AI-PredictionClient

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lib/AI/PredictionClient.pm  view on Meta::CPAN

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This container has the Inception model already loaded and ready to go.
 
Start this container and run the following commands within it to get the server running:
 
 $ cd /serving
 $ bazel-bin/tensorflow_serving/model_servers/tensorflow_model_server --port=9000 --model_name=inception --model_base_path=inception-export &> inception_log &
 
A longer article on setting up a server is here:
 
 
=head1 ADDITIONAL INFO
 
The design of this client is to be fairly easy for a developer to see how the data is formed and received.
The TensorFlow interface is based on Protocol Buffers and gRPC.
That implementation is built on a complex architecture of nested protofiles.
 
In this design I flattened the architecture out and where the native data handling of Perl is best,
the modules use plain old Perl data structures rather than creating another layer of accessors.

lib/AI/PredictionClient/Docs/Overview.pod  view on Meta::CPAN

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This container has the Inception model already loaded and ready to go.
 
Start this container and run the following commands within it to get the server running:
 
 $ cd /serving
 $ bazel-bin/tensorflow_serving/model_servers/tensorflow_model_server --port=9000 --model_name=inception --model_base_path=inception-export &> inception_log &
 
A longer article on setting up a server is here:
 
 
=head1 ADDITIONAL INFO
 
The design of this client is to be fairly easy for a developer to see how the data is formed and received.
The TensorFlow interface is based on Protocol Buffers and gRPC.
That implementation is built on a complex architecture of nested protofiles.
 
In this design I flattened the architecture out and where the native data handling of Perl is best,
the modules use plain old Perl data structures rather than creating another layer of accessors.



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