https://gitlab.com/andreafavia/yaket

YAKET: YAML Keras Trainer (or Yet Another Keras Trainer) is a simple and lightweight trainer module to help you quickly develop Keras models by defining parameters directly from a YAML file.
https://gitlab.com/andreafavia/yaket

keras python3 tensorflow trainer

Last synced: 4 months ago
JSON representation

YAKET: YAML Keras Trainer (or Yet Another Keras Trainer) is a simple and lightweight trainer module to help you quickly develop Keras models by defining parameters directly from a YAML file.

• Host: gitlab.com
• URL: https://gitlab.com/andreafavia/yaket
• Owner: andreafavia
• License: mit
• Created: 2022-07-14T19:51:16.070Z (almost 4 years ago)
• Default Branch: main
• Last Synced: 2026-01-05T05:31:57.618Z (6 months ago)
• Topics: keras, python3, tensorflow, trainer
• Stars: 2
• Forks: 0
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# YAKET: Yaml Keras Trainer (or Yet Another Keras Trainer)

[![pipeline status](https://gitlab.com/andreafavia/yaket/badges/main/pipeline.svg)](https://gitlab.com/andreafavia/yaket/-/commits/main) 

[![coverage report](https://gitlab.com/andreafavia/yaket/badges/main/coverage.svg)](https://gitlab.com/andreafavia/yaket/-/commits/main) 

[![Latest Release](https://gitlab.com/andreafavia/yaket/-/badges/release.svg)](https://gitlab.com/andreafavia/yaket/-/releases)

## Installation 💻

    pip install yaket

## Description 🔥

Yaket is a lightweight and simple module to train Keras modules by defining parameters directly using YAML file. 

YAML parameters are validated using Pydantic, hence typos or not allowed parameters will throw errors at the beginning of the execution.

This allows developer to focus uniquely on what matters: data and model development.

Data Scientists and ML Engineer won't need to add manually all training parameters, such as optimizer, callbacks, schedulers, thus reducing the

likelihood of human-induced code bugs.

## Features 🎊

1. Train models with tensorflow default optimizers, metrics, callbacks, and losses.

2. Convert the saved model to **ONNX** or **Tensorflow-Lite** for on edge-deploymnet or faster inference.

3. Quickly use distributed multi-gpu and TPU training with `tf.distributed.strategy` *(Experimental)*

4. Train models with custom modules defined in python script.

5. Log training parameters, models, and results using `mlflow.tensorflow.autolog()` module. The run will be saved in `mlruns` folder. 

6. Save the model in a particular folder and particular format (i.e., SavedModel,H5, or .pb)

7. Train with `sample_weight_mode = 'temporal'` when training sequence models.

8. More to come!

## Visuals 📖

The YAML file contains most of the parameters used in Keras model.fit, such as epochs, verbose, callbacks. Below an example:

```yaml

    autolog: False

    optimizer: 

    - Adam:

       learning_rate: 0.001

    batch_size: 64 

    loss: 

    SparseCategoricalCrossentropy: 

        from_logits: True

    callbacks:

        - EarlyStopping:

            monitor: val_accuracy

            patience: 2

            restore_best_weights: True  

    verbose: 1 

    epochs: 100

    shuffle: True

    accelerator: mgpu 

```

The usage is very simple using python:

```python

    

    model = ... # define your tf.keras.Model

    # Define path to yaml file

    path = "/yaket/examples/files/trainer.yaml"

    # Initialize trainer

    trainer = Trainer(

        config_path=path,

        train_dataset=(x_train, y_train),

        val_dataset=(x_test, y_test),

        model=model, 

    )

    trainer.train() # train based on the parameters defined in the yaml file

    trainer.clear_ram() # clear RAM after training

    trainer.convert_model(format_model = 'onnx') # Convert to ONNX

    

```

Other scenarios are visible in **examples** folder.

## Asking for help

If you have any questions please:

1. [Read the docs](https://andreafavia.gitlab.io/yaket/reference) (WIP)

2. Create an issue. 

## License

MIT License