Special! Apr 30 at 12pm (NSH 3305) -- Olatunji Ruwase (Microsoft Research) -- DeepSpeed: Enabling efficient trillion parameter scale training for deep learning models

[Victor Akinwande]

Quick reminder that this talk is happening later today.

On Wed, Apr 24, 2024 at 11:59 AM Victor Akinwande <vakinwan at andrew.cmu.edu>
wrote:

> Dear all,
>
> We look forward to seeing you next Tuesday (04/30) from 12:00-1:00 PM
> (ET) for the next talk of this semester's CMU AI Seminar, sponsored by
> SambaNova Systems (https://sambanova.ai). The seminar will be held in NSH
> 3305 with pizza provided and will be streamed on Zoom.
>
> To learn more about the seminar series or to see the future schedule,
> please visit the seminar website (http://www.cs.cmu.edu/~aiseminar/).
>
> Next Tuesday (04/30),  Olatunji (Tunji) Ruwase (Microsoft Research) will
> be giving a virtual talk titled "DeepSpeed: Enabling efficient trillion
> parameter scale training for deep learning models".
>
> ///////////////////
>
> *Talk Abstract: *Deep Learning is driving unprecedented progress in a
> wide range of Artificial Intelligence domains, including natural language
> processing, vision, speech, and multimodal. Sustaining this rapid pace of
> AI revolution, however, requires practical solutions to the extreme demands
> of model scaling on the compute, memory, communication and storage
> components of modern computing hardware. To address this challenge, we
> created a deep learning optimization library called DeepSpeed to make
> distributed model training and inference efficient, effective, and easy on
> commodity hardware. This talk will focus on DeepSpeed optimizations for
> improving memory, compute, communication, and data efficiency of
> extreme-scale model training.
>
> *Speaker Bio: *Olatunji (Tunji) Ruwase is the lead and co-founder of the
> DeepSpeed project at Microsoft. His broad industry and research background
> spans compilers, operating systems, and hardware accelerators. His current
> focus is on systems and convergence optimizations, and frameworks for
> efficient distributed training and inference of deep learning models.  His
> research results on deep learning training, inference, and hyperparameter
> search are used in multiple Microsoft systems and products, such as Azure,
> Ads, Bing, Catapult, and HyperDrive. Tunji earned a PhD in Computer Science
> from Carnegie Mellon University under the guidance of Professor Todd Mowry.
>
> ///////////////////
>
>
> *In person: NSH 3305Zoom Link:
>  https://cmu.zoom.us/j/99510233317?pwd=ZGx4aExNZ1FNaGY4SHI3Qlh0YjNWUT09
> <https://cmu.zoom.us/j/99510233317?pwd=ZGx4aExNZ1FNaGY4SHI3Qlh0YjNWUT09>*
>
> - Victor & Asher
>
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