id: version-3.1.0-download-singa title: Download SINGA original_id: download-singa
Verify
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.
V3.1.0 (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