| Release Notes - SINGA - Version singa-3.3.0 |
| |
| SINGA is a distributed deep learning library. |
| |
| This release includes following changes: |
| |
| * New examples: |
| * Add one CNN example for the BloodMnist dataset, a sub set of MedMNIST. |
| * Add one example for the medical image analysis. |
| |
| * Enhance distributed training |
| * Add key information printing, e.g., throughput and communication time, for distributed training. |
| * Optimize printing and logging modules for faster distributed training. |
| |
| * Enhance example code |
| * Add more datasets and model implementations for the cifar_distributed_cnn example. |
| * Update the running script for the cifar_distributed_cnn example to include more models. |
| * Update the dataset path for the largedataset_cnn example for more flexibility. |
| * Add more model implementations for the largedataset_cnn example. |
| |
| * Enhance the webpage |
| * Reconstruct the singa webpage to include project features. |
| * Update the Git web site by deploying it via .asf.yaml. |
| * Update the Chinese and Vietnamese documentations. |
| |
| * Debug and add assertions for input tensor data types in the opt.py. |
| |
| * Change pointer type to void for generalizing data types. |
| |
| * Fix bugs |
| * Fix the python test error due to operations not implemented for some data types. |
| * Fix the model of pad from bytes to str. |
| |
| ---------------------------------------------------------------------------------------------- |
| |
| Release Notes - SINGA - Version singa-3.2.0 |
| |
| SINGA is a distributed deep learning library. |
| |
| This release includes following changes: |
| |
| * New examples: |
| * Add one cifar-10 distributed CNN example for benchmarking the performance of the distributed |
| training. |
| * Add one large CNN example for training with a dataset from the filesysetm. |
| |
| * Enhance distributed training |
| * Improve the data augmentation module for faster distributed training. |
| * Add device synchronization for more accurate time measurements during the distributed training. |
| |
| * Add Support for half-precision floating-point format (fp16) in deep learning models and |
| computational kernels. |
| |
| * Update new onnx APIs and fix onnx examples accordingly, namely, DenseNet121, ShuffleNetv1, |
| ShuffleNetv2, SqueezeNet, VGG19. |
| |
| * Add a new method to resize images by given width and height. |
| |
| * Use docusaurus versioning to simplify the process of generating the project homepage. |
| |
| * Promote code quality |
| * Unify the formats of docstrings that describe the contents and usage of the module. |
| * Unify the parameters of command-line arguments. |
| |
| * Fix bugs |
| * Fix the CI build error by downloading the tbb binaries. |
| * Add disabling graph option for accessing parameter or gradient tensors during distributed |
| training. |
| * Solve the warnings of deprecated functions in the distributed optimizer module. |
| |
| ---------------------------------------------------------------------------------------------- |
| |
| Release Notes - SINGA - Version singa-3.1.0 |
| |
| SINGA is a distributed deep learning library. |
| |
| This release includes following changes: |
| |
| * 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, including |
| CosSim, DepthToSpace, Embedding, Erf, Expand, Floor, Pad, Round, Rounde, SpaceToDepth, UpSample, Where. |
| The corresponding ONNX operators are thus supported by SINGA. |
| |
| * Add Embedding and Gemm into the layer module. |
| |
| * Add SGD operators to opt module, including RMSProp, Adam, and AdaGrad. |
| |
| * Extend the sonnx module to support |
| DenseNet121, ShuffleNetv1, ShuffleNetv2, SqueezeNet, VGG19, GPT2, and RoBERTa, |
| |
| * 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. |
| |
| * Add one example that trains a BiLSTM model over the InsuranceQA data. |
| |
| * Replace the Travis CI with Github workflow. Add quality and coverage management. |
| |
| * Add compiling and packaging scripts to creat 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. |
| |
| |
| ---------------------------------------------------------------------------------------------- |
| |
| Release Notes - SINGA - Version singa-3.0.0 |
| |
| SINGA is a distributed deep learning library. |
| |
| This release includes following changes: |
| |
| * Code quality has been promoted by introducing linting check in CI and auto code formatter. |
| For linting, the tools, `cpplint` and `pylint`, are used and configured to comply |
| [google coding styles](http://google.github.io/styleguide/) details in `tool/linting/`. |
| Similarly, formatting tools, `clang-format` and `yapf` configured with google coding styles, |
| are the recommended one for developers to clean code before submitting changes, |
| details in `tool/code-format/`. [LGTM](https://lgtm.com) is enabled on Github for |
| code quality check; License check is also enabled. |
| |
| * New Tensor APIs are added for naming consistency, and feature enhancement: |
| - size(), mem_size(), get_value(), to_proto(), l1(), l2(): added for the sake of naming consistency |
| - AsType(): convert data type between `float` and `int` |
| - ceil(): perform element-wise ceiling of the input |
| - concat(): concatenate two tensor |
| - index selector: e.g. tensor1[:,:,1:,1:] |
| - softmax(in, axis): allow to perform softmax on a axis on a multi-dimensional tensor |
| |
| * 14 new operators are added into the autograd module: Gemm, GlobalAveragePool, ConstantOfShape, |
| Dropout, ReduceSum, ReduceMean, Slice, Ceil, Split, Gather, Tile, NonZero, Cast, OneHot. |
| Their unit tests are added as well. |
| |
| * 14 new operators are added to sonnx module for both backend and frontend: |
| [Gemm](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Gemm), |
| [GlobalAveragePool](https://github.com/onnx/onnx/blob/master/docs/Operators.md#GlobalAveragePool), |
| [ConstantOfShape](https://github.com/onnx/onnx/blob/master/docs/Operators.md#ConstantOfShape), |
| [Dropout](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Dropout), |
| [ReduceSum](https://github.com/onnx/onnx/blob/master/docs/Operators.md#ReduceSum), |
| [ReduceMean](https://github.com/onnx/onnx/blob/master/docs/Operators.md#ReduceMean), |
| [Slice](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Slice), |
| [Ceil](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Ceil), |
| [Split](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Split), |
| [Gather](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Gather), |
| [Tile](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Tile), |
| [NonZero](https://github.com/onnx/onnx/blob/master/docs/Operators.md#NonZero), |
| [Cast](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Cast), |
| [OneHot](https://github.com/onnx/onnx/blob/master/docs/Operators.md#OneHot). |
| Their tests are added as well. |
| |
| * Some ONNX models are imported into SINGA, including |
| [Bert-squad](https://github.com/onnx/models/tree/master/text/machine_comprehension/bert-squad), |
| [Arcface](https://github.com/onnx/models/tree/master/vision/body_analysis/arcface), |
| [FER+ Emotion](https://github.com/onnx/models/tree/master/vision/body_analysis/emotion_ferplus), |
| [MobileNet](https://github.com/onnx/models/tree/master/vision/classification/mobilenet), |
| [ResNet18](https://github.com/onnx/models/tree/master/vision/classification/resnet), |
| [Tiny Yolov2](https://github.com/onnx/models/tree/master/vision/object_detection_segmentation/tiny_yolov2), |
| [Vgg16](https://github.com/onnx/models/tree/master/vision/classification/vgg), and Mnist. |
| |
| * Some operators now support [multidirectional broadcasting](https://github.com/onnx/onnx/blob/master/docs/Broadcasting.md#multidirectional-broadcasting), |
| including Add, Sub, Mul, Div, Pow, PRelu, Gemm |
| |
| * [Distributed training with communication optimization]. [DistOpt](./python/singa/opt.py) |
| has implemented multiple optimization techniques, including gradient sparsification, |
| chunk transmission, and gradient compression. |
| |
| * Computational graph construction at the CPP level. The operations submitted to the Device are buffered. |
| After analyzing the dependency, the computational graph is created, which is further analyzed for |
| speed and memory optimization. To enable this feature, use the [Module API](./python/singa/module.py). |
| |
| * New website based on Docusaurus. The documentation files are moved to a separate repo [singa-doc](https://github.com/apache/singa-doc). |
| The static website files are stored at [singa-site](https://github.com/apache/singa-site). |
| |
| * DNNL([Deep Neural Network Library](https://github.com/intel/mkl-dnn)), powered by Intel, |
| is integrated into `model/operations/[batchnorm|pooling|convolution]`, |
| the changes is opaque to the end users. The current version is dnnl v1.1 |
| which replaced previous integration of mkl-dnn v0.18. The framework could |
| boost the performance of dl operations when executing on CPU. The dnnl dependency |
| is installed through conda. |
| |
| * Some Tensor APIs are marked as deprecated which could be replaced by broadcast, |
| and it can support better on multi-dimensional operations. These APIs are |
| add_column(), add_row(), div_column(), div_row(), mult_column(), mult_row() |
| |
| * Conv and Pooling are enhanced to support fine-grained padding like (2,3,2,3), |
| and [SAME_UPPER, SAME_LOWER](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Conv) |
| pad mode and shape checking. |
| |
| * Reconstruct soonx, |
| - Support two types of weight value (Initializer and Constant Node); |
| - For some operators (BatchNorm, Reshape, Clip, Slice, Gather, Tile, OneHot), |
| move some inputs to its attributes; |
| - Define and implement the type conversion map. |
| |
| ------------------------------------------------------------------------ |
| Release Notes - SINGA - Version singa-incubating-2.0.0 |
| |
| SINGA is a general distributed deep learning platform for training big deep |
| learning models over large datasets. |
| |
| This release includes following features: |
| |
| * Core components |
| * [SINGA-434] Support tensor broadcasting |
| * [SINGA-370] Improvement to tensor reshape and various misc. changes related to SINGA-341 and 351 |
| |
| * Model components |
| * [SINGA-333] Add support for Open Neural Network Exchange (ONNX) format |
| * [SINGA-385] Add new python module for optimizers |
| * [SINGA-394] Improve the CPP operations via Intel MKL DNN lib |
| * [SINGA-425] Add 3 operators , Abs(), Exp() and leakyrelu(), for Autograd |
| * [SINGA-410] Add two function, set_params() and get_params(), for Autograd Layer class |
| * [SINGA-383] Add Separable Convolution for autograd |
| * [SINGA-388] Develop some RNN layers by calling tiny operations like matmul, addbias. |
| * [SINGA-382] Implement concat operation for autograd |
| * [SINGA-378] Implement maxpooling operation and its related functions for autograd |
| * [SINGA-379] Implement batchnorm operation and its related functions for autograd |
| |
| * Utility functions and CI |
| * [SINGA-432] Update depdent lib versions in conda-build config |
| * [SINGA-429] Update docker images for latest cuda and cudnn |
| * [SINGA-428] Move Docker images under Apache user name |
| |
| * Documentation and usability |
| * [SINGA-395] Add documentation for autograd APIs |
| * [SINGA-344] Add a GAN example |
| * [SINGA-390] Update installation.md |
| * [SINGA-384] Implement ResNet using autograd API |
| * [SINGA-352] Complete SINGA documentation in Chinese version |
| |
| * Bugs fixed |
| * [SINGA-431] Unit Test failed - Tensor Transpose |
| * [SINGA-422] ModuleNotFoundError: No module named "_singa_wrap" |
| * [SINGA-418] Unsupportive type 'long' in python3. |
| * [SINGA-409] Basic `singa-cpu` import throws error |
| * [SINGA-408] Unsupportive function definition in python3 |
| * [SINGA-380] Fix bugs from Reshape |
| |
| --------------------------------------------------------------- |
| Release Notes - SINGA - Version singa-incubating-1.2.0 |
| |
| SINGA is a general distributed deep learning platform for training big deep |
| learning models over large datasets. |
| |
| This release includes following features: |
| |
| * Core components |
| * [SINGA-290] Upgrade to Python 3 |
| * [SINGA-341] Added stride functionality to tensors for CPP |
| * [SINGA-347] Create a function that supports einsum |
| * [SINGA-351] Added stride support and cudnn codes to cuda |
| |
| * Model components |
| * [SINGA-300] Add residual networks for imagenet classification |
| * [SINGA-312] Rename layer parameters |
| * [SINGA-313] Add L2 norm layer |
| * [SINGA-315] Reduce memory footprint by Python generator for parameter |
| * [SINGA-316] Add SigmoidCrossEntropy |
| * [SINGA-324] Extend RNN layer to accept variant seq length across batches |
| * [SINGA-326] Add Inception V4 for ImageNet classification |
| * [SINGA-328] Add VGG models for ImageNet classification |
| * [SINGA-329] Support layer freezing during training (fine-tuning) |
| * [SINGA-346] Update cudnn from V5 to V7 |
| * [SINGA-349] Create layer operations for autograd |
| * [SINGA-363] Add DenseNet for Imagenet classification |
| |
| * Utility functions and CI |
| * [SINGA-274] Improve Debian packaging with CPack |
| * [SINGA-303] Create conda packages |
| * [SINGA-337] Add test cases for code |
| * [SINGA-348] Support autograd MLP Example |
| * [SINGA-345] Update Jenkins and fix bugs in compliation |
| * [SINGA-354] Update travis scripts to use conda-build for all platforms |
| * [SINGA-358] Consolidated RUN steps and cleaned caches in Docker containers |
| * [SINGA-359] Create alias for conda packages |
| |
| * Documentation and usability |
| * [SINGA-223] Fix side navigation menu in the website |
| * [SINGA-294] Add instructions to run CUDA unit tests on Windows |
| * [SINGA-305] Add jupyter notebooks for SINGA V1 tutorial |
| * [SINGA-319] Fix link errors on the index page |
| * [SINGA-352] Complete SINGA documentation in Chinese version |
| * [SINGA-361] Add git instructions for contributors and committers |
| |
| * Bugs fixed |
| * [SINGA-330] fix openblas building on i7 7700k |
| * [SINGA-331] Fix the bug of tensor division operation |
| * [SINGA-350] Error from python3 test |
| * [SINGA-356] Error using travis tool to build SINGA on mac os |
| * [SINGA-363] Fix some bugs in imagenet examples |
| * [SINGA-368] Fix the bug in Cifar10 examples |
| * [SINGA-369] the errors of examples in testing |
| |
| --------------------------------------------------------------- |
| Release Notes - SINGA - Version singa-incubating-1.1.0 |
| |
| SINGA is a general distributed deep learning platform for training big deep learning models over large datasets. |
| |
| This release includes following features: |
| |
| * Core components |
| * [SINGA-296] Add sign and to_host function for pysinga tensor module |
| |
| * Model components |
| * [SINGA-254] Implement Adam for V1 |
| * [SINGA-264] Extend the FeedForwardNet to accept multiple inputs |
| * [SINGA-267] Add spatial mode in batch normalization layer |
| * [SINGA-271] Add Concat and Slice layers |
| * [SINGA-275] Add Cross Entropy Loss for multiple labels |
| * [SINGA-278] Convert trained caffe parameters to singa |
| * [SINGA-287] Add memory size check for cudnn convolution |
| |
| * Utility functions and CI |
| * [SINGA-242] Compile all source files into a single library. |
| * [SINGA-244] Separating swig interface and python binding files |
| * [SINGA-246] Imgtool for image augmentation |
| * [SINGA-247] Add windows support for singa |
| * [SINGA-251] Implement image loader for pysinga |
| * [SINGA-252] Use the snapshot methods to dump and load models for pysinga |
| * [SINGA-255] Compile mandatory depedent libaries together with SINGA code |
| * [SINGA-259] Add maven pom file for building java classes |
| * [SINGA-261] Add version ID into the checkpoint files |
| * [SINGA-266] Add Rafiki python toolkits |
| * [SINGA-273] Improve license and contributions |
| * [SINGA-284] Add python unittest into Jenkins and link static libs into whl file |
| * [SINGA-280] Jenkins CI support |
| * [SINGA-288] Publish wheel of PySINGA generated by Jenkins to public servers |
| |
| * Documentation and usability |
| * [SINGA-263] Create Amazon Machine Image |
| * [SINGA-268] Add IPython notebooks to the documentation |
| * [SINGA-276] Create docker images |
| * [SINGA-289] Update SINGA website automatically using Jenkins |
| * [SINGA-295] Add an example of image classification using GoogleNet |
| |
| * Bugs fixed |
| * [SINGA-245] float as the first operand can not multiply with a tensor object |
| * [SINGA-293] Bug from compiling PySINGA on Mac OS X with multiple version of Python |
| |
| --------------------------------------------------------------- |
| Release Notes - SINGA - Version singa-incubating-1.0.0 |
| |
| SINGA is a general distributed deep learning platform for training big deep learning models over large datasets. |
| |
| 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 |
| |
| ---------------------------------------------------------- |
| Release Notes - SINGA - Version singa-incubating-0.3.0 |
| |
| SINGA is a general distributed deep learning platform for training big deep learning models over large datasets. |
| |
| This release includes following features: |
| |
| * GPU Support |
| * [SINGA-131] Implement and optimize hybrid training using both CPU and GPU |
| * [SINGA-136] Support cuDNN v4 |
| * [SINGA-134] Extend SINGA to run over a GPU cluster |
| * [SINGA-157] Change the priority of cudnn library and install libsingagpu.so |
| |
| * Remove Dependencies |
| * [SINGA-156] Remove the dependency on ZMQ for single process training |
| * [SINGA-155] Remove zookeeper for single-process training |
| |
| * Python Binding |
| * [SINGA-126] Python Binding for Interactive Training |
| |
| * Other Improvements |
| * [SINGA-80] New Blob Level and Address Level Math Operation Interface |
| * [SINGA-130] Data Prefetching |
| * [SINGA-145] New SGD based optimization Updaters: AdaDelta, Adam, AdamMax |
| |
| * Bugs Fixed |
| * [SINGA-148] Race condition between Worker threads and Driver |
| * [SINGA-150] Mesos Docker container failed |
| * [SIGNA-141] Undesired Hash collision when locating process id to worker… |
| * [SINGA-149] Docker build fail |
| * [SINGA-143] The compilation cannot detect libsingagpu.so file |
| |
| |
| ----------------------------------------- |
| Release Notes - SINGA - Version singa-incubating-0.2.0 |
| |
| 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: |
| |
| * Programming model |
| * [SINGA-80] New Blob Level and Address Level Math Operation Interface |
| * [SINGA-82] Refactor input layers using data store abstraction |
| * [SINGA-87] Replace exclude field to include field for layer configuration |
| * [SINGA-110] Add Layer member datavec_ and gradvec_ |
| * [SINGA-120] Implemented GRU and BPTT (BPTTWorker) |
| |
| * Neuralnet layers |
| * [SINGA-91] Add SoftmaxLayer and ArgSortLayer |
| * [SINGA-106] Add dummy layer for test purpose |
| * [SINGA-120] Implemented GRU and BPTT (GRULayer and OneHotLayer) |
| |
| * GPU training support |
| * [SINGA-100] Implement layers using CUDNN for GPU training |
| * [SINGA-104] Add Context Class |
| * [SINGA-105] Update GUN make files for compiling cuda related code |
| * [SINGA-98] Add Support for AlexNet ImageNet Classification Model |
| |
| |
| * Model/Hybrid partition |
| * [SINGA-109] Refine bridge layers |
| * [SINGA-111] Add slice, concate and split layers |
| * [SINGA-113] Model/Hybrid Partition Support |
| |
| * Python binding |
| * [SINGA-108] Add Python wrapper to singa |
| |
| * Predict-only mode |
| * [SINGA-85] Add functions for extracting features and test new data |
| |
| * Integrate with third-party tools |
| * [SINGA-11] Start SINGA on Apache Mesos |
| * [SINGA-78] Use Doxygen to generate documentation |
| * [SINGA-89] Add Docker support |
| |
| * Unit test |
| * [SINGA-95] Add make test after building |
| |
| * Other improvment |
| * [SINGA-84] Header Files Rearrange |
| * [SINGA-93] Remove the asterisk in the log tcp://169.254.12.152:*:49152 |
| * [SINGA-94] Move call to google::InitGoogleLogging() from Driver::Init() to main() |
| * [SINGA-96] Add Momentum to Cifar10 Example |
| * [SINGA-101] Add ll (ls -l) command in .bashrc file when using docker |
| * [SINGA-114] Remove short logs in tmp directory |
| * [SINGA-115] Print layer debug information in the neural net graph file |
| * [SINGA-118] Make protobuf LayerType field id easy to assign |
| * [SIGNA-97] Add HDFS Store |
| |
| * Bugs fixed |
| * [SINGA-85] Fix compilation errors in examples |
| * [SINGA-90] Miscellaneous trivial bug fixes |
| * [SINGA-107] Error from loading pre-trained params for training stacked RBMs |
| * [SINGA-116] Fix a bug in InnerProductLayer caused by weight matrix sharing |
| |
| ----------------------------------------- |
| |
| Features included in singa-incubating-0.1.0: |
| |
| * Job management |
| * [SINGA-3] Use Zookeeper to check stopping (finish) time of the system |
| * [SINGA-16] Runtime Process id Management |
| * [SINGA-25] Setup glog output path |
| * [SINGA-26] Run distributed training in a single command |
| * [SINGA-30] Enhance easy-to-use feature and support concurrent jobs |
| * [SINGA-33] Automatically launch a number of processes in the cluster |
| * [SINGA-34] Support external zookeeper service |
| * [SINGA-38] Support concurrent jobs |
| * [SINGA-39] Avoid ssh in scripts for single node environment |
| * [SINGA-43] Remove Job-related output from workspace |
| * [SINGA-56] No automatic launching of zookeeper service |
| * [SINGA-73] Refine the selection of available hosts from host list |
| |
| * Installation with GNU Auto tool |
| * [SINGA-4] Refine thirdparty-dependency installation |
| * [SINGA-13] Separate intermediate files of compilation from source files |
| * [SINGA-17] Add root permission within thirdparty/install. |
| * [SINGA-27] Generate python modules for proto objects |
| * [SINGA-53] Add lmdb compiling options |
| * [SINGA-62] Remove building scrips and auxiliary files |
| * [SINGA-67] Add singatest into build targets |
| |
| * Distributed training |
| * [SINGA-7] Implement shared memory Hogwild algorithm |
| * [SINGA-8] Implement distributed Hogwild |
| * [SINGA-19] Slice large Param objects for load-balance |
| * [SINGA-29] Update NeuralNet class to enable layer partition type customization |
| * [SINGA-24] Implement Downpour training framework |
| * [SINGA-32] Implement AllReduce training framework |
| * [SINGA-57] Improve Distributed Hogwild |
| |
| * Training algorithms for different model categories |
| * [SINGA-9] Add Support for Restricted Boltzman Machine (RBM) model |
| * [SINGA-10] Add Support for Recurrent Neural Networks (RNN) |
| |
| * Checkpoint and restore |
| * [SINGA-12] Support Checkpoint and Restore |
| |
| * Unit test |
| * [SINGA-64] Add the test module for utils/common |
| |
| * Programming model |
| * [SINGA-36] Refactor job configuration, driver program and scripts |
| * [SINGA-37] Enable users to set parameter sharing in model configuration |
| * [SINGA-54] Refactor job configuration to move fields in ModelProto out |
| * [SINGA-55] Refactor main.cc and singa.h |
| * [SINGA-61] Support user defined classes |
| * [SINGA-65] Add an example of writing user-defined layers |
| |
| * Other features |
| * [SINGA-6] Implement thread-safe singleton |
| * [SINGA-18] Update API for displaying performance metric |
| * [SINGA-77] Integrate with Apache RAT |
| |
| Some bugs are fixed during the development of this release |
| * [SINGA-2] Check failed: zsock_connect |
| * [SINGA-5] Server early terminate when zookeeper singa folder is not initially empty |
| * [SINGA-15] Fixg a bug from ConnectStub function which gets stuck for connecting layer_dealer_ |
| * [SINGA-22] Cannot find openblas library when it is installed in default path |
| * [SINGA-23] Libtool version mismatch error. |
| * [SINGA-28] Fix a bug from topology sort of Graph |
| * [SINGA-42] Issue when loading checkpoints |
| * [SINGA-44] A bug when reseting metric values |
| * [SINGA-46] Fix a bug in updater.cc to scale the gradients |
| * [SINGA-47] Fix a bug in data layers that leads to out-of-memory when group size is too large |
| * [SINGA-48] Fix a bug in trainer.cc that assigns the same NeuralNet instance to workers from diff groups |
| * [SINGA-49] Fix a bug in HandlePutMsg func that sets param fields to invalid values |
| * [SINGA-66] Fix bugs in Worker::RunOneBatch function and ClusterProto |
| * [SINGA-79] Fix bug in singatool that can not parse -conf flag |
| |
| Features planned for the next release |
| * [SINGA-11] Start SINGA using Mesos |
| * [SINGA-31] Extend Blob to support xpu (cpu or gpu) |
| * [SINGA-35] Add random number generators |
| * [SINGA-40] Support sparse Param update |
| * [SINGA-41] Support single node single GPU training |
