This is great. Thanks to everybody involved.
On Tue, May 22, 2018, 9:15 AM sandeep krishnamurthy <s...@apache.org> wrote: > Hello MXNet community, > > Keras users can now use the high-performance MXNet deep learning engine for > the distributed training of convolutional neural networks (CNNs) and > recurrent neural networks (RNNs). With an update of a few lines of code, > Keras developers can increase training speed by using MXNet's multi-GPU > distributed training capabilities. Saving an MXNet model is another > valuable feature of the release. You can design in Keras, train with > Keras-MXNet, and run inference in production, at-scale with MXNet. > > From our initial benchmarks, CNNs with Keras-MXNet is up to 3X faster on > GPUs compared to the default backend. See the benchmark module > <https://github.com/awslabs/keras-apache-mxnet/tree/master/benchmark> for > more details. > > RNN support in this release is experimental with few known > issues/unsupported functionalities. See using RNN with Keras-MXNet > limitations and workarounds doc > < > https://github.com/awslabs/keras-apache-mxnet/blob/master/docs/mxnet_backend/using_rnn_with_mxnet_backend.md > > > for more details. > > See Release Notes > <https://github.com/awslabs/keras-apache-mxnet/releases/tag/v2.1.6> for > more details on unsupported operators and known issues. We will continue > our efforts in the future releases to close the gaps. > > Thank you for all the contributors - Lai Wei <https://github.com/roywei>, > Karan > Jariwala <https://github.com/karan6181/>, Jiajie Chen > <https://github.com/jiajiechen>, Kalyanee Chendke < > https://github.com/kalyc>, > Junyuan Xie <https://github.com/piiswrong> > > We welcome your contributions - > https://github.com/awslabs/keras-apache-mxnet. Here is the issue with the > list of operators to be implemented. Do check it out and create a PR - > https://github.com/awslabs/keras-apache-mxnet/issues/18 > > Best, > Sandeep >
