add release note for v3.0.0.rc1
diff --git a/docs-site/docs/releases/RELEASE_NOTES_3.0.0.rc1.md b/docs-site/docs/releases/RELEASE_NOTES_3.0.0.rc1.md
new file mode 100644
index 0000000..e7519ce
--- /dev/null
+++ b/docs-site/docs/releases/RELEASE_NOTES_3.0.0.rc1.md
@@ -0,0 +1,100 @@
+Release Notes - SINGA - Version singa-3.0.0.rc1
+
+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.
