AI-PredictionClient
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--- The Artistic License 1.0 ---
This software is Copyright (c) 2017 by Tom Stall.
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"license" : [
"perl_5"
],
"meta-spec" : {
"url" : "http://search.cpan.org/perldoc?CPAN::Meta::Spec",
"version" : 2
},
"name" : "AI-PredictionClient",
"prereqs" : {
"configure" : {
"requires" : {
"AI::PredictionClient::Alien::TensorFlowServingProtos" : "0.05",
"Alien::Google::GRPC" : "0.06",
"ExtUtils::MakeMaker" : "0",
"Inline" : "0",
"Inline::CPP" : "0",
"Inline::MakeMaker" : "0"
}
},
"develop" : {
"requires" : {
"Test::MinimumVersion" : "0",
"Test::Perl::Critic" : "0",
"Test::Pod" : "1.41",
"Test::Spelling" : "0.12"
}
},
"runtime" : {
"requires" : {
"AI::PredictionClient::Alien::TensorFlowServingProtos" : "0",
"Alien::Google::GRPC" : "0",
"Cwd" : "0",
"Data::Dumper" : "0",
"Inline" : "0",
"JSON" : "0",
"MIME::Base64" : "0",
"Moo" : "0",
"Moo::Role" : "0",
"MooX::Options" : "0",
"Perl6::Form" : "0",
"perl" : "5.01",
"strict" : "0",
"warnings" : "0"
}
},
"test" : {
"requires" : {
"Test::More" : "0"
}
}
},
"provides" : {
"AI::PredictionClient" : {
"file" : "lib/AI/PredictionClient.pm",
"version" : "0.05"
},
"AI::PredictionClient::CPP::PredictionGrpcCpp" : {
"AI::PredictionClient::Testing::Camel" : {
"file" : "lib/AI/PredictionClient/Testing/Camel.pm",
"version" : "0.05"
},
"AI::PredictionClient::Testing::PredictionLoopback" : {
"file" : "lib/AI/PredictionClient/Testing/PredictionLoopback.pm",
"version" : "0.05"
}
},
"release_status" : "stable",
"resources" : {
"homepage" : "https://github.com/mountaintom/AI-PredictionClient",
"repository" : {
"type" : "git",
"url" : "https://github.com/mountaintom/AI-PredictionClient.git",
"web" : "https://github.com/mountaintom/AI-PredictionClient"
}
},
"version" : "0.05",
"x_serialization_backend" : "Cpanel::JSON::XS version 3.0233"
}
---
abstract: 'A Perl Prediction client for Google TensorFlow Serving.'
author:
- 'Tom Stall <stall@cpan.org>'
build_requires:
Test::More: '0'
configure_requires:
AI::PredictionClient::Alien::TensorFlowServingProtos: '0.05'
Alien::Google::GRPC: '0.06'
ExtUtils::MakeMaker: '0'
Inline: '0'
Inline::CPP: '0'
Inline::MakeMaker: '0'
dynamic_config: 0
generated_by: 'Dist::Zilla version 6.009, CPAN::Meta::Converter version 2.143240'
license: perl
meta-spec:
version: '0.05'
AI::PredictionClient::Roles::PredictionRole:
file: lib/AI/PredictionClient/Roles/PredictionRole.pm
version: '0.05'
AI::PredictionClient::Testing::Camel:
file: lib/AI/PredictionClient/Testing/Camel.pm
version: '0.05'
AI::PredictionClient::Testing::PredictionLoopback:
file: lib/AI/PredictionClient/Testing/PredictionLoopback.pm
version: '0.05'
requires:
AI::PredictionClient::Alien::TensorFlowServingProtos: '0'
Alien::Google::GRPC: '0'
Cwd: '0'
Data::Dumper: '0'
Inline: '0'
JSON: '0'
MIME::Base64: '0'
Moo: '0'
Moo::Role: '0'
MooX::Options: '0'
Perl6::Form: '0'
perl: '5.01'
strict: '0'
warnings: '0'
resources:
homepage: https://github.com/mountaintom/AI-PredictionClient
repository: https://github.com/mountaintom/AI-PredictionClient.git
version: '0.05'
x_serialization_backend: 'YAML::Tiny version 1.70'
bin/Inception.pl view on Meta::CPAN
is => 'ro',
required => 1,
format => 's',
doc => '* Required: Path to image to be processed'
);
option host => (
is => 'ro',
required => 0,
format => 's',
default => $default_host,
doc => "IP address of the server [Default: $default_host]"
);
option port => (
is => 'ro',
required => 0,
format => 's',
default => $default_port,
doc => "Port number of the server [Default: $default_port]"
);
option model_name => (
is => 'ro',
bin/Inception.pl view on Meta::CPAN
$client->model_signature($self->model_signature);
$client->debug_verbose($self->debug_verbose);
$client->loopback($self->debug_loopback_interface);
$client->camel($self->debug_camel);
printf("Sending image %s to server at host:%s port:%s\n",
$self->image_file, $self->host, $self->port);
if ($client->call_inception($image_ref)) {
my $results_ref = $client->inception_results;
my $classifications_ref = $results_ref->{'classes'};
my $scores_ref = $results_ref->{'scores'};
my $comments = 'Clasification Results for ' . $self->image_file;
my $results_text
= form
'.===========================================================================.',
'| Class | Score |',
'|-----------------------------------------------------------+---------------|',
'| {[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[} |{]].[[[[[[[[} |',
$classifications_ref, $scores_ref,
'|===========================================================================|',
'| {[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[} |',
$comments,
"'==========================================================================='";
print $results_text;
} else {
printf("Failed. Status: %s, Status Code: %s, Status Message: %s \n",
$client->status, $client->status_code, $client->status_message);
return 1;
}
return 0;
}
sub read_image {
requires "AI::PredictionClient::Alien::TensorFlowServingProtos" => "0";
requires "Alien::Google::GRPC" => "0";
requires "Cwd" => "0";
requires "Data::Dumper" => "0";
requires "Inline" => "0";
requires "JSON" => "0";
requires "MIME::Base64" => "0";
requires "Moo" => "0";
requires "Moo::Role" => "0";
requires "MooX::Options" => "0";
requires "Perl6::Form" => "0";
requires "perl" => "5.01";
requires "strict" => "0";
requires "warnings" => "0";
on 'test' => sub {
requires "Test::More" => "0";
};
on 'configure' => sub {
requires "AI::PredictionClient::Alien::TensorFlowServingProtos" => "0.05";
requires "Alien::Google::GRPC" => "0.06";
requires "ExtUtils::MakeMaker" => "0";
requires "Inline" => "0";
requires "Inline::CPP" => "0";
requires "Inline::MakeMaker" => "0";
};
on 'develop' => sub {
requires "Test::MinimumVersion" => "0";
requires "Test::Perl::Critic" => "0";
requires "Test::Pod" => "1.41";
requires "Test::Spelling" => "0.12";
};
[PodWeaver]
[ReadmeAnyFromPod]
type = pod
filename = README.pod
location = root
[Prereqs]
perl = 5.01
[Prereqs / ConfigureRequires]
Inline = 0
Inline::CPP = 0
Inline::MakeMaker = 0
Alien::Google::GRPC = 0.06
AI::PredictionClient::Alien::TensorFlowServingProtos = 0.05
[Test::MinimumVersion]
max_target_perl = 5.10.1
[MetaProvides::Package]
lib/AI/PredictionClient.pm view on Meta::CPAN
AI::PredictionClient - A Perl Prediction client for Google TensorFlow Serving.
=head1 VERSION
version 0.05
=head1 DESCRIPTION
This is a package for creating Perl clients for TensorFlow Serving model servers.
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.
lib/AI/PredictionClient.pm view on Meta::CPAN
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:
Inception.pl --image_file=zzzzz --debug_camel --host=107.170.xx.xxx --port=9000
Sending image zzzzz to server at host:107.170.xx.xxx port:9000
.===========================================================================.
| Class | Score |
|-----------------------------------------------------------+---------------|
| Arabian camel, dromedary, Camelus dromedarius | 11.968746 |
| triumphal arch | 4.0692205 |
| panpipe, pandean pipe, syrinx | 3.4675434 |
| thresher, thrasher, threshing machine | 3.4537551 |
| sorrel | 3.1359406 |
|===========================================================================|
| Classification Results for zzzzz |
'==========================================================================='
=head2 SETTING UP A TEST SERVER
You can set up a server by following the instructions on the TensorFlow Serving site:
https://www.tensorflow.org/deploy/tfserve
lib/AI/PredictionClient.pm view on Meta::CPAN
https://www.tomstall.com/content/create-a-globally-distributed-tensorflow-serving-cluster-with-nearly-no-pain/
=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.
The Tensor interface is used repetitively so this package includes a simplified Tensor class
to pack and unpack data to and from the models.
In the case of most clients, the Tensor class is simply sending and receiving rank one tensors - vectors.
In the case of higher rank tensors, the tensor data is sent and received flattened.
The size property would be used for importing/exporting the tensors in/out of a math package.
The design takes advantage of the native JSON serialization capabilities built into the C++ Protocol Buffers.
Serialization allows a much simpler more robust interface to be created between the Perl environment
and the C++ environment.
One of the biggest advantages is for the developer who would like to quickly extend what this package does.
You can see how the data structures are built and directly manipulate them in Perl.
Of course, if you can be more forward looking, building the proper roles and classes and contributing them would be great.
=head1 DEPENDENCIES
This module is dependent on gRPC. This module will use the cpan module Alien::Google::GRPC to
either use an existing gRPC installation on your system or if not found, the Alien::Google::GRPC
module will download and build a private copy.
The system dependencies needed for this module to build are most often already installed.
If not, the following dependencies need to be installed.
lib/AI/PredictionClient/CPP/PredictionGrpcCpp.pm view on Meta::CPAN
std::unique_ptr<PredictionService::Stub> stub_;
std::string to_base64(std::string text);
};
PredictionClient::PredictionClient(std::string server_port)
: stub_(PredictionService::NewStub(grpc::CreateChannel(
server_port, grpc::InsecureChannelCredentials()))) {}
std::string PredictionClient::callPredict(std::string serialized_request_object) {
PredictRequest predictRequest;
PredictResponse response;
ClientContext context;
std::string serialized_result_object;
google::protobuf::util::JsonPrintOptions jprint_options;
google::protobuf::util::JsonParseOptions jparse_options;
google::protobuf::util::Status request_serialized_status =
google::protobuf::util::JsonStringToMessage(
serialized_request_object, &predictRequest, jparse_options);
if (!request_serialized_status.ok()) {
std::string error_result =
"{\"Status\": \"Error:object:request_deserialization:protocol_buffers\", ";
error_result += "\"StatusCode\": \"" +
std::to_string(request_serialized_status.error_code()) +
"\", ";
error_result += "\"StatusMessage\":" +
to_base64(request_serialized_status.error_message()) +
"}";
return error_result;
}
Status status = stub_->Predict(&context, predictRequest, &response);
if (status.ok()) {
google::protobuf::util::Status response_serialize_status =
google::protobuf::util::MessageToJsonString(
response, &serialized_result_object, jprint_options);
if (!response_serialize_status.ok()) {
std::string error_result =
"{\"Status\": \"Error:object:response_serialization:protocol_buffers\", ";
error_result += "\"StatusCode\": \"" +
std::to_string(response_serialize_status.error_code()) +
"\", ";
error_result += "\"StatusMessage\":" +
to_base64(response_serialize_status.error_message()) +
"}";
return error_result;
}
std::string success_result = "{\"Status\": \"OK\", ";
success_result += "\"StatusCode\": \"\", ";
success_result += "\"StatusMessage\": \"\", ";
success_result += "\"Result\": " + serialized_result_object + "}";
return success_result;
} else {
std::string error_result = "{\"Status\": \"Error:transport:grpc\", ";
error_result +=
"\"StatusCode\": \"" + std::to_string(status.error_code()) + "\", ";
error_result += "\"StatusMessage\":" + to_base64(status.error_message()) + "}";
return error_result;
}
}
std::string PredictionClient::to_base64(std::string text) {
base64::Base64Proto base64pb;
std::string serialized_base64_message;
google::protobuf::util::JsonPrintOptions jprint_options;
base64pb.add_base64(text.c_str(), text.size());
lib/AI/PredictionClient/Classes/SimpleTensor.pm view on Meta::CPAN
DT_FLOAT => 'floatVal',
DT_DOUBLE => 'doubleVal',
DT_INT16 => 'intVal',
DT_INT8 => 'intVal',
DT_UINT8 => 'intVal',
DT_STRING => 'stringVal',
DT_COMPLEX64 => 'scomplexVal',
DT_INT64 => 'int64Val',
DT_BOOL => 'boolVal',
DT_COMPLEX128 => 'dcomplexVal',
DT_RESOURCE => 'resourceHandleVal'
};
});
sub value {
my ($self, $value_aref) = @_;
my $decoded_aref;
my $value_type = $self->dtype_values->{ $self->dtype };
my $tensor_value_ref = \$self->tensor_ds->{$value_type};
lib/AI/PredictionClient/Docs/Overview.pod view on Meta::CPAN
Overview.pod - A Perl Prediction client for Google TensorFlow Serving.
=head1 VERSION
version 0.05
head1 DESCRIPTION
This is a package for creating Perl clients for TensorFlow Serving model servers.
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.
lib/AI/PredictionClient/Docs/Overview.pod view on Meta::CPAN
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:
Inception.pl --image_file=zzzzz --debug_camel --host=107.170.xx.xxx --port=9000
Sending image zzzzz to server at host:107.170.xx.xxx port:9000
.===========================================================================.
| Class | Score |
|-----------------------------------------------------------+---------------|
| Arabian camel, dromedary, Camelus dromedarius | 11.968746 |
| triumphal arch | 4.0692205 |
| panpipe, pandean pipe, syrinx | 3.4675434 |
| thresher, thrasher, threshing machine | 3.4537551 |
| sorrel | 3.1359406 |
|===========================================================================|
| Classification Results for zzzzz |
'==========================================================================='
=head2 SETTING UP A TEST SERVER
You can set up a server by following the instructions on the TensorFlow Serving site:
https://www.tensorflow.org/deploy/tfserve
lib/AI/PredictionClient/Docs/Overview.pod view on Meta::CPAN
https://www.tomstall.com/content/create-a-globally-distributed-tensorflow-serving-cluster-with-nearly-no-pain/
=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.
The Tensor interface is used repetitively so this package includes a simplified Tensor class
to pack and unpack data to and from the models.
In the case of most clients, the Tensor class is simply sending and receiving rank one tensors - vectors.
In the case of higher rank tensors, the tensor data is sent and received flattened.
The size property would be used for importing/exporting the tensors in/out of a math package.
The design takes advantage of the native JSON serialization capabilities built into the C++ Protocol Buffers.
Serialization allows a much simpler more robust interface to be created between the Perl environment
and the C++ environment.
One of the biggest advantages is for the developer who would like to quickly extend what this package does.
You can see how the data structures are built and directly manipulate them in Perl.
Of course, if you can be more forward looking, building the proper roles and classes and contributing them would be great.
=head1 DEPENDENCIES
This module is dependent on gRPC. This module will use the cpan module Alien::Google::GRPC to
either use an existing gRPC installation on your system or if not found, the Alien::Google::GRPC
module will download and build a private copy.
The system dependencies needed for this module to build are most often already installed.
If not, the following dependencies need to be installed.
lib/AI/PredictionClient/InceptionClient.pm view on Meta::CPAN
use 5.010;
use Data::Dumper;
use Moo;
use AI::PredictionClient::Classes::SimpleTensor;
use AI::PredictionClient::Testing::Camel;
extends 'AI::PredictionClient::Predict';
has inception_results => (is => 'rwp');
has camel => (is => 'rw',);
sub call_inception {
my $self = shift;
my $image = shift;
my $tensor = AI::PredictionClient::Classes::SimpleTensor->new();
$tensor->shape([ { size => 1 } ]);
$tensor->dtype("DT_STRING");
lib/AI/PredictionClient/InceptionClient.pm view on Meta::CPAN
$tensor->value([ $camel_test->camel_jpeg_ref ]);
} else {
$tensor->value([$image]);
}
$self->inputs({ images => $tensor });
if ($self->callPredict()) {
my $predict_output_map_href = $self->outputs;
my $inception_results_href;
foreach my $key (keys %$predict_output_map_href) {
$inception_results_href->{$key} = $predict_output_map_href->{$key}
->value; #Because returns Tensor objects.
}
$self->_set_inception_results($inception_results_href);
return 1;
} else {
return 0;
}
}
1;
lib/AI/PredictionClient/Roles/PredictRole.pm view on Meta::CPAN
use strict;
use warnings;
package AI::PredictionClient::Roles::PredictRole;
$AI::PredictionClient::Roles::PredictRole::VERSION = '0.05';
# ABSTRACT: Implements the Predict service specific interface
use AI::PredictionClient::Classes::SimpleTensor;
use Moo::Role;
requires 'request_ds', 'reply_ds';
sub inputs {
my ($self, $inputs_href) = @_;
my $inputs_converted_href;
foreach my $inkey (keys %$inputs_href) {
$inputs_converted_href->{$inkey} = $inputs_href->{$inkey}->tensor_ds;
}
$self->request_ds->{"inputs"} = $inputs_converted_href;
return;
}
sub callPredict {
my $self = shift;
my $request_ref = $self->serialize_request();
my $result_ref = $self->perception_client_object->callPredict($request_ref);
return $self->deserialize_reply($result_ref);
}
sub outputs {
my $self = shift;
my $predict_outputs_ref = $self->reply_ds->{outputs};
my $tensorsout_href;
foreach my $outkey (keys %$predict_outputs_ref) {
lib/AI/PredictionClient/Testing/PredictionLoopback.pm view on Meta::CPAN
return $class->$orig(@_);
}
};
has server_port => (is => 'rw',);
sub callPredict {
my ($self, $request_data) = @_;
my $test_return01
= '{"outputs":{"classes":{"dtype":"DT_STRING","tensorShape":{"dim":[{"size":"1"},{"size":"6"}]},"stringVal":["bG9vcGJhY2sgdGVzdCBkYXRhCg==","bWlsaXRhcnkgdW5pZm9ybQ==","Ym93IHRpZSwgYm93LXRpZSwgYm93dGll","bW9ydGFyYm9hcmQ=","c3VpdCwgc3VpdCBvZiBjbG90...
my $test_return02
= '{"outputs":{"classes":{"dtype":"DT_STRING","tensorShape":{"dim":[{"size":"1"},{"size":"5"}]},"stringVal":["bG9hZCBpdAo=","Y2hlY2sgaXQK","cXVpY2sgLSByZXdyaXRlIGl0Cg==","dGVjaG5vbG9naWMK","dGVjaG5vbG9naWMK"]},"scores":{"dtype":"DT_FLOAT","tensor...
my $return_ser = '{"Status": "OK", ';
$return_ser .= '"StatusCode": "42", ';
$return_ser .= '"StatusMessage": "", ';
$return_ser .= '"DebugRequestLoopback": ' . $request_data . ', ';
if ($self->server_port eq 'technologic:2004') {
$return_ser .= '"Result": ' . $test_return02 . '}';
} else {
$return_ser .= '"Result": ' . $test_return01 . '}';
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