Tensorflow Freezes during a lengthy backtest

My long running backtest which continuously forecast over a long loop freezes after so many requests (a long time). When I force break (control c) I get the following dump. Any help?

File “/Users/erezkatz/miniconda3/envs/ekbase/lib/python3.11/site-packages/keras/src/layers/normalization/batch_normalization.py”, line 597, in call
outputs = self._fused_batch_norm(
File “/Users/erezkatz/miniconda3/envs/ekbase/lib/python3.11/site-packages/keras/src/layers/normalization/batch_normalization.py”, line 990, in _fused_batch_norm
output, mean, variance = control_flow_util.smart_cond(
File “/Users/erezkatz/miniconda3/envs/ekbase/lib/python3.11/site-packages/keras/src/utils/control_flow_util.py”, line 108, in smart_cond
return tf.internal.smart_cond.smart_cond(
File “/Users/erezkatz/miniconda3/envs/ekbase/lib/python3.11/site-packages/tensorflow/python/framework/smart_cond.py”, line 55, in smart_cond
return false_fn()
File “/Users/erezkatz/miniconda3/envs/ekbase/lib/python3.11/site-packages/keras/src/layers/normalization/batch_normalization.py”, line 979, in _fused_batch_norm_inference
return tf.compat.v1.nn.fused_batch_norm(
File “/Users/erezkatz/miniconda3/envs/ekbase/lib/python3.11/site-packages/tensorflow/python/util/traceback_utils.py”, line 150, in error_handler
return fn(*args, **kwargs)
File “/Users/erezkatz/miniconda3/envs/ekbase/lib/python3.11/site-packages/tensorflow/python/util/dispatch.py”, line 1260, in op_dispatch_handler
return dispatch_target(*args, **kwargs)
File “/Users/erezkatz/miniconda3/envs/ekbase/lib/python3.11/site-packages/tensorflow/python/ops/nn_impl.py”, line 1580, in fused_batch_norm
y, running_mean, running_var, _, _, _ = gen_nn_ops.fused_batch_norm_v3(
File “/Users/erezkatz/miniconda3/envs/ekbase/lib/python3.11/site-packages/tensorflow/python/ops/gen_nn_ops.py”, line 5175, in fused_batch_norm_v3
_result = pywrap_tfe.TFE_Py_FastPathExecute(

Hi @Erez_Katz ,

The frezing issue you are experiencing durig your long-running backtest could be related to the memory usage of the BatchNormalization layers in you model. where the model is contnuously making predictions over an extended period, the BatchNormalization layers can acumulate memory usage over time. This is because the moving averages are updated with each batch of data procesed, and the memory allocated for these averages keeps growing.


Official TensorFlow 2.16 Repo via JARaaS Hybrid RAG - Documentation - code current 6/18/2024

Note: Sources at the end of the response

When experiencing freezes during lengthy backtests in TensorFlow, it is often due to computational overhead or bottlenecks in processing that accumulate over time. Below are a few troubleshooting and optimization steps you might consider:

Possible Causes and Solutions:

  1. Memory Leaks: A common issue with long-running processes is memory leaks. Ensure that your code does not hold references to data unnecessarily.

    • Solution: Monitor the memory usage of your process. You can use tools like memory_profiler or tracemalloc in Python to track memory consumption.
  2. TensorFlow Sessions: In TensorFlow 1.x, the creation and improper handling of sessions can cause memory to leak.

    • Solution: If using TensorFlow 1.x, make sure to properly close sessions using session.close(). Consider using context managers (with tf.Session() as sess:) to ensure sessions are closed after they are used.
  3. Batch Normalization: The traceback you provided indicates issues with batch normalization during inference.

    • Solution: Ensure that batch normalization layers are correctly set to inference mode during testing. Use model.evaluate or model.predict which automatically set the training argument to False.
  4. Garbage Collection: Python’s garbage collector might not run often enough in a long-running loop.

    • Solution: Explicitly call Python’s garbage collector:
      import gc
  5. Model Checkpoints: Regularly save model checkpoints to avoid losing progress if your process crashes.

    • Solution: Use tf.keras.callbacks.ModelCheckpoint to save model at different training intervals.
  6. Debugging Freezes: Use TensorFlow profiler or other tools to identify bottlenecks.

    • Solution: Profile your TensorFlow code to identify any slow operations. TensorFlow provides a built-in profiler that can help with this.

Here is a more detailed debugging approach using some of the above points:

Improving Model Training with TensorFlow Callbacks:

import tensorflow as tf
import numpy as np

# Dummy Data
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

# Model Definition
model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(512, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')


# Callbacks to avoid freezing
checkpoint = tf.keras.callbacks.ModelCheckpoint('model.h5', save_best_only=True)
tensorboard = tf.keras.callbacks.TensorBoard(log_dir='./logs')
garbage_collect = tf.keras.callbacks.LambdaCallback(on_epoch_end=lambda epoch, logs: gc.collect())

model.fit(x_train, y_train, epochs=5, callbacks=[checkpoint, tensorboard, garbage_collect])

# Evaluate the model
model.evaluate(x_test, y_test)

Profiling the Code:

Profiling can provide insights into where the bottlenecks are occurring.

  1. TensorFlow Profiler:

If none of the above solutions help, consider dividing your long backtest into smaller chunks and periodically restarting the process. This can mitigate cumulative issues such as memory leaks.


I hope these suggestions help you troubleshoot and resolve the issue you’re facing.