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Code to Build Tensorflow Serving from source within a Docker container
mkdir -p /work/
# Clone the source from Github
cd /work/ && git clone — recurse-submodules
# Pin the version of Tensorflow Serving and its submodule
cd /work/serving && git checkout $TENSOR_SERVING_COMMIT_HASH
cd /work/serving/tensorflow && git checkout $TENSORFLOW_COMMIT_HASH
cd /work/serving/tensorflow && ./configure
# Tensorflow Serving uses Bazel as the build tool. The Docker image already have Bazel installed in it.
# Run the following command to build the source with Bazel
cd /work/serving && bazel build -c opt //tensorflow_serving/model_servers:tensorflow_model_server --jobs 10 --curses no --discard_analysis_cache
# The following bazel option flags was added:
# -c (compilation_mode): the compilation mode flag affects the the C++ generation. ‘opt’ compilation mode is selected to enable optimization and disable the assert calls.
# — discard_analysis_cache: will discard the analysis cache immediately after the analysis phase completes. This reduces memory usage by ~10%, but makes further incremental builds slower. 
# — jobs: The default number of jobs spawned by bazel is 200. Depending on the system configuration of your host, you might like to update this parameter. We tune ours to 10.
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alanmarazzi commented Oct 2, 2017

In line 5 — recurse-submodules is wrong, it should be --recursive 😃

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