TRAINING.rst - Include exact command for getting mmap tool
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@ -181,7 +181,11 @@ Making a mmap-able model for inference
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The ``output_graph.pb`` model file generated in the above step will be loaded in memory to be dealt with when running inference.
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This will result in extra loading time and memory consumption. One way to avoid this is to directly read data from the disk.
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TensorFlow has tooling to achieve this: it requires building the target ``//tensorflow/contrib/util:convert_graphdef_memmapped_format`` (binaries are produced by our TaskCluster for some systems including Linux/amd64 and macOS/amd64), use ``util/taskcluster.py`` tool to download, specifying ``tensorflow`` as a source and ``convert_graphdef_memmapped_format`` as artifact.
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TensorFlow has tooling to achieve this: it requires building the target ``//tensorflow/contrib/util:convert_graphdef_memmapped_format`` (binaries are produced by our TaskCluster for some systems including Linux/amd64 and macOS/amd64), use ``util/taskcluster.py`` tool to download:
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.. code-block::
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$ python3 util/taskcluster.py --source tensorflow --artifact convert_graphdef_memmapped_format --branch r1.14 --target .
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Producing a mmap-able model is as simple as:
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