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# Download SINGA
* To verify the downloaded tar.gz file, download the [KEYS]( and ASC files and then execute the following commands
% 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.
* v2.0.0 (20 April 2019):
* [Apache SINGA 2.0.0](
* [Release Notes 2.0.0](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 depdent lib versions in conda-build config
* v1.2.0 (6 June 2018):
* [Apache SINGA 1.2.0](
* [Release Notes 1.2.0](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
* v1.1.0 (12 February 2017):
* [Apache SINGA 1.1.0](
* [Release Notes 1.1.0](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 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
* v1.0.0 (8 September 2016):
* [Apache SINGA 1.0.0](
* [Release Notes 1.0.0](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
* v0.3.0 (20 April 2016):
* [Apache SINGA 0.3.0](
* [Release Notes 0.3.0](releases/RELEASE_NOTES_0.3.0.html)
* New features and major updates,
* [Training on GPU cluster](v0.3.0/gpu.html) enables training of deep learning models over a GPU cluster.
* [Python wrapper improvement](v0.3.0/python.html) makes it easy to configure the job, including neural net and SGD algorithm.
* [New SGD updaters](v0.3.0/updater.html) are added, including Adam, AdaDelta and AdaMax.
* [Installation](v0.3.0/installation.html) has fewer dependent libraries for single node training.
* Heterogeneous training with CPU and GPU.
* Support cuDNN V4.
* Data prefetching.
* Fix some bugs.
* v0.2.0 (14 January 2016):
* [Apache SINGA 0.2.0](
* [Release Notes 0.2.0](releases/RELEASE_NOTES_0.2.0.html)
* New features and major updates,
* [Training on GPU](v0.2.0/gpu.html) enables training of complex models on a single node with multiple GPU cards.
* [Hybrid neural net partitioning](v0.2.0/hybrid.html) supports data and model parallelism at the same time.
* [Python wrapper](v0.2.0/python.html) makes it easy to configure the job, including neural net and SGD algorithm.
* [RNN model and BPTT algorithm](v0.2.0/general-rnn.html) are implemented to support applications based on RNN models, e.g., GRU.
* [Cloud software integration](v0.2.0/distributed-training.html) 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.
* v0.1.0 (8 October 2015):
* [Apache SINGA 0.1.0](
* [Amazon EC2 image](
* [Release Notes 0.1.0](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
Apache SINGA is an effort undergoing incubation at The Apache Software
Foundation (ASF), sponsored by the name of Apache Incubator PMC. Incubation is
required of all newly accepted projects until a further review indicates that
the infrastructure, communications, and decision making process have stabilized
in a manner consistent with other successful ASF projects. While incubation
status is not necessarily a reflection of the completeness or stability of the
code, it does indicate that the project has yet to be fully endorsed by the