Thanks, that's a nice summary. Great job and good to know the progress. I think we can do some exciting stuff in terms of parsing the Python AST and converting to a computational graph. Maybe we could brainstorm on that further on the linked ticket.
On Wed, May 22, 2019 at 12:12 AM Jun Wu <wujun....@gmail.com> 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
