deep-trainer 0.2.0

Yet another simple & effective PyTorch trainer

Deep-Trainer

Lightweight training utilities for PyTorch projects.

deep-trainer provides a minimal yet flexible training loop abstraction for PyTorch projects, including:

• Training & evaluation loops
• Automatic Mixed Precision (AMP) support
• Checkpointing (best / last / all)
• Metric handling system with aggregation
• TensorBoard logging (or custom loggers)
• Easy subclassing for custom training behavior

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⚠️ Project Status

This project was originally developed as a personal baseline training framework.

• The codebase is functional but relatively old
• APIs may evolve in future versions
• Some refactoring and cleanup are planned
• Backward compatibility is not guaranteed for future major updates

If you use this project in production or research, please consider pinning a version.

Contributions and improvements are welcome.

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🚀 Installation

Install with pip

pip install deep-trainer

Install from source

git clone https://github.com/raphaelreme/deep-trainer.git
cd deep-trainer
pip install .

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🏁 Getting Started

Below is a minimal training example for a classification task.

import torch
from deep_trainer import PytorchTrainer

# ======================
# Dataset
# ======================

trainset = ...
valset = ...
testset = ...

train_loader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)
val_loader = torch.utils.data.DataLoader(valset, batch_size=256)
test_loader = torch.utils.data.DataLoader(testset, batch_size=256)

# ======================
# Model
# ======================

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model = ...
model.to(device)

# ======================
# Optimizer & Scheduler
# ======================

optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)

# Step every batch (scheduler is stepped per training step)
scheduler = torch.optim.lr_scheduler.StepLR(
    optimizer,
    step_size=len(train_loader) * 50,  # decay every 50 epochs
    gamma=0.1,
)

# ======================
# Loss
# ======================

criterion = torch.nn.CrossEntropyLoss()

# ======================
# Training
# ======================

trainer = PytorchTrainer(
    model,
    optimizer,
    scheduler=scheduler,
    save_mode="small",   # keep best + last checkpoint
    device=device,
    use_amp=True,        # optional mixed precision
)

trainer.train(
    epochs=150,
    train_loader=train_loader,
    criterion=criterion,
    val_loader=val_loader,
)

# ======================
# Testing (Best model)
# ======================

trainer.load("experiments/checkpoints/best.ckpt")  # Reload best checkpoint
test_metrics = trainer.evaluate(test_loader)

print(test_metrics)

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Features Overview

✔ Simple Trainer Abstraction

The PytorchTrainer handles:

• Forward / backward passes
• Optimizer and scheduler stepping
• Mixed precision scaling
• Metric tracking
• Validation & best checkpoint selection
• Logging

You probably will need to override the following method:

• process_train_batch
• train_step
• backward
• eval_step

to customize behavior (multi-loss, gradient accumulation, multiple optimizers, self-supervised learning, etc.).

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✔ Flexible Metric System

The metric system supports:

• Per-batch metrics
• Aggregated metrics
• Validation metric selection
• Custom metrics via subclassing

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✔ Logging

By default, logs are written to TensorBoard.

tensorboard --logdir experiments/logs/

You can also use:

• DictLogger (in-memory logging)
• MultiLogger (combine multiple loggers)
• Or implement your own logger by subclassing TrainLogger

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Example

An example training script is available in:

example/example.py

It demonstrates training a PreActResNet18 on CIFAR-10.

To use it:

# Show available hyperparameters
python example.py -h

# Launch training
python example.py

# Monitor training
tensorboard --logdir experiments/logs/

With default parameters, it reaches approximately 94--95% validation accuracy on CIFAR-10.

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Design Philosophy

deep-trainer aims to be:

• Minimal (no heavy abstractions)
• Transparent (easy to read & debug)
• Hackable (easy to override core behavior)
• Suitable for research baselines

It is not intended to replace full-featured training frameworks like:

• PyTorch Lightning
• HuggingFace Trainer
• Accelerate

Instead, it provides a lightweight middle ground between raw PyTorch loops and larger ecosystems.

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Contributing

Contributions are welcome!

If you'd like to:

• Improve documentation
• Refactor old components
• Add new metrics
• Improve testing
• Modernize APIs

Feel free to open an issue or submit a pull request.

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📜 License

MIT License. See LICENSE file for details.