Deploy Deep Learning Models with TensorFlow Serving and Docker

Official repository of TensorFlow Serving with Docker for Model Deployment Coursera Project

Data Set

The dataset for TensorFlow Serving with Docker for Model Deployment is available here. It is assumed that the contents of the uncompressed file (train.csv, test.csv) are saved in the sample folder as train.py file.

Getting Started

$ virtualenv -p python3 tf-serving-coursera
$ source tf-serving-coursera/bin/activate
$ pip3 install -r requirements.txt

Model Structure

We predict Amazon product ratings based on plaintext reviews.

hub_layer = hub.KerasLayer("https://tfhub.dev/google/tf2-preview/nnlm-en-dim128/1", output_shape=[128], 
                 input_shape=[], dtype=tf.string, name='input', trainable=False)

model = tf.keras.Sequential()
model.add(hub_layer)
model.add(tf.keras.layers.Dense(64, activation='relu'))
model.add(tf.keras.layers.Dense(3, activation='softmax', name='output'))
model.compile(loss='categorical_crossentropy',
        optimizer='Adam', metrics=['accuracy'])

Test the Model

Negative Review

>> test_sentence = "horrible book, waste of time"
>> model.predict([test_sentence])
[0.87390379  0.02980554  0.09629067]

Positive Review

>> test_sentence = "Awesome product. Loved it! :D"
>> model.predict([test_sentence)
[0.00827967  0.01072392  0.98099641]

Steps to Deploy the Model

Export the Model as Protobuf

base_path = "amazon_review/"
path = os.path.join(base_path, str(int(time.time())))
tf.saved_model.save(model, path)

Start TensorFlow Serving with Docker

$ docker pull tensorflow/serving

$ docker run -p 8500:8500 \
             -p 8501:8501 \
             --mount type=bind,\
             source=/path/to/amazon_review,\
             target=/models/amazon_review \
             -e MODEL_NAME=amazon_review
             -t tensorflow/serving

Setup a client (either gRPC or REST based)

import sys
import grpc
from grpc.beta import implementations
import tensorflow as tf
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2, get_model_metadata_pb2
from tensorflow_serving.apis import prediction_service_pb2_grpc


def get_stub(host='127.0.0.1', port='8500'):
    channel = grpc.insecure_channel('127.0.0.1:8500') 
    stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
    return stub


def get_model_prediction(model_input, stub, model_name='amazon_review', signature_name='serving_default'):
    request = predict_pb2.PredictRequest()
    request.model_spec.name = model_name
    request.model_spec.signature_name = signature_name
    request.inputs['input_input'].CopyFrom(tf.make_tensor_proto(model_input))
    response = stub.Predict.future(request, 5.0)  # 5 seconds
    return response.result().outputs["output"].float_val


def get_model_version(model_name, stub):
    request = get_model_metadata_pb2.GetModelMetadataRequest()
    request.model_spec.name = 'amazon_review'
    request.metadata_field.append("signature_def")
    response = stub.GetModelMetadata(request, 10)
    # signature of loaded model is available here: response.metadata['signature_def']
    return response.model_spec.version.value

if __name__ == '__main__':
    print("\nCreate RPC connection ...")
    stub = get_stub()
    while True:
        print("\nEnter an Amazon review [:q for Quit]")
        if sys.version_info[0] <= 3:
            sentence = raw_input() if sys.version_info[0] < 3 else input()
        if sentence == ':q':
            break
        model_input = [sentence]
        model_prediction = get_model_prediction(model_input, stub)
        print("The model predicted ...")
        print(model_prediction)

Run the client

$ python3 tf_serving_grpc_client.py

Create RPC connection ...

Enter an Amazon review [:q for Quit]
horrible book, waste of time

The model predicted ...
[0.87390379  0.02980554  0.09629067]

Enter an Amazon review [:q for Quit]
Awesome product. Loved it! :D

The model predicted ...
[0.00827967  0.01072392  0.98099641]

Enter an Amazon review [:q for Quit]
:q

shamshad/Deploy-Deep-Learning-Models-TF-Serving-Docker