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A small library that wraps Keras models to pickle them.

Project description

# `keras_pickle_wrapper`

This small library exposes a KerasPickleWrapper class that allows keras models to be pickled, and even used across multiprocessing (or with a library like [job\_stream](

It won't be necessary if is ever properly resolved.

## Installation

`pip install keras-pickle-wrapper`

## Usage


import keras
import pickle

import KerasPickleWrapper from keras_pickle_wrapper

ins = keras.layers.Input((2,))
x = ins
x = keras.layers.Dense(3)(x)
x = keras.layers.Dense(1)(x)
m = keras.models.Model(inputs=ins, outputs=x)
m.compile(loss='mse', optimizer='sgd')

# Wrap a compiled model
mw = KerasPickleWrapper(m)

# Calling the object returns the wrapped Keras model
mw().fit([[0,0], [0,1], [1,0], [1,1]], [[0], [1], [1], [0]])

# Pickle / unpickle the wrapper
data = pickle.dumps(mw)
mw2 = pickle.loads(data)
output_1 = mw().predict(np.asarray([[0, 0]]))
output_2 = mw2().predict(np.asarray([[0, 0]]))

# You can unload the object from memory as well

# The object will remain unloaded until requested again
output_3 = mw().predict(np.asarray([[0, 0]]))

print("All outputs:")

# If using tensorflow and pickling / unpickling a lot, be sure to clear the
# session:

# When using a custom layer class, be sure to register it so the pickler works
class MyLayer(keras.layers.Layer):
pass # ...

If your model takes 1GB of RAM, the default approach should require 2GB additional RAM to encode, as it dumps to shared memory by default. To disable this, set `KerasPickleWrapper.NO_SHM = True`. Temporary files will then be written to the standard temporary directory. Using `KerasPickleWrapper.unload(clear_session=True)` prior to pickling combined with `NO_SHM` should eliminate excess memory consumption, but clears the session (session clearing only applies to Tensorflow at the moment).

## Changelog

* 2017-8-17 - Renamed to keras-pickle-wrapper because PyPI.
* 2017-8-17 - Packaged up KerasWrapper for PyPI distribution.

Project details

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