tf serving tutorial | tensorflow serving tutorial | Deep Learning Tutorial 48 (Tensorflow, Python)

Introduction

In this tutorial, we will explore TensorFlow Serving, a powerful tool developed by Google's TensorFlow team for serving machine learning models. Unlike traditional frameworks like Flask or FastAPI, TensorFlow Serving simplifies model version management and enhances performance through features like batch inference. We will cover the theoretical background and provide a practical step-by-step guide on how to set up and use TensorFlow Serving effectively.

Chapter 1: Understanding the Problem TensorFlow Serving Solves

When deploying machine learning models, version management can become complex. For example, if you build an email classification model to distinguish between spam and non-spam emails, the typical workflow involves:

• Data Collection: Gather and clean the data.
• Feature Engineering: Transform data into a suitable format for model training.
• Model Training: Train your model using a framework like TensorFlow.
• Model Export: Save the trained model using the model.save() method.

Once the model is in production, new data may prompt the need for a newer version. In traditional setups, this could mean significant changes to your server code to accommodate multiple versions, leading to increased complexity.

With TensorFlow Serving, you can:

• Easily manage model versions: Switch between different versions without rewriting server code.
• Utilize batch inference: Handle multiple requests simultaneously, optimizing resource use.

Chapter 2: Installing TensorFlow Serving

To get started with TensorFlow Serving, follow these steps to install it using Docker:

1. Install Docker: Make sure Docker is installed on your system. You can download it from Docker's official website.

2. Pull TensorFlow Serving Image: Open your terminal and run the following command:

   docker pull tensorflow/serving
   

3. Verify Installation: Check that the TensorFlow Serving image is present in your Docker Desktop.

4. Prepare Your Model: Save your TensorFlow model versions using the model.save() method. For example:

   model.save('saved_model/my_model')
   

5. Open PowerShell: Use Windows PowerShell (or your terminal of choice) to run the Docker container.

Chapter 3: Running TensorFlow Serving

To run TensorFlow Serving with your model, execute the following command in your terminal:

docker run -p 8601:8601 --name=tf_serving \
  --mount type=bind,source=/path/to/your/model,destination=/models/my_model \
  -e MODEL_NAME=my_model -t tensorflow/serving

Breakdown of the Command

• -p 8601:8601: Maps port 8601 on your host to port 8601 on the container.
• --mount...: Binds your model directory to the Docker container.
• -e MODEL_NAME=my_model: Specifies the name of your model.
• -t tensorflow/serving: Runs the TensorFlow Serving image.

Chapter 4: Making Predictions

Once TensorFlow Serving is running, you can make predictions using tools like Postman or curl.

1. Open Postman: Create a new POST request.
2. Set URL: Use the following format:

   http://localhost:8601/v1/models/my_model:predict
   

3. Set Body: Choose "raw" and set the content type to JSON. Use the following format for your request body:

   {
     "instances": [
       {"email": "example@example.com"},
       {"email": "spam@spam.com"}
     ]
   }
   

4. Send Request: Click "Send" to receive predictions from your model.

Chapter 5: Serving Multiple Model Versions

To serve multiple model versions, you can create a model configuration file (e.g., model.config) with the following structure:

model_config_list: {
  config: {
    name: "my_model",
    base_path: "/models/my_model",
    model_platform: "tensorflow",
    model_version_policy: {
      all: {}
    }
  }
}

Run TensorFlow Serving with the Config File

Use the following command to start TensorFlow Serving with your model configuration:

docker run -p 8601:8601 --name=tf_serving \
  --mount type=bind,source=/path/to/your/model,destination=/models/my_model \
  -e MODEL_CONFIG_FILE=/models/my_model/model.config -t tensorflow/serving

Chapter 6: Using Version Labels

To utilize version labels (e.g., production and beta), modify your model configuration file as follows:

model_config_list: {
  config: {
    name: "my_model",
    base_path: "/models/my_model",
    model_platform: "tensorflow",
    model_version_policy: {
      specific: {
        versions: [1, 2]
      }
    }
  }
}

You can now call your models using labels instead of version numbers.

Conclusion

In this tutorial, we covered the importance of TensorFlow Serving for managing machine learning models, installation instructions, and how to make predictions. We also discussed how to handle multiple model versions and utilize version labels to streamline deployment.

As the next step, practice setting up TensorFlow Serving with your own models and explore the configuration options available. This hands-on experience will deepen your understanding of model serving in production environments.