Author: Team PyTorch
The first PyTorch Ecosystem Day, a virtual event designed for the PyTorch ecosystem and industry communities to showcase their work and discover new opportunities to collaborate, kicks off next week on Wednesday, April 21st Pacific Time. Attendees have the opportunity to engage in discussions on new developments, trends, challenges, and best practices with PyTorch through poster sessions, keynotes, and a unique networking opportunity hosted through Gather.Town.
Ahead of the event, our team sat down with a few of the speakers to learn more about their work on PyTorch, trends they are seeing in the ever-growing community, and…
NVIDIA GTC 2021 is a week-long event that shares breakthroughs in AI, data center, accelerated computing, healthcare, gaming technology, and more. This year, GTC 2021 is hosting over 50 different sessions related to PyTorch.
See below for the list of sessions that mention or include PyTorch. You can register for the event for free and view the full listing of the session catalogue. Make sure you are logged in and the links below will take you directly to the relevant session.
Authored by Cassie Breviu, Microsoft Cloud Developer Advocate
We have collaborated with PyTorch and the PyTorch community to create a new tutorial to help new and experienced machine learning practitioners get started with PyTorch. We are excited to announce the new machine learning tutorial that is now available! We love contributing to open source and this collaboration was a blast! Let’s take a look at the journey that got us here and learn a bit more about the tutorial that was created.
First let’s talk about our “Learn the Basics” tutorial co-authored with PyTorch that aims to help both…
The recent addition of Glow compiler support within NXP’s eIQ™ machine learning (ML) software development platform has been well received by the ML development community. Glow, which is a compiler that interfaces to the PyTorch machine learning framework. The Glow ML compiler is designed to help developers optimize neural network graphs and generate code for target hardware devices such as low-power MCUs. The generated code can then be integrated into a high-level framework such as an NXP MCUXpresso Software Development Kit (SDK) project.
Last fall we explored how developers can use Glow for inferencing neural network models on low-power NXP…
We’re proud to announce our first PyTorch Ecosystem Day. The virtual, one-day event will focus completely on our Ecosystem and Industry PyTorch communities!
PyTorch is a deep learning framework of choice for academics and companies, all thanks to its rich ecosystem of tools and strong community. As with our developers, our ecosystem partners play a pivotal role in the development and growth of the community.
We will be hosting our first PyTorch Ecosystem Day, a virtual event designed for our ecosystem and industry communities to showcase their work and discover new opportunities to collaborate.
PyTorch Ecosystem Day will be held…
Authors: John Trenkle, Jaya Kawale and the Tubi ML Team
In this blog series, our aim is to highlight the nuances of Machine Learning in Tubi’s Ad-based Video on Demand (AVOD) space as practiced at Tubi. Machine Learning helps solve myriad problems involving recommendations, content understanding and ads. We extensively use PyTorch for several of these use cases as it provides us the flexibility, computational speed and ease of implementation to train large scale deep neural networks using GPUs.
With 33 million active monthly users and over 2.5 billion hours of content watched last year, Tubi is one of the…
Authors: David Novotny, Roman Shapovalov, Nikhila Ravi, Shubham Goel, Georgia Gkioxari, Justin Johnson, Jeremy Reizenstein, Patrick Labatut, Wan-Yen Lo
PyTorch3D is a highly modular and optimized library with unique capabilities designed to facilitate 3D deep learning with PyTorch. PyTorch3D provides a set of frequently used 3D operators and loss functions for 3D data that are fast and differentiable, as well as a modular differentiable rendering API. Researchers can use these features in deep learning systems right away. (For a primer on PyTorch3D and differentiable rendering have a look at our tutorial at the PyTorch hackathon).
Implicit Shape Rendering
Machine learning today requires distributed computing. Whether you’re training networks, tuning hyperparameters, serving models, or processing data, machine learning is computationally intensive and can be prohibitively slow without access to a cluster. Ray is a popular framework for distributed Python that can be paired with PyTorch to rapidly scale machine learning applications.
This post covers various elements of the Ray ecosystem and how it can be used with PyTorch!
Authors: Sridharan, Srinivas; Tsai, Louie; Kalamkar, Dhiraj D; Shiryaev, Mikhail; Durnov, Dmitry
The modern deep learning models are growing at an exponential rate, and those latest models could grow their parameters from million to billions. To train those modern models within hours, distributed training is a better option for those big models.
In this article, we distributed the DLRM model by using PyTorch with different backends, and shed light on the performance benefit of oneCCL backend.
The Intel® oneAPI Collective Communications Library (oneCCL) enables developers and researchers to more quickly train newer and deeper models. …
Authors: Jiong Gong at Intel, Vitaly Fedyunin at Facebook, Nikita Shustrov at Intel
Intel and Facebook previously collaborated to enable BF16, a first-class data type in PyTorch. It supports basic math and tensor operations and adds CPU optimization with multi-threading, vectorization, and neural network kernels from oneAPI Deep Neural Network Library (oneDNN, formerly known as MKL-DNN). The related work was published in an earlier blog during the launch of the 3rd Gen Intel® Xeon® scalable processors (formerly codename Cooper Lake). In that blog, we introduced the HW advancements for native BF16 support in Cooper Lake with BF16->FP32 fused multiply-add (FMA)…
PyTorch is an open source machine learning platform that provides a seamless path from research prototyping to production deployment.