Thanks Jun for the summary. Apologies for delayed response. Having skimmed through a bunch of PRs revolving around "Numpy-compatibility Infra" (#15581 <https://github.com/apache/incubator-mxnet/pull/15581>, #14758 <https://github.com/apache/incubator-mxnet/pull/14758>, #14924 <https://github.com/apache/incubator-mxnet/pull/14924>)
I had 3 questions. 1. Looks like by making Numpy compatible APIs, MXNet would be more "usable", "easy-to-use" or "user-friendly". Having MXNet's Numpy version seems to be one big bet in our roadmap. So now my question is, would this be an addition or a replacement? So instead of mx.nd.zeros would be discontinuing with that and rather use mx.np.zeros? 2. Are we going to deprecate our mx.nd.* ops in 2.0 or upcoming releases? The reason why I'm asking this is - I have a pending PR on mx.nd.cumsum op (but now that Hao's #15581 has mx.np.cumsum in the pipeline should I close my PR if itβs not going to be used in future? 3. I understand making our operators "numpy-compatible" is an urgent need and will be greatly appreciated by the users/community. But going forward, are there going to be 2 ways of using MXNet operators? or is it going to be the de-facto method. I would assume we should only have one (to prevent confusing our users) and ensure all our existing ops are numpy-compatible. Thanks once again! Chai On Wed, 22 May 2019 at 09:25, Junru Shao <[email protected]> wrote: > ππ Nice progress Jun! > > On Wed, May 22, 2019 at 12:12 AM Jun Wu <[email protected]> wrote: > > > Dear Community, > > > > A few months ago, we submitted this RFC > > <https://github.com/apache/incubator-mxnet/issues/14253> proposing > > introducing NumPy-compatible coding experience into MXNet. As it has been > > some time since the proposal, we would like to share the progress with > the > > community and listen to feedbacks and suggestions to enhance technical > > implementation as well as the way the project is operated. > > > > We set our first milestone by tackling the problem of MXNet not > supporting > > scalar and zero-size tensors. Last month, we submitted the PR > > <https://github.com/apache/incubator-mxnet/pull/14661> providing the > > infrastructure to support those two types of tensors in MXNet. This work > > has affected almost every file and all language bindings in MXNet > codebase. > > It would be impossible to provide a complete solution hadn't there any > > contributions from many MXNet developers across different organizations. > > > > With the infrastructure of supporting scalar and zero-size tensors, we > are > > currently working on implementing NumPy operators in MXNet. We created a > > list of operators < > https://github.com/apache/incubator-mxnet/issues/14327> > > to be implemented from the D2L book <http://www.d2l.ai/>, and hope that > we > > will be able to provide full NumPy operator coverage for the book by the > > end of next month. > > > > In the future, we plan to provide NumPy operator support for GluonCV > > <https://github.com/dmlc/gluon-cv> and GluonNLP > > <https://github.com/dmlc/gluon-nlp>. We also intend to explore the > > opportunities of extending our work to support the libraries that heavily > > depend on NumPy, not only from the deep learning world, but also a > broader > > data science community, where the techniques employed by deep learning, > > such as auto differentiation, symbolic programming, GPU computing, and so > > forth can be beneficial. > > > > Thank you very much for your time to read this email and care about our > > efforts on making MXNet a super user-friendly deep learning framework. We > > look forward to your comments, suggestions and contributions for this > > project. > > > > Best, > > Developers of MXNet NumPy Project > > > > References > > [1] Development branch: > > https://github.com/apache/incubator-mxnet/tree/numpy > > [2] PR for supporting scalar and zero-size tensors: > > https://github.com/apache/incubator-mxnet/pull/14661 > > [3] First batch of NumPy operators to be implemented: > > https://github.com/apache/incubator-mxnet/issues/14327 > > [4] The D2L book: https://github.com/d2l-ai/d2l-en > > [5] GluonCV: https://github.com/dmlc/gluon-cv > > [6] GluonNLP: https://github.com/dmlc/gluon-nlp > > > -- *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/>
