Easy installation usingConda,Pip,Docker andfrom Source
Models are trained with SINGA and can be queried in the RDBMS
Various domain specific deep learning models, e.g., healthcare and science, are provided in SINGA repo onGithub and onGoogle Colab
SINGA supports data parallel training across multiple GPUs (on a single node or across different nodes)
SINGA records thecomputation graph and applies the backward propagation automatically after forward propagation
The optimization of memory are implemented in theDevice class
SINGA supports various popular optimizers including stochastic gradient descent with momentum, Adam, RMSProp, and AdaGrad, etc
SINGA supports loadingONNX format models and saving models defined using SINGA APIs into ONNX format, which enables AI developers to use models across different libraries and tools
SINGA supports the time profiling of each of the operators buffered in the graph
SINGA has a well architected software stack and easy-to-use Python interface to improve usability
SINGA trains the deep learning models which can be queried as stored procedures of the RDBMS
SINGA parallelizes the training and optimizes the communication cost to improve training scalability
SINGA builds a computational graph to optimize the training speed and memory footprint
Apache SINGA powers the following organizations and companies...
