Hello everyone and
welcome to this special PyTorch
edition of oneAPI Dev News
for the summer of 2023.
We'll do one of these updates
every few months to keep you in the loop
with all things PyTorch.
Before we get going,
I will let you know
that you can learn more
about the things we share in this video
by visiting the links in the description.
The first topic is the Introduction
to PyTorch
episode of our Bone Hunt:
Discovering Dinosaurs with AI series.
Watch the episode to learn
how resident amateur
paleontologist and oneAPI
evangelist Bob Cheesebrough uses PyTorch
and the Intel Extension
for PyTorch in his dinosaur bone
hunting adventures.
Watch the episode
and check out the entire Bone
Hunt series.
Next, our friend
Jack Erickson shares a video
where you'll learn how to start running
PyTorch Inference
on an Intel Flex series
GPU using Intel Extension for PyTorch.
You'll see how this extension
brings the latest
and greatest features
for Intel hardware
to open source PyTorch, illustrated
using a transformers model
based on one of the published examples
in the documentation for the extension.
You'll be able to access more information
and how to download this free
extension for GPU support
by following the links
in the description of the video
and for a direct link,
you can check out
developer.intel.com/pytorch.
And now,
congratulations to the PyTorch Foundation
for its release of PyTorch 2.0!
In her blog
Celebrate PyTorch 2.0
with New Performance
Features for AI Developers.
our friend and colleague
Susan Khaler
discusses the four features
in which Intel
made significant contributions
to PyTorch 2.0: TorchInductor, GNN, Int8
Inference Optimization
and the oneDNN Graph API.
Read the blog
to get in-depth details
about each of these contributions.
All of us at Intel are delighted
to be a part of the PyTorch community
and we appreciate the collaboration
and feedback
from our colleagues at Meta
as we co-developed these features.
Moving on, dig into the technical article
Language Identification:
Building an End to End
AI Solution Using PyTorch
from Ramya Ravi and team.
Language identification is the process
of identifying
the primary language from multiple audio
input samples
in natural language processing.
Language identification
is an important and challenging problem.
There are many language
related tasks
such as entering text on your phone,
finding news articles
you enjoy,
or discovering answers
to questions that you may have.
All of these tasks
are powered by NLP models.
We must perform language identification
to decide
which model to invoke
at a particular point in time.
This article presents
an in-depth solution and code
sample for language identification
using Intel Extension for PyTorch,
which is a version of PyTorch
optimized for use on Intel processors
and Intel Neural Compressor.
Read the article and check out the code
sample demonstrating how to train a model
to perform
language identification
using the Hugging Face
SpeechBrain Toolkit
and optimize it using the Intel
Analytics Toolkit.
Our final topic is an article
by Benjamin
Consalvo,
Accelerated Image
Segmentation Using PyTorch.
In this article,
Ben shares how PyTorch delivers great CPU
performance
and can be further accelerated
with Intel Extension for PyTorch.
To illustrate this, Ben trained in
AI image segmentation model using PyTorch
1.13.1 with ResNet34
and UNet Architecture
to identify roads
and speed limits from satellite
images on a 4th Gen Intel
Xeon Scalable processor.
In this article,
Ben walks you through the steps to work
with a satellite image
dataset called SpaceNet 5
and shows how he optimized the code
to make deep learning workloads
feasible on CPUs
by flipping a few key switches.
That's all for this episode.
Remember,
you can learn more
about all of these topics
by following the links
in the description.
And while you're here, remember to like
and share this video
and subscribe
to the Intel Software YouTube channel.
Thanks for watching
and I'll see you in a few months
for the next PyTorch
edition of oneAPI Dev News.