tf.make_tensor_proto

create_python_files.sh

pip install grpcio-tools

wget https://github.com/tensorflow/tensorflow/archive/v1.9.0.zip -O tf-190.zip
unzip tf-190.zip && rm tf-190.zip

wget https://github.com/tensorflow/serving/archive/1.9.0.zip -O tf-serving-190.zip
unzip tf-serving-190.zip && rm tf-serving-190.zip

mv serving-1.9.0/tensorflow_serving tensorflow-1.9.0

mkdir tfserving_proto


cd tensorflow-1.9.0

TF_SERVING_PROTO=../tfserving_proto

python -m grpc.tools.protoc \
    ./tensorflow/core/framework/*.proto \
    --python_out=${TF_SERVING_PROTO} \
    --grpc_python_out=${TF_SERVING_PROTO} \
    --proto_path=.

python -m grpc.tools.protoc \
    ./tensorflow/core/example/*.proto \
    --python_out=${TF_SERVING_PROTO} \
    --grpc_python_out=${TF_SERVING_PROTO} \
    --proto_path=.

python -m grpc.tools.protoc \
    ./tensorflow/core/protobuf/*.proto \
    --python_out=${TF_SERVING_PROTO} \
    --grpc_python_out=${TF_SERVING_PROTO} \
    --proto_path=.

python -m grpc.tools.protoc \
    ./tensorflow_serving/apis/*.proto \
    --python_out=${TF_SERVING_PROTO} \
    --grpc_python_out=${TF_SERVING_PROTO} \
    --proto_path=.

# now move ${TF_SERVING_PROTO} to your project

pyfile.py

from tensorflow.core.framework import tensor_pb2, tensor_shape_pb2, types_pb2
from tensorflow_serving.apis import predict_pb2, prediction_service_pb2
from tensorflow_serving.apis import prediction_service_pb2_grpc


def dtypes_as_dtype(dtype):
    if dtype == 'float32':
        return types_pb2.DT_FLOAT
    raise Exception('dtype %s is not supported' % dtype)


def make_tensor_proto(data):
    # return tf.make_tensor_proto(data, shape=data.shape)

    shape = data.shape
    dims = [tensor_shape_pb2.TensorShapeProto.Dim(size=i) for i in shape]
    proto_shape= tensor_shape_pb2.TensorShapeProto(dim=dims)

    proto_dtype = dtypes_as_dtype(data.dtype)

    tensor_proto = tensor_pb2.TensorProto(
        dtype=proto_dtype,
        tensor_shape=proto_shape)
    tensor_proto.tensor_content = data.tostring()

    return tensor_proto


def beta_create_PredictionService_stub(channel):
    return prediction_service_pb2_grpc.PredictionServiceStub(channel._channel)

This is inspired by https://towardsdatascience.com/tensorflow-serving-client-make-it-slimmer-and-faster-b3e5f71208fb