21-9-7 download page
diff --git a/docs-site/docs/download.md b/docs-site/docs/download.md
index d58524a..6d9b108 100644
--- a/docs-site/docs/download.md
+++ b/docs-site/docs/download.md
@@ -18,11 +18,41 @@
You can also check the SHA512 or MD5 values to see if the download is completed.
+## V3.2.0 (15 August 2021):
+
+- [Apache SINGA 3.2.0](http://www.apache.org/dyn/closer.cgi/singa/3.2.0/apache-singa-3.2.0.tar.gz)
+ [\[SHA512\]](https://www.apache.org/dist/singa/3.2.0/apache-singa-3.2.0.tar.gz.sha512)
+ [\[ASC\]](https://www.apache.org/dist/singa/3.2.0/apache-singa-3.2.0.tar.gz.asc)
+- [Release Notes 3.2.0](http://singa.apache.org/docs/releases/RELEASE_NOTES_3.2.0)
+- Major changes:
+ * New examples
+ - Add one cifar-10 distributed CNN example for benchmarking the performance
+ of the distributed training.
+ - Add one large CNN example for training with a dataset from the filesysetm.
+ * Enhance distributed training
+ - Improve the data augmentation module for faster distributed training.
+ - Add device synchronization for more accurate time measurements during the distributed
+ training.
+ * Add Support for half-precision floating-point format (fp16) in deep learning models and
+ computational kernels.
+ * Update new onnx APIs and fix onnx examples accordingly, namely, DenseNet121, ShuffleNetv1,
+ ShuffleNetv2, SqueezeNet, VGG19.
+ * Add a new method to resize images by given width and height.
+ * Use docusaurus versioning to simplify the process of generating the project homepage.
+ * Promote code quality
+ - Unify the formats of docstrings that describe the contents and usage of the module.
+ - Unify the parameters of command-line arguments.
+ * Fix bugs
+ - Fix the CI build error by downloading the tbb binaries.
+ - Add disabling graph option for accessing parameter or gradient tensors during distributed
+ training.
+ - Solve the warnings of deprecated functions in the distributed optimizer module.
+
## V3.1.0 (30 October 2020):
-- [Apache SINGA 3.1.0](http://www.apache.org/dyn/closer.cgi/singa/3.1.0/apache-singa-3.1.0.tar.gz)
- [\[SHA512\]](https://www.apache.org/dist/singa/3.1.0/apache-singa-3.1.0.tar.gz.sha512)
- [\[ASC\]](https://www.apache.org/dist/singa/3.1.0/apache-singa-3.1.0.tar.gz.asc)
+- [Apache SINGA 3.1.0](https://archive.apache.org/dist/singa/3.1.0/apache-singa-3.1.0.tar.gz)
+ [\[SHA512\]](https://archive.apache.org/dist/singa/3.1.0/apache-singa-3.1.0.tar.gz.sha512)
+ [\[ASC\]](https://archive.apache.org/dist/singa/3.1.0/apache-singa-3.1.0.tar.gz.asc)
- [Release Notes 3.1.0](http://singa.apache.org/docs/releases/RELEASE_NOTES_3.1.0)
- Major changes:
- Update Tensor core:
diff --git a/docs-site/website/versioned_docs/version-3.2.0.rc1/download.md b/docs-site/website/versioned_docs/version-3.2.0.rc1/download.md
new file mode 100644
index 0000000..3c9f7b3
--- /dev/null
+++ b/docs-site/website/versioned_docs/version-3.2.0.rc1/download.md
@@ -0,0 +1,238 @@
+---
+id: version-3.2.0.rc1-downloads
+title: Download SINGA
+original_id: downloads
+---
+
+<!--- Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to you under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. -->
+
+## Verify
+
+To verify the downloaded tar.gz file, download the
+[KEYS](https://www.apache.org/dist/singa/KEYS) and ASC files and then execute
+the following commands
+
+```shell
+% gpg --import KEYS
+% gpg --verify downloaded_file.asc downloaded_file
+```
+
+You can also check the SHA512 or MD5 values to see if the download is completed.
+
+## V3.2.0 (15 August 2021):
+
+- [Apache SINGA 3.2.0](http://www.apache.org/dyn/closer.cgi/singa/3.2.0/apache-singa-3.2.0.tar.gz)
+ [\[SHA512\]](https://www.apache.org/dist/singa/3.2.0/apache-singa-3.2.0.tar.gz.sha512)
+ [\[ASC\]](https://www.apache.org/dist/singa/3.2.0/apache-singa-3.2.0.tar.gz.asc)
+- [Release Notes 3.2.0](http://singa.apache.org/docs/releases/RELEASE_NOTES_3.2.0)
+- Major changes:
+ * New examples
+ - Add one cifar-10 distributed CNN example for benchmarking the performance
+ of the distributed training.
+ - Add one large CNN example for training with a dataset from the filesysetm.
+ * Enhance distributed training
+ - Improve the data augmentation module for faster distributed training.
+ - Add device synchronization for more accurate time measurements during the distributed
+ training.
+ * Add Support for half-precision floating-point format (fp16) in deep learning models and
+ computational kernels.
+ * Update new onnx APIs and fix onnx examples accordingly, namely, DenseNet121, ShuffleNetv1,
+ ShuffleNetv2, SqueezeNet, VGG19.
+ * Add a new method to resize images by given width and height.
+ * Use docusaurus versioning to simplify the process of generating the project homepage.
+ * Promote code quality
+ - Unify the formats of docstrings that describe the contents and usage of the module.
+ - Unify the parameters of command-line arguments.
+ * Fix bugs
+ - Fix the CI build error by downloading the tbb binaries.
+ - Add disabling graph option for accessing parameter or gradient tensors during distributed
+ training.
+ - Solve the warnings of deprecated functions in the distributed optimizer module.
+
+## V3.1.0 (30 October 2020):
+
+- [Apache SINGA 3.1.0](https://archive.apache.org/dist/singa/3.1.0/apache-singa-3.1.0.tar.gz)
+ [\[SHA512\]](https://archive.apache.org/dist/singa/3.1.0/apache-singa-3.1.0.tar.gz.sha512)
+ [\[ASC\]](https://archive.apache.org/dist/singa/3.1.0/apache-singa-3.1.0.tar.gz.asc)
+- [Release Notes 3.1.0](http://singa.apache.org/docs/releases/RELEASE_NOTES_3.1.0)
+- Major changes:
+ - Update Tensor core:
+ - Support tensor transformation (reshape, transpose) for tensors up to 6
+ dimensions.
+ - Implement traverse_unary_transform in Cuda backend, which is similar to
+ CPP backend one.
+ - Add new tensor operators into the autograd module.
+ - Reconstruct sonnx to
+ - Support creating operators from both layer and autograd.
+ - Re-write SingaRep to provide a more powerful intermediate representation
+ of SINGA.
+ - Add a SONNXModel which implements from Model to provide uniform API and
+ features.
+ * Replace the Travis CI with Github workflow. Add quality and coverage
+ management.
+ * Add compiling and packaging scripts to create wheel packages for
+ distribution.
+ * Fix bugs
+ - Fix IMDB LSTM model example training script.
+ - Fix Tensor operation Mult on Broadcasting use cases.
+ - Gaussian function on Tensor now can run on Tensor with odd size.
+ - Updated a testing helper function gradients() in autograd to lookup param
+ gradient by param python object id for testing purpose.
+
+## V3.0.0 (18 April 2020):
+
+- [Apache SINGA 3.0.0](https://archive.apache.org/dist/singa/3.0.0/apache-singa-3.0.0.tar.gz)
+ [\[SHA512\]](https://archive.apache.org/dist/singa/3.0.0/apache-singa-3.0.0.tar.gz.sha512)
+ [\[ASC\]](https://archive.apache.org/dist/singa/3.0.0/apache-singa-3.0.0.tar.gz.asc)
+- [Release Notes 3.0.0](http://singa.apache.org/docs/releases/RELEASE_NOTES_3.0.0)
+- New features and major changes,
+ - Enhanced ONNX. Multiple ONNX models have been tested in SINGA.
+ - Distributed training with MPI and NCCL Communication optimization through
+ gradient sparsification and compression, and chunk transmission.
+ - Computational graph construction and optimization for speed and memory using
+ the graph.
+ - New documentation website (singa.apache.org) and API reference website
+ (apache-singa.rtfd.io).
+ - CI for code quality check.
+ - Replace MKLDNN with DNNL
+ - Update tensor APIs to support broadcasting operations.
+ - New autograd operators to support ONNX models.
+
+## Incubating v2.0.0 (20 April 2019):
+
+- [Apache SINGA 2.0.0 (incubating)](https://archive.apache.org/dist/incubator/singa/2.0.0/apache-singa-incubating-2.0.0.tar.gz)
+ [\[SHA512\]](https://archive.apache.org/dist/incubator/singa/2.0.0/apache-singa-incubating-2.0.0.tar.gz.sha512)
+ [\[ASC\]](https://archive.apache.org/dist/incubator/singa/2.0.0/apache-singa-incubating-2.0.0.tar.gz.asc)
+- [Release Notes 2.0.0 (incubating)](http://singa.apache.org/docs/releases/RELEASE_NOTES_2.0.0.html)
+- New features and major updates,
+ - Enhance autograd (for Convolution networks and recurrent networks)
+ - Support ONNX
+ - Improve the CPP operations via Intel MKL DNN lib
+ - Implement tensor broadcasting
+ - Move Docker images under Apache user name
+ - Update dependent lib versions in conda-build config
+
+## Incubating v1.2.0 (6 June 2018):
+
+- [Apache SINGA 1.2.0 (incubating)](https://archive.apache.org/dist/incubator/singa/1.2.0/apache-singa-incubating-1.2.0.tar.gz)
+ [\[SHA512\]](https://archive.apache.org/dist/incubator/singa/1.2.0/apache-singa-incubating-1.2.0.tar.gz.sha512)
+ [\[ASC\]](https://archive.apache.org/dist/incubator/singa/1.2.0/apache-singa-incubating-1.2.0.tar.gz.asc)
+- [Release Notes 1.2.0 (incubating)](http://singa.apache.org/docs/releases/RELEASE_NOTES_1.2.0.html)
+- New features and major updates,
+ - Implement autograd (currently support MLP model)
+ - Upgrade PySinga to support Python 3
+ - Improve the Tensor class with the stride field
+ - Upgrade cuDNN from V5 to V7
+ - Add VGG, Inception V4, ResNet, and DenseNet for ImageNet classification
+ - Create alias for conda packages
+ - Complete documentation in Chinese
+ - Add instructions for running Singa on Windows
+ - Update the compilation, CI
+ - Fix some bugs
+
+## Incubating v1.1.0 (12 February 2017):
+
+- [Apache SINGA 1.1.0 (incubating)](https://archive.apache.org/dist/incubator/singa/1.1.0/apache-singa-incubating-1.1.0.tar.gz)
+ [\[MD5\]](https://archive.apache.org/dist/incubator/singa/1.1.0/apache-singa-incubating-1.1.0.tar.gz.md5)
+ [\[ASC\]](https://archive.apache.org/dist/incubator/singa/1.1.0/apache-singa-incubating-1.1.0.tar.gz.asc)
+- [Release Notes 1.1.0 (incubating)](http://singa.apache.org/docs/releases/RELEASE_NOTES_1.1.0.html)
+- New features and major updates,
+ - Create Docker images (CPU and GPU versions)
+ - Create Amazon AMI for SINGA (CPU version)
+ - Integrate with Jenkins for automatically generating Wheel and Debian
+ packages (for installation), and updating the website.
+ - Enhance the FeedFowardNet, e.g., multiple inputs and verbose mode for
+ debugging
+ - Add Concat and Slice layers
+ - Extend CrossEntropyLoss to accept instance with multiple labels
+ - Add image_tool.py with image augmentation methods
+ - Support model loading and saving via the Snapshot API
+ - Compile SINGA source on Windows
+ - Compile mandatory dependent libraries together with SINGA code
+ - Enable Java binding (basic) for SINGA
+ - Add version ID in checkpointing files
+ - Add Rafiki toolkit for providing RESTFul APIs
+ - Add examples pretrained from Caffe, including GoogleNet
+
+## Incubating v1.0.0 (8 September 2016):
+
+- [Apache SINGA 1.0.0 (incubating)](https://archive.apache.org/dist/incubator/singa/1.0.0/apache-singa-incubating-1.0.0.tar.gz)
+ [\[MD5\]](https://archive.apache.org/dist/incubator/singa/1.0.0/apache-singa-incubating-1.0.0.tar.gz.md5)
+ [\[ASC\]](https://archive.apache.org/dist/incubator/singa/1.0.0/apache-singa-incubating-1.0.0.tar.gz.asc)
+- [Release Notes 1.0.0 (incubating)](http://singa.apache.org/docs/releases/RELEASE_NOTES_1.0.0.html)
+- New features and major updates,
+ - Tensor abstraction for supporting more machine learning models.
+ - Device abstraction for running on different hardware devices, including CPU,
+ (Nvidia/AMD) GPU and FPGA (to be tested in later versions).
+ - Replace GNU autotool with cmake for compilation.
+ - Support Mac OS
+ - Improve Python binding, including installation and programming
+ - More deep learning models, including VGG and ResNet
+ - More IO classes for reading/writing files and encoding/decoding data
+ - New network communication components directly based on Socket.
+ - Cudnn V5 with Dropout and RNN layers.
+ - Replace website building tool from maven to Sphinx
+ - Integrate Travis-CI
+
+## Incubating v0.3.0 (20 April 2016):
+
+- [Apache SINGA 0.3.0 (incubating)](https://archive.apache.org/dist/incubator/singa/0.3.0/apache-singa-incubating-0.3.0.tar.gz)
+ [\[MD5\]](https://archive.apache.org/dist/incubator/singa/0.3.0/apache-singa-incubating-0.3.0.tar.gz.md5)
+ [\[ASC\]](https://archive.apache.org/dist/incubator/singa/0.3.0/apache-singa-incubating-0.3.0.tar.gz.asc)
+- [Release Notes 0.3.0 (incubating)](http://singa.apache.org/docs/releases/RELEASE_NOTES_0.3.0.html)
+- New features and major updates,
+ - Training on GPU cluster enables training of deep learning models over a GPU
+ cluster.
+ - Python wrapper improvement makes it easy to configure the job, including
+ neural net and SGD algorithm.
+ - New SGD updaters are added, including Adam, AdaDelta and AdaMax.
+ - Installation has fewer dependent libraries for single node training.
+ - Heterogeneous training with CPU and GPU.
+ - Support cuDNN V4.
+ - Data prefetching.
+ - Fix some bugs.
+
+## Incubating v0.2.0 (14 January 2016):
+
+- [Apache SINGA 0.2.0 (incubating)](https://archive.apache.org/dist/incubator/singa/0.2.0/apache-singa-incubating-0.2.0.tar.gz)
+ [\[MD5\]](https://archive.apache.org/dist/incubator/singa/0.2.0/apache-singa-incubating-0.2.0.tar.gz.md5)
+ [\[ASC\]](https://archive.apache.org/dist/incubator/singa/0.2.0/apache-singa-incubating-0.2.0.tar.gz.asc)
+- [Release Notes 0.2.0 (incubating)](http://singa.apache.org/docs/releases/RELEASE_NOTES_0.2.0.html)
+- New features and major updates,
+ - Training on GPU enables training of complex models on a single node with
+ multiple GPU cards.
+ - Hybrid neural net partitioning supports data and model parallelism at the
+ same time.
+ - Python wrapper makes it easy to configure the job, including neural net and
+ SGD algorithm.
+ - RNN model and BPTT algorithm are implemented to support applications based
+ on RNN models, e.g., GRU.
+ - Cloud software integration includes Mesos, Docker and HDFS.
+ - Visualization of neural net structure and layer information, which is
+ helpful for debugging.
+ - Linear algebra functions and random functions against Blobs and raw data
+ pointers.
+ - New layers, including SoftmaxLayer, ArgSortLayer, DummyLayer, RNN layers and
+ cuDNN layers.
+ - Update Layer class to carry multiple data/grad Blobs.
+ - Extract features and test performance for new data by loading previously
+ trained model parameters.
+ - Add Store class for IO operations.
+
+## Incubating v0.1.0 (8 October 2015):
+
+- [Apache SINGA 0.1.0 (incubating)](https://archive.apache.org/dist/incubator/singa/apache-singa-incubating-0.1.0.tar.gz)
+ [\[MD5\]](https://archive.apache.org/dist/incubator/singa/apache-singa-incubating-0.1.0.tar.gz.md5)
+ [\[ASC\]](https://archive.apache.org/dist/incubator/singa/apache-singa-incubating-0.1.0.tar.gz.asc)
+- [Amazon EC2 image](https://console.aws.amazon.com/ec2/v2/home?region=ap-southeast-1#LaunchInstanceWizard:ami=ami-b41001e6)
+- [Release Notes 0.1.0 (incubating)](http://singa.apache.org/docs/releases/RELEASE_NOTES_0.1.0.html)
+- Major features include,
+ - Installation using GNU build utility
+ - Scripts for job management with zookeeper
+ - Programming model based on NeuralNet and Layer abstractions.
+ - System architecture based on Worker, Server and Stub.
+ - Training models from three different model categories, namely, feed-forward
+ models, energy models and RNN models.
+ - Synchronous and asynchronous distributed training frameworks using CPU
+ - Checkpoint and restore
+ - Unit test using gtest
diff --git a/docs-site/website/versioned_docs/version-3.2.0/download.md b/docs-site/website/versioned_docs/version-3.2.0/download.md
new file mode 100644
index 0000000..8886ab7
--- /dev/null
+++ b/docs-site/website/versioned_docs/version-3.2.0/download.md
@@ -0,0 +1,238 @@
+---
+id: version-3.2.0-downloads
+title: Download SINGA
+original_id: downloads
+---
+
+<!--- Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to you under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. -->
+
+## Verify
+
+To verify the downloaded tar.gz file, download the
+[KEYS](https://www.apache.org/dist/singa/KEYS) and ASC files and then execute
+the following commands
+
+```shell
+% gpg --import KEYS
+% gpg --verify downloaded_file.asc downloaded_file
+```
+
+You can also check the SHA512 or MD5 values to see if the download is completed.
+
+## V3.2.0 (15 August 2021):
+
+- [Apache SINGA 3.2.0](http://www.apache.org/dyn/closer.cgi/singa/3.2.0/apache-singa-3.2.0.tar.gz)
+ [\[SHA512\]](https://www.apache.org/dist/singa/3.2.0/apache-singa-3.2.0.tar.gz.sha512)
+ [\[ASC\]](https://www.apache.org/dist/singa/3.2.0/apache-singa-3.2.0.tar.gz.asc)
+- [Release Notes 3.2.0](http://singa.apache.org/docs/releases/RELEASE_NOTES_3.2.0)
+- Major changes:
+ * New examples
+ - Add one cifar-10 distributed CNN example for benchmarking the performance
+ of the distributed training.
+ - Add one large CNN example for training with a dataset from the filesysetm.
+ * Enhance distributed training
+ - Improve the data augmentation module for faster distributed training.
+ - Add device synchronization for more accurate time measurements during the distributed
+ training.
+ * Add Support for half-precision floating-point format (fp16) in deep learning models and
+ computational kernels.
+ * Update new onnx APIs and fix onnx examples accordingly, namely, DenseNet121, ShuffleNetv1,
+ ShuffleNetv2, SqueezeNet, VGG19.
+ * Add a new method to resize images by given width and height.
+ * Use docusaurus versioning to simplify the process of generating the project homepage.
+ * Promote code quality
+ - Unify the formats of docstrings that describe the contents and usage of the module.
+ - Unify the parameters of command-line arguments.
+ * Fix bugs
+ - Fix the CI build error by downloading the tbb binaries.
+ - Add disabling graph option for accessing parameter or gradient tensors during distributed
+ training.
+ - Solve the warnings of deprecated functions in the distributed optimizer module.
+
+## V3.1.0 (30 October 2020):
+
+- [Apache SINGA 3.1.0](https://archive.apache.org/dist/singa/3.1.0/apache-singa-3.1.0.tar.gz)
+ [\[SHA512\]](https://archive.apache.org/dist/singa/3.1.0/apache-singa-3.1.0.tar.gz.sha512)
+ [\[ASC\]](https://archive.apache.org/dist/singa/3.1.0/apache-singa-3.1.0.tar.gz.asc)
+- [Release Notes 3.1.0](http://singa.apache.org/docs/releases/RELEASE_NOTES_3.1.0)
+- Major changes:
+ - Update Tensor core:
+ - Support tensor transformation (reshape, transpose) for tensors up to 6
+ dimensions.
+ - Implement traverse_unary_transform in Cuda backend, which is similar to
+ CPP backend one.
+ - Add new tensor operators into the autograd module.
+ - Reconstruct sonnx to
+ - Support creating operators from both layer and autograd.
+ - Re-write SingaRep to provide a more powerful intermediate representation
+ of SINGA.
+ - Add a SONNXModel which implements from Model to provide uniform API and
+ features.
+ * Replace the Travis CI with Github workflow. Add quality and coverage
+ management.
+ * Add compiling and packaging scripts to create wheel packages for
+ distribution.
+ * Fix bugs
+ - Fix IMDB LSTM model example training script.
+ - Fix Tensor operation Mult on Broadcasting use cases.
+ - Gaussian function on Tensor now can run on Tensor with odd size.
+ - Updated a testing helper function gradients() in autograd to lookup param
+ gradient by param python object id for testing purpose.
+
+## V3.0.0 (18 April 2020):
+
+- [Apache SINGA 3.0.0](https://archive.apache.org/dist/singa/3.0.0/apache-singa-3.0.0.tar.gz)
+ [\[SHA512\]](https://archive.apache.org/dist/singa/3.0.0/apache-singa-3.0.0.tar.gz.sha512)
+ [\[ASC\]](https://archive.apache.org/dist/singa/3.0.0/apache-singa-3.0.0.tar.gz.asc)
+- [Release Notes 3.0.0](http://singa.apache.org/docs/releases/RELEASE_NOTES_3.0.0)
+- New features and major changes,
+ - Enhanced ONNX. Multiple ONNX models have been tested in SINGA.
+ - Distributed training with MPI and NCCL Communication optimization through
+ gradient sparsification and compression, and chunk transmission.
+ - Computational graph construction and optimization for speed and memory using
+ the graph.
+ - New documentation website (singa.apache.org) and API reference website
+ (apache-singa.rtfd.io).
+ - CI for code quality check.
+ - Replace MKLDNN with DNNL
+ - Update tensor APIs to support broadcasting operations.
+ - New autograd operators to support ONNX models.
+
+## Incubating v2.0.0 (20 April 2019):
+
+- [Apache SINGA 2.0.0 (incubating)](https://archive.apache.org/dist/incubator/singa/2.0.0/apache-singa-incubating-2.0.0.tar.gz)
+ [\[SHA512\]](https://archive.apache.org/dist/incubator/singa/2.0.0/apache-singa-incubating-2.0.0.tar.gz.sha512)
+ [\[ASC\]](https://archive.apache.org/dist/incubator/singa/2.0.0/apache-singa-incubating-2.0.0.tar.gz.asc)
+- [Release Notes 2.0.0 (incubating)](http://singa.apache.org/docs/releases/RELEASE_NOTES_2.0.0.html)
+- New features and major updates,
+ - Enhance autograd (for Convolution networks and recurrent networks)
+ - Support ONNX
+ - Improve the CPP operations via Intel MKL DNN lib
+ - Implement tensor broadcasting
+ - Move Docker images under Apache user name
+ - Update dependent lib versions in conda-build config
+
+## Incubating v1.2.0 (6 June 2018):
+
+- [Apache SINGA 1.2.0 (incubating)](https://archive.apache.org/dist/incubator/singa/1.2.0/apache-singa-incubating-1.2.0.tar.gz)
+ [\[SHA512\]](https://archive.apache.org/dist/incubator/singa/1.2.0/apache-singa-incubating-1.2.0.tar.gz.sha512)
+ [\[ASC\]](https://archive.apache.org/dist/incubator/singa/1.2.0/apache-singa-incubating-1.2.0.tar.gz.asc)
+- [Release Notes 1.2.0 (incubating)](http://singa.apache.org/docs/releases/RELEASE_NOTES_1.2.0.html)
+- New features and major updates,
+ - Implement autograd (currently support MLP model)
+ - Upgrade PySinga to support Python 3
+ - Improve the Tensor class with the stride field
+ - Upgrade cuDNN from V5 to V7
+ - Add VGG, Inception V4, ResNet, and DenseNet for ImageNet classification
+ - Create alias for conda packages
+ - Complete documentation in Chinese
+ - Add instructions for running Singa on Windows
+ - Update the compilation, CI
+ - Fix some bugs
+
+## Incubating v1.1.0 (12 February 2017):
+
+- [Apache SINGA 1.1.0 (incubating)](https://archive.apache.org/dist/incubator/singa/1.1.0/apache-singa-incubating-1.1.0.tar.gz)
+ [\[MD5\]](https://archive.apache.org/dist/incubator/singa/1.1.0/apache-singa-incubating-1.1.0.tar.gz.md5)
+ [\[ASC\]](https://archive.apache.org/dist/incubator/singa/1.1.0/apache-singa-incubating-1.1.0.tar.gz.asc)
+- [Release Notes 1.1.0 (incubating)](http://singa.apache.org/docs/releases/RELEASE_NOTES_1.1.0.html)
+- New features and major updates,
+ - Create Docker images (CPU and GPU versions)
+ - Create Amazon AMI for SINGA (CPU version)
+ - Integrate with Jenkins for automatically generating Wheel and Debian
+ packages (for installation), and updating the website.
+ - Enhance the FeedFowardNet, e.g., multiple inputs and verbose mode for
+ debugging
+ - Add Concat and Slice layers
+ - Extend CrossEntropyLoss to accept instance with multiple labels
+ - Add image_tool.py with image augmentation methods
+ - Support model loading and saving via the Snapshot API
+ - Compile SINGA source on Windows
+ - Compile mandatory dependent libraries together with SINGA code
+ - Enable Java binding (basic) for SINGA
+ - Add version ID in checkpointing files
+ - Add Rafiki toolkit for providing RESTFul APIs
+ - Add examples pretrained from Caffe, including GoogleNet
+
+## Incubating v1.0.0 (8 September 2016):
+
+- [Apache SINGA 1.0.0 (incubating)](https://archive.apache.org/dist/incubator/singa/1.0.0/apache-singa-incubating-1.0.0.tar.gz)
+ [\[MD5\]](https://archive.apache.org/dist/incubator/singa/1.0.0/apache-singa-incubating-1.0.0.tar.gz.md5)
+ [\[ASC\]](https://archive.apache.org/dist/incubator/singa/1.0.0/apache-singa-incubating-1.0.0.tar.gz.asc)
+- [Release Notes 1.0.0 (incubating)](http://singa.apache.org/docs/releases/RELEASE_NOTES_1.0.0.html)
+- New features and major updates,
+ - Tensor abstraction for supporting more machine learning models.
+ - Device abstraction for running on different hardware devices, including CPU,
+ (Nvidia/AMD) GPU and FPGA (to be tested in later versions).
+ - Replace GNU autotool with cmake for compilation.
+ - Support Mac OS
+ - Improve Python binding, including installation and programming
+ - More deep learning models, including VGG and ResNet
+ - More IO classes for reading/writing files and encoding/decoding data
+ - New network communication components directly based on Socket.
+ - Cudnn V5 with Dropout and RNN layers.
+ - Replace website building tool from maven to Sphinx
+ - Integrate Travis-CI
+
+## Incubating v0.3.0 (20 April 2016):
+
+- [Apache SINGA 0.3.0 (incubating)](https://archive.apache.org/dist/incubator/singa/0.3.0/apache-singa-incubating-0.3.0.tar.gz)
+ [\[MD5\]](https://archive.apache.org/dist/incubator/singa/0.3.0/apache-singa-incubating-0.3.0.tar.gz.md5)
+ [\[ASC\]](https://archive.apache.org/dist/incubator/singa/0.3.0/apache-singa-incubating-0.3.0.tar.gz.asc)
+- [Release Notes 0.3.0 (incubating)](http://singa.apache.org/docs/releases/RELEASE_NOTES_0.3.0.html)
+- New features and major updates,
+ - Training on GPU cluster enables training of deep learning models over a GPU
+ cluster.
+ - Python wrapper improvement makes it easy to configure the job, including
+ neural net and SGD algorithm.
+ - New SGD updaters are added, including Adam, AdaDelta and AdaMax.
+ - Installation has fewer dependent libraries for single node training.
+ - Heterogeneous training with CPU and GPU.
+ - Support cuDNN V4.
+ - Data prefetching.
+ - Fix some bugs.
+
+## Incubating v0.2.0 (14 January 2016):
+
+- [Apache SINGA 0.2.0 (incubating)](https://archive.apache.org/dist/incubator/singa/0.2.0/apache-singa-incubating-0.2.0.tar.gz)
+ [\[MD5\]](https://archive.apache.org/dist/incubator/singa/0.2.0/apache-singa-incubating-0.2.0.tar.gz.md5)
+ [\[ASC\]](https://archive.apache.org/dist/incubator/singa/0.2.0/apache-singa-incubating-0.2.0.tar.gz.asc)
+- [Release Notes 0.2.0 (incubating)](http://singa.apache.org/docs/releases/RELEASE_NOTES_0.2.0.html)
+- New features and major updates,
+ - Training on GPU enables training of complex models on a single node with
+ multiple GPU cards.
+ - Hybrid neural net partitioning supports data and model parallelism at the
+ same time.
+ - Python wrapper makes it easy to configure the job, including neural net and
+ SGD algorithm.
+ - RNN model and BPTT algorithm are implemented to support applications based
+ on RNN models, e.g., GRU.
+ - Cloud software integration includes Mesos, Docker and HDFS.
+ - Visualization of neural net structure and layer information, which is
+ helpful for debugging.
+ - Linear algebra functions and random functions against Blobs and raw data
+ pointers.
+ - New layers, including SoftmaxLayer, ArgSortLayer, DummyLayer, RNN layers and
+ cuDNN layers.
+ - Update Layer class to carry multiple data/grad Blobs.
+ - Extract features and test performance for new data by loading previously
+ trained model parameters.
+ - Add Store class for IO operations.
+
+## Incubating v0.1.0 (8 October 2015):
+
+- [Apache SINGA 0.1.0 (incubating)](https://archive.apache.org/dist/incubator/singa/apache-singa-incubating-0.1.0.tar.gz)
+ [\[MD5\]](https://archive.apache.org/dist/incubator/singa/apache-singa-incubating-0.1.0.tar.gz.md5)
+ [\[ASC\]](https://archive.apache.org/dist/incubator/singa/apache-singa-incubating-0.1.0.tar.gz.asc)
+- [Amazon EC2 image](https://console.aws.amazon.com/ec2/v2/home?region=ap-southeast-1#LaunchInstanceWizard:ami=ami-b41001e6)
+- [Release Notes 0.1.0 (incubating)](http://singa.apache.org/docs/releases/RELEASE_NOTES_0.1.0.html)
+- Major features include,
+ - Installation using GNU build utility
+ - Scripts for job management with zookeeper
+ - Programming model based on NeuralNet and Layer abstractions.
+ - System architecture based on Worker, Server and Stub.
+ - Training models from three different model categories, namely, feed-forward
+ models, energy models and RNN models.
+ - Synchronous and asynchronous distributed training frameworks using CPU
+ - Checkpoint and restore
+ - Unit test using gtest
diff --git a/docs-site/website/versioned_docs/version-3.2.0_Chinese/installation.md b/docs-site/website/versioned_docs/version-3.2.0_Chinese/installation.md
index a6a380c..4875ac9 100644
--- a/docs-site/website/versioned_docs/version-3.2.0_Chinese/installation.md
+++ b/docs-site/website/versioned_docs/version-3.2.0_Chinese/installation.md
@@ -16,14 +16,14 @@
[](https://colab.research.google.com/drive/1Ntkhi-Z6XTR8WYPXiLwujHd2dOm0772V?usp=sharing)
```shell
-$ conda install -c nusdbsystem -c conda-forge singa-cpu=3.1.0
+$ conda install -c nusdbsystem -c conda-forge singa-cpu=3.2.0.rc1
```
2. 使用带CUDA和cuDNN的GPU（需要CUDA驱动>=384.81）
[](https://colab.research.google.com/drive/1do_TLJe18IthLOnBOsHCEe-FFPGk1sPJ?usp=sharing)
```shell
-$ conda install -c nusdbsystem -c conda-forge singa-gpu=3.1.0
+$ conda install -c nusdbsystem -c conda-forge singa-gpu=3.2.0.rc1
```
3. 安装特定版本的SINGA，下面的命令列出了所有可用的SINGA软件包：
@@ -33,9 +33,9 @@
Loading channels: done
# Name Version Build Channel
-singa 3.1.0.rc2 cpu_py36 nusdbsystem
-singa 3.1.0.rc2 cudnn7.6.5_cuda10.2_py36 nusdbsystem
-singa 3.1.0.rc2 cudnn7.6.5_cuda10.2_py37 nusdbsystem
+singa 3.2.0.rc1 cpu_py36 nusdbsystem
+singa 3.2.0.rc1 cudnn7.6.5_cuda10.2_nccl2.6.4.1_mpich3.3.2_py36 nusdbsystem
+singa 3.2.0 cpu_py36 nusdbsystem
```
<!--- > Please note that using the nightly built images is not recommended except for SINGA development and testing. Using stable releases is recommended. -->
@@ -62,7 +62,7 @@
pip install singa -f http://singa.apache.org/docs/next/wheel-cpu.html --trusted-host singa.apache.org
```
-您可以通过`singa==<version>`安装特定版本的SINGA，其中`<version>`字段应被替换，例如`3.1.0`。可用的SINGA版本在链接中列出。
+您可以通过`singa==<version>`安装特定版本的SINGA，其中`<version>`字段应被替换，例如`3.2.0`。可用的SINGA版本在链接中列出。
要安装最新的开发版本，请将链接替换为
http://singa.apache.org/docs/next/wheel-cpu-dev.html
@@ -74,7 +74,7 @@
pip install singa -f http://singa.apache.org/docs/next/wheel-gpu.html --trusted-host singa.apache.org
```
-您也可以配置SINGA版本和CUDA版本，比如`singa==3.1.0+cuda10.2`，SINGA版本和CUDA版本的可用组合在链接中列出。
+您也可以配置SINGA版本和CUDA版本，比如`singa==3.2.0+cuda10.2`，SINGA版本和CUDA版本的可用组合在链接中列出。
要安装最新的开发版本，请将链接替换为
http://singa.apache.org/docs/next/wheel-gpu-dev.html
