3> Source Customization (TensorFlow 2.0)
from grpc.beta import implementations
import numpy
import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2
import matplotlib.pyplot as plt
import os
1> Conda Environment Configuration
tensorflow 2.0 Environment Configuration (Client)
conda create -n keras keras tensorflow
source activate keras
pip install numpy scipy scikit-learn pillow h5py
pip install grpcio
pip install tensorflow-serving-api
pip install tensorflow_datasets
conda deactivate
2> Docker Server Environment Configuration ------------------------- DockerFile -------------------------- FROM ubuntu:16.04
RUN apt-get update && apt-get install -y software-properties-common && add-apt-repository ppa:deadsnakes/ppa && \ apt-get update && apt-get install -y python3.6 python3.6-dev python3-pip git
RUN ln -sfn /usr/bin/python3.6 /usr/bin/python3 && ln -sfn /usr/bin/python3 /usr/bin/python && ln -sfn /usr/bin/pip3 /usr/bin/pip
docker build -t docker-ubuntu16-python3.6 . docker run -it docker-ubuntu16-python3.6 bash apt-get install pkg-config zip g++ zlib1g-dev unzip
apt-get install wget wget https://github.com/bazelbuild/bazel/releases/download/0.15.0/bazel-0.15.0-installer-linux-x86_64.sh chmod +x bazel-0.15.0-installer-linux-x86_64.sh ./bazel-0.15.0-installer-linux-x86_64.sh export PATH="$PATH:$HOME/bin" apt-get install sudo sudo apt-get update && sudo apt-get install -y \ automake \ build-essential \ curl \ libcurl3-dev \ git \ libtool \ libfreetype6-dev \ libpng12-dev \ libzmq3-dev \ pkg-config \ python-dev \ python-numpy \ python-pip \ software-properties-common \ swig \ zip \ zlib1g-dev pip install --upgrade pip pip install grpcio git clone --recursive https://github.com/tensorflow/serving echo "deb [arch=amd64] http://storage.googleapis.com/tensorflow-serving-apt stable tensorflow-model-server tensorflow-model-server-universal" | sudo tee /etc/apt/sources.list.d/tensorflow-serving.list curl https://storage.googleapis.com/tensorflow-serving-apt/tensorflow-serving.release.pub.gpg | sudo apt-key add - sudo apt-get update && sudo apt-get install tensorflow-model-server pip install tensorflow-serving-api cd serving/ python tensorflow_serving/example/mnist_saved_model.py /tmp/mnist_model
tensorflow_model_server --port=9000 --model_name=mnist --model_base_path=/tmp/mnist_model
3> Source Customization (TensorFlow 2.0) from grpc.beta import implementations import numpy import tensorflow as tf import tensorflow_datasets as tfds from tensorflow_serving.apis import predict_pb2 from tensorflow_serving.apis import prediction_service_pb2 import matplotlib.pyplot as plt import os
mnist = tfds.load("mnist", shuffle_files=False, as_supervised=False) mnist.keys()
print(mnist.keys())
concurrency = 1 num_tests = 100 host = '' port = 9000 work_dir = './tmp/4'
try: if not(os.path.isdir(work_dir)): os.makedirs(os.path.join(work_dir)) except OSError: print('Failed to create directory!' + work_dir)
def _create_rpc_callback(): def _callback(result): response = numpy.array( result.result().outputs['y'].float_val) prediction = numpy.argmax(response) print(prediction) return _callback
train_data, test_data = mnist["train"], mnist["test"]
test_data_set = mnist["test"]
test_image = mnist.test.images[0]
train_data, test_data = tfds.load( "mnist", split=(tfds.Split.TRAIN, tfds.Split.TEST), shuffle_files=False, as_supervised=False ) print(train_data.output_types) print(train_data.output_shapes)
data = [] iterator = iter(train_data.batch(2)) data.append(next(iterator)) data.append(next(iterator)) data.append(next(iterator))
print(data[0].keys()) print(data[0]["image"].shape) print(data[0]["label"].shape)
plt.figure(figsize=(10,2)) for i in range(3): for j in range(2): plt.subplot(1,6,2*i+j+1) plt.imshow(data[i]["image"][j,:,:,0]) plt.axis("off")
plt.tight_layout()
plt.show()
print(data[0]["label"].numpy(), data[1]["label"].numpy(), data[2]["label"].numpy())
test_image = data[0]["image"].numpy()
print('test_image:'+ test_image)
predict_request = predict_pb2.PredictRequest() predict_request.model_spec.name = 'mnist' predict_request.model_spec.signature_name = 'prediction'
predict_channel = implementations.insecure_channel(host, int(port)) predict_stub = prediction_service_pb2.beta_create_PredictionService_stub(predict_channel)
print(test_image)
predict_request.inputs['x'].CopyFrom( tf.make_tensor_proto(test_image, shape=[1, test_image.size])) result = predict_stub.Predict.future(predict_request, 3.0) result.add_done_callback( _create_rpc_callback())