| ## Download SINGA |
| |
| * Latest code: please clone the dev branch from [Github](https://github.com/apache/incubator-singa) |
| |
| * v1.1.0 (12 February 2017): |
| * [Apache SINGA 1.1.0](http://www.apache.org/dyn/closer.cgi/incubator/singa/1.1.0/apache-singa-incubating-1.1.0.tar.gz) |
| [\[MD5\]](https://dist.apache.org/repos/dist/release/incubator/singa/1.1.0/apache-singa-incubating-1.1.0.tar.gz.md5) |
| [\[KEYS\]](https://dist.apache.org/repos/dist/release/incubator/singa/1.1.0/KEYS) |
| * [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 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 |
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| |
| * v1.0.0 (8 September 2016): |
| * [Apache SINGA 1.0.0](https://archive.apache.org/dist/incubator/singa/1.0.0/apache-singa-incubating-1.0.0.tar.gz) |
| [\[MD5\]](https://archive.apache.org/dist/incubator/singa/1.0.0/apache-singa-incubating-1.0.0.tar.gz.md5) |
| [\[KEYS\]](https://archive.apache.org/dist/incubator/singa//1.0.0/KEYS) |
| * [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](https://archive.apache.org/dist/incubator/singa/0.3.0/apache-singa-incubating-0.3.0.tar.gz) |
| [\[MD5\]](https://archive.apache.org/dist/incubator/singa/0.3.0/apache-singa-incubating-0.3.0.tar.gz.md5) |
| [\[KEYS\]](https://archive.apache.org/dist/incubator/singa/0.3.0/KEYS) |
| * [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. |
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| |
| * v0.2.0 (14 January 2016): |
| * [Apache SINGA 0.2.0](https://archive.apache.org/dist/incubator/singa/0.2.0/apache-singa-incubating-0.2.0.tar.gz) |
| [\[MD5\]](https://archive.apache.org/dist/incubator/singa/0.2.0/apache-singa-incubating-0.2.0.tar.gz.md5) |
| [\[KEYS\]](https://archive.apache.org/dist/incubator/singa/0.2.0/KEYS) |
| * [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. |
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| * v0.1.0 (8 October 2015): |
| * [Apache SINGA 0.1.0](https://archive.apache.org/dist/incubator/singa/apache-singa-incubating-0.1.0.tar.gz) |
| [\[MD5\]](https://archive.apache.org/dist/incubator/singa/apache-singa-incubating-0.1.0.tar.gz.md5) |
| [\[KEYS\]](https://archive.apache.org/dist/incubator/singa/KEYS) |
| * [Amazon EC2 image](https://console.aws.amazon.com/ec2/v2/home?region=ap-southeast-1#LaunchInstanceWizard:ami=ami-b41001e6) |
| * [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 |
| |
| **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. |
