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