Hello Community, Me along with fellow MXNet contributors (Jake <https://github.com/stu1130>, Karan <https://github.com/karan6181>) are working on the following problem: 1. Some of the data transformations used in training is applicable during inference. Most commonly transformations on validation data is same as transformations required during inference. 2. MXNet models do not contain data transformations as part of the graph. Making it harder, time consuming and duplicated effort to re create data transformation during inference. This problem is more evident in cross language use cases. Training in Gluon (Python) and inference in Java/C++.
After few initial discussions with some of MXNet contributors (Zhi <https://github.com/zhreshold>, Naveen <https://github.com/nswamy>, Sina <https://github.com/safrooze>), design proposal, development plan, tasks, milestones and more details are captured in this document. https://cwiki.apache.org/confluence/display/MXNET/MXNet+end+to+end+models Please do provide your feedback via comments in the document or on this e-mail. All contributions are welcome. I will be creating JIRA stories and issues for initial tasks identified. -- Sandeep Krishnamurthy
