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Continuous integration support
* remove grpc git submodule * update tensorflow * add build status * add build artifacts to .gitignore * add continuous integration build documentation
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.gitignore

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node_modules
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/bazel-bin
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/bazel-ci_build-cache
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/bazel-genfiles
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/bazel-out
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/bazel-serving
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/bazel-tensorflow
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/bazel-testlogs
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/bazel-tf
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/bazel-workspace
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/third_party/py/numpy/numpy_include
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/tools/bazel.rc
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/util/python/python_include

.gitmodules

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[submodule "tensorflow"]
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path = tensorflow
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url = https://github.com/tensorflow/tensorflow.git
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[submodule "grpc"]
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path = grpc
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url = https://github.com/grpc/grpc.git

README.md

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#TensorFlow Serving
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[![Build Status](http://ci.tensorflow.org/buildStatus/icon?job=serving-master-cpu)](http://ci.tensorflow.org/job/serving-master-cpu)
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TensorFlow Serving is an open-source software library for serving
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machine learning models. It deals with the *inference* aspect of machine
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learning, taking models after *training* and managing their lifetimes, providing

WORKSPACE

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remote = "https://github.com/mdsteele/boringssl-bazel.git",
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)
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bind(
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name = "protobuf_clib",
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actual = "@tf//google/protobuf:protobuf",
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)
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bind(
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name = "zlib",
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actual = "@zlib_archive//:zlib",

grpc

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tensorflow

Submodule tensorflow updated 67 files

tensorflow_serving/example/BUILD

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@@ -34,7 +34,7 @@ cc_library(
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"mnist_inference.pb.h",
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],
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deps = [
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"//grpc:grpc++",
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"@grpc//:grpc++",
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],
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)
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"@tf//tensorflow/core:protos_all_cc",
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"@tf//tensorflow/core:tensorflow",
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":mnist_grpc_cc",
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"//grpc",
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"@grpc//:grpc",
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"//tensorflow_serving/session_bundle",
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"//tensorflow_serving/session_bundle:manifest_proto",
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"//tensorflow_serving/session_bundle:signature",
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"@tf//tensorflow/core:protos_all_cc",
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"@tf//tensorflow/core:tensorflow",
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":mnist_grpc_cc",
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"//grpc",
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"@grpc//:grpc",
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"//tensorflow_serving/batching:batch_scheduler",
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"//tensorflow_serving/batching:batch_scheduler_retrier",
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"//tensorflow_serving/batching:streaming_batch_scheduler",

tensorflow_serving/example/mnist_inference.cc

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#include <utility>
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#include <vector>
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#include "grpc/include/grpc++/security/server_credentials.h"
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#include "grpc/include/grpc++/server.h"
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#include "grpc/include/grpc++/server_builder.h"
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#include "grpc/include/grpc++/server_context.h"
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#include "grpc/include/grpc++/support/status.h"
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#include "grpc/include/grpc++/support/status_code_enum.h"
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#include "grpc/include/grpc/grpc.h"
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#include "grpc++/security/server_credentials.h"
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#include "grpc++/server.h"
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#include "grpc++/server_builder.h"
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#include "grpc++/server_context.h"
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#include "grpc++/support/status.h"
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#include "grpc++/support/status_code_enum.h"
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#include "grpc/grpc.h"
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#include "tensorflow/core/framework/tensor.h"
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#include "tensorflow/core/framework/tensor_shape.h"
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#include "tensorflow/core/framework/tensor_types.h"

tensorflow_serving/example/mnist_inference_2.cc

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#include <string>
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#include <vector>
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#include "grpc/include/grpc++/completion_queue.h"
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#include "grpc/include/grpc++/security/server_credentials.h"
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#include "grpc/include/grpc++/server.h"
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#include "grpc/include/grpc++/server_builder.h"
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#include "grpc/include/grpc++/server_context.h"
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#include "grpc/include/grpc++/support/async_unary_call.h"
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#include "grpc/include/grpc++/support/status.h"
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#include "grpc/include/grpc++/support/status_code_enum.h"
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#include "grpc/include/grpc/grpc.h"
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#include "grpc/include/grpc/support/log.h"
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#include "grpc++/completion_queue.h"
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#include "grpc++/security/server_credentials.h"
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#include "grpc++/server.h"
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#include "grpc++/server_builder.h"
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#include "grpc++/server_context.h"
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#include "grpc++/support/async_unary_call.h"
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#include "grpc++/support/status.h"
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#include "grpc++/support/status_code_enum.h"
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#include "grpc/grpc.h"
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#include "grpc/support/log.h"
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#include "tensorflow/core/framework/tensor.h"
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#include "tensorflow/core/framework/tensor_types.h"
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#include "tensorflow/core/framework/types.pb.h"

tensorflow_serving/g3doc/setup.md

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See the [basic tutorial](serving_basic.md) and [advanced tutorial](serving_advanced.md)
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for more in-depth examples of running TensorFlow Serving.
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### Continuous integration build
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Our [continuous integration build](http://ci.tensorflow.org/view/Serving/job/serving-master-cpu/)
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using tensorflow [ci_build](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/ci_build)
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infrastructure offer you simplified development using docker. All you need is
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git and docker. No need to install all other dependencies manually.
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~~~shell
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git clone --recursive https://github.com/tensorflow/serving
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cd serving
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CI_TENSORFLOW_SUBMODULE_PATH=tensorflow tensorflow/tensorflow/tools/ci_build/ci_build.sh CPU bazel test //tensorflow_serving/...
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~~~
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Note: The `serving` directory is mapped into the container. You can develop
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outside the docker container (in your favourite editor) and when you run this
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build it will build with your changes.

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