AI-PredictionClient
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bin/Inception.pl view on Meta::CPAN
#!/usr/bin/env perl
use strict;
use warnings;
use AI::PredictionClient::InceptionClient;
# PODNAME: Inception.pl
# ABSTRACT: Runnable example command line Inception client
use Moo;
use MooX::Options;
use 5.010;
use Data::Dumper qw(Dumper);
use Perl6::Form;
my $default_host = '127.0.0.1';
my $default_port = '9000';
my $default_model = 'inception';
bin/Inception.pl view on Meta::CPAN
exit main->new_with_options->run;
__END__
=pod
=encoding UTF-8
=head1 NAME
Inception.pl - Runnable example command line Inception client
=head1 VERSION
version 0.05
=head1 PLEASE SEE FOR DOCUMENTATION
L<AI::PredictionClient>
=head1 AUTHOR
lib/AI/PredictionClient.pm view on Meta::CPAN
TensorFlow Serving is the system that allows TensorFlow neural network AI models
to be moved from the research environment to your production environment.
Currently this package implements a client for the Predict service and a model specific Inception client.
The Predict service 'Predict.pm' is the most versatile of the TensorFlow Serving Prediction services.
A large portion of the model specific clients are implemented from this service.
The model specific client 'InceptionClient.pm' is implemented. This is the most popular client.
Additionally, a command line Inception client 'Inception.pl' is included
as an example of a complete client built form this package.
=head2 Using the example client
The example client is installed in your local bin directory and
will allow you to send an image to an Inception model server and display
the classifications of what the Inception neural network model "thought" it saw.
This client implements a command line interface to the
InceptionClient module 'AI::PredictionClient::InceptionClient', and provides
a working example of using this module for building your own clients.
The commands for the Inception client can be displayed by running the Inception.pl client with no arguments.
$ Inception.pl
image_file is missing
USAGE: Inception.pl [-h] [long options ...]
--debug_camel Test using camel image
--debug_loopback_interface Test loopback through dummy server
--debug_verbose Verbose output
--host=String IP address of the server [Default:
127.0.0.1]
--image_file=String * Required: Path to image to be processed
--model_name=String Model to process image [Default: inception]
--model_signature=String API signature for model [Default:
predict_images]
--port=String Port number of the server [Default: 9000]
-h show a compact help message
Some typical command line examples include:
Inception.pl --image_file=anything --debug_camel --host=xx7.x11.xx3.x14 --port=9000
Inception.pl --image_file=grace_hopper.jpg --host=xx7.x11.xx3.x14 --port=9000
Inception.pl --image_file=anything --debug_camel --debug_loopback --port 2004 --host technologic
=head3 In the examples above, the following points are demonstrated:
If you don't have an image handy --debug_camel will provide a sample image to send to the server.
The image file argument still needs to be provided to make the command line parser happy.
If you don't have a server to talk to, but want to see if most everything else is working use
the --debug_loopback_interface. This will provide a sample response you can test the client with.
The module can use the same loopback interface for debugging your bespoke clients.
The --debug_verbose option will dump the data structures of the request and response to allow
you to see what is going on.
=head3 The response from a live server to the camel image looks like this:
lib/AI/PredictionClient.pm view on Meta::CPAN
https://www.tensorflow.org/deploy/tfserve
https://tensorflow.github.io/serving/setup
https://tensorflow.github.io/serving/docker
I have a prebuilt Docker container available here:
docker pull mountaintom/tensorflow-serving-inception-docker-swarm-demo
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:
https://www.tomstall.com/content/create-a-globally-distributed-tensorflow-serving-cluster-with-nearly-no-pain/
=head1 ADDITIONAL INFO
lib/AI/PredictionClient/Docs/Overview.pod view on Meta::CPAN
TensorFlow Serving is the system that allows TensorFlow neural network AI models
to be moved from the research environment to your production environment.
Currently this package implements a client for the Predict service and a model specific Inception client.
The Predict service 'Predict.pm' is the most versatile of the TensorFlow Serving Prediction services.
A large portion of the model specific clients are implemented from this service.
The model specific client 'InceptionClient.pm' is implemented. This is the most popular client.
Additionally, a command line Inception client 'Inception.pl' is included
as an example of a complete client built form this package.
=head2 Using the example client
The example client is installed in your local bin directory and
will allow you to send an image to an Inception model server and display
the classifications of what the Inception neural network model "thought" it saw.
This client implements a command line interface to the
InceptionClient module 'AI::PredictionClient::InceptionClient', and provides
a working example of using this module for building your own clients.
The commands for the Inception client can be displayed by running the Inception.pl client with no arguments.
$ Inception.pl
image_file is missing
USAGE: Inception.pl [-h] [long options ...]
--debug_camel Test using camel image
--debug_loopback_interface Test loopback through dummy server
--debug_verbose Verbose output
--host=String IP address of the server [Default:
127.0.0.1]
--image_file=String * Required: Path to image to be processed
--model_name=String Model to process image [Default: inception]
--model_signature=String API signature for model [Default:
predict_images]
--port=String Port number of the server [Default: 9000]
-h show a compact help message
Some typical command line examples include:
Inception.pl --image_file=anything --debug_camel --host=xx7.x11.xx3.x14 --port=9000
Inception.pl --image_file=grace_hopper.jpg --host=xx7.x11.xx3.x14 --port=9000
Inception.pl --image_file=anything --debug_camel --debug_loopback --port 2004 --host technologic
=head3 In the examples above, the following points are demonstrated:
If you don't have an image handy --debug_camel will provide a sample image to send to the server.
The image file argument still needs to be provided to make the command line parser happy.
If you don't have a server to talk to, but want to see if most everything else is working use
the --debug_loopback_interface. This will provide a sample response you can test the client with.
The module can use the same loopback interface for debugging your bespoke clients.
The --debug_verbose option will dump the data structures of the request and response to allow
you to see what is going on.
=head3 The response from a live server to the camel image looks like this:
lib/AI/PredictionClient/Docs/Overview.pod view on Meta::CPAN
https://www.tensorflow.org/deploy/tfserve
https://tensorflow.github.io/serving/setup
https://tensorflow.github.io/serving/docker
I have a prebuilt Docker container available here:
docker pull mountaintom/tensorflow-serving-inception-docker-swarm-demo
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:
https://www.tomstall.com/content/create-a-globally-distributed-tensorflow-serving-cluster-with-nearly-no-pain/
=head1 ADDITIONAL INFO
( run in 0.569 second using v1.01-cache-2.11-cpan-fe3c2283af0 )