How can I avoid reloading a TensorFlow model during inference for production use?

Question

I am in the process of taking a TensorFlow model into production, and I want to optimize the inference process to avoid reloading the model every time a prediction is required. Currently, I am loading the model using the tf.keras.models.load_model function for each inference request, but this adds significant overhead. I used the below script to avoid reloading but it's not working.

import tensorflow as tf

# Define a global variable to store the loaded model
global loaded_model
loaded_model = None

# Function to load the model if it hasn't been loaded
def load_cached_model():
    global loaded_model
    
    if loaded_model is None:
        loaded_model = tf.keras.models.load_model('path_to_model.h5')
    
    return loaded_model

# Example inference function
def inference(input_data):
    # Load or retrieve the cached model
    model = load_cached_model()
    
    # Perform inference with the model
    predictions = model.predict(input_data)
    
    return predictions

# Example usage in a production scenario
if __name__ == "__main__":
    input_data_1 = ...  # Prepare input data for the first request
    output_1 = inference(input_data_1)  # Inference with the cached model
    
    input_data_2 = ...  # Prepare input data for the second request
    output_2 = inference(input_data_2)  # Inference with the cached model

Is there a best practice or recommended approach for avoiding the model reload during inference for production deployments? What strategies or caching mechanisms can I implement to optimize the inference process and improve the model's efficiency in a production environment?

I'd appreciate any insights, examples, or best practices for managing model caching and avoiding redundant model reloading in a production context.

Answer 1

In short, the answer to your question is it depends on the way you are planning to deploy your model. Lets assume for simplicity that you are deploying your model as a custom container. Then, you could define a class, upon creating an instance of which the model would be loaded as an attribute and then used during the inference. For instance:

class Analyzer:

    def __init__(self, model_path):
        self.model = self.load_model(model_path)

    def load_model(self, model_path):
        <your_code>

    def inference(self, input_data):
        <your_code>

It seems from your code, that you are rather at the beginning of your ML journey, so I would advice to approach the learning of these things more structurally; below are a great course from deeplearning.ai and a very useful book to provide you with valuable insights:

• Machine Learning Engineering for Production (MLOps) Specialization
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition