LayerZero

Performance Focussed PyTorch training framework.

Features

Trainer

• Model compilation via torch.compile() (PyTorch 2.0+)
• Mixed precision training (AMP)
• Automatic GPU augmentation integration
• Asynchronous CUDA data transfers
• Real-time TensorBoard logging
• PyTorch Profiler integration (GPU/CPU/memory analysis)
• Metric tracking and logging
• Model checkpointing
• Custom callbacks

ImageDataLoader

• GPU-accelerated augmentation using Kornia
• Configurable augmentation modes
• Automatic worker detection
• Torchvision dataset support
• Pinned memory for GPU training

Helper

• Training/validation metric tracking
• Loss curve visualization
• Experiment logging

---

Performance Optimizations

Applied automatically:

• torch.compile() for model compilation
• Mixed precision (FP16) training
• Non-blocking CUDA transfers
• GPU-based augmentation (Kornia)
• Optimized DataLoader configuration

Monitoring & Analysis:

• Real-time TensorBoard logging (loss, metrics, lr)
• PyTorch Profiler integration (find bottlenecks)
• GPU/CPU utilization tracking
• Memory usage profiling

---

Installation

pip install torch torchvision matplotlib tensorboard torch-tb-profiler

# Optional: GPU augmentation
pip install kornia kornia-rs

Or install from PyPI:

pip install LayerZero

Note: torch-tb-profiler is required to view PyTorch Profiler traces in TensorBoard.

---

Usage

Basic Example

import torch
from torch import nn
from LayerZero import ImageDataLoader, ImageLoaderConfig, Trainer, TrainerConfig
from torchvision.datasets import CIFAR10

# Model
model = nn.Sequential(
    nn.Flatten(),
    nn.Linear(3*32*32, 128),
    nn.ReLU(),
    nn.Linear(128, 10)
)

# Data
config = ImageLoaderConfig(
    data_dir='./data',
    batch_size=128,
    use_gpu_augmentation='auto',  # Automatic GPU acceleration
)
loader = ImageDataLoader(CIFAR10, image_size=32, config=config)

train_loader, test_loader = loader.get_loaders()

# Training configuration
config = TrainerConfig(
    epochs=10,
    amp=True,
    compile_model='auto',
    use_tensorboard=True  # TensorBoard enabled by default!
)

# Train
trainer = Trainer(
    model=model,
    loss_fn=nn.CrossEntropyLoss(),
    optimizer=torch.optim.Adam(model.parameters()),
    config=config
)

results = trainer.fit(
    train_loader, 
    test_loader,
    data_loader=loader  # Auto-detects GPU augmentation!
)

View Training in Real-Time:

# Google Colab / Kaggle (inline in notebook - two separate commands):
%load_ext tensorboard
%tensorboard --logdir runs

# Local / Terminal:
tensorboard --logdir=runs
# Then open: http://localhost:6006

---

Configuration

Augmentation Modes

from LayerZero import ImageDataLoader, ImageLoaderConfig, AugmentationMode

config = ImageLoaderConfig(
    augmentation_mode=AugmentationMode.MINIMAL,  # Flip + Crop
)
loader = ImageDataLoader(CIFAR10, image_size=224, config=config)

GPU Augmentation

# Automatic integration with Trainer (Recommended)
config = ImageLoaderConfig(
    use_gpu_augmentation='auto',  # Auto-detect GPU and Kornia
    auto_install_kornia=True      # Install if missing
)
loader = ImageDataLoader(CIFAR10, image_size=224, config=config)

train_loader, test_loader = loader.get_loaders()

# GPU augmentation auto-detected when fit() is called!
trainer = Trainer(
    model=model,
    loss_fn=nn.CrossEntropyLoss(),
    optimizer=torch.optim.Adam(model.parameters()),
    config=config
)

trainer.fit(
    train_loader,
    test_loader,
    data_loader=loader  # ← Pass loader here, Trainer auto-detects GPU aug!
)

# Manual usage in custom training loops
gpu_aug = loader.get_gpu_augmentation(device='cuda')

for X, y in train_loader:
    X = X.to(device)
    X = gpu_aug(X)
    # ... training code ...

Mixed Precision

config = TrainerConfig(
    amp=True,   # Enable (default)
    # amp=False  # Disable for debugging
)

Model Compilation

config = TrainerConfig(
    compile_model='auto',        # Auto-detect PyTorch 2.0+
    compile_mode='default',      # Compilation mode
    # compile_mode='reduce-overhead'
    # compile_mode='max-autotune'
)

TensorBoard (Real-Time Monitoring) 📊

Automatically enabled by default! Works seamlessly in Google Colab, Kaggle, and local environments.

🎯 Google Colab / Kaggle Usage (Recommended)

# Step 1: Load TensorBoard extension (run once at top of notebook)
%load_ext tensorboard

# Step 2: Train your model (TensorBoard logs automatically)
trainer = Trainer(model, loss_fn, optimizer, config=TrainerConfig(epochs=10))
trainer.fit(train_loader, val_loader)

# Step 3: View TensorBoard inline in your notebook
%tensorboard --logdir runs

That's it! TensorBoard will display directly in your Colab/Kaggle notebook with real-time updates.

💻 Local / Terminal Usage

# Terminal 1: Start training
python train.py

# Terminal 2: Start TensorBoard
tensorboard --logdir=runs

# Open browser to: http://localhost:6006

⚙️ Configuration

config = TrainerConfig(
    use_tensorboard=True,              # Enable/disable (default: True)
    tensorboard_log_dir="runs",        # Log directory
    tensorboard_comment="experiment1", # Experiment name/tag
    tensorboard_log_graph=True,        # Log model graph
    tensorboard_log_gradients=False,   # Log gradient histograms (slower)
)

📈 What Gets Logged

• ✅ Train & validation losses (real-time, per epoch)
• ✅ All custom metrics (accuracy, F1, etc.)
• ✅ Learning rate changes over time
• ✅ Model graph visualization (optional)
• ✅ Gradient & weight histograms (optional)
• ✅ PyTorch Profiler (optional - GPU/CPU utilization, memory, bottlenecks)

🔧 Advanced Options

Disable TensorBoard:

config = TrainerConfig(
    use_tensorboard=False  # Turn off TensorBoard logging
)

Multiple experiments with names:

# Experiment 1
config1 = TrainerConfig(tensorboard_comment="resnet50_lr0.001")
trainer1.fit(train_loader, val_loader)

# Experiment 2
config2 = TrainerConfig(tensorboard_comment="resnet50_lr0.01")
trainer2.fit(train_loader, val_loader)

# View both: %tensorboard --logdir runs

Manual callback control:

from LayerZero import Trainer, TensorBoardCallback

tb_callback = TensorBoardCallback(
    log_dir="my_experiments",
    comment="custom_experiment",
    log_gradients=True  # Enable gradient logging
)

trainer = Trainer(
    model=model,
    loss_fn=loss_fn,
    optimizer=optimizer,
    config=TrainerConfig(use_tensorboard=False),  # Disable auto-init
    callbacks=[tb_callback]  # Add manually
)

📱 Colab/Kaggle Quick Start

# Complete Colab/Kaggle example
%load_ext tensorboard

from LayerZero import ImageDataLoader, Trainer, TrainerConfig
from torchvision.datasets import CIFAR10
import torch.nn as nn

# Setup model and data
model = nn.Sequential(...)
loader = ImageDataLoader(CIFAR10, root='./data', batch_size=128)
train_loader, val_loader = loader.get_loaders()

# Train with TensorBoard (automatic)
trainer = Trainer(
    model=model,
    loss_fn=nn.CrossEntropyLoss(),
    optimizer=torch.optim.Adam(model.parameters()),
    config=TrainerConfig(epochs=10)  # TensorBoard enabled by default!
)

trainer.fit(train_loader, val_loader, data_loader=loader)

# View results inline
%tensorboard --logdir runs

🔬 PyTorch Profiler Integration (Performance Analysis)

NEW! Analyze GPU/CPU utilization, memory usage, and identify bottlenecks - all in TensorBoard!

# Enable profiler with TensorBoard
config = TrainerConfig(
    epochs=10,
    use_tensorboard=True,
    use_profiler=True,  # Enable PyTorch Profiler
)

trainer = Trainer(model, loss_fn, optimizer, config=config)
trainer.fit(train_loader, val_loader)

# View profiler traces in TensorBoard
%tensorboard --logdir runs
# Look for the "PYTORCH_PROFILER" or "PROFILE" tab (requires torch-tb-profiler)

What you'll see:

• 📊 GPU/CPU utilization timeline
• 💾 Memory usage over time (allocated/reserved)
• ⚡ Operation timing breakdown
• 🔍 Bottleneck identification (slow ops highlighted)
• 📈 Kernel execution trace

Requirements:

• torch-tb-profiler must be installed: pip install torch-tb-profiler
• The profiler tab will appear after profiling data is generated

Fine-tune profiler schedule:

config = TrainerConfig(
    use_profiler=True,
    profiler_schedule_wait=1,      # Skip first N batches
    profiler_schedule_warmup=1,    # Warmup for N batches
    profiler_schedule_active=3,    # Profile for N batches
    profiler_schedule_repeat=2,    # Repeat cycle N times
)

Why use the profiler?

• Find GPU idle time (data loading bottlenecks)
• Identify slow operations
• Optimize memory usage
• Compare different model architectures
• Debug performance issues

⚠️ Performance Note:

• TensorBoard (default): < 1% overhead ✅
• Gradient logging: ~5-10% overhead (disabled by default)
• Profiler: ~10-15% overhead (disabled by default)
• Logging happens once per epoch, not per batch
• Safe to keep TensorBoard enabled for all training

Example: Optimizing based on profiler insights

# Before profiling: Found data loading is slow
# Solution: Increase num_workers

loader = ImageDataLoader(
    CIFAR10,
    batch_size=128,
    num_workers=4,  # Increased from default
    use_gpu_augmentation='auto'  # Move augmentation to GPU
)

Custom Metrics

def accuracy_fn(y_pred, y_true):
    return (y_pred.argmax(1) == y_true).float().mean().item() * 100

config = TrainerConfig(
    metrics={'accuracy': accuracy_fn}
)

Callbacks

def save_checkpoint(model, epoch, metrics):
    torch.save(model.state_dict(), f'model_epoch_{epoch}.pt')

config = TrainerConfig(
    callbacks={'on_epoch_end': save_checkpoint}
)

---

API Reference

ImageDataLoader

ImageDataLoader(
    dataset_cls,                          # Torchvision dataset class
    root='./data',                        # Data directory
    image_size=224,                       # Image size
    batch_size=64,                        # Batch size
    augmentation_mode=AugmentationMode.BASIC,
    use_gpu_augmentation='auto',
    auto_install_kornia=True,
    num_workers=None,                     # Auto-detect
    download=False,
)

TrainerConfig

TrainerConfig(
    epochs=10,
    amp=True,                          # Mixed precision
    compile_model='auto',              # torch.compile()
    compile_mode='default',
    device='auto',
    save_dir='./checkpoints',
    # TensorBoard settings
    use_tensorboard=True,              # Enable TensorBoard (default: True)
    tensorboard_log_dir='runs',        # TensorBoard log directory
    tensorboard_comment='',            # Experiment name/comment
    tensorboard_log_graph=True,        # Log model graph
    tensorboard_log_gradients=False,   # Log gradient histograms
    # PyTorch Profiler settings (integrates with TensorBoard)
    use_profiler=False,                # Enable PyTorch Profiler (default: False)
    profiler_schedule_wait=1,          # Batches to skip before profiling
    profiler_schedule_warmup=1,        # Warmup batches
    profiler_schedule_active=3,        # Active profiling batches
    profiler_schedule_repeat=2,        # Number of profiling cycles
)

Trainer

Trainer(
    model,
    loss_fn,
    optimizer,
    config,
    metrics=None,
    callbacks=None,
)

# Run training with optional GPU augmentation auto-detection
trainer.fit(
    train_loader, 
    val_loader,
    epochs=None,        # Optional: Override config.epochs
    data_loader=None    # Optional: ImageDataLoader for GPU aug auto-detection
)

trainer.evaluate(dataloader)  # Evaluate on data
trainer.predict(dataloader)   # Get predictions

KorniaHelper

from LayerZero import (
    is_kornia_available,
    install_kornia,
    ensure_kornia,
    get_kornia_version,
)

if ensure_kornia(auto_install=True):
    # Kornia available
    pass

---

Architecture

LayerZero/
├── Trainer.py              # Training loop
├── ImageDataLoader.py      # Data loading
├── GPUAugmentation.py      # Kornia augmentation
├── AugmentationMode.py     # Augmentation enums
├── KorniaHelper.py         # Kornia management
└── Helper.py               # Metrics tracking

---

Troubleshooting

Kornia installation fails

pip install kornia kornia-rs

torch.compile not available

Requires PyTorch 2.0+:

pip install --upgrade torch torchvision

Out of memory

Reduce batch size or enable mixed precision:

config = TrainerConfig(amp=True)

Slow on CPU

Use minimal augmentation:

config = ImageLoaderConfig(augmentation_mode=AugmentationMode.MINIMAL)
loader = ImageDataLoader(CIFAR10, image_size=224, config=config)

---

Releasing New Versions

# Bump version (bug fixes: 0.1.3 → 0.1.4)
make bump-patch

# Push to trigger PyPI release
make release

See RELEASE_WORKFLOW.md for complete guide.

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License

MIT