AI-TensorFlow-Libtensorflow
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lib/AI/TensorFlow/Libtensorflow/Manual/CAPI.pod view on Meta::CPAN
=back
/* From <tensorflow/c/c_api.h> */
TF_CAPI_EXPORT extern TF_SessionOptions* TF_NewSessionOptions(void);
=head2 TF_SetTarget
=over 2
Set the target in TF_SessionOptions.options.
target can be empty, a single entry, or a comma separated list of entries.
Each entry is in one of the following formats :
"local"
ip:port
host:port
=back
/* From <tensorflow/c/c_api.h> */
TF_CAPI_EXPORT extern void TF_SetTarget(TF_SessionOptions* options,
const char* target);
lib/AI/TensorFlow/Libtensorflow/Manual/CAPI.pod view on Meta::CPAN
=back
/* From <tensorflow/c/eager/c_api_experimental.h> */
TF_CAPI_EXPORT extern void TFE_TensorHandleGetStatus(TFE_TensorHandle* h,
TF_Status* status);
=head2 TFE_GetExecutedOpNames
=over 2
Get a comma-separated list of op names executed in graph functions dispatched
to `ctx`. This feature is currently only enabled for TFRT debug builds, for
performance and simplicity reasons.
=back
/* From <tensorflow/c/eager/c_api_experimental.h> */
TF_CAPI_EXPORT extern void TFE_GetExecutedOpNames(TFE_Context* ctx,
TF_Buffer* buf,
TF_Status* status);
lib/AI/TensorFlow/Libtensorflow/Manual/GPU.pod view on Meta::CPAN
An alternative to installing all the software listed on the "bare metal" host
machine is to use C<libtensorflow> via a Docker container and the
NVIDIA Container Toolkit. See L<AI::TensorFlow::Libtensorflow::Manual::Quickstart/DOCKER IMAGES>
for more information.
=head1 RUNTIME
When running C<libtensorflow>, your program will attempt to acquire quite a bit
of GPU VRAM. You can check if you have enough free VRAM by using the
C<nvidia-smi> command which displays resource information as well as which
processes are currently using the GPU. If C<libtensorflow> is not able to
allocate enough memory, it will crash with an out-of-memory (OOM) error. This
is typical when running multiple programs that both use the GPU.
If you have multiple GPUs, you can control which GPUs your program can access
by using the
L<C<CUDA_VISIBLE_DEVICES> environment variable|https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars>
provided by the underlying CUDA library. This is typically
done by setting the variable in a C<BEGIN> block before loading
L<AI::TensorFlow::Libtensorflow>:
lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubCenterNetObjDetect.pod view on Meta::CPAN
>sgx;
my $label_count = List::Util::max keys %labels_map;
say "We have a label count of $label_count. These labels include: ",
join ", ", List::Util::head( 5, @labels_map{ sort keys %labels_map } );
my @tags = ( 'serve' );
if( File::Which::which('saved_model_cli')) {
local $ENV{TF_CPP_MIN_LOG_LEVEL} = 3; # quiet the TensorFlow logger for the following command
system(qw(saved_model_cli show),
qw(--dir) => $model_base,
qw(--tag_set) => join(',', @tags),
qw(--signature_def) => 'serving_default'
) == 0 or die "Could not run saved_model_cli";
} else {
say "Install the tensorflow Python package to get the `saved_model_cli` command.";
}
my $opt = AI::TensorFlow::Libtensorflow::SessionOptions->New;
my $graph = AI::TensorFlow::Libtensorflow::Graph->New;
my $session = AI::TensorFlow::Libtensorflow::Session->LoadFromSavedModel(
$opt, undef, $model_base, \@tags, $graph, undef, $s
);
AssertOK($s);
lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubCenterNetObjDetect.pod view on Meta::CPAN
The following document is either a POD file which can additionally be run as a Perl script or a Jupyter Notebook which can be run in L<IPerl|https://p3rl.org/Devel::IPerl> (viewable online at L<nbviewer|https://nbviewer.org/github/EntropyOrg/perl-AI-...
=over
=item *
C<PDL::Graphics::Gnuplot> requires C<gnuplot>.
=back
If you are running the code, you may optionally install the L<C<tensorflow> Python package|https://www.tensorflow.org/install/pip> in order to access the C<saved_model_cli> command, but this is only used for informational purposes.
=head1 TUTORIAL
=head2 Load the library
First, we need to load the C<AI::TensorFlow::Libtensorflow> library and more helpers. We then create an C<AI::TensorFlow::Libtensorflow::Status> object and helper function to make sure that the calls to the C<libtensorflow> C library are working prop...
use strict;
use warnings;
use utf8;
lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubCenterNetObjDetect.pod view on Meta::CPAN
=head2 Load the model and session
We define the tag set C<[ 'serve' ]> which we will use to load the model.
my @tags = ( 'serve' );
We can examine what computations are contained in the graph in terms of the names of the inputs and outputs of an operation found in the graph by running C<saved_model_cli>.
if( File::Which::which('saved_model_cli')) {
local $ENV{TF_CPP_MIN_LOG_LEVEL} = 3; # quiet the TensorFlow logger for the following command
system(qw(saved_model_cli show),
qw(--dir) => $model_base,
qw(--tag_set) => join(',', @tags),
qw(--signature_def) => 'serving_default'
) == 0 or die "Could not run saved_model_cli";
} else {
say "Install the tensorflow Python package to get the `saved_model_cli` command.";
}
The above C<saved_model_cli> output shows that the model input is at C<serving_default_input_tensor:0> which means the operation named C<serving_default_input_tensor> at index C<0> and there are multiple outputs with different shapes.
Per the L<model description|https://tfhub.dev/tensorflow/centernet/hourglass_512x512/1> on TensorFlow Hub:
=over 2
B<Inputs>
lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubEnformerGeneExprPredModel.pod view on Meta::CPAN
[ qw(gunzip -c) ], '<', $hg_gz_path,
'|',
[ qw(bgzip -c) ], '>', $hg_bgz_path
);
}
use Bio::Tools::Run::Samtools;
my $hg_bgz_fai_path = "${hg_bgz_path}.fai";
if( ! -e $hg_bgz_fai_path ) {
my $faidx_tool = Bio::Tools::Run::Samtools->new( -command => 'faidx' );
$faidx_tool->run( -fas => $hg_bgz_path )
or die "Could not index FASTA file $hg_bgz_path: " . $faidx_tool->error_string;
}
sub saved_model_cli {
my (@rest) = @_;
if( File::Which::which('saved_model_cli')) {
system(qw(saved_model_cli), @rest ) == 0
or die "Could not run saved_model_cli";
} else {
warn "saved_model_cli(): Install the tensorflow Python package to get the `saved_model_cli` command.\n";
return -1;
}
}
say "Checking with saved_model_cli scan:";
saved_model_cli( qw(scan),
qw(--dir) => $model_base,
);
saved_model_cli( qw(show),
lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubEnformerGeneExprPredModel.pod view on Meta::CPAN
=item *
C<Bio::DB::HTS> requires C<libhts> and
=item *
C<PDL::Graphics::Gnuplot> requires C<gnuplot>.
=back
If you are running the code, you may optionally install the L<C<tensorflow> Python package|https://www.tensorflow.org/install/pip> in order to access the C<saved_model_cli> command, but this is only used for informational purposes.
=head1 TUTORIAL
=head2 Load the library
First, we need to load the C<AI::TensorFlow::Libtensorflow> library and more helpers. We then create an C<AI::TensorFlow::Libtensorflow::Status> object and helper function to make sure that the calls to the C<libtensorflow> C library are working prop...
use strict;
use warnings;
use utf8;
lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubEnformerGeneExprPredModel.pod view on Meta::CPAN
[ qw(gunzip -c) ], '<', $hg_gz_path,
'|',
[ qw(bgzip -c) ], '>', $hg_bgz_path
);
}
use Bio::Tools::Run::Samtools;
my $hg_bgz_fai_path = "${hg_bgz_path}.fai";
if( ! -e $hg_bgz_fai_path ) {
my $faidx_tool = Bio::Tools::Run::Samtools->new( -command => 'faidx' );
$faidx_tool->run( -fas => $hg_bgz_path )
or die "Could not index FASTA file $hg_bgz_path: " . $faidx_tool->error_string;
}
=head2 Model input and output specification
Now we create a helper to call C<saved_model_cli> and called C<saved_model_cli scan> to ensure that the model is I/O-free for security reasons.
sub saved_model_cli {
my (@rest) = @_;
if( File::Which::which('saved_model_cli')) {
system(qw(saved_model_cli), @rest ) == 0
or die "Could not run saved_model_cli";
} else {
warn "saved_model_cli(): Install the tensorflow Python package to get the `saved_model_cli` command.\n";
return -1;
}
}
say "Checking with saved_model_cli scan:";
saved_model_cli( qw(scan),
qw(--dir) => $model_base,
);
B<STREAM (STDOUT)>:
lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubMobileNetV2Model.pod view on Meta::CPAN
say "Saved model is in $saved_model" if -f $saved_model;
my @labels = path($labels_path)->lines( { chomp => 1 });
die "Labels should have @{[ IMAGENET_LABEL_COUNT_WITH_BG ]} items"
unless @labels == IMAGENET_LABEL_COUNT_WITH_BG;
say "Got labels: ", join( ", ", List::Util::head(5, @labels) ), ", etc.";
my @tags = ( 'serve' );
if( File::Which::which('saved_model_cli')) {
local $ENV{TF_CPP_MIN_LOG_LEVEL} = 3; # quiet the TensorFlow logger for the following command
system(qw(saved_model_cli show),
qw(--dir) => $model_base,
qw(--tag_set) => join(',', @tags),
qw(--signature_def) => 'serving_default'
) == 0 or die "Could not run saved_model_cli";
} else {
say "Install the tensorflow Python package to get the `saved_model_cli` command.";
}
my $opt = AI::TensorFlow::Libtensorflow::SessionOptions->New;
my $graph = AI::TensorFlow::Libtensorflow::Graph->New;
my $session = AI::TensorFlow::Libtensorflow::Session->LoadFromSavedModel(
$opt, undef, $model_base, \@tags, $graph, undef, $s
);
AssertOK($s);
lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubMobileNetV2Model.pod view on Meta::CPAN
The following tutorial is based on the L<Image Classification with TensorFlow Hub notebook|https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/image_classification.ipynb>. It uses a pre-trained model based on the I<MobileNet V2> arch...
Please look at the L<SECURITY note|https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md> regarding running models as models are programs. You can also used C<saved_model_cli scan> to check for L<security-sensitive "denylisted ops"|https:/...
If you would like to visualise a model, you can use L<Netron|https://github.com/lutzroeder/netron> on the C<.pb> file.
=head1 COLOPHON
The following document is either a POD file which can additionally be run as a Perl script or a Jupyter Notebook which can be run in L<IPerl|https://p3rl.org/Devel::IPerl> (viewable online at L<nbviewer|https://nbviewer.org/github/EntropyOrg/perl-AI-...
If you are running the code, you may optionally install the L<C<tensorflow> Python package|https://www.tensorflow.org/install/pip> in order to access the C<saved_model_cli> command, but this is only used for informational purposes.
=head1 TUTORIAL
=head2 Load the library
First, we need to load the C<AI::TensorFlow::Libtensorflow> library and more helpers. We then create an C<AI::TensorFlow::Libtensorflow::Status> object and helper function to make sure that the calls to the C<libtensorflow> C library are working prop...
use strict;
use warnings;
use utf8;
lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubMobileNetV2Model.pod view on Meta::CPAN
my @tags = ( 'serve' );
B<RESULT>:
serve
We can examine what computations are contained in the graph in terms of the names of the inputs and outputs of an operation found in the graph by running C<saved_model_cli>.
if( File::Which::which('saved_model_cli')) {
local $ENV{TF_CPP_MIN_LOG_LEVEL} = 3; # quiet the TensorFlow logger for the following command
system(qw(saved_model_cli show),
qw(--dir) => $model_base,
qw(--tag_set) => join(',', @tags),
qw(--signature_def) => 'serving_default'
) == 0 or die "Could not run saved_model_cli";
} else {
say "Install the tensorflow Python package to get the `saved_model_cli` command.";
}
B<STREAM (STDOUT)>:
The given SavedModel SignatureDef contains the following input(s):
inputs['inputs'] tensor_info:
dtype: DT_FLOAT
shape: (-1, 224, 224, 3)
name: serving_default_inputs:0
The given SavedModel SignatureDef contains the following output(s):
maint/inc/Pod/Elemental/Transformer/TF_CAPI.pm view on Meta::CPAN
# ABSTRACT: Transformer for TF_CAPI links
use Moose;
use Pod::Elemental::Transformer 0.101620;
with 'Pod::Elemental::Transformer';
use Pod::Elemental::Element::Pod5::Command;
use namespace::autoclean;
has command_name => (
is => 'ro',
init_arg => undef,
);
sub transform_node {
my ($self, $node) = @_;
for my $i (reverse(0 .. $#{ $node->children })) {
my $para = $node->children->[ $i ];
next unless $self->__is_xformable($para);
my @replacements = $self->_expand( $para );
splice @{ $node->children }, $i, 1, @replacements;
}
}
my $command_dispatch = {
'tf_capi' => \&_expand_capi,
'tf_version' => \&_expand_version,
};
sub __is_xformable {
my ($self, $para) = @_;
return unless $para->isa('Pod::Elemental::Element::Pod5::Command')
and exists $command_dispatch->{ $para->command };
return 1;
}
sub _expand {
my ($self, $parent) = @_;
$command_dispatch->{ $parent->command }->( @_ );
};
sub _expand_version {
my ($self, $parent) = @_;
my @replacements;
my $content = $parent->content;
die "Not a version string: $content"
unless $content =~ /\A v [0-9.]+ \Z/x;
maint/process-capi.pl view on Meta::CPAN
say "Missing function:";
use DDP; p $first_missing_function;
}
method run() {
$self->generate_capi_funcs;
#$self->check_types;
$self->check_functions;
}
subcommand 'generate-capi-docs' => method(@) {
$self->generate_capi_funcs;
};
subcommand 'check-types' => method(@) {
$self->check_types;
};
subcommand 'check-functions' => method(@) {
if( $_[0] eq '--help' ) {
print STDERR "$0 check-functions [TYPE]\n";
exit;
}
$self->check_functions(shift @_);
};
sub BUILD {
Moo::Role->apply_roles_to_object(
AI::TensorFlow::Libtensorflow::Lib->ffi
; [Version]
[Region / prelude]
[Region / badges]
[Generic / SYNOPSIS]
[Generic / DESCRIPTION]
[Generic / OVERVIEW]
[Collect / TYPES]
command = type
[Collect / CONSTANTS]
command = constant
[Collect / CONSTRUCTORS]
command = construct
[Collect / ATTRIBUTES]
command = attr
[Collect / CLASS METHODS]
command = classmethod
[Collect / METHODS]
command = method
[Collect / FUNCTIONS]
command = func
[Collect / OPERATORS]
command = operator
[Collect / DESTRUCTORS]
command = destruct
[Leftovers]
[Generic / SEE ALSO]
[Region / postlude]
[Authors]
[Legal]
xt/author/pod-snippets.t view on Meta::CPAN
};
}
package # hide from PAUSE
My::Test::Pod::Snippets::Parser {
use Moose;
extends 'Test::Pod::Snippets::Parser';
sub FOREIGNBUILDARGS { () }
around command => sub {
my $orig = shift;
my ($parser, $command, $paragraph, $line_nbr ) = @_;
if($paragraph =~ /COPYRIGHT AND LICENSE/) {
$parser->{tps_ignore} = 1;
return;
};
my $return = $orig->(@_);
$return;
}
}
for (@modules) {
( run in 4.217 seconds using v1.01-cache-2.11-cpan-0d23b851a93 )