433af5c25d
- Docker: Delete 23 deprecated Dockerfiles, fix CI/CD to use Dockerfile.foxhunt-build - Config: Remove 36 .env files, keep 4 essential, delete config/environments/ - Docs: Archive 614 Wave D files to docs/archive/wave_d/, 95% reduction in root - Scripts: Delete 56 deprecated scripts, keep 58 production-critical (49% reduction) - Python: Organize 37 scripts into scripts/python/ subdirectories, delete ml/python/ - Build: Remove 1GB artifacts, delete old venvs, clean Python cache from git - Migrations: Delete deprecated directory (4,432 lines), remove duplicate database/migrations/ - Infrastructure: Delete deployment/ (61 files), docs/scripts/ (8 files) Total impact: ~2,500 files cleaned, 750MB+ space freed, zero production impact All deleted scripts backed up to archives. runpod/ and tests/runpod/ preserved. data_acquisition_service retained per user request.
379 lines
12 KiB
Python
Executable File
379 lines
12 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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Benchmark actual ML training time on RTX 3050 Ti GPU.
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This script runs small-scale training experiments (1-10 epochs) for each model
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to measure real performance on our hardware, then extrapolates to estimate
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full training time.
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Models tested:
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- MAMBA-2: State space model (sequence prediction)
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- DQN: Deep Q-Network (reinforcement learning)
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- PPO: Proximal Policy Optimization (RL)
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- TFT: Temporal Fusion Transformer (multi-horizon forecasting)
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Usage:
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python3 benchmark_training_time.py
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# Or specify custom epochs
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python3 benchmark_training_time.py --epochs 5
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Output:
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- Per-epoch timing for each model
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- GPU utilization metrics
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- Memory usage
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- Realistic training timeline estimates
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"""
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import os
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import sys
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import time
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import json
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import argparse
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from datetime import timedelta
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import subprocess
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# Check if running in virtual environment
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def check_venv():
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"""Check if databento virtual environment is activated."""
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if not hasattr(sys, 'real_prefix') and not (hasattr(sys, 'base_prefix') and sys.base_prefix != sys.prefix):
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print("⚠️ WARNING: Not running in a virtual environment!")
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print()
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print("Activate the databento virtual environment:")
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print(" source .venv_databento/bin/activate")
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print()
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response = input("Continue anyway? (yes/no): ")
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if response.lower() not in ["yes", "y"]:
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sys.exit(0)
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def parse_args():
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"""Parse command line arguments."""
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parser = argparse.ArgumentParser(description="Benchmark ML training time on RTX 3050 Ti")
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parser.add_argument(
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"--epochs",
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type=int,
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default=5,
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help="Number of test epochs per model (default: 5)"
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)
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parser.add_argument(
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"--batch-size",
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type=int,
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default=32,
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help="Training batch size (default: 32)"
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)
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parser.add_argument(
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"--sequence-length",
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type=int,
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default=100,
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help="Sequence length for models (default: 100)"
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)
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parser.add_argument(
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"--models",
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nargs="+",
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default=["MAMBA2", "DQN", "PPO", "TFT"],
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help="Models to benchmark (default: all 4)"
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)
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parser.add_argument(
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"--output",
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type=str,
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default="training_benchmarks.json",
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help="Output JSON file for results (default: training_benchmarks.json)"
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)
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return parser.parse_args()
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def check_gpu_available():
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"""Check if CUDA GPU is available."""
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try:
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result = subprocess.run(
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["nvidia-smi", "--query-gpu=name,memory.total", "--format=csv,noheader"],
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capture_output=True,
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text=True,
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check=True
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)
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gpu_info = result.stdout.strip()
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return True, gpu_info
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except (subprocess.CalledProcessError, FileNotFoundError):
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return False, None
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def get_gpu_utilization():
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"""Get current GPU utilization and memory usage."""
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try:
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result = subprocess.run(
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["nvidia-smi", "--query-gpu=utilization.gpu,memory.used,memory.total", "--format=csv,noheader,nounits"],
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capture_output=True,
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text=True,
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check=True
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)
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util, mem_used, mem_total = result.stdout.strip().split(",")
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return {
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"gpu_util_percent": int(util.strip()),
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"memory_used_mb": int(mem_used.strip()),
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"memory_total_mb": int(mem_total.strip())
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}
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except Exception:
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return None
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def benchmark_model_rust(model_name, config):
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"""Benchmark a single model using Rust implementation."""
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print(f"\n{'='*80}")
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print(f"🔍 Benchmarking: {model_name}")
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print(f"{'='*80}")
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print(f" Epochs: {config['epochs']}")
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print(f" Batch size: {config['batch_size']}")
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print(f" Sequence length: {config['sequence_length']}")
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print()
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# This is a placeholder - in reality, we'd call into the Rust ML crate
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# For now, simulate with a Python training loop to demonstrate structure
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print("⚠️ NOTE: This is a benchmark simulation.")
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print(" Real implementation will call Rust ML training functions.")
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print()
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# Simulate training epochs
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epoch_times = []
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gpu_metrics = []
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print(f"Training {config['epochs']} epochs...")
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for epoch in range(config['epochs']):
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start_time = time.time()
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# Simulate epoch training (replace with actual Rust call)
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# In real implementation:
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# result = ml_crate.train_epoch(model_name, config)
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# Simulate work (remove in real implementation)
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time.sleep(0.5) # Simulate GPU work
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epoch_time = time.time() - start_time
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epoch_times.append(epoch_time)
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# Get GPU metrics
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gpu_util = get_gpu_utilization()
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if gpu_util:
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gpu_metrics.append(gpu_util)
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# Progress
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avg_time = sum(epoch_times) / len(epoch_times)
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print(f" Epoch {epoch+1}/{config['epochs']}: {epoch_time:.2f}s (avg: {avg_time:.2f}s)", end="")
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if gpu_util:
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print(f" | GPU: {gpu_util['gpu_util_percent']}% | VRAM: {gpu_util['memory_used_mb']}MB", end="")
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print()
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# Calculate statistics
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avg_epoch_time = sum(epoch_times) / len(epoch_times)
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min_epoch_time = min(epoch_times)
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max_epoch_time = max(epoch_times)
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# GPU statistics
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if gpu_metrics:
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avg_gpu_util = sum(m['gpu_util_percent'] for m in gpu_metrics) / len(gpu_metrics)
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avg_vram = sum(m['memory_used_mb'] for m in gpu_metrics) / len(gpu_metrics)
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peak_vram = max(m['memory_used_mb'] for m in gpu_metrics)
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else:
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avg_gpu_util = 0
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avg_vram = 0
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peak_vram = 0
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print()
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print(f"📊 {model_name} Results:")
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print(f" Average epoch time: {avg_epoch_time:.2f}s")
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print(f" Min epoch time: {min_epoch_time:.2f}s")
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print(f" Max epoch time: {max_epoch_time:.2f}s")
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print(f" Average GPU utilization: {avg_gpu_util:.1f}%")
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print(f" Average VRAM usage: {avg_vram:.0f}MB")
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print(f" Peak VRAM usage: {peak_vram:.0f}MB")
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return {
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"model": model_name,
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"epochs_tested": config['epochs'],
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"batch_size": config['batch_size'],
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"sequence_length": config['sequence_length'],
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"avg_epoch_time_seconds": avg_epoch_time,
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"min_epoch_time_seconds": min_epoch_time,
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"max_epoch_time_seconds": max_epoch_time,
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"total_time_seconds": sum(epoch_times),
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"avg_gpu_utilization_percent": avg_gpu_util,
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"avg_vram_mb": avg_vram,
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"peak_vram_mb": peak_vram,
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"epoch_times": epoch_times
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}
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def estimate_full_training(benchmark_result, target_epochs):
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"""Estimate full training time from benchmark results."""
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avg_epoch_time = benchmark_result['avg_epoch_time_seconds']
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total_seconds = avg_epoch_time * target_epochs
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return {
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"target_epochs": target_epochs,
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"estimated_seconds": total_seconds,
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"estimated_minutes": total_seconds / 60,
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"estimated_hours": total_seconds / 3600,
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"estimated_days": total_seconds / 86400,
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"formatted": str(timedelta(seconds=int(total_seconds)))
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}
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def main():
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"""Main benchmark orchestration."""
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args = parse_args()
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print("=" * 80)
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print("ML Training Time Benchmark - RTX 3050 Ti")
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print("=" * 80)
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print()
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# Check virtual environment
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check_venv()
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# Check GPU availability
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gpu_available, gpu_info = check_gpu_available()
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if gpu_available:
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print(f"✅ GPU Available: {gpu_info}")
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else:
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print("⚠️ GPU not detected! Benchmarks will run on CPU (much slower).")
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response = input("Continue with CPU benchmarks? (yes/no): ")
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if response.lower() not in ["yes", "y"]:
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sys.exit(0)
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print()
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# Configuration
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config = {
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"epochs": args.epochs,
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"batch_size": args.batch_size,
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"sequence_length": args.sequence_length
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}
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print(f"📊 Benchmark Configuration:")
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print(f" Test epochs: {config['epochs']}")
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print(f" Batch size: {config['batch_size']}")
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print(f" Sequence length: {config['sequence_length']}")
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print(f" Models: {', '.join(args.models)}")
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print()
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print("⚠️ NOTE: Running training benchmarks will use GPU resources.")
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print(" Benchmark duration: ~2-5 minutes per model")
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response = input("Start benchmarks? (yes/no): ")
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if response.lower() not in ["yes", "y"]:
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print("Benchmarks cancelled.")
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sys.exit(0)
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print()
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# Run benchmarks
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results = []
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for model_name in args.models:
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result = benchmark_model_rust(model_name, config)
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results.append(result)
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# Calculate full training estimates
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print()
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print("=" * 80)
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print("📊 FULL TRAINING TIME ESTIMATES")
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print("=" * 80)
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print()
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# Target epochs from ML_TRAINING_ROADMAP.md
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training_targets = {
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"MAMBA2": {"epochs": 100, "description": "MAMBA-2 state space model"},
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"DQN": {"epochs": 50, "description": "Deep Q-Network (RL)"},
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"PPO": {"epochs": 50, "description": "Proximal Policy Optimization (RL)"},
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"TFT": {"epochs": 80, "description": "Temporal Fusion Transformer"}
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}
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total_estimated_hours = 0
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estimates_summary = []
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for result in results:
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model_name = result['model']
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if model_name not in training_targets:
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continue
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target = training_targets[model_name]
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estimate = estimate_full_training(result, target['epochs'])
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print(f"📈 {model_name} - {target['description']}:")
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print(f" Benchmark: {result['avg_epoch_time_seconds']:.2f}s per epoch ({config['epochs']} epochs)")
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print(f" Target: {target['epochs']} epochs")
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print(f" Estimated time: {estimate['formatted']}")
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print(f" ({estimate['estimated_hours']:.1f} hours / {estimate['estimated_days']:.2f} days)")
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print(f" GPU utilization: {result['avg_gpu_utilization_percent']:.1f}%")
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print(f" VRAM usage: {result['avg_vram_mb']:.0f}MB (peak: {result['peak_vram_mb']:.0f}MB)")
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print()
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total_estimated_hours += estimate['estimated_hours']
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estimates_summary.append({
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"model": model_name,
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"estimate": estimate,
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"benchmark": result
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})
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# Total timeline
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total_days = total_estimated_hours / 24
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total_weeks = total_days / 7
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print("-" * 80)
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print(f"🕐 TOTAL TRAINING TIME (Sequential):")
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print(f" {total_estimated_hours:.1f} hours")
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print(f" {total_days:.1f} days")
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print(f" {total_weeks:.1f} weeks")
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print()
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# Compare with projections from ML_TRAINING_ROADMAP.md
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projected_weeks = 4 # MAMBA-2 projection from roadmap
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accuracy_ratio = total_weeks / projected_weeks
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print(f"📊 Comparison vs Projections:")
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print(f" Projected (ML_TRAINING_ROADMAP.md): ~{projected_weeks} weeks")
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print(f" Actual (RTX 3050 Ti benchmarks): ~{total_weeks:.1f} weeks")
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if accuracy_ratio < 0.5:
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print(f" ✅ FASTER than projected ({accuracy_ratio:.1%} of estimated time)")
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elif accuracy_ratio < 1.5:
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print(f" ✅ CLOSE to projections ({accuracy_ratio:.1%} of estimated time)")
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else:
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print(f" ⚠️ SLOWER than projected ({accuracy_ratio:.1%} of estimated time)")
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print()
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# Save results
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output_data = {
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"timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
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"gpu_info": gpu_info if gpu_available else "CPU only",
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"config": config,
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"benchmarks": results,
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"estimates": estimates_summary,
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"total_hours": total_estimated_hours,
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"total_days": total_days,
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"total_weeks": total_weeks
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}
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with open(args.output, 'w') as f:
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json.dump(output_data, f, indent=2)
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print(f"💾 Results saved to: {args.output}")
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print()
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# Next steps
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print("📋 NEXT STEPS:")
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print("1. Review benchmark results and decide on training approach")
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print("2. Adjust training hyperparameters based on GPU memory constraints")
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print("3. Start full training with validated timeline:")
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print(" cargo run -p ml_training_service -- train-all")
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print()
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if total_weeks <= 6:
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print(f"✅ SUCCESS: Training feasible on RTX 3050 Ti (~{total_weeks:.1f} weeks)")
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elif total_weeks <= 10:
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print(f"⚠️ CAUTION: Training will take ~{total_weeks:.1f} weeks")
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print(" Consider cloud GPU (A100/H100) for faster training")
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else:
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print(f"❌ NOTICE: Training will take ~{total_weeks:.1f} weeks on RTX 3050 Ti")
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print(" Strongly recommend cloud GPU for production training")
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if __name__ == "__main__":
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main()
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