Published on June 23, 2023

Fix Failure of Keras Model.fit with Mirrored Strategy

If you're experiencing issues with training a mirrored strategy in Keras and the model.fit() function is not working as expected, there could be several reasons for this.

Here are a few common troubleshooting steps you can take:

Verify TensorFlow version: Ensure that you have the latest version of TensorFlow installed. Mirrored strategy requires TensorFlow 2.x or above. You can check your TensorFlow version using tf.__version__. If you have an older version, consider upgrading to the latest stable release.
Check GPU availability: Mirrored strategy is designed to work with multiple GPUs. Make sure you have multiple GPUs available and properly set up for TensorFlow to use. You can check if TensorFlow detects your GPUs by running tf.config.list_physical_devices('GPU'). If no GPUs are detected, make sure your GPU drivers are up to date and properly installed.
Check batch size: When using mirrored strategy, the batch size should be divisible by the number of GPUs. If the batch size is not divisible, it can cause issues. Ensure that your batch size is a multiple of the number of GPUs you have. For example, if you have 2 GPUs, and your desired batch size is 64, you should use a batch size of 32 per GPU.
Verify model compatibility: Not all Keras models are compatible with mirrored strategy. Ensure that the layers and operations used in your model can be mirrored across multiple GPUs. For example, some custom or third-party layers might not be supported. Use the tf.distribute.Strategy.run function to check if your model can run successfully on each replica. If any errors occur, review the model architecture and make necessary adjustments.
```
# Example code to check model compatibility
strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
    model = create_model()  # Create your Keras model
    strategy.run(model, args=())  # Check if the model can run successfully on each replica
```

Set up strategy correctly: Double-check that you have set up the mirrored strategy correctly before calling model.fit(). This includes creating an instance of tf.distribute.MirroredStrategy() and using it as a context manager with strategy.scope().

import tensorflow as tf
 
# Create mirrored strategy
strategy = tf.distribute.MirroredStrategy()
 
# Define and compile your Keras model within the strategy's scope
with strategy.scope():
    model = create_model()  # Create your Keras model
    model.compile(...)  # Compile the model with optimizer, loss, and metrics
 
# Use model.fit() with the strategy
model.fit(...)

By following these troubleshooting steps and utilizing the provided example code, you should be able to identify and resolve the issues encountered when using the mirrored strategy with model.fit() in Keras.

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