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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.

V4.3.0 (7 September 2024):

  • Apache SINGA 4.3.0 [SHA512] [ASC]
  • Release Notes 4.3.0
  • Major changes:

    • Add the implementation for the Transformer example.

    • Enhance examples

      • Update the readme file for the dynamic model slicing example.
      • Update the HFL example by setting the maximum number of epochs.
      • Add the multiprocess training implementation for the cnn ms example.
      • Add the sparsification version of the model for the cnn ms example.

    • Extend the matrix multiplication operator to more dimensions.

    • Update the data types and tensor operations for model training.

    • Add the implementation for the new sum error loss.

    • Update the website

      • Add the news for the SIGMOD Systems Award.

    • Fix bugs

      • Fix the Github Actions for online code testing.

V4.2.0 (15 March 2024):

  • Apache SINGA 4.2.0 [SHA512] [ASC]

  • Release Notes 4.2.0

  • Major changes:

    • Add support for deep learning models running on top of PolarDB

      • Implement efficient model selection for a given dataset stored in the database
      • Add support for dynamic model creation
      • Add support for flexible setting of model training configurations
      • Optimize the in-database analytics modules for scalability, efficiency and memory consumption

    • New example

      • Add a horizontal federated learning example using the Bank dataset

    • Enhance examples

      • Add sample training data for testing the model selection application

    • Update the website

      • Update the star button in the main page
      • Refine the display of star statistics

    • Update the python versions for wheel files

    • Fix bugs

      • Fix the rat check files
      • Update the license files

V4.1.0 (05 November 2023):

  • Apache SINGA 4.1.0 [SHA512] [ASC]

  • Release Notes 4.1.0

  • Major changes:

    • New examples

      • Add an example for malaria detection using cell images.
      • Add an example for structured data learning.

    • Add support for models running on top of RDBMS

      • Add support for in-database model definition and selection in RDBMS.
      • Implement training-free model evaluation metrics for in-database model selection.
      • Implement a coordinator to balance between training-free and training-based model evaluations for in-database model selection.

    • Enhance distributed training

      • Add implementations for the sum error loss.
      • Improve the optimizer to return model gradients.
      • Improve the iterative checking for tensors and strings in the ModelMeta class.

    • Enhance example code

      • Add support for flexible setting of training configurations for models, e.g., learning rates, weight decay, momentum, etc.
      • Add implementations for dynamic models with varying layer sizes.

    • Update the website

      • Add illustrations for database integration.
      • Update users of Apache SINGA.

    • Fix bugs

      • Update the NVIDIA_GPGKEY in the Dockerfile for building wheel files.
      • Update the versions of dependencies in the wheel file.
      • Fix the collections module in the model.py file.

V4.0.0 (07 April 2023):

  • Apache SINGA 4.0.0 [SHA512] [ASC]

  • Release Notes 4.0.0

  • Major changes:

    • Enhance distributed training

      • Add support for configuration of number of GPUs to be used.
      • Increase max epoch for better convergence.
      • Print intermediate mini-batch information.
      • Add support for switching between CPU and GPU devices.

    • Enhance example code

      • Update the args of normalize forward function in the transforms of the BloodMnist example.
      • Update the xceptionnet in the cnn example.
      • Add arguments for weight decay, momentum and learning rates in the cnn example.
      • Add training scripts for more datasets and model types in the cnn example.
      • Add resnet dist version for the large dataset cnn example.
      • Add cifar 10 multi process for the large dataset cnn example.
      • Add sparsification implementation for mnist in the large dataset cnn example.
      • Update the cifar datasets downloading to local directories.
      • Extend the cifar datasets load function for customized directorires.

    • Enhance the webpage

      • Update online documentation for distributed training.

    • Promote code quality

      • Update inline comments for prepreocessing and data loading

    • Update the PIL image module

    • Update the runtime Dockerfile

    • Update the conda files

V3.3.0 (07 June 2022):

  • Apache SINGA 3.3.0 [SHA512] [ASC]
  • Release Notes 3.3.0
  • Major changes:
    • New examples
      • Add one CNN example for the BloodMnist dataset, a sub set of MedMNIST.
      • Add one example for the medical image analysis.
    • Enhance distributed training
      • Add key information printing, e.g., throughput and communication time, for distributed training.
      • Optimize printing and logging modules for faster distributed training.
    • Enhance example code
      • Add more datasets and model implementations for the cifar_distributed_cnn example.
      • Update the running script for the cifar_distributed_cnn example to include more models.
      • Update the dataset path for the largedataset_cnn example for more flexibility.
      • Add more model implementations for the largedataset_cnn example.
    • Enhance the webpage
      • Reconstruct the singa webpage to include project features.
      • Update the Git web site by deploying it via .asf.yaml.
      • Update the Chinese and Vietnamese documentations.
    • Debug and add assertions for input tensor data types in the opt.py.
    • Change pointer type to void for generalizing data types.
    • Fix bugs
      • Fix the python test error due to operations not implemented for some data types.
      • Fix the model of pad from bytes to str.

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):

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
Last updated on 9/12/2024
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