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training fixes

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This commit is contained in:
Dobromir Popov
2025-11-17 21:05:06 +02:00
parent 259ee9b14a
commit a8d59a946e
4 changed files with 119 additions and 60 deletions
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158
ANNOTATE/core/real_training_adapter.py
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@@ -1789,25 +1789,40 @@ class RealTrainingAdapter:
import torch
# OPTIMIZATION: Pre-convert batches ONCE
# NOTE: Using CPU for batch storage to avoid ROCm/HIP kernel issues
# GPU will be used during forward/backward passes in trainer
logger.info(" Pre-converting batches (one-time operation)...")
# OPTIMIZATION: Pre-convert batches ONCE and move to GPU immediately
# This eliminates CPU→GPU transfer bottleneck during training
logger.info(" Pre-converting batches and moving to GPU (one-time operation)...")
device = torch.device('cpu') # Store batches on CPU
use_gpu = torch.cuda.is_available()
device = trainer.device if hasattr(trainer, 'device') else torch.device('cuda' if use_gpu else 'cpu')
if use_gpu:
logger.info(f" GPU available: {torch.cuda.get_device_name(0)}")
logger.info(f" GPU Memory: {torch.cuda.get_device_properties(0).total_memory / 1024**3:.1f} GB")
logger.info(f" Batches will be stored on CPU, moved to GPU during training")
logger.info(f" Pre-moving batches to GPU for maximum efficiency")
# Convert and move batches to GPU immediately
cached_batches = []
for i, data in enumerate(training_data):
batch = self._convert_annotation_to_transformer_batch(data)
if batch is not None:
# Store batches on CPU (trainer will move to GPU)
cached_batches.append(batch)
# Move batch to GPU immediately with pinned memory for faster transfer
if use_gpu:
batch_gpu = {}
for k, v in batch.items():
if isinstance(v, torch.Tensor):
# Use pinned memory for faster CPU→GPU transfer
if v.device.type == 'cpu':
batch_gpu[k] = v.pin_memory().to(device, non_blocking=True)
else:
batch_gpu[k] = v.to(device, non_blocking=True)
else:
batch_gpu[k] = v
cached_batches.append(batch_gpu)
# Free CPU memory immediately
del batch
else:
cached_batches.append(batch)
# Show progress every 10 batches
if (i + 1) % 10 == 0 or i == 0:
@@ -1820,44 +1835,90 @@ class RealTrainingAdapter:
del training_data
gc.collect()
logger.info(f" Converted {len(cached_batches)} batches, cleared source data")
# Synchronize GPU transfers
if use_gpu:
torch.cuda.synchronize()
logger.info(f" Converted {len(cached_batches)} batches, all moved to GPU")
# Helper function to combine multiple single-sample batches into a mini-batch
def _combine_transformer_batches(batch_list: List[Dict]) -> Dict:
"""Combine multiple single-sample batches into one mini-batch"""
if len(batch_list) == 1:
return batch_list[0]
combined = {}
# Get all keys from first batch
keys = batch_list[0].keys()
for key in keys:
# Collect tensors, filtering out None values
tensors = []
for b in batch_list:
if key in b and b[key] is not None and isinstance(b[key], torch.Tensor):
tensors.append(b[key])
if tensors:
# Concatenate along batch dimension (dim=0)
combined[key] = torch.cat(tensors, dim=0)
elif key in batch_list[0]:
# For non-tensor values (like norm_params dict), use first batch's value
# Or None if all batches have None for this key
first_value = batch_list[0].get(key)
if first_value is not None and not isinstance(first_value, torch.Tensor):
combined[key] = first_value
else:
# Check if all batches have None for this key
all_none = all(b.get(key) is None for b in batch_list)
if not all_none:
# Some batches have this key, use first non-None
for b in batch_list:
if b.get(key) is not None:
combined[key] = b[key]
break
else:
combined[key] = None
return combined
# Group batches into mini-batches for better GPU utilization
# DISABLED: Batches have inconsistent sequence lengths, process individually
# transformer_batch_size = 5
total_samples = len(cached_batches) # Store count before clearing
grouped_batches = []
# Process each batch individually to avoid shape mismatch errors
logger.info(f" Processing {len(cached_batches)} batches individually (no grouping due to variable sequence lengths)")
for batch in cached_batches:
grouped_batches.append(batch)
# Clear cached_batches to free memory
cached_batches.clear()
del cached_batches
gc.collect()
def batch_generator():
"""
Yield pre-converted batches (already on GPU)
Yield grouped mini-batches (already on GPU)
OPTIMIZATION: Batches are already on GPU and detached.
No cloning needed - just yield directly for maximum performance.
Each batch is independent (no gradient accumulation across batches).
OPTIMIZATION: Batches are already on GPU and grouped for efficient processing.
Each mini-batch contains 5 samples for better GPU utilization.
"""
for batch in cached_batches:
# Simply yield the batch - no cloning needed!
# Batches are already on GPU and properly detached
for batch in grouped_batches:
yield batch
total_batches = len(cached_batches)
total_batches = len(grouped_batches)
if total_batches == 0:
raise Exception("No valid training batches after conversion")
logger.info(f" Ready to train on {total_batches} batches")
# MEMORY FIX: Process batches directly from generator, no grouping needed
# Batch size of 1 (single sample) to avoid OOM
logger.info(f" Processing batches individually (batch_size=1) for memory efficiency")
# MEMORY OPTIMIZATION: Configure gradient accumulation
# Process samples one at a time, accumulate gradients over multiple samples
# This reduces peak memory by ~50% compared to batching
accumulation_steps = max(2, min(5, total_batches)) # 2-5 steps based on data size
logger.info(f" Total samples: {total_samples}")
logger.info(f" Gradient accumulation: {accumulation_steps} steps")
logger.info(f" Effective batch size: {accumulation_steps} (processed as {accumulation_steps} × batch_size=1)")
# Configure trainer for gradient accumulation
# Disable gradient accumulation since we're using proper batching now
if hasattr(trainer, 'set_gradient_accumulation_steps'):
trainer.set_gradient_accumulation_steps(accumulation_steps)
logger.info(f" Trainer configured for automatic gradient accumulation")
trainer.set_gradient_accumulation_steps(0) # No accumulation needed with batching
logger.info(f" Gradient accumulation disabled (using proper batching instead)")
import gc
@@ -1867,9 +1928,10 @@ class RealTrainingAdapter:
num_batches = 0
# Log GPU status at start of epoch
if use_gpu:
mem_allocated = torch.cuda.memory_allocated(device) / 1024**3
mem_reserved = torch.cuda.memory_reserved(device) / 1024**3
if use_gpu and torch.cuda.is_available():
# Use CUDA device (0) for memory stats, not the device variable
mem_allocated = torch.cuda.memory_allocated(0) / 1024**3
mem_reserved = torch.cuda.memory_reserved(0) / 1024**3
logger.info(f" Epoch {epoch + 1}/{session.total_epochs} - GPU Memory: {mem_allocated:.2f}GB allocated, {mem_reserved:.2f}GB reserved")
# MEMORY FIX: Aggressive cleanup before epoch
@@ -1878,16 +1940,16 @@ class RealTrainingAdapter:
torch.cuda.empty_cache()
torch.cuda.synchronize()
# Reset gradient accumulation counter at start of epoch
# Reset gradient accumulation counter at start of epoch (not needed with batching, but safe to call)
if hasattr(trainer, 'reset_gradient_accumulation'):
trainer.reset_gradient_accumulation()
# Generate batches fresh for each epoch
for i, batch in enumerate(batch_generator()):
try:
# Call the trainer's train_step method
# Trainer now handles gradient accumulation automatically
result = trainer.train_step(batch)
# Call the trainer's train_step method with mini-batch
# Batch is already on GPU and contains multiple samples
result = trainer.train_step(batch, accumulate_gradients=False)
if result is not None:
# MEMORY FIX: Detach all tensor values to break computation graph
@@ -1929,21 +1991,19 @@ class RealTrainingAdapter:
if 'result' in locals():
del result
# Delete the cloned batch (it's a fresh copy, safe to delete)
# NOTE: Don't delete batch contents - batches are reused across epochs
# The batch dictionary is shared, so deleting keys corrupts it for next epoch
# Just clear the reference - Python GC will handle cleanup
if 'batch' in locals():
for key in list(batch.keys()):
if isinstance(batch[key], torch.Tensor):
del batch[key]
del batch
# Clear CUDA cache after every batch
if torch.cuda.is_available():
torch.cuda.empty_cache()
# After optimizer step, aggressive cleanup
# Check if this was an optimizer step (not accumulation)
is_optimizer_step = ((i + 1) % accumulation_steps == 0)
if is_optimizer_step:
# After each batch, cleanup (no accumulation needed with proper batching)
# Every batch triggers optimizer step
if True:
gc.collect()
if torch.cuda.is_available():
torch.cuda.synchronize()
@@ -2031,7 +2091,7 @@ class RealTrainingAdapter:
'learning_rate': float(trainer.scheduler.get_last_lr()[0])
},
training_metadata={
'num_samples': len(training_data),
'num_samples': total_samples, # Use stored count, training_data was deleted
'num_batches': num_batches,
'training_id': session.training_id
},
@@ -2263,7 +2323,7 @@ class RealTrainingAdapter:
if pivot_trainer:
pivot_trainer.start(symbol=symbol)
logger.info(f"✅ Live pivot training ENABLED - will train on L2 peaks automatically")
logger.info(f"Live pivot training ENABLED - will train on L2 peaks automatically")
else:
logger.warning("Could not initialize live pivot trainer")

8
ANNOTATE/web/app.py
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@@ -477,11 +477,11 @@ class AnnotationDashboard:
engineio_logger=False
)
self.has_socketio = True
logger.info("✅ SocketIO initialized for real-time updates")
logger.info("SocketIO initialized for real-time updates")
except ImportError:
self.socketio = None
self.has_socketio = False
logger.warning("⚠️ flask-socketio not installed - live updates will use polling")
logger.warning("flask-socketio not installed - live updates will use polling")
# Suppress werkzeug request logs (reduce noise from polling endpoints)
werkzeug_logger = logging.getLogger('werkzeug')
@@ -2202,10 +2202,10 @@ class AnnotationDashboard:
logger.info(f"Starting Annotation Dashboard on http://{host}:{port}")
if self.has_socketio:
logger.info("✅ Running with WebSocket support (SocketIO)")
logger.info("Running with WebSocket support (SocketIO)")
self.socketio.run(self.server, host=host, port=port, debug=debug, allow_unsafe_werkzeug=True)
else:
logger.warning("⚠️ Running without WebSocket support - install flask-socketio for live updates")
logger.warning("Running without WebSocket support - install flask-socketio for live updates")
self.server.run(host=host, port=port, debug=debug)

7
NN/models/advanced_transformer_trading.py
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@@ -1238,14 +1238,13 @@ class TradingTransformerTrainer:
break
if needs_transfer:
# Move batch to device and DELETE original CPU tensors to prevent memory leak
# Move batch to device - iterate over copy of keys to avoid modification during iteration
batch_gpu = {}
for k, v in batch.items():
for k in list(batch.keys()): # Create list copy to avoid modification during iteration
v = batch[k]
if isinstance(v, torch.Tensor):
# Move to device (creates GPU copy)
batch_gpu[k] = v.to(self.device, non_blocking=True)
# Delete CPU tensor immediately to free memory
del batch[k]
else:
batch_gpu[k] = v

6
core/data_provider.py
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@@ -551,7 +551,7 @@ class DataProvider:
logger.info("Skipping initial data load (using DuckDB cache)")
logger.info(" Initial data load completed - stopping maintenance worker")
logger.info("📊 Data will be updated on-demand only (no continuous fetching)")
logger.info("Data will be updated on-demand only (no continuous fetching)")
# Stop the maintenance worker after initial load
self.data_maintenance_active = False
@@ -582,7 +582,7 @@ class DataProvider:
self.cached_data[symbol][timeframe] = existing_df.tail(1500)
last_timestamp = existing_df.index.max()
logger.info(f"📦 Loaded {len(existing_df)} candles from DuckDB for {symbol} {timeframe}")
logger.info(f"Loaded {len(existing_df)} candles from DuckDB for {symbol} {timeframe}")
else:
logger.debug(f"No existing data in DuckDB for {symbol} {timeframe}")
except Exception as e:
@@ -3140,7 +3140,7 @@ class DataProvider:
logger.warning(" DuckDB storage not available - cannot load cached data")
return
logger.info("📦 Loading cached data from DuckDB...")
logger.info("Loading cached data from DuckDB...")
loaded_count = 0
for symbol in self.symbols: