singa-incubating-1.0.0 Release Notes

SINGA is a general distributed deep learning platform for training big deep learning models over large datasets. It is designed with an intuitive programming model based on the layer abstraction. SINGA supports a wide variety of popular deep learning models.

This release includes following features:

  • Core abstractions including Tensor and Device

    • [SINGA-207] Update Tensor functions for matrices

    • [SINGA-205] Enable slice and concatenate operations for Tensor objects

    • [SINGA-197] Add CNMem as a submodule in lib/

    • [SINGA-196] Rename class Blob to Block

    • [SINGA-194] Add a Platform singleton

    • [SINGA-175] Add memory management APIs and implement a subclass using CNMeM

    • [SINGA-173] OpenCL Implementation

    • [SINGA-171] Create CppDevice and CudaDevice

    • [SINGA-168] Implement Cpp Math functions APIs

    • [SINGA-162] Overview of features for V1.x

    • [SINGA-165] Add cross-platform timer API to singa

    • [SINGA-167] Add Tensor Math function APIs

    • [SINGA-166] light built-in logging for making glog optional

    • [SINGA-164] Add the base Tensor class

  • IO components for file read/write, network and data pre-processing

    • [SINGA-233] New communication interface

    • [SINGA-215] Implement Image Transformation for Image Pre-processing

    • [SINGA-214] Add LMDBReader and LMDBWriter for LMDB

    • [SINGA-213] Implement Encoder and Decoder for CSV

    • [SINGA-211] Add TextFileReader and TextFileWriter for CSV files

    • [SINGA-210] Enable checkpoint and resume for v1.0

    • [SINGA-208] Add DataIter base class and a simple implementation

    • [SINGA-203] Add OpenCV detection for cmake compilation

    • [SINGA-202] Add reader and writer for binary file

    • [SINGA-200] Implement Encoder and Decoder for data pre-processing

  • Module components including layer classes, training algorithms and Python binding

    • [SINGA-235] Unify the engines for cudnn and singa layers

    • [SINGA-230] OpenCL Convolution layer and Pooling layer

    • [SINGA-222] Fixed bugs in IO

    • [SINGA-218] Implementation for RNN CUDNN version

    • [SINGA-204] Support the training of feed-forward neural nets

    • [SINGA-199] Implement Python classes for SGD optimizers

    • [SINGA-198] Change Layer::Setup API to include input Tensor shapes

    • [SINGA-193] Add Python layers

    • [SINGA-192] Implement optimization algorithms for SINGA v1 (nesterove, adagrad, rmsprop)

    • [SINGA-191] Add “autotune” for CudnnConvolution Layer

    • [SINGA-190] Add prelu layer and flatten layer

    • [SINGA-189] Generate python outputs of proto files

    • [SINGA-188] Add Dense layer

    • [SINGA-187] Add popular parameter initialization methods

    • [SINGA-186] Create Python Tensor class

    • [SINGA-184] Add Cross Entropy loss computation

    • [SINGA-183] Add the base classes for optimizer, constraint and regularizer

    • [SINGA-180] Add Activation layer and Softmax layer

    • [SINGA-178] Add Convolution layer and Pooling layer

    • [SINGA-176] Add loss and metric base classes

    • [SINGA-174] Add Batch Normalization layer and Local Response Nomalization layer.

    • [SINGA-170] Add Dropout layer and CudnnDropout layer.

    • [SINGA-169] Add base Layer class for V1.0

  • Examples

    • [SINGA-232] Alexnet on Imagenet

    • [SINGA-231] Batchnormlized VGG model for cifar-10

    • [SINGA-228] Add Cpp Version of Convolution and Pooling layer

    • [SINGA-227] Add Split and Merge Layer and add ResNet Implementation

  • Documentation

    • [SINGA-239] Transfer documentation files of v0.3.0 to github

    • [SINGA-238] RBM on mnist

    • [SINGA-225] Documentation for installation and Cifar10 example

    • [SINGA-223] Use Sphinx to create the website

  • Tools for compilation and some utility code

    • [SINGA-229] Complete install targets

    • [SINGA-221] Support for Travis-CI

    • [SINGA-217] build python package with setup.py

    • [SINGA-216] add jenkins for CI support

    • [SINGA-212] Disable the compilation of libcnmem if USE_CUDA is OFF

    • [SINGA-195] Channel for sending training statistics

    • [SINGA-185] Add CBLAS and GLOG detection for singav1

    • [SINGA-181] Add NVCC supporting for .cu files

    • [SINGA-177] Add fully cmake supporting for the compilation of singa_v1

    • [SINGA-172] Add CMake supporting for Cuda and Cudnn libs