Ml Debug

ML Training Debugging

Systematic debugging guide for machine learning training issues with PyTorch Lightning.

Quick Diagnosis

Identify your problem category:

1. Loss Issues

NaN or Inf Loss

Diagnosis:

# Check training logs for NaN
grep -i "nan\|inf" logs/train.log

# Test with lower learning rate
python src/train.py model.optimizer.lr=0.0001

Solutions (try in order):

1. Reduce learning rate

model:
  optimizer:
    lr: 0.0001  # Start small

1. Enable gradient clipping

trainer:
  gradient_clip_val: 1.0
  gradient_clip_algorithm: "norm"

1. Fix numerical stability

# In model forward pass
# Bad: Division without epsilon
output = x / y

# Good: Add epsilon
output = x / (y + 1e-8)

# Bad: Manual log(softmax)
output = torch.log(F.softmax(x, dim=-1))

# Good: Use stable version
output = F.log_softmax(x, dim=-1)

1. Try full precision

python src/train.py trainer.precision=32

1. Check for inf/nan in data

# In DataModule.setup()
sample = self.train_dataset[0]
assert not torch.isnan(sample[0]).any(), "NaN in input data"
assert not torch.isinf(sample[0]).any(), "Inf in input data"

See examples/nan-loss-debugging.md for detailed guide.

Exploding Gradients

Diagnosis:

# Enable gradient tracking
trainer = Trainer(
    gradient_clip_val=1.0,
    track_grad_norm=2,  # Log L2 norm
    log_every_n_steps=10
)

Solutions:

• If gradient norm >100: reduce learning rate or increase clipping
• If gradients explode in specific layer: check weight initialization
• Use gradient accumulation to reduce per-step updates

Vanishing Gradients

Symptoms: Gradients close to zero, no learning in early layers.

Solutions:

# 1. Use ReLU instead of Sigmoid/Tanh
self.activation = nn.ReLU()

# 2. Add skip connections (ResNet-style)
def forward(self, x):
    identity = x
    out = self.layer(x)
    return out + identity  # Skip connection

# 3. Use batch normalization
self.bn = nn.BatchNorm1d(hidden_dim)

# 4. Better initialization
for layer in self.layers:
    nn.init.kaiming_normal_(layer.weight, mode='fan_out', nonlinearity='relu')

2. Performance Issues

Overfitting (val_loss > train_loss)

Diagnosis:

# Plot losses
import matplotlib.pyplot as plt
import pandas as pd

metrics = pd.read_csv("logs/metrics.csv")
plt.plot(metrics["train_loss"], label="Train")
plt.plot(metrics["val_loss"], label="Val")
plt.legend()
plt.savefig("overfit_analysis.png")

# Overfit ratio
ratio = metrics["val_loss"].iloc[-1] / metrics["train_loss"].iloc[-1]
if ratio > 1.5:
    print("⚠️  SEVERE OVERFITTING")

Solutions:

# 1. Add regularization
model:
  dropout: 0.3  # Increase dropout
  optimizer:
    weight_decay: 0.0001  # L2 regularization

# 2. Data augmentation
data:
  augmentation:
    random_crop: true
    horizontal_flip: true
    mixup_alpha: 0.2

# 3. Early stopping
callbacks:
  early_stopping:
    monitor: "val/loss"
    patience: 10
    mode: "min"

# 4. Reduce model size
model:
  hidden_dims: [256, 128]  # Smaller model

Underfitting (both losses high)

Solutions:

# 1. Increase model capacity
model:
  hidden_dims: [2048, 1024, 512, 256]
  num_layers: 6

# 2. Train longer
trainer:
  max_epochs: 200

# 3. Increase learning rate
model:
  optimizer:
    lr: 0.01

# 4. Remove excessive regularization
model:
  dropout: 0.1
  optimizer:
    weight_decay: 0.00001

Slow Convergence

Try different optimizers:

# AdamW (default, good for most)
optimizer = torch.optim.AdamW(params, lr=0.001, weight_decay=0.01)

# SGD with momentum (good for vision)
optimizer = torch.optim.SGD(params, lr=0.1, momentum=0.9, nesterov=True)

# RMSprop (good for RNNs)
optimizer = torch.optim.RMSprop(params, lr=0.001)

Add learning rate scheduler:

model:
  scheduler:
    # Cosine annealing
    _target_: torch.optim.lr_scheduler.CosineAnnealingLR
    T_max: 100
    eta_min: 1e-6

    # Or OneCycle (often fastest convergence)
    # _target_: torch.optim.lr_scheduler.OneCycleLR
    # max_lr: 0.01
    # total_steps: ${trainer.max_steps}

3. Speed Issues

Profile Training

# Use Lightning profiler
python src/train.py \
  trainer.profiler="advanced" \
  trainer.max_epochs=1

# Check GPU utilization
watch -n 1 nvidia-smi

Data Loading Bottleneck

Symptoms: GPU utilization <80%, slow batch iteration.

Solutions:

data:
  num_workers: 8  # Increase workers
  pin_memory: true
  persistent_workers: true
  prefetch_factor: 2

# Use faster data formats
# HDF5, LMDB, or preprocessed tensors

Slow Forward/Backward

Solutions:

# 1. Use torch.compile (PyTorch 2.0+)
def configure_model(self):
    self.model = torch.compile(self.model, mode="reduce-overhead")

# 2. Avoid Python loops - use vectorization
# Bad
for i in range(batch_size):
    result[i] = self.layer(x[i])

# Good
result = self.layer(x)

# 3. Mixed precision
# trainer.precision = "16-mixed"

# 4. Gradient accumulation
# trainer.accumulate_grad_batches = 4

See scripts/profile_training.py for profiling script.

4. Memory Issues

Out of Memory (OOM)

Diagnosis:

# Check GPU memory
nvidia-smi

# Profile memory usage
python src/train.py trainer.profiler="pytorch" trainer.max_epochs=1

Solutions (in order):

# 1. Reduce batch size
data:
  batch_size: 32  # Was 128

# 2. Mixed precision
trainer:
  precision: "16-mixed"

# 3. Gradient accumulation (maintains effective batch size)
trainer:
  accumulate_grad_batches: 4  # Effective: 32 * 4 = 128

# 4. Gradient checkpointing (in model)
# self.model.gradient_checkpointing_enable()

# 5. Reduce model size
model:
  hidden_dims: [512, 256]

Memory Leak

Common causes:

# Bad: Keeps computation graph
loss_history = []
for batch in dataloader:
    loss = model(batch)
    loss_history.append(loss)  # ❌

# Good: Only store scalar
loss_history = []
for batch in dataloader:
    loss = model(batch)
    loss_history.append(loss.item())  # ✅

# Clear cache periodically
if self.global_step % 100 == 0:
    torch.cuda.empty_cache()

Checkpoint management:

callbacks:
  model_checkpoint:
    save_top_k: 3  # Only keep 3 best

5. Data Issues

Check Data Shapes

# In DataModule.setup()
def setup(self, stage=None):
    self.train_dataset = ...

    # Validate
    sample = self.train_dataset[0]
    print(f"Input shape: {sample[0].shape}")
    print(f"Label shape: {sample[1].shape}")
    print(f"Label range: [{sample[1].min()}, {sample[1].max()}]")

    # Check for NaN
    assert not torch.isnan(sample[0]).any(), "NaN in input"

    # Label distribution
    labels = [self.train_dataset[i][1] for i in range(min(1000, len(self.train_dataset)))]
    print(f"Distribution: {pd.Series(labels).value_counts()}")

Visualize Data

# Show a batch
import matplotlib.pyplot as plt

dm = MyDataModule()
dm.setup()
loader = dm.train_dataloader()
batch = next(iter(loader))
inputs, labels = batch

fig, axes = plt.subplots(4, 4, figsize=(10, 10))
for i, ax in enumerate(axes.flat):
    ax.imshow(inputs[i].permute(1, 2, 0))  # CHW -> HWC
    ax.set_title(f"Label: {labels[i].item()}")
    ax.axis('off')
plt.savefig("data_samples.png")

6. PyTorch Geometric Specific Issues

Over-smoothing in GNNs

Symptoms: All node representations become similar after many layers.

Solutions:

# 1. Reduce layers
model:
  num_layers: 2  # Instead of 5+

# 2. Add skip connections
# In model forward:
# x = conv(x, edge_index) + x_orig

# 3. Use jumping knowledge
model:
  jk_mode: "cat"  # or "max", "lstm"

Large Graph OOM

# Use mini-batch training with sampling
data:
  use_sampling: true
  num_neighbors: [15, 10, 5]  # Per-layer sampling
  batch_size: 1024  # Mini-batches of nodes

See examples/gnn-debugging.md for GNN-specific guide.

Debugging Checklist

Data:

• Loads without errors
• Shapes are correct
• No NaN or Inf values
• Labels in correct range (0 to num_classes-1)
• Augmentation works
• Splits are correct

Model:

• Forward pass works with dummy data
• Output shape matches expected
• Reasonable number of parameters
• Gradients flow through all layers

Training:

• Loss decreases in first epoch
• Validation runs correctly
• Checkpoints save
• Metrics logged
• GPU utilization high (>80%)

Config:

• Learning rate appropriate (0.0001-0.01)
• Batch size fits in memory
• Enough epochs

Common Error Messages

Generate Debug Report

Use the debug report script:

python scripts/debug_report.py --log-dir logs/

See scripts/debug_report.py for implementation.

Success Criteria

• Problem identified and categorized
• Root cause determined
• Solution applied
• Training resumes successfully
• Metrics improve
• No errors in logs

Debugging complete - training is back on track!