id: RELEASE_NOTES_1.0.0 title: 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