Initial push of TensorFlow Serving.

.gitignore

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

.gitmodules

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

AUTHORS

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+# This is the official list of TensorFlow Serving authors for copyright purposes.
+# This file is distinct from the CONTRIBUTORS files.
+# See the latter for an explanation.
+
+# Names should be added to this file as:
+# Name or Organization <email address>
+# The email address is not required for organizations.
+
+Google Inc.

CONTRIBUTING.md

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+# Contributing guidelines
+
+## How to become a contributor and submit your own code
+
+### Contributor License Agreements
+
+We'd love to accept your patches! Before we can take them, we have to jump a couple of legal hurdles.
+
+Please fill out either the individual or corporate Contributor License Agreement (CLA).
+
+  * If you are an individual writing original source code and you're sure you own the intellectual property, then you'll need to sign an [individual CLA](http://code.google.com/legal/individual-cla-v1.0.html).
+  * If you work for a company that wants to allow you to contribute your work, then you'll need to sign a [corporate CLA](http://code.google.com/legal/corporate-cla-v1.0.html).
+
+Follow either of the two links above to access the appropriate CLA and instructions for how to sign and return it. Once we receive it, we'll be able to accept your pull requests.
+
+***NOTE***: Only original source code from you and other people that have signed the CLA can be accepted into the main repository.
+
+### Contributing code
+
+If you have improvements to TensorFlow Serving, send us your pull requests!
+For those just getting started, Github has a [howto](https://help.github.com/articles/using-pull-requests/).
+
+If you want to contribute but you're not sure where to start, take a look at the
+[issues with the "contributions welcome" label](https://github.com/tensorflow/serving/labels/contributions%20welcome).
+These are issues that we believe are particularly well suited for outside
+contributions, often because we probably won't get to them right now. If you
+decide to start on an issue, leave a comment so that other people know that
+you're working on it. If you want to help out, but not alone, use the issue
+comment thread to coordinate.

LICENSE

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+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
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+   Copyright 2016, The TensorFlow Serving Authors.
+
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+   you may not use this file except in compliance with the License.
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+
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+
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+   See the License for the specific language governing permissions and
+   limitations under the License.

README.md

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+#TensorFlow Serving
+
+TensorFlow Serving is an open-source software library for serving
+machine-learned models. It deals with the *inference* aspect of machine
+learning, taking models after *training* and managing their lifetimes, providing
+clients with versioned access via a high-performance, reference-counted lookup
+table.
+
+Multiple models, or indeed multiple versions of the same model, can be served
+simultaneously. This flexibility facilitates canarying new versions,
+non-atomically migrating clients to new models or versions, and A/B testing
+experimental models.
+
+The primary use-case is high-performance production serving, but the same
+serving infrastructure can also be used in bulk-processing (e.g. map-reduce)
+jobs to pre-compute inference results or analyze model performance. In both
+scenarios, GPUs can substantially increase inference throughput. TensorFlow
+Serving comes with a scheduler that groups individual inference requests into
+batches for joint execution on a GPU, with configurable latency controls.
+
+TensorFlow Serving has out-of-the-box support for TensorFlow models (naturally),
+but at its core it manages arbitrary versioned items (*servables*) with
+pass-through to their native APIs. In addition to trained TensorFlow models,
+servables can include other assets needed for inference such as embeddings,
+vocabularies and feature transformation configs, or even non-TensorFlow-based
+machine-learned models.
+
+The architecture is highly modular. You can use some parts individually (e.g.
+batch scheduling) or use all the parts together. There are numerous plug-in
+points; perhaps the most useful ways to extend the system are:
+(a) [creating a new type of servable](tensorflow_serving/g3doc/custom_servable.md);
+(b) [creating a custom source of servable versions](tensorflow_serving/g3doc/custom_source.md).
+
+**If you'd like to contribute to TensorFlow Serving, be sure to review the
+[contribution guidelines](CONTRIBUTING.md).**
+
+**We use [GitHub issues](https://github.com/tensorflow/serving/issues) for
+tracking requests and bugs.
+
+# Download and Setup
+
+See [install instructions](tensorflow_serving/g3doc/setup.md).
+
+##For more information
+
+* [Serving architecture overview](tensorflow_serving/g3doc/architecture_overview.md)
+* [TensorFlow website](http://tensorflow.org)

RELEASE.md

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+# Release 0.4.0
+
+Initial release of TensorFlow Serving.