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base: Machine-Learning:c92fa68e923589f78653812c65b5b1a5d57068c3
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Machine-Learning:main
Machine-Learning:fixes
Machine-Learning:feat/save_fix
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compare: Machine-Learning:e7b9da37d644df6accd43b3814e27406c773d7ef
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Machine-Learning:fixes
Machine-Learning:main
Machine-Learning:feat/save_fix

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c92fa68e92 ... e7b9da37d6

Author SHA1 Message Date
Falko Victor Habel e7b9da37d6 Merge pull request 'updated memory fix' (#26) from feat/save_fix into main
Run VectorLoader Script / Explore-Gitea-Actions (push) Successful in 20s Details
Gitea Actions For AIIA / Explore-Gitea-Actions (push) Successful in 9m36s Details 
Reviewed-on: #26
 2025-07-03 11:50:07 +00:00
Falko Victor Habel 159ada872b updated memory fix
Gitea Actions For AIIA / Explore-Gitea-Actions (push) Successful in 9m43s Details
2025-07-03 13:49:45 +02:00
1 changed files with 18 additions and 5 deletions
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23
src/aiunn/finetune/memory_trainer.py
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@ -237,9 +237,10 @@ class MemoryOptimizedTrainer(aiuNNTrainer):
start_idx = start_batch if epoch == start_epoch else 0 start_idx = start_batch if epoch == start_epoch else 0
progress_bar = tqdm(train_batches[start_idx:], progress_bar = tqdm(train_batches[start_idx:],
initial=start_idx, initial=start_idx,
total=len(train_batches), total=len(train_batches),
desc=f"Epoch {epoch + 1}/{epochs}") desc=f"Epoch {epoch + 1}/{epochs}")
for batch_idx, (low_res, high_res) in progress_bar: for batch_idx, (low_res, high_res) in progress_bar:
# Move data to device # Move data to device
@ -251,8 +252,10 @@ class MemoryOptimizedTrainer(aiuNNTrainer):
if hasattr(self, 'use_checkpointing') and self.use_checkpointing: if hasattr(self, 'use_checkpointing') and self.use_checkpointing:
low_res.requires_grad_() low_res.requires_grad_()
outputs = checkpoint(self.model, low_res) outputs = checkpoint(self.model, low_res)
outputs = outputs.clone() # <-- Clone added here
else: else:
outputs = self.model(low_res) outputs = self.model(low_res)
outputs = outputs.clone() # <-- Clone added here
loss = self.criterion(outputs, high_res) loss = self.criterion(outputs, high_res)
# Scale loss for gradient accumulation # Scale loss for gradient accumulation
@ -266,8 +269,8 @@ class MemoryOptimizedTrainer(aiuNNTrainer):
# Update weights every accumulation_steps or at the end of epoch # Update weights every accumulation_steps or at the end of epoch
should_step = (not self.use_gradient_accumulation or should_step = (not self.use_gradient_accumulation or
(batch_idx + 1) % self.accumulation_steps == 0 or (batch_idx + 1) % self.accumulation_steps == 0 or
batch_idx == len(train_batches) - 1) batch_idx == len(train_batches) - 1)
if should_step: if should_step:
self.scaler.step(self.optimizer) self.scaler.step(self.optimizer)
@ -348,6 +351,16 @@ class MemoryOptimizedTrainer(aiuNNTrainer):
return self.best_loss return self.best_loss
# Stop memory monitoring
if self.use_memory_profiling:
self.stop_monitoring = True
if self.memory_monitor_thread:
self.memory_monitor_thread.join(timeout=1)
print(f"Training completed. Peak GPU memory usage: {self.peak_memory:.2f}GB")
return self.best_loss
def get_memory_summary(self): def get_memory_summary(self):
"""Get a summary of memory usage during training""" """Get a summary of memory usage during training"""
if not self.memory_stats: if not self.memory_stats: