aaronmarkham commented on a change in pull request #13657: update with release notes for 1.4.0 release URL: https://github.com/apache/incubator-mxnet/pull/13657#discussion_r242242634
########## File path: NEWS.md ########## @@ -1,6 +1,565 @@ MXNet Change Log ================ +## 1.4.0 +### New Features +#### Java Inference API + +Model inference is run and managed by software engineers in a production eco-system which is built with tools and frameworks that use Java/Scala as a primary language. Inference on a trained model has two different use-cases: + + 1. Real time or Online Inference - tasks that require immediate feedback, such as fraud detection + 2. Batch or Offline Inference - tasks that don't require immediate feedback, these are use-cases where you have massive amounts of data and want to run Inference or pre-compute inference results +Batch Inference is performed on big data platforms such as Spark using Scala or Java while Real time Inference is typically performed and deployed on popular web frameworks such as Tomcat, Netty, Jetty, etc. which use Java. With this project, we want to build a new set of APIs which are Java friendly, compatible with Java 7+, are easy to use for inference, and lowers the entry barrier of consuming MXNet for production use-cases. More details can be found at the [Java Inference API document](https://cwiki.apache.org/confluence/display/MXNET/MXNet+Java+Inference+API). + +#### Julia API + +MXNet.jl is the Julia package of Apache MXNet. MXNet.jl brings flexible and efficient GPU computing and state-of-art deep learning to Julia. Some highlight of features include: + + * Efficient tensor/matrix computation across multiple devices, including multiple CPUs, GPUs and distributed server nodes. + * Flexible symbolic manipulation to composite and construct state-of-the-art deep learning models. + +#### Control Flow Operators + +Today we observe more and more dynamic neural network models, especially in the fields of natural language processing and graph analysis. The dynamics in these models come from multiple sources, including: + + * Models are expressed with control flow, such as conditions and loops; + * NDArrays in a model may have dynamic shapes, meaning the NDArrays of a model or some of the NDArrays have different shapes for different batches; + * Models may want to use more dynamic data structures, such as lists or dictionaries. +It's natural to express the dynamic models in frameworks with the imperative programming interface (e.g., Gluon, Pytorch, TensorFlow Eager). In this interface, users can simply use Python control flows, or NDArrays with any shape at any moment, or use Python lists and dictionaries to store data as they want. The problem of this approach is that it highly depends on the front-end programming languages (mainly Python). A model implemented in one language can only run in the same language. A common use case is that machine learning scientists want to develop their models in Python but engineers who deploy the models usually have to use a different language (e.g., Java and C). Gluon tries to close the gap between the model development and deployment. Machine learning scientists design and implement their models in Python with the imperative interface and Gluon turns the implementations into symbolic implementations by simply invoking hybridize() for model exporting. + +The goal of this project is to enhance Gluon to turn a dynamic neural network into a static computation graph (where the dynamic control flows are expressed by control flow operators) with Gluon hybridization and export them for deployment. More information can be found at [Optimize dynamic neural network models with control flow operators](https://cwiki.apache.org/confluence/display/MXNET/Optimize+dynamic+neural+network+models+with+control+flow+operators) Review comment: Besides my minor updates to this section... why not move the goal to the top of the discussion? You intro the problem and go kind of deep - what about a light intro to the problem and then the goal/solution. Then go deep, and then summarize. ```suggestion The goal of this project is to enhance Gluon to turn a dynamic neural network into a static computation graph. The dynamic control flows are expressed by control flow operators with Gluon hybridization, and these are exported for deployment. More information can be found at [Optimize dynamic neural network models with control flow operators](https://cwiki.apache.org/confluence/display/MXNET/Optimize+dynamic+neural+network+models+with+control+flow+operators) ``` ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected] With regards, Apache Git Services
