Is there a comparison between SINGA and Tensorflow?

1. TF uses a more basic abstraction in the programming model, i.e., TF.Varaible, than SINGA which is based on the Layer abstraction. Hence their data flow graphs have some differences. 2. SINGA supports distributed training better than the current version of TensorFlow, I think. Both synchronous and asynchronous distributed training frameworks are supported in SINGA. 3. Performance (efficiency, memory cost and scalability) would be compared in v0.3.

With the parallelism model and abstraction it has to support model/data or hybrid partitioning, and synchronous/asynchronous or hybrid training, it should be easy to extend to GPU cluster. However, training is required only periodically, and if it can be done on existing clusters as efficiently, why not?

A recent survey on in-memory big data processing systems: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7097...

SINGA provides an abstraction for defining all known models,a neural net structure that is easy for model and data partitioning, and a parallelism model that supports both synchronous, asynchronous and hybrid training frameworks. Processing at each node could the be CPU, GPU, or CPU-GPU based.