Exporting and serving TensorFlow models

TensorFlow is an open-source machine learning framework developed by Google. It provides a comprehensive ecosystem of tools, libraries, and resources to build and deploy various machine learning models. One crucial aspect of using TensorFlow models in real-world applications is the ability to export and serve these models efficiently. This article will guide you through the process of exporting and serving TensorFlow models, enabling you to utilize your models effectively.

Exporting TensorFlow Models

When you train a TensorFlow model, it is usually saved in a format specific to TensorFlow, known as a SavedModel. This SavedModel contains the complete graph structure and the model parameters. To export a TensorFlow model, you can use the tf.saved_model.save() function. Here's an example of how to export a model:

import tensorflow as tf

# Assuming 'model' is your trained TensorFlow model
tf.saved_model.save(model, 'path_to_export_directory')

In the above code, model represents your trained TensorFlow model, and 'path_to_export_directory' is the directory where you want to save the exported model. TensorFlow will generate the SavedModel inside this directory, which you can later use for serving.

Serving TensorFlow Models

After exporting your TensorFlow model, you can serve it to make predictions or use it in real-time applications. TensorFlow Serving is a powerful system designed specifically for model serving. It provides flexibility and efficiency in hosting your models as a server-client setup.

To serve your exported TensorFlow model using TensorFlow Serving, you need to follow these steps:

1. Install TensorFlow Serving

First, you need to install TensorFlow Serving. You can do this via pip:

$ pip install tensorflow-serving-api

2. Start the TensorFlow Serving server

Once TensorFlow Serving is installed, you can start the server by running the following command:

$ tensorflow_model_server --port=port_number --model_name=model_name --model_base_path=path_to_export_directory

In the above command, you need to replace port_number with the desired port number, model_name with the name you want to assign to the model, and path_to_export_directory with the directory path where you previously exported the SavedModel.

3. Make predictions using the served model

Now that the TensorFlow Serving server is running, you can make predictions by sending HTTP POST requests to the server endpoint. Here's an example using the Python requests library:

import requests
import json

# Assuming 'data' is the input data for prediction
data = {'instances': [{'input': your_input_data}]}

# Send POST request to server endpoint
response = requests.post('http://localhost:port_number/v1/models/model_name:predict', data=json.dumps(data))

# Get predictions from the response
predictions = json.loads(response.text)['predictions']

In the above code snippet, port_number should match the port number you specified while starting the TensorFlow Serving server, and model_name should match the name you assigned to the model during server startup.

Conclusion

Exporting and serving TensorFlow models is a crucial step in deploying machine learning models in production environments. TensorFlow provides simple yet powerful functionalities to export and serve models using the SavedModel format and TensorFlow Serving. By following the steps outlined in this article, you can easily export your trained models and deploy them as efficient server-client setups, allowing you to leverage the full potential of your TensorFlow models.