Hi All,
Please review the Podling Report for August 2017 so that we can file on time. Feel free to make updates to this shared Google doc directly. https://docs.google.com/document/d/1PGhs96klZB6DXhpK9_biPh4-aCm8-bWwFzexnOW_GMA/edit Given below is the current snapshot of the report – it may change as updates are made directly to the above shared Google doc. --snip— *MXNet* MXNet is an open-source deep learning framework that allows you to define, train, and deploy deep neural networks on a wide array of devices, from cloud infrastructure to mobile devices. It is highly scalable, allowing for fast model training, and supports a flexible programming model and multiple languages. MXNet allows you to mix symbolic and imperative programming flavors to maximize both efficiency and productivity. MXNet is built on a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. The MXNet library is portable and lightweight, and it scales to multiple GPUs and multiple machines. MXNet has been incubating since 2017-01-23. *Three most important issues to address in the move towards graduation:* 1. Migrate code (GitHub) and website to Apache Infra. 2. Establish a predictable release process consistent with Apache Way. 3. Grow the community. *Any issues that the Incubator PMC (IPMC) or ASF Board wish/need to be* *aware of?* None *How has the community developed since the last report?* 1. Various Slack channels and dev@ mailing lists are being used actively. 2. A new blog published on OReilly web-site on 27-July having step-by-step instructions to implement a convolutional neural network to classify traffic signs with Apache MXNet: https://www.oreilly.com/ideas/classifying-traffic-signs-with-mxnet-an-introduction-to-computer-vision-with-neural-networks 1. A new blog post published on 28-July showing users how to exploit the unique features of Apache MXNet with a cheat sheet: https://aws.amazon.com/blogs/ai/exploiting-the-unique-features-of-the-apache-mxnet-deep-learning-framework-with-a-cheat-sheet/ *How has the project developed since the last report?* 1. The code base was migrated from http://github.com/dmlc/mxnet to https://github.com/apache/incubator-mxnet on 28-July, 2017. 2. From a statistics perspective, 54 authors have pushed 140 commits to master, with updates to 358 files including 22K additions and 3K deletions. 3. Documentation- Architecture guides, How To’s, Tutorials, and APIs continue to be improved. 4. More features (e.g. operators, algorithms) and bug-fixes requested by the user community continue to be added. *How would you assess the podling's maturity?* Podling's still getting established in Apache - so maturity == Low. Please feel free to add your own commentary. [X] Initial setup [ ] Working towards first release [ ] Community building [ ] Nearing graduation [X] Other: A maintenance release is being planned for August 2017 *Date of last release:* A maintenance release MXNet 0.10.0 Post 2 with few bug-fixes was released on 17-July, 2017. https://github.com/apache/incubator-mxnet/releases/tag/0.10.0.post2 *When were the last committers or PPMC members elected?* Ly Nguyen added as a committer and PPMC member in June 2017. *Signed-off-by:* [ ](mxnet) Sebastian Schelter Comments: [ ](mxnet) Suneel Marthi Comments: [ ](mxnet) Markus Weimer Comments: [ ](mxnet) Henri Yandell Comments: --snip— Thanks, Bhavin Thaker.
