Agreed!
I will mention this to my colleagues at Amazon that can help with that.

On Mon, Apr 8, 2019 at 1:32 PM Chaitanya Bapat <chai.ba...@gmail.com> wrote:

> 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 <lupe...@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 <apefor...@gmail.com> wrote:
> >
> > > Chai,
> > >
> > > Thanks for sharing. This is awesome news!
> > >
> > > Lin
> > >
> > > On Mon, Apr 8, 2019 at 8:48 AM Chaitanya Bapat <chai.ba...@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/>
>

Reply via email to