[GitHub] mbrookhart commented on a change in pull request #13657: update with release notes for 1.4.0 release

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mbrookhart 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_r242271526
 
 

 ##########
 File path: NEWS.md
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 @@ -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)
+
+#### SVRG Optimization
+
+SVRG stands for Stochastic Variance Reduced Gradient, which was first introduced in the paper [Accelerating Stochastic Gradient Descent using Predicative Variance Reduction in 2013](https://papers.nips.cc/paper/4937-accelerating-stochastic-gradient-descent-using-predictive-variance-reduction.pdf). It is an optimization technique that complements SGD. SGD is known for large scale optimization but it suffers from slow convergence asymptotically due to the inherent variance. SGD approximates the full gradient using a small batch of samples which introduces variance. In order to converge faster, SGD often needs to start with a smaller learning rate. SVRG remedies the problem by keeping a version of the estimated weights that is close to the optimal parameters and maintain average of full gradient over full pass of data. The average of full gradients of all data is calculated w.r.t to parameters of last mth epochs. It has provable guarantees for strongly convex smooth functions, and a more detailed proof can be found in section 3 of the paper. SVRG uses a different update rule: gradients w.r.t current parameters minus gradients w.r.t parameters from the last mth epoch, plus the average of gradients over all data. Key Characteristics of SVRG:
+
+  * Explicit variance reduction 
+  * Ability to use relatively large learning rate compared to SGD, which leads to faster convergence.
+More details can be found at [SVRG Optimization in MXNet Python Module](https://cwiki.apache.org/confluence/display/MXNET/Unified+integration+with+external+backend+libraries)
+
+#### Subgraph API
+
+MXNet can integrate with many different kinds of backend libraries, including TVM, MKLDNN, TensorRT, Intel nGraph and more. These backend in general support a limited number of operators, and thus running computation in a model usually involves in interaction between backend-supported operators and MXNet operators. These backend libraries share some common requirements:
+
+TVM , MKLDNN and nGraph uses customized data formats. Interaction between these backends with MXNet requires data format conversion.
+TVM, MKLDNN, TensorRT and nGraph fuses operators.
+Integration with these backends should happen in the granularity of subgraphs instead of in the granularity of operators. To fuse operators, it's obvious that we need to divide a graph into subgraphs so that the operators in a subgraph can be fused into a single operator. To handle customized data formats, we should partition a computation graph into subgraphs as well. Each subgraph contains only TVM, MKLDNN or ngraph operators. In this way, MXNet converts data formats only when entering such a subgraph and the operators inside a subgraph handle format conversion themselves if necessary. This makes interaction of TVM and MKLDNN with MXNet much easier. Neither the MXNet executor nor the MXNet operators need to deal with customized data formats. Even though invoking these libraries from MXNet requires similar steps, the partitioning rule and the subgraph execution of these backends can be different. As such, we define the following interface for backends to customize graph partitioning and subgraph execution inside an operator. More details can be found at PR 12157 and [Subgraph API](https://cwiki.apache.org/confluence/display/MXNET/Unified+integration+with+external+backend+libraries).
+
+#### MXNet nGraph integration
+
+As the diversity of deep learning hardware accelerators increase, it is important to have an efficient abstraction layer so developers can avoid having to enable each accelerator/compute separately. Intel nGraph enables that vision. The primary goal of this integration is to provide a seamless development and deployment experience to data scientists and machine learning engineers to leverage Intel nGraph ecosystem with MXNet. As Subgraph API seamlessly integrates with MXNet frontend API, users should just be able to use or switch nGraph backend with any existing MXNet scripts, models and deployments using the symbolic interface. For more details see  [MXNet nGraph integration using subgraph backend interface](https://cwiki.apache.org/confluence/display/MXNET/MXNet+nGraph+integration+using+subgraph+backend+interface)
+
+#### JVM Memory Management
 
 Review comment:
   Hasn't been merged, we've been having trouble getting @zheng-da and @reminisce attention around ReInvent/NeurIPS ;) Probably shouldn't be in the 1.4 release notes, it's not in 1.4.

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