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An adapter callback for Keras ModelCheckpoint that allows checkpointing an alternate model (often submodel of a multi-GPU model).

Project description


An adapter callback for Keras ModelCheckpoint that allows checkpointing an alternate model (often submodel of a multi-GPU model).


pip install alt-model-checkpoint


You must provide your own Keras or Tensorflow installation. See Pipfile for preferred versions.

If using the Keras bundled in Tensorflow:

from alt_model_checkpoint.tensorflow import AltModelCheckpoint

If using Keras standalone:

from alt_model_checkpoint.keras import AltModelCheckpoint

Common usage involving multi-GPU models built with Keras multi_gpu_model():

from alt_model_checkpoint.keras import AltModelCheckpoint
from keras.models import Model
from keras.utils import multi_gpu_model

def compile_model(m):
    """Implement with your model compile logic; both base and GPU models should be compiled identically"""

base_model = Model(...)
gpu_model = multi_gpu_model(base_model)
compile_model(gpu_model), callbacks=[
    AltModelCheckpoint('save/path/for/model.hdf5', base_model)

Constructor args


Model save file path; see underlying ModelCheckpoint docs for details.


Keras model to save instead of the default. This is used especially when training multi-gpu models built with Keras multi_gpu_model(). In that case, you would pass the original "template model" to be saved each checkpoint.


If TRUE (default), saves the optimizer of the base model (e.g. a multi-gpu model) with the alternate model. This is necessary if you want to be able to resume training on a saved alternate model. If FALSE, the alternate model's optimizer will be saved as-is.

*args, **kwargs

These are passed as-is to the underlying ModelCheckpoint constructor.

Dev environment setup

  1. Install pipenv.
  2. Run make test (runs make test-build automatically to ensure deps)

Project details

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