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From Chaitanya Bapat <chai.ba...@gmail.com>
Subject Re: Fujitsu Breaks ImageNet Record using MXNet (under 75 sec)
Date Mon, 08 Apr 2019 20:31:48 GMT
Yes. Moreover, we should be pushing it on our Twitter, Reddit, Medium, etc
social channels.

On Mon, 8 Apr 2019 at 15:55, Hagay Lupesko <lupesko@gmail.com> wrote:

> That's super cool Chai - thanks for sharing!
> I also noticed that, and was seeing how we can reach out to the Fujitsu
> guys so they can contribute back into MXNet...
>
> On Mon, Apr 8, 2019 at 10:14 AM Lin Yuan <apeforest@gmail.com> wrote:
>
> > Chai,
> >
> > Thanks for sharing. This is awesome news!
> >
> > Lin
> >
> > On Mon, Apr 8, 2019 at 8:48 AM Chaitanya Bapat <chai.bapat@gmail.com>
> > wrote:
> >
> > > Greetings!
> > >
> > > Great start to a Monday morning, as I came across this news on Import
> AI,
> > > an AI newsletter.
> > >
> > > The newsletter talked about Apache MXNet, hence thought of sharing it
> > with
> > > our community. This seems to be a great achievement worth paying
> > attention
> > > to.
> > >
> > > *75 seconds: How long it takes to train a network against ImageNet:*
> > > *...Fujitsu Research claims state-of-the-art ImageNet training
> scheme...*
> > > Researchers with Fujitsu Laboratories in Japan have further reduced the
> > > time it takes to train large-scale, supervised learning AI models;
> their
> > > approach lets them train a residual network to around 75% accuracy on
> the
> > > ImageNet dataset after 74.7 seconds of training time. This is a big
> leap
> > > from where we were in 2017 (an hour), and is impressive relative to
> > > late-2018 performance (around 4 minutes: see issue #121
> > > <
> > >
> >
> https://twitter.us13.list-manage.com/track/click?u=67bd06787e84d73db24fb0aa5&id=28edafc07a&e=0b77acb987
> > > >
> > > ).
> > >
> > > *How they did it: *The researchers trained their system across *2,048
> > Tesla
> > > V100 GPUs* via the Amazon-developed MXNet deep learning framework. They
> > > used a large mini-batch size of 81,920, and also implemented layer-wise
> > > adaptive scaling (LARS) and a 'warming up' period to increase learning
> > > efficiency.
> > >
> > > *Why it matters:* Training large models on distributed infrastructure
> is
> > a
> > > key component of modern AI research, and the reduction in time we've
> seen
> > > on ImageNet training is striking - I think this is emblematic of the
> > > industrialization of AI, as people seek to create systematic approaches
> > to
> > > efficiently training models across large amounts of computers. This
> trend
> > > ultimately leads to a speedup in the rate of research reliant on
> > > large-scale experimentation, and can unlock new paths of research.
> > > *  Read more:* Yet Another Accelerated SGD: ResNet-50 Training on
> > ImageNet
> > > in 74.7 seconds (Arxiv)
> > > <
> > >
> >
> https://twitter.us13.list-manage.com/track/click?u=67bd06787e84d73db24fb0aa5&id=d2b13c879f&e=0b77acb987
> > > >
> > > .
> > >
> > > NVIDIA article -
> > >
> > >
> >
> https://news.developer.nvidia.com/fujitsu-breaks-imagenet-record-with-v100-tensor-core-gpus/
> > >
> > > Hope that gives further impetus to strive harder!
> > > Have a good week!
> > > Chai
> > >
> > >  --
> > > *Chaitanya Prakash Bapat*
> > > *+1 (973) 953-6299*
> > >
> > > [image: https://www.linkedin.com//in/chaibapat25]
> > > <https://github.com/ChaiBapchya>[image:
> > https://www.facebook.com/chaibapat
> > > ]
> > > <https://www.facebook.com/chaibapchya>[image:
> > > https://twitter.com/ChaiBapchya] <https://twitter.com/ChaiBapchya
> > >[image:
> > > https://www.linkedin.com//in/chaibapat25]
> > > <https://www.linkedin.com//in/chaibapchya/>
> > >
> >
>


-- 
*Chaitanya Prakash Bapat*
*+1 (973) 953-6299*

[image: https://www.linkedin.com//in/chaibapat25]
<https://github.com/ChaiBapchya>[image: https://www.facebook.com/chaibapat]
<https://www.facebook.com/chaibapchya>[image:
https://twitter.com/ChaiBapchya] <https://twitter.com/ChaiBapchya>[image:
https://www.linkedin.com//in/chaibapat25]
<https://www.linkedin.com//in/chaibapchya/>

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