Keras predict loop memory leak using tf.data.Dataset but not with a numpy array

Question

I encounter a memory leak and decreasing performance when looping over a Keras model predict function when using a tf.data.Dataset to feed the model, but not when feeding it with a numpy array.

Does anyone understand what is causing this and/or how to resolve the issue?

Minimal reproducible code snippet (copy/paste runnable):

import tensorflow as tf
import numpy as np
import time

SIZE = 5000

inp = tf.keras.layers.Input(shape=(SIZE,), dtype='float32')
x = tf.keras.layers.Dense(units=SIZE)(inp)

model = tf.keras.Model(inputs=inp, outputs=x)

np_data = np.random.rand(1, SIZE)
ds = tf.data.Dataset.from_tensor_slices(np_data).batch(1).repeat()

debug_time = time.time()
while True:
    model.predict(x=ds, steps=1)
    print('Processing {:.2f}'.format(time.time() - debug_time))
    debug_time = time.time()

Result: Predict loop timing starts around 0.04s per iteration, within a minute or two it's up to about 0.5s and process memory continues to increase from a few hundred MB to close to a GB.

Swap out the tf.data.Dataset for an equivalent numpy array and runtime is ~0.01s consistently.

Working case code snippet (copy/paste runnable):

import tensorflow as tf
import numpy as np
import time

SIZE = 5000

inp = tf.keras.layers.Input(shape=(SIZE,), dtype='float32')
x = tf.keras.layers.Dense(units=SIZE)(inp)

model = tf.keras.Model(inputs=inp, outputs=x)

np_data = np.random.rand(1, SIZE)

debug_time = time.time()
while True:
    model.predict(x=np_data)  # using numpy array directly
    print('Processing {:.2f}'.format(time.time() - debug_time))
    debug_time = time.time()

Related discussions:

Memory leak tf.data + Keras - Doesn't seem to address the core issue, but the question appears similar.
https://github.com/tensorflow/tensorflow/issues/22098 - Possibly an open issue in Keras/Github, but I can't confirm it, changing inter_op_paralellism as suggested in that thread has no impact on the results posted here.

Additional info:

I can reduce the rate of performance degradation by around 10x by passing in an iterator instead of a dataset object. I noticed in training_utils.py:1314 the Keras code is creating an iterator each call to predict.

TF 1.14.0

What’s your question? Would you like an explanation of why this phenomena occurs? Please clarify in your post. — Matthew Anderson
– Matthew Anderson, Commented Jul 6, 2019 at 3:40

David Parks · Accepted Answer · 2019-07-06 17:53:22Z

The root of the problem appears to be that Keras is creating dataset operations each predict loop. Notice at training_utils.py:1314 a dataset iterator is created in each predict loop.

The problem can be reduced in severity by passing in an iterator, and is solved entirely by passing in the iterators get_next() tensor.

I have posted the issue on the Tensorflow Github page: https://github.com/tensorflow/tensorflow/issues/30448

Here is the solution, this example runs in constant time using the TF dataset, you just can't pass in the dataset object:

import tensorflow as tf
import numpy as np
import time

SIZE = 5000

inp = tf.keras.layers.Input(shape=(SIZE,), dtype='float32')
x = tf.keras.layers.Dense(units=SIZE)(inp)

model = tf.keras.Model(inputs=inp, outputs=x)

np_data = np.random.rand(1, SIZE)
ds = tf.data.Dataset.from_tensor_slices(np_data).batch(1).repeat()
it = tf.data.make_one_shot_iterator(ds)
tensor = it.get_next()

debug_time = time.time()
while True:
    model.predict(x=tensor, steps=1)
    print('Processing {:.2f}'.format(time.time() - debug_time))
    debug_time = time.time()

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