Merge pull request #666 from nudles/dev

Update release notes for v3.0.0RC1

RELEASE_NOTES

@@ -1,3 +1,91 @@
+Release Notes - SINGA - Version singa-3.0.0
+
+SINGA is a distributed deep learning library.
+
+This release includes following changes:
+
+  * Code quality has been promoted by introducing linting check in CI and auto code formatter. 
+    For linting, the tools, `cpplint` and `pylint`, are used and configured to comply 
+    [google coding styles](http://google.github.io/styleguide/)  details in `tool/linting/`. 
+    Similarly, formatting tools, `clang-format` and `yapf` configured with google coding styles, 
+    are the recommended one for developers to clean code before submitting changes, 
+    details in `tool/code-format/`. [LGTM](https://lgtm.com) is enabled on Github for 
+    code quality check; License check is also enabled.
+
+ * New Tensor APIs are added for naming consistency, and feature enhancement: 
+   - size(), mem_size(), get_value(), to_proto(), l1(), l2(): added for the sake of naming consistency
+   - AsType(): convert data type between `float` and `int`
+   - ceil(): perform element-wise ceiling of the input
+   - concat(): concatenate two tensor
+   - index selector: e.g. tensor1[:,:,1:,1:]
+   - softmax(in, axis): allow to perform softmax on a axis on a multi-dimensional tensor
+
+  * 14 new operators are added into the autograd module: Gemm, GlobalAveragePool, ConstantOfShape, 
+    Dropout, ReduceSum, ReduceMean, Slice, Ceil, Split, Gather, Tile, NonZero, Cast, OneHot. 
+    Their unit tests are added as well.
+
+  * 14 new operators are added to sonnx module for both backend and frontend: 
+    [Gemm](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Gemm), 
+    [GlobalAveragePool](https://github.com/onnx/onnx/blob/master/docs/Operators.md#GlobalAveragePool), 
+    [ConstantOfShape](https://github.com/onnx/onnx/blob/master/docs/Operators.md#ConstantOfShape), 
+    [Dropout](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Dropout), 
+    [ReduceSum](https://github.com/onnx/onnx/blob/master/docs/Operators.md#ReduceSum), 
+    [ReduceMean](https://github.com/onnx/onnx/blob/master/docs/Operators.md#ReduceMean), 
+    [Slice](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Slice), 
+    [Ceil](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Ceil), 
+    [Split](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Split), 
+    [Gather](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Gather), 
+    [Tile](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Tile), 
+    [NonZero](https://github.com/onnx/onnx/blob/master/docs/Operators.md#NonZero), 
+    [Cast](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Cast), 
+    [OneHot](https://github.com/onnx/onnx/blob/master/docs/Operators.md#OneHot). 
+    Their tests are added as well.
+
+  * Some ONNX models are imported into SINGA, including 
+    [Bert-squad](https://github.com/onnx/models/tree/master/text/machine_comprehension/bert-squad), 
+    [Arcface](https://github.com/onnx/models/tree/master/vision/body_analysis/arcface), 
+    [FER+ Emotion](https://github.com/onnx/models/tree/master/vision/body_analysis/emotion_ferplus), 
+    [MobileNet](https://github.com/onnx/models/tree/master/vision/classification/mobilenet), 
+    [ResNet18](https://github.com/onnx/models/tree/master/vision/classification/resnet), 
+    [Tiny Yolov2](https://github.com/onnx/models/tree/master/vision/object_detection_segmentation/tiny_yolov2), 
+    [Vgg16](https://github.com/onnx/models/tree/master/vision/classification/vgg), and Mnist.
+
+  * Some operators now support [multidirectional broadcasting](https://github.com/onnx/onnx/blob/master/docs/Broadcasting.md#multidirectional-broadcasting), 
+    including Add, Sub, Mul, Div, Pow, PRelu, Gemm 
+
+  * [Distributed training with communication optimization]. [DistOpt](./python/singa/opt.py) 
+    has implemented multiple optimization techniques, including gradient sparsification, 
+    chunk transmission, and gradient compression.
+
+  * Computational graph construction at the CPP level. The operations submitted to the Device are buffered.
+    After analyzing the dependency, the computational graph is created, which is further analyzed for
+    speed and memory optimization. To enable this feature, use the [Module API](./python/singa/module.py).
+
+  * New website based on Docusaurus. The documentation files are moved to a separate repo [singa-doc]](https://github.com/apache/singa-doc).
+    The static website files are stored at [singa-site](https://github.com/apache/singa-site).
+
+  * DNNL([Deep Neural Network Library](https://github.com/intel/mkl-dnn)), powered by Intel, 
+    is integrated into `model/operations/[batchnorm|pooling|convolution]`, 
+    the changes is opaque to the end users. The current version is dnnl v1.1 
+    which replaced previous integration of mkl-dnn v0.18. The framework could 
+    boost the performance of dl operations when executing on CPU. The dnnl dependency 
+    is installed through conda.
+
+  * Some Tensor APIs are marked as deprecated which could be replaced by broadcast, 
+    and it can support better on multi-dimensional operations. These APIs are
+    add_column(), add_row(), div_column(), div_row(), mult_column(), mult_row()
+
+  * Conv and Pooling are enhanced to support fine-grained padding like (2,3,2,3), 
+    and [SAME_UPPER, SAME_LOWER](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Conv) 
+    pad mode and shape checking.
+
+  * Reconstruct soonx, 
+    - Support two types of weight value (Initializer and Constant Node); 
+    - For some operators (BatchNorm, Reshape, Clip, Slice, Gather, Tile, OneHot), 
+      move some inputs to its attributes; 
+    - Define and implement the type conversion map. 
+
+------------------------------------------------------------------------
 Release Notes - SINGA - Version singa-incubating-2.0.0
 
 SINGA is a general distributed deep learning platform for training big deep