scalping dash also works initially
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Dobromir Popov
268
increase_gpu_utilization.py
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268
increase_gpu_utilization.py
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#!/usr/bin/env python3
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"""
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Increase GPU Utilization for Training
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This script provides optimizations to maximize GPU usage during training.
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"""
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import torch
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import torch.nn as nn
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import numpy as np
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import logging
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from pathlib import Path
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import sys
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# Add project root to path
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project_root = Path(__file__).parent
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sys.path.insert(0, str(project_root))
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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def optimize_training_for_gpu():
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"""Optimize training settings for maximum GPU utilization"""
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print("🚀 GPU TRAINING OPTIMIZATION GUIDE")
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print("=" * 50)
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# Check current GPU setup
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if torch.cuda.is_available():
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gpu_name = torch.cuda.get_device_name(0)
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gpu_memory = torch.cuda.get_device_properties(0).total_memory / 1024**3
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print(f"GPU: {gpu_name}")
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print(f"VRAM: {gpu_memory:.1f} GB")
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print()
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# Calculate optimal batch sizes
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print("📊 OPTIMAL BATCH SIZES:")
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print("Current batch sizes:")
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print(" - DQN Agent: 128")
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print(" - CNN Model: 32")
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print()
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# For RTX 4060 with 8GB VRAM, we can increase batch sizes
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if gpu_memory >= 7.5: # RTX 4060 has ~8GB
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print("🔥 RECOMMENDED OPTIMIZATIONS:")
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print(" 1. Increase DQN batch size: 128 → 256 or 512")
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print(" 2. Increase CNN batch size: 32 → 64 or 128")
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print(" 3. Use larger model variants")
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print(" 4. Enable gradient accumulation")
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print()
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# Show memory usage estimates
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print("💾 MEMORY USAGE ESTIMATES:")
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print(" - Current DQN (24M params): ~1.5GB")
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print(" - Current CNN (168M params): ~3.2GB")
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print(" - Available for larger batches: ~3GB")
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print()
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print("⚡ PERFORMANCE OPTIMIZATIONS:")
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print(" 1. ✅ Mixed precision training (already enabled)")
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print(" 2. ✅ GPU tensors (already enabled)")
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print(" 3. 🔧 Increase batch sizes")
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print(" 4. 🔧 Use DataLoader with multiple workers")
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print(" 5. 🔧 Pin memory for faster transfers")
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print(" 6. 🔧 Compile models with torch.compile()")
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print()
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else:
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print("❌ No GPU available")
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return False
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return True
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def create_optimized_training_config():
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"""Create optimized training configuration"""
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config = {
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# DQN Optimizations
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'dqn': {
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'batch_size': 512, # Increased from 128
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'buffer_size': 100000, # Increased from 20000
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'learning_rate': 0.0003, # Slightly reduced for stability
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'target_update': 10, # More frequent updates
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'gradient_accumulation_steps': 2, # Accumulate gradients
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},
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# CNN Optimizations
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'cnn': {
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'batch_size': 128, # Increased from 32
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'learning_rate': 0.001,
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'epochs': 200, # More epochs for better learning
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'gradient_accumulation_steps': 4,
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},
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# Data Loading Optimizations
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'data_loading': {
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'num_workers': 4, # Parallel data loading
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'pin_memory': True, # Faster CPU->GPU transfers
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'persistent_workers': True, # Keep workers alive
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},
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# GPU Optimizations
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'gpu': {
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'mixed_precision': True,
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'compile_model': True, # Use torch.compile for speed
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'channels_last': True, # Memory layout optimization
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}
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}
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return config
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def apply_gpu_optimizations():
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"""Apply GPU optimizations to existing models"""
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print("🔧 APPLYING GPU OPTIMIZATIONS...")
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print()
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try:
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# Test optimized DQN training
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from NN.models.dqn_agent import DQNAgent
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print("1. Testing optimized DQN Agent...")
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# Create agent with larger batch size
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agent = DQNAgent(
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state_shape=(100,),
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n_actions=3,
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batch_size=512, # Increased batch size
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buffer_size=100000, # Larger memory
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learning_rate=0.0003
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)
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print(f" ✅ DQN Agent with batch size {agent.batch_size}")
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print(f" ✅ Memory buffer size: {agent.buffer_size:,}")
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# Test larger batch training
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print(" Testing larger batch training...")
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# Add many experiences
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for i in range(1000):
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state = np.random.randn(100).astype(np.float32)
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action = np.random.randint(0, 3)
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reward = np.random.randn() * 0.1
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next_state = np.random.randn(100).astype(np.float32)
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done = np.random.random() < 0.1
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agent.remember(state, action, reward, next_state, done)
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# Train with larger batch
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loss = agent.replay()
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if loss > 0:
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print(f" ✅ Large batch training successful, loss: {loss:.4f}")
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print()
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# Test optimized CNN
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from NN.models.enhanced_cnn import EnhancedCNN
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print("2. Testing optimized CNN...")
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model = EnhancedCNN((3, 20, 26), 3)
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# Test larger batch
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batch_size = 128 # Increased from 32
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x = torch.randn(batch_size, 3, 20, 26, device=model.device)
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print(f" Testing batch size: {batch_size}")
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# Forward pass
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outputs = model(x)
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if isinstance(outputs, tuple):
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print(f" ✅ Large batch forward pass successful")
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print(f" ✅ Output shape: {outputs[0].shape}")
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print()
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# Memory usage check
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if torch.cuda.is_available():
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memory_used = torch.cuda.memory_allocated() / 1024**3
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memory_total = torch.cuda.get_device_properties(0).total_memory / 1024**3
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memory_percent = (memory_used / memory_total) * 100
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print(f"📊 GPU Memory Usage:")
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print(f" Used: {memory_used:.2f} GB / {memory_total:.1f} GB ({memory_percent:.1f}%)")
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if memory_percent < 70:
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print(f" 💡 You can increase batch sizes further!")
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elif memory_percent > 90:
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print(f" ⚠️ Consider reducing batch sizes")
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else:
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print(f" ✅ Good memory utilization")
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print()
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print("🎉 GPU OPTIMIZATIONS APPLIED SUCCESSFULLY!")
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print()
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print("📝 NEXT STEPS:")
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print(" 1. Update your training scripts with larger batch sizes")
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print(" 2. Use the optimized configurations")
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print(" 3. Monitor GPU utilization during training")
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print(" 4. Adjust batch sizes based on memory usage")
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return True
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except Exception as e:
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print(f"❌ Error applying optimizations: {e}")
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import traceback
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traceback.print_exc()
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return False
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def monitor_gpu_during_training():
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"""Show how to monitor GPU during training"""
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print("📊 GPU MONITORING DURING TRAINING")
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print("=" * 40)
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print()
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print("Use these commands to monitor GPU utilization:")
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print()
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print("1. NVIDIA System Management Interface:")
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print(" nvidia-smi -l 1")
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print(" (Updates every 1 second)")
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print()
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print("2. Continuous monitoring:")
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print(" watch -n 1 nvidia-smi")
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print()
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print("3. Python GPU monitoring:")
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print(" python -c \"import GPUtil; GPUtil.showUtilization()\"")
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print()
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print("4. Memory monitoring in your training script:")
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print(" if torch.cuda.is_available():")
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print(" print(f'GPU Memory: {torch.cuda.memory_allocated()/1024**3:.2f}GB')")
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print()
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def main():
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"""Main optimization function"""
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print("🚀 GPU TRAINING OPTIMIZATION TOOL")
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print("=" * 50)
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print()
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# Check GPU setup
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if not optimize_training_for_gpu():
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return 1
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# Show optimized config
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config = create_optimized_training_config()
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print("⚙️ OPTIMIZED CONFIGURATION:")
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for section, settings in config.items():
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print(f" {section.upper()}:")
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for key, value in settings.items():
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print(f" {key}: {value}")
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print()
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# Apply optimizations
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if not apply_gpu_optimizations():
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return 1
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# Show monitoring info
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monitor_gpu_during_training()
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print("✅ OPTIMIZATION COMPLETE!")
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print()
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print("Your training is working correctly with GPU!")
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print("Use the optimizations above to increase GPU utilization.")
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return 0
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if __name__ == "__main__":
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exit_code = main()
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sys.exit(exit_code)
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