How to deploy a model in Python using TensorFlow Serving?

Deploying machine learning models is critical to making artificial intelligence applications functional, and to effectively serve models in production environments, TensorFlow Serving provides a reliable solution. When a model is trained and ready to be deployed, it is critical to serve it efficiently to handle real-time requests. TensorFlow Serving is a powerful tool that helps smoothly deploy machine learning models in production environments.

In this article, we’ll take a deep dive into the steps involved in deploying a model in Python using TensorFlow Serving.

What is model deployment?

Model deployment involves making a trained machine learning model available for real-time predictions. This means moving the model from a development environment to a production system where it can efficiently handle incoming requests. TensorFlow Serving is a purpose-built, high-performance system designed specifically for deploying machine learning models.

Set up TensorFlow service

First, we need to install TensorFlow Serving on our system. Please follow the steps below to set up TensorFlow Serving -

Step 1: Install TensorFlow Serving

First use the package manager pip to install TensorFlow Serving. Open a command prompt or terminal and enter the following command -

pip install tensorflow-serving-api

Step 2: Start the TensorFlow service server

After installation, start the TensorFlow Serving server by running the following command -

tensorflow_model_server --rest_api_port=8501 --model_name=my_model --model_base_path=/path/to/model/directory

Replace `/path/to/model/directory` with the path where the trained model is stored.

Prepare to deploy model

Before deploying the model, it needs to be saved in a format that TensorFlow Serving can understand. Follow these steps to prepare your model for deployment -

Save the model in SavedModel format

In the Python script, use the following code to save the trained model into SavedModel format -

import tensorflow as tf

# Assuming `model` is your trained TensorFlow model
tf.saved_model.save(model, '/path/to/model/directory')

Define model signature

Model signature provides information about the model input and output tensors. Use the `tf.saved_model.signature_def_utils.build_signature_def` function to define the model signature. Here is an example -

inputs = {'input': tf.saved_model.utils.build_tensor_info(model.input)}
outputs = {'output': tf.saved_model.utils.build_tensor_info(model.output)}

signature = tf.saved_model.signature_def_utils.build_signature_def(
   inputs=inputs,
   outputs=outputs,
   method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME
)

Save model using signature

To save the model along with the signature, use the following code -

builder = tf.saved_model.builder.SavedModelBuilder('/path/to/model/directory')
builder.add_meta_graph_and_variables(
   sess=tf.keras.backend.get_session(),
   tags=[tf.saved_model.tag_constants.SERVING],
   signature_def_map={
      tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: signature
   }
)
builder.save

()

Use TensorFlow Serving to serve the model

Now that our model is ready, it’s time to serve it using TensorFlow Serving. Please follow the steps below -

Establish a connection with TensorFlow Serving

In the Python script, use the gRPC protocol to establish a connection with TensorFlow Serving. Here is an example -

from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2_grpc

channel = grpc.insecure_channel('localhost:8501')
stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)

Create request

To make predictions, create a request protobuf message and specify the model name and signature name. Here is an example -

request = predict_pb2.PredictRequest()
request.model_spec.name = 'my_model'
request.model_spec.signature_name = tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
request.inputs['input'].CopyFrom(tf.contrib.util.make_tensor_proto(data, shape=data.shape))

Replace `data` with the input data you want to predict.

Send a request and get a response

Send the request to TensorFlow Serving and retrieve the response. Here is an example -

response = stub.Predict(request, timeout_seconds)
output = tf.contrib.util.make_ndarray(response.outputs['output'])

`timeout_seconds`The parameter specifies the maximum time to wait for a response.

Test the deployed model

To ensure that the deployed model functions properly, it must be tested with sample input. Here's how to test a deployed model -

Prepare sample data

Create a set of sample input data that matches the model's expected input format.

Send a request to the deployed model

Create a request and send it to the deployed model.

request = predict_pb2.PredictRequest()
request.model_spec.name = 'my_model'
request.model_spec.signature_name = tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
request.inputs['input'].CopyFrom(tf.contrib.util.make_tensor_proto(data, shape=data.shape))

Evaluate output

Compare the output received from the deployed model with the expected output. This step ensures that the model makes accurate predictions.

Scaling and Monitoring Deployments

As forecast demand increases, it is critical to scale your deployment to handle large volumes of incoming requests. Additionally, monitoring deployments helps track the performance and health of deployed models. Consider implementing the following scaling and monitoring strategies -

Zoom

• Use multiple instances of TensorFlow Serving for load balancing.

• Containerization using platforms such as Docker and Kubernetes.

monitor

• Collect metrics such as request latency, error rate, and throughput.

• Set alerts and notifications for critical events.

Example

The following program example shows how to use TensorFlow service to deploy the model -

import tensorflow as tf
from tensorflow import keras

# Load the trained model
model = keras.models.load_model("/path/to/your/trained/model")

# Convert the model to the TensorFlow SavedModel format
export_path = "/path/to/exported/model"
tf.saved_model.save(model, export_path)

# Start the TensorFlow Serving server
import os
os.system("tensorflow_model_server --port=8501 --model_name=your_model --model_base_path={}".format(export_path))

In the above example, you need to replace "/path/to/your/trained/model" with the actual path to the trained model. The model will be loaded using Keras’ load_model() function.

Next, the model will be converted to TensorFlow SavedModel format and saved in the specified export path.

Then use the os.system() function to start the TensorFlow Serving server, which executes the tensorflow_model_server command. This command specifies the server port, model name (your_model), and the base path where the exported model is located.

Please make sure you have TensorFlow Serving installed and replace the file path with the appropriate value for your system.

Expected output

After the server starts successfully, it will be ready to provide prediction services. You can use other programs or APIs to send prediction requests to the server, and the server will respond with prediction output based on the loaded model.

in conclusion

In conclusion, it is important to deploy machine learning models in production environments to leverage their predictive capabilities. In this article, we explore the process of deploying models in Python using TensorFlow Serving. We discussed installing TensorFlow Serving, preparing to deploy the model, serving the model, and testing its performance. With the following steps, we can successfully deploy the TensorFlow model and make accurate real-time predictions.

The above is the detailed content of How to deploy a model in Python using TensorFlow Serving?. For more information, please follow other related articles on the PHP Chinese website!