This Docker-based starter kit provides a runtime for machine learning work in Jupyter notebooks.
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24 alternativesPyToune
PyToune provides a simplified framework and utilities for working with PyTorch.
About PyToune
Poutyne is an open-source framework that reduces boilerplate when training PyTorch neural networks. Users define a standard PyTorch module, then wrap it in Poutyne's Model class, which handles training steps, statistics, and callbacks in a Keras-like workflow.
It provides fit for training with validation data, evaluate for testing, and predict for new data, plus callbacks for checkpointing, logging, and early stopping. A ModelBundle class automates checkpointing and logging. It requires Python 3.10 or newer and works with the latest PyTorch version.
Key features
- Wraps standard PyTorch modules in a Model class for training
- Trains with fit using training and validation data
- Evaluates performance and predicts on new data
- Uses callbacks for checkpointing and logging training statistics
- Provides ModelBundle for automatic checkpointing and logging
- Supports batch and epoch metrics such as accuracy
Best for: PyTorch users who want Keras-like training with less boilerplate code.
Good for
- Reduces boilerplate code for training PyTorch networks
- Follows a Keras-like Model workflow for training and evaluation
- Includes callbacks and ModelBundle for checkpointing and logging
Watch out for
- Requires Python 3.10 or newer
- Users must still create their own PyTorch network
PyToune at a glance
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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PyToune FAQ
How does Poutyne work with PyTorch?
You create a PyTorch module as usual, then pass it to Poutyne's Model, which handles training steps, statistics, and callbacks.
Which Python and PyTorch versions are supported?
It is compatible with the latest version of PyTorch and Python 3.10 or newer.
What do callbacks provide?
Callbacks can save models, perform early stopping, save checkpoints, and log training statistics.
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