Keras model fails to predict if called in a thread

When using Keras models for prediction in a multi-threaded environment, you may encounter issues where the model fails to predict correctly. This is due to the way Keras handles threads and its internal state.

One common reason for this issue is that Keras models are not thread-safe. This means that if you call model.predict() in a thread other than the main thread, it can lead to unexpected behavior and incorrect predictions.

To avoid this issue, it is recommended to always call model.predict() in the main thread or synchronize access to the model using locks or other synchronization mechanisms.

Another approach is to use a separate Keras session or graph for each thread, ensuring that each thread has its own independent instance of the model. This can help prevent issues with shared internal states and ensure accurate predictions in a multi-threaded environment.

In conclusion, be mindful of how you use Keras models in multi-threaded applications to ensure reliable and accurate predictions. By following best practices and handling threads properly, you can avoid issues with model prediction and achieve consistent results.