Hello MXNet community, MXNet users can now use Dynamic Training(DT) for Deep learning models with Apache MXNet. DT helps to reducing training cost and training time by adding elasticity to the distributed training cluster. DT also helps in increasing instance pool utilization. With DT unused instances can be used to speed up training and then instances can be removed from training cluster at a later time to be used by some other application. For details, refer to DT blog<https://aws.amazon.com/blogs/machine-learning/introducing-dynamic-training-for-deep-learning-with-amazon-ec2/>. Developers should be able to integrate Dynamic training in their existing distributed training code, with introduction of few extra lines of code<https://github.com/awslabs/dynamic-training-with-apache-mxnet-on-aws#writing-a-distributed-training-script>.
Thank you for all the contributors – Vikas Kumar <https://github.com/Vikas89 >, Haibin Lin < https://github.com/eric-haibin-lin>, Andrea Olgiati < https://github.com/andreaolgiati/><https://github.com/andreaolgiati/> , Mu Li < https://github.com/mli >, Hagay Lupesko <https://github.com/lupesko>, Markham Aaron < https://github.com/aaronmarkham > , Sergey Sokolov < https://github.com/Ishitori> This is an effort towards making training neural networks cheap and fast. We welcome your contributions to the repo - https://github.com/awslabs/dynamic-training-with-apache-mxnet-on-aws . We would love to hear feedback and ideas in this direction. Thanks Vikas
