torchflow-core 0.1.0

Lightweight PyTorch training utilities (Trainer, Callbacks, Tuner)

torchflow

A lightweight, dependency-minimal PyTorch training framework providing a clean Trainer API, a set of commonly-used training callbacks, and an Optuna-backed tuner helper for simple hyperparameter searches.

This repository and source package are available at https://github.com/mobadara/torchflow. The package is published on PyPI under the name torchflow-core; import it as torchflow in your code.

See the full changelog in CHANGELOG.md for release history.

Contents

• Features
• Installation
• Quick start
• Callbacks
• Tuner (Optuna)
• Examples
• Testing
• Contributing
• License

Features

• Simple, readable Trainer for training and validation loops
• Callback system with lifecycle hooks (on_train_begin, on_epoch_begin, on_batch_end, on_validation_end, on_epoch_end, on_train_end)
• Built-in callbacks: EarlyStopping, ModelCheckpoint, LearningRateScheduler, ReduceLROnPlateau, CSVLogger, TensorBoardCallback
• Safe, lazy imports for optional heavy dependencies (TensorBoard, Optuna)
• Small Optuna tuner helper that builds a new Trainer for each trial using a user-supplied build_fn(trial)

Installation

Install from PyPI (package name is torchflow-core):

pip install torchflow-core

Then import normally:

import torchflow

For development from source:

git clone https://github.com/mobadara/torchflow.git
cd torchflow
pip install -e .[dev]

Optional extras:

• TensorBoard logging: pip install tensorboard
• Hyperparameter tuning: pip install optuna

Quick start

Minimal training example (pseudo-code):

import torch
from torch import nn, optim
from torchflow.trainer import Trainer

model = nn.Sequential(nn.Linear(10, 1))
criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=1e-2)

trainer = Trainer(model, criterion, optimizer, device='cpu')
trainer.train(train_loader, val_loader=val_loader, num_epochs=5)

Callbacks

Callbacks are simple objects with lifecycle hooks that the Trainer calls at key moments during training. They are passed to Trainer as a list and can perform logging, checkpointing, learning-rate changes, early stopping, and more.

Example with TensorBoard logging and early stopping:

from torchflow.callbacks import TensorBoardCallback, EarlyStopping

tb = TensorBoardCallback(log_dir='runs/myrun')  # uses a safe SummaryWriter factory
python examples/simple_train.py

trainer = Trainer(model, criterion, optimizer, callbacks=[tb, early])
trainer.train(train_loader, val_loader=val_loader, num_epochs=20)

The library exposes a few convenience callbacks out of the box:

• EarlyStopping
• ModelCheckpoint
• LearningRateScheduler
• ReduceLROnPlateau
• CSVLogger
• TensorBoardCallback

Tuner (Optuna)

torchflow.tuner provides a small wrapper around Optuna. The contract is:

• build_fn(trial) should return a dict with at least model, optimizer, and criterion
• Optional keys device, callbacks, writer, metrics, mlflow_tracking may also be returned

Example usage:

from torchflow.tuner import tune, example_build_fn

# `example_build_fn` is a tiny helper included for demonstration.
study = tune(example_build_fn, train_loader, val_loader, n_trials=10, num_epochs=3)

The tuner imports Optuna lazily; importing torchflow.tuner does not require Optuna to be installed. Calling tune() will raise a clear error if Optuna is missing.

Examples

Run the included example scripts in the examples/ directory:

python examples/simple_train.py
python examples/lr_and_logging.py
python examples/tensorboard_example.py

Note: examples/tensorboard_example.py will try to use TensorBoard; install python examples/lr_and_logging.py

Testing

Tests use pytest and are located in the tests/ directory. Some tests skip when optional dependencies (like torch or tensorboard) are not available.

Run tests locally:

pip install -e .[dev]
pytest -q

Contributing

Contributions are welcome. See CONTRIBUTING.md for contribution guidelines, the project's coding conventions, and testing instructions.

License

This project is released under the terms of the license in the LICENSE file. By contributing you agree to license your changes under the same terms.

Maintainers

• Author: Muyiwa J. Obadara
• Repository: https://github.com/mobadara/torchflow

If you'd like to contact the maintainer, open an issue or mention the handle on Twitter: @m_obadara

Project & Contact

• GitHub: https://github.com/mobadara/torchflow
• Twitter: https://twitter.com/m_obadara

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