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# ABSTRACT: Start here for an overview of the library
# PODNAME: AI::TensorFlow::Libtensorflow::Manual::Quickstart

__END__

=pod

=encoding UTF-8

=head1 NAME

AI::TensorFlow::Libtensorflow::Manual::Quickstart - Start here for an overview of the library

=head1 DESCRIPTION

This provides a tour of C<libtensorflow> to help get started with using the
library.

=head1 CONVENTIONS

The library uses UpperCamelCase naming convention for method names in order to
match the underlying C library (for compatibility with future API changes) and
to make translating code from C easier as this is a low-level API.

As such, constructors for objects that correspond to C<libtensorflow> data
structures are typically called C<New>. For example, a new
L<AI::TensorFlow::Libtensorflow::Status> object can be created as follows

  use AI::TensorFlow::Libtensorflow::Status;
  my $status = AI::TensorFlow::Libtensorflow::Status->New;

  ok defined $status, 'Created new Status';

These C<libtensorflow> data structures use L<destructors|perlobj/Destructors> where necessary.

=head1 OBJECT TYPES

=over 4

=item L<AI::TensorFlow::Libtensorflow::Status>

Used for error-handling. Many methods take this as the final argument which is
then checked after the method call to ensure that it completed successfully.

=item L<AI::TensorFlow::Libtensorflow::Tensor>, L<AI::TensorFlow::Libtensorflow::DataType>

A C<TFTensor> is a multi-dimensional data structure that stores the data for inputs and outputs.
Each element has the same data type
which is defined by L<AI::TensorFlow::Libtensorflow::DataType>
thus a C<TFTensor> is considered to be "homogeneous data structure".
See L<Introduction to Tensors|https://www.tensorflow.org/guide/tensor> for more.

=item L<AI::TensorFlow::Libtensorflow::OperationDescription>, L<AI::TensorFlow::Libtensorflow::Operation>

An operation is a function that has inputs and outputs. It has a user-defined
name (such as C<MyAdder>) and library-defined type (such as C<AddN>).
L<AI::TensorFlow::Libtensorflow::OperationDescription> is used to build an
operation that will be added to a graph of other operations where those other
operations can set the operation's inputs and get the operation's outputs.
These inputs and outputs have types and dimension specifications, so that the
operations only accept and emit certain C<TFTensor>s.

=item L<AI::TensorFlow::Libtensorflow::Graph>

A set of operations with inputs and outputs linked together. This computation
can be serialized along with parameters as part of
a L<SavedModel|https://www.tensorflow.org/guide/saved_model>.

=item L<AI::TensorFlow::Libtensorflow::Session>, L<AI::TensorFlow::Libtensorflow::SessionOptions>

A session drives the execution of a L<AI::TensorFlow::Libtensorflow::Graph>.
Specifics of how the session executes can be set via L<AI::TensorFlow::Libtensorflow::SessionOptions>.

=back

=head1 TUTORIALS

The object types in L</OBJECT TYPES> are used in the following tutorials:

=over 4

=item L<InferenceUsingTFHubMobileNetV2Model|AI::TensorFlow::Libtensorflow::Manual::Notebook::InferenceUsingTFHubMobileNetV2Model>: image classification tutorial

This tutorial demonstrates using a pre-trained SavedModel and creating a L<AI::TensorFlow::Libtensorflow::Session> with the
L<LoadFromSavedModel|AI::TensorFlow::Libtensorflow::Session/LoadFromSavedModel>
method. It also demonstrates how to prepare image data for use as an input C<TFTensor>.

=item L<InferenceUsingTFHubEnformerGeneExprPredModel|AI::TensorFlow::Libtensorflow::Manual::Notebook::InferenceUsingTFHubEnformerGeneExprPredModel>: gene expression prediction tutorial

This tutorial builds on L<InferenceUsingTFHubMobileNetV2Model|AI::TensorFlow::Libtensorflow::Manual::Notebook::InferenceUsingTFHubMobileNetV2Model>.
It shows how to convert a pre-trained SavedModel from one that does not have a
usable signature to a new model that does. It also demonstrates how to prepare
genomic data for use as an input C<TFTensor>.

=back

=head1 DOCKER IMAGES

Docker (or equivalent runtime) images for the library along with all the
dependencies to run the above tutorials are available at
L<Quay.io|https://quay.io/repository/entropyorg/perl-ai-tensorflow-libtensorflow>
under various tags which can be run as

C<<
  docker run --rm -it -p 8888:8888 quay.io/entropyorg/perl-ai-tensorflow-libtensorflow:latest-nb-omnibus
>>

and when the links come up on the terminal, click the link to
C<http://127.0.0.1:8888/> in order to connect to the Jupyter Notebook interface
via the web browser. In the browser, click on the C<notebook> folder to access
the notebooks.

=head2 GPU Docker support

If using the GPU Docker image for NVIDIA support, make sure that the
L<TensorFlow Docker requirements|https://www.tensorflow.org/install/docker#tensorflow_docker_requirements>
are met and that the correct flags are passed to C<docker run>, for example

C<<
  docker run --rm --gpus all [...]
>>

More information about NVIDIA Docker containers can be found in the
NVIDIA Container Toolkit
L<Installation Guide|https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html>
(specifically L<Setting up NVIDIA Container Toolkit|https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#setting-up-nvidia-container-toolkit>)
and
L<User Guide|https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/user-guide.html>.

=head3 Diagnostics

When using the Docker GPU image, you may come across the error

C<<
  nvidia-container-cli: initialization error: load library failed: libnvidia-ml.so.1: cannot open shared object file: no such file or directory: unknown.
>>

Make sure that you have installed the NVIDIA Container Toolkit correctly
via the Installation Guide. Also make sure that you only have one Docker daemon
installed.  The recommended approach is to install via the official Docker
releases at L<https://docs.docker.com/engine/install/>. Note that in some
cases, you may have other unofficial Docker installations such as the
C<docker.io> package or the Snap C<docker> package, which may conflict with
the official vendor-provided NVIDIA Container Runtime.

=head2 Docker Tags

=over 4

=item C<latest>: base image with only C<libtensorflow> installed.

=item C<latest-nb-image-class>: image containing dependencies needed to run

L<InferenceUsingTFHubMobileNetV2Model|AI::TensorFlow::Libtensorflow::Manual::Notebook::InferenceUsingTFHubMobileNetV2Model>.

=item C<latest-nb-gene-expr-pred>: image containing dependencies needed to run

L<InferenceUsingTFHubEnformerGeneExprPredModel|AI::TensorFlow::Libtensorflow::Manual::Notebook::InferenceUsingTFHubEnformerGeneExprPredModel>.

=item C<latest-nb-obj-detect>: image containing dependencies needed to run

L<InferenceUsingTFHubCenterNetObjDetect|AI::TensorFlow::Libtensorflow::Manual::Notebook::InferenceUsingTFHubCenterNetObjDetect>.

=item C<latest-nb-omnibus>: image containing dependencies for all of the above

notebooks.

=item C<latest-gpu-nb-omnibus>: same as C<latest-nb-omnibus> but with NVIDIA GPU support.

=back

=head1 SEE ALSO

=over 4

=item L<TensorFlow|https://www.tensorflow.org/> home page

=back

=head1 AUTHOR

Zakariyya Mughal <zmughal@cpan.org>

=head1 COPYRIGHT AND LICENSE

This software is Copyright (c) 2022-2023 by Auto-Parallel Technologies, Inc.

This is free software, licensed under: