From training to deployment: A tutorial on serving TensorFlow models with TensorFlow Serving

This tutorial assumes that you have TensorFlow and TensorFlow Serving installed and configured.

Step 1: Define the model

First, we need to define the model that we will use to predict the next number in the series. In this case, we will use a simple linear model with a single weight and bias:

import tensorflow as tf

# Define the model
def model(x):
  return x * W + b

# Define the loss function
def loss(y_pred, y_true):
  return tf.reduce_mean(tf.square(y_pred - y_true))

# Define the optimization operation
def optimize(learning_rate=0.01):
  return tf.compat.v1.train.AdamOptimizer(learning_rate).minimize(loss)

Step 2: Generate synthetic data

Next, we need to generate some synthetic data that we can use to train our model. For this tutorial, we will generate a simple sequence of numbers starting from 0 and ending at 99:

# Generate synthetic data
x_train = list(range(100))
y_train = [x + 1 for x in x_train]

Step 3: Train the model

Now we are ready to train our model. We will use the synthetic data that we generated earlier to train the model using mini-batch gradient descent:

# Initialize the variables
W = tf.compat.v1.Variable(0.0, name='W')
b = tf.compat.v1.Variable(0.0, name='b')

# Set the learning rate
learning_rate = 0.01

# Define the optimization operation
optimizer = optimize(learning_rate)

# Initialize the global variables
init = tf.compat.v1.global_variables_initializer()

# Start a TensorFlow session
with tf.compat.v1.Session() as sess:
  # Run the initialization operation
  sess.run(init)

  # Training loop
  for i in range(100):
    # Select a random batch of data
    indices = np.random.choice(len(x_train), size=32)
    x_batch = [x_train[i] for i in indices]
    y_batch = [y_train[i] for i in indices]

    # Run the optimization operation
    sess.run(optimizer, feed_dict={'x:0': x_batch, 'y:0': y_batch})

Step 4: Save the trained model

After training the model, we need to save it to disk so that we can serve it later. We will use the tf.saved_model.save function to save the model in a format that is compatible with TensorFlow Serving:

# Save the model
tf.saved_model.save(sess, './model')

Step 5: Serve the model using TensorFlow Serving

To serve the model using TensorFlow Serving, we need to start the TensorFlow Serving server and point it to the directory where the saved model.

# Start the TensorFlow Serving server
tensorflow_model_server --model_name=sequential_number_predictor --model_base_path=./model

This will start the TensorFlow Serving server and load the model from the ./model directory. The server will start listening for requests on the default port (8501).

To test the server, we can use the curl command to send a request to the server and get a prediction:

# Send a request to the server and get a prediction
curl -d '{"instances": [1.0, 2.0, 3.0]}' -X POST http://localhost:8501/v1/models/sequential_number_predictor:predict

This should return a response with the predictions for the next numbers in the series:

{
  "predictions": [2.0, 3.0, 4.0]
}

Step 6: Consume the model from your application

To consume the model from your application, you can use the TensorFlow Serving REST API to send requests and get predictions. Here is an example of how you can do this using the Python requests library:

import requests

# Send a request to the server and get a prediction
response = requests.post('http://localhost:8501/v1/models/sequential_number_predictor:predict', json={'instances': [1.0, 2.0, 3.0]})

# Print the predictions
print(response.json()['predictions'])

This should print the same predictions that we got earlier using the curl command.

That’s it! You have now trained and served a TensorFlow model using TensorFlow Serving. You can now use the model to make predictions from your application or from other systems.

Happy Coding!