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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.

V3.2.0 (15 August 2021):

• Apache SINGA 3.2.0 [SHA512] [ASC]
• 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 [SHA512] [ASC]
• 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 [SHA512] [ASC]
• 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) [SHA512] [ASC]
• Release Notes 2.0.0 (incubating)
• 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) [SHA512] [ASC]
• Release Notes 1.2.0 (incubating)
• 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) [MD5] [ASC]
• Release Notes 1.1.0 (incubating)
• 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) [MD5] [ASC]
• Release Notes 1.0.0 (incubating)
• 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) [MD5] [ASC]
• Release Notes 0.3.0 (incubating)
• 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) [MD5] [ASC]
• Release Notes 0.2.0 (incubating)
• 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) [MD5] [ASC]
• Amazon EC2 image
• Release Notes 0.1.0 (incubating)
• 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