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
