, min_batch, max_batch)
            print(f"Optimized Batch Size: {batch_size}")
            print(f"Inference Results: {results}")

        schedule.every(10).seconds.do(inference_task)

        print("Starting inference scheduler...")
        while True:
            schedule.run_pending()
            time.sleep(1)

    except Exception as e:
        print(f"Error scheduling inference: {e}")

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Energy-Optimized Inference Scheduler")
    parser.add_argument("--model", required=True, help="Path to the PyTorch model file")
    parser.add_argument("--input", required=True, help="Path to the input data file (numpy array)")
    parser.add_argument("--min_batch", type=int, required=True, help="Minimum batch size")
    parser.add_argument("--max_batch", type=int, required=True, help="Maximum batch size")

    args = parser.parse_args()

    schedule_inference(args.model, args.input, args.min_batch, args.max_batch)