It's been another month, and still no discussion on this thread. This seems to leave open 3 open - a move to the attic for this project. If that's the path that you're leaning towards, please do announce this intention on this list, and on the users@ list, so that this does not come as a surprise to anyone relying on this project.
--Rich On 2023/06/21 02:39:10 Sheng Zha wrote: > Dear MXNet community, > > I would like to start a conversation about the future of Apache MXNet > and where we should head next. > > # What we built > > Apache MXNet is an open-source deep learning framework used to train > and deploy deep learning models developed by contributors from > multiple organizations. MXNet is known for its efficiency at scale. > MXNet supports multiple languages including Python, Scala, R, Julia, > Perl, and more, which makes it accessible to a wide variety of > developers and data scientists. Its core is written in C++ for > performance, but it provides a flexible interface that allows users to > write code in their preferred language. > > Another important feature of MXNet is its support for both imperative > and symbolic programming styles. Imperative programming (like PyTorch) > is more intuitive and flexible, allowing users to write code as they > would in standard Python, whereas symbolic programming (like Theano, > TensorFlow) is more efficient in terms of runtime and memory usage and > allows for certain optimizations like graph-level optimizations and > auto-differentiation. This allows users to choose the programming > style that best suits their needs. Gluon interface further attempts at > unifying these paradigms. > > As an attempt to address the legacy issues, the community started > working on the development of MXNet 2.0 in 2020. Some of the updates > include a new design for data loading in Gluon, a unified distributed > data parallel interface, parameterizable probability distributions, a > refactored MXNet np interface, and enhanced support for 3rd-party > functionality. We've also improved the development process with a new > CMake build system, a memory profiler, and more Pythonic exception > handling. > > Along the journey, many people who share the enthusiasm about deep > learning joined our cause, and we managed to develop a community with > 875 contributors, 87 committers and 51 PMC members. Many of the > community members continue to play important roles outside of MXNet in > the generative AI and deep learning system spaces. We graduated from > Apache Incubator in Sept 2022 to a top-level project. Our project is > the culmination of the hard work of many people over the years. > > # Where we are > > Since late 2022 the code development has mostly halted and community > engagement slowed. Despite the boom in generative AI and related > spaces such as deep learning frameworks and distributed training > solutions, the project's current positioning is not enough in > sustaining the growth of the project, especially in light of the > development in the open source deep learning framework space. > > # What can we do > > There are a few choices that we as a community can make: > 1) we can continue on the current path by finding a critical mass to > continue drive maintenance > 2) we can discuss alternative positions that MXNet can pivot to so > that this community can bring value beyond existing offerings. > 3) we can retire as an Apache TLP into Attic. > > Thanks, > Sheng > ---------- > This is a re-post of https://github.com/apache/mxnet/issues/21206 >
