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From Xingjian SHI <xsh...@connect.ust.hk>
Subject Re: Request for comments - Keras-MXNet as submodule in MXNet
Date Fri, 23 Mar 2018 19:02:39 GMT
-1. We do not need to get the eyeballs this way. Asking users to use features that are not
mature enough will only hurt the future development of MXNet.


Xingjian

________________________________
From: Naveen Swamy <mnnaveen@gmail.com>
Sent: Saturday, March 24, 2018 2:54 AM
To: dev@mxnet.incubator.apache.org
Subject: Re: Request for comments - Keras-MXNet as submodule in MXNet

The proposal is about bringing a forked version of Keras(that works only
with MXNet) into Apache MXNet repo submodule that way MXNet gets more
eyeballs from existing Keras users and eventually Gluon, etc., , like
Sandeep mentioned Keras has a large user base which MXNet could tap into.

On Fri, Mar 23, 2018 at 11:50 AM, Yao Wang <kevinthesunwy@gmail.com> wrote:

> -1 Creating Keras as submodule of MXNet will provide users a feeling that
> MXNet depends on Keras. Keras is a frontend library which can be supported
> by various different backend framework. It would be better to add backend
> framework as Keras's submodule(Keras depends on MXNet) rather than
> opposite.
>
> Best,
> Yao
>
> 2018-03-23 11:44 GMT-07:00 Xingjian SHI <xshiab@connect.ust.hk>:
>
> > -1. I think we should wait until it's merged into keras-team/keras. The
> > repo is still not mature enough.
> >
> >
> > Best,
> >
> > Xingjian
> >
> >
> > ________________________________
> > From: sandeep krishnamurthy <sandeep.krishna98@gmail.com>
> > Sent: Friday, March 23, 2018 1:49 PM
> > To: dev@mxnet.incubator.apache.org
> > Subject: Request for comments - Keras-MXNet as submodule in MXNet
> >
> > Hello MXNet Community,
> >
> > Along with Lai, Karan and other MXNet contributors, I am working on
> adding
> > MXNet backend for Keras. Currently supporting around ~70% of Keras APIs
> > across CNNs and RNNs.
> > https://github.com/deep-learning-tools/keras/tree/keras2_mxnet_backend
[https://avatars0.githubusercontent.com/u/32447491?s=400&v=4]<https://github.com/deep-learning-tools/keras/tree/keras2_mxnet_backend>

deep-learning-tools/keras<https://github.com/deep-learning-tools/keras/tree/keras2_mxnet_backend>
github.com
keras - Deep Learning library for Python. Runs on TensorFlow, Theano, or CNTK.



> > [https://avatars0.githubusercontent.com/u/32447491?s=400&v=4]<https://
> > github.com/deep-learning-tools/keras/tree/keras2_mxnet_backend>
> >
> > deep-learning-tools/keras<https://github.com/deep-learning-
> > tools/keras/tree/keras2_mxnet_backend>
> > github.com
> > keras - Deep Learning library for Python. Runs on TensorFlow, Theano, or
> > CNTK.
> >
> >
> >
> >
> > We wanted to gather the community feedback on the proposal for including
> > this keras-mxnet package as a submodule in Apache MXNet. This will enable
> > providing the Keras interface for MXNet users. MXNet users can choose
> Keras
> > interface for building their Neural Networks in Symbolic Mode (Ex:
> > mx.keras).
> >
> > *Advantages:*
> >
> > 1. Keras is widely popular interface that many DL practitioners are
> > familiar. By including keras interface within MXNet natively, we enable
> > many users to use MXNet with 0 learning curve.
> >
> > 2.  Adding as submodule and exposing natively within MXNet pip package,
> > would greatly enhance user experience and get more users as compared to
> > releasing a fork repository independently.
> >
> > 3. Why submodule? - Helps in easily managing with patching the latest
> > parent keras-team/keras developments and releases. Thereby helping us
> > provide users the core keras experience. Operational management.
> >
> > 4. Other minor advantages - Operational maintenance, pip, CI and quality
> > control.
> >
> > Please do share your comments on the proposal.
> >
> > Best,
> > Sandeep
> >
> > *Note: *We tried merging with keras-team/keras and we created a PR
> > <https://github.com/keras-team/keras/pull/9291> as well. However, due to
> > multiple design incompatibility challenges, we need significant re-work
> on
> > MXNet Module, KVStore, Optimizers to address keras-team design concerns.
> > Since, we are adhering to keras API interface exposed to users, we are
> > planning release on the forked repo for now. More details on the design
> > challenges and workaround tried -
> > https://docs.google.com/document/d/1Vn5ip5MzCKcN29KCCnwjB2d59y-
> > VevdLrdn_eNd3nE4/edit?usp=sharing
> >
>

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