IAflow

This library help to create models with identifiers, checkpoints, logs and metadata automatically, in order to make the training process more efficient and traceable.

To install the library, you can use pip:

pip install iaflow

Then you can create the ia_make with the following code:

ia_maker = IAFlow(
  models_folder='./models',
  checkpoint_params={
    'monitor': 'val_loss',
    'save_best_only': True,
    'save_weights_only': True
  },
  tensorboard_params={
    'histogram_freq': 1,
    'write_graph': True,
    'write_images': True
  }
)

And then you can add the model with the following code:

model_1_data = ia_maker.add_model(
  model_name='model_1',
  model_params={ 'input_shape': (2, 1) },
  load_model_params={},
  compile_params={
    'metrics': ['accuracy'],
    'optimizer': 'adam', 'loss': 'mse'
  },
)

Finally, you can train the model with the following code:

ia_maker.train(
  model_1_data,
  epochs=5,
  dataset_name='dataset_1'
)

This library has integration with Notifier Status Function, so you can send notifications to Telegram when the training process is finished. To check how to use it and more feature as managing Dataset and Models with the library, you can check the documentation here.

Tech used

• Python

• Tensorflow

• Telegram API

• Notifier Status Function (personal lib)

• Webhooks