Download SINGA
Latest code: please clone the dev branch from Github
v1.1.0 (12 February 2017):
- Apache SINGA 1.1.0 [MD5] [KEYS]
- Release Notes 1.1.0
- 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
v1.0.0 (8 September 2016):
- Apache SINGA 1.0.0 [MD5] [KEYS]
- Release Notes 1.0.0
- 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):
v0.2.0 (14 January 2016):
- Apache SINGA 0.2.0 [MD5] [KEYS]
- Release Notes 0.2.0
- 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.
v0.1.0 (8 October 2015):
- Apache SINGA 0.1.0 [MD5] [KEYS]
- Amazon EC2 image
- Release Notes 0.1.0
- 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
Disclaimer
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 ASF.