IBM Joins the PyTorch Foundation as a Premier Member

OpenTeams July 27, 2023

The PyTorch Foundation, part of The Linux Foundation, is pleased to announce that IBM has joined as a premier member.

IBM Logo

The foundation serves as a neutral space for the deep learning community to collaborate on the open source PyTorch framework and ecosystem. With its extensive industry expertise and leadership in open source and AI, IBM is committed to actively contributing to the PyTorch community.

IBM offers a comprehensive portfolio of enterprise AI solutions and recently released watsonx, its next-generation data and AI platform. IBM’s watsonx platform leverages PyTorch to offer an enterprise-grade software stack for end-to-end training and fine-tuning of AI foundation models.

“By joining the PyTorch Foundation, we aim to contribute our expertise and resources to further advance PyTorch’s capabilities and make AI more accessible in hybrid cloud environments with flexible hardware options,” said Priya Nagpurkar, Vice President, Hybrid Cloud Platform and Developer Productivity, IBM Research. “We intend for our collaboration with PyTorch to bring the power of foundation models and generative AI to enterprises using the watsonx platform to drive business transformation.”

IBM and PyTorch have already collaborated on two projects. The first enables foundation models with billions of parameters to train efficiently on standard cloud networking infrastructure, such as Ethernet networking. Together, IBM and PyTorch have also worked on ways to make checkpointing for AI training considerably more cost-effective, by fixing the distributed checkpointing within PyTorch to support certain types of object storage.

“We’re happy to welcome IBM as a premier member. IBM’s expertise and dedication to advancing the field of artificial intelligence align perfectly with the mission of the PyTorch community,” said PyTorch Foundation Executive Director Ibrahim Haddad. “Their commitment to open collaboration and innovation will strengthen our collective efforts to empower developers and researchers worldwide.”

As a premier member, IBM is granted one seat to the PyTorch Foundation Governing Board. The Board sets policy through our bylaws, mission and vision statements, describing the overarching scope of foundation initiatives, technical vision, and direction.

Raghu Ganti Headshot

We’re happy to welcome Raghu Ganti, Principal Research Scientist at IBM Research, to our board. Raghu co-leads IBM Research’s foundation model training and validation platform, built on Red Hat OpenShift. His team primarily contributes to the PyTorch training components, with the mission of democratizing training and validation of foundation models.

To learn more about how you can be a part of the PyTorch Foundation, visit our website.

Categories: Artificial Intelligence & Machine Learning Posts, PyTorch
Tags: , ,

Related Articles

PyMC Open Source Development

In this episode of Open Source Directions, we were joined by Thomas Wiecki once again who talked about the work being done with PyMC. PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning focusing on advanced Markov chain Monte Carlo (MCMC) and variational inference (VI) algorithms. Its flexibility and extensibility make it applicable to a large suite of problems.

OpenTeams September 7, 2020

Interfaces for Explaining Transformer Language Models

Interfaces for exploring transformer language models by looking at input saliency and neuron activation. Explorable #1: Input saliency of a list of countries generated by a language model Tap or hover over the output tokens: Explorable #2: Neuron activation analysis reveals four groups of neurons, each is associated with generating a certain type of token Tap or hover over the sparklines on the left to isolate a certain factor: The Transformer architecture has been powering a number of the recent advances in NLP. A breakdown of this architecture is provided here . Pre-trained language models based on the architecture, in both its auto-regressive (models that use their own output as input to next time-steps and that process tokens from left-to-right, like GPT2) and denoising (models trained by corrupting/masking the input and that process tokens bidirectionally, like BERT) variants continue to push the envelope in various tasks in NLP and, more recently, in computer vision. Our understanding of why these models work so well, however, still lags behind these developments. This exposition series continues the pursuit to interpret and visualize the inner-workings of transformer-based language models. We illustrate how some key interpretability methods apply to transformer-based language models. This article focuses on auto-regressive models, but these methods are applicable to other architectures and tasks as well. This is the first article in the series. In it, we present explorables and visualizations aiding the intuition of: Input Saliency methods that score input tokens importance to generating a token. Neuron Activations and how individual and groups of model neurons spike in response to inputs and to produce outputs. The next article addresses Hidden State Evolution across the layers of the model and what it may tell us about each layer’s role.

OpenTeams December 16, 2020

124 Artificial Intelligence and Machine Learning Technology Influencers

As of 2023, the field of Artificial Intelligence (AI) and Machine Learning (ML) has witnessed rapid growth, innovation, and adoption across various industries. Many individuals have played pivotal roles in shaping and advancing this dynamic field. These influencers have made significant contributions through their groundbreaking research, influential publications, thought leadership, and active participation in the AI/ML community. In this article, we will highlight 124 AI and ML technology influencers who have had a profound impact on the industry.

OpenTeams August 2, 2023

OpenTeams bridges the gap between enterprises and the open source community, facilitating adoption and optimization of open source technologies. By fostering trust and enhancing communication, OpenTeams enables companies to leverage the full potential of open source solutions, driving innovation and efficiency in the enterprise.

Twitter Facebook Youtube Linkedin Envelope
Resources
Company

© 2024 OpenTeams. All Rights Reserved.