benchmarking-ml-models

name	benchmarking-ml-models
description	Runs ML model benchmarks and evaluations. Measures inference speed, memory usage, and accuracy metrics. Use for "벤치마크", "모델 평가", "성능 테스트", "inference 속도" requests.

ML Benchmark

Model performance benchmarking.

Quick Benchmark

import time
import torch

# Warmup
for _ in range(10):
    model(sample_input)

# Benchmark
start = time.time()
for _ in range(100):
    model(sample_input)
torch.cuda.synchronize()
elapsed = time.time() - start

print(f"Avg latency: {elapsed/100*1000:.2f}ms")

Metrics

Metric	Description	Command
Latency	Inference time	time.time()
Throughput	Samples/sec	samples / elapsed
Memory	VRAM usage	torch.cuda.max_memory_allocated()
Accuracy	Model quality	accuracy_score(y_true, y_pred)

Benchmark Script

# Run standard benchmark
python benchmark.py --model ./model.pt --batch-size 32 --iterations 100

Output Format

## Benchmark Results: {model_name}

| Metric | Value |
|--------|-------|
| Latency (p50) | 15.2ms |
| Latency (p99) | 22.1ms |
| Throughput | 65 samples/sec |
| Memory | 4.2 GB |
| Accuracy | 92.3% |

### Configuration
- GPU: NVIDIA A100
- Batch size: 32
- Precision: FP16

Compare Models

results = {}
for model_name, model in models.items():
    results[model_name] = benchmark(model)

# Generate comparison table

Best Practices

• Always warmup before measuring
• Use torch.cuda.synchronize() for GPU
• Report p50/p99 latencies
• Document hardware configuration