You are viewing a plain text version of this content. The canonical link for it is here.
Posted to dev@singa.apache.org by "wangwei (JIRA)" <ji...@apache.org> on 2016/10/06 12:00:25 UTC

[jira] [Closed] (SINGA-234) Unify the engines for cudnn and singa layers

     [ https://issues.apache.org/jira/browse/SINGA-234?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

wangwei closed SINGA-234.
-------------------------
    Resolution: Duplicate

> Unify the engines for cudnn and singa layers
> --------------------------------------------
>
>                 Key: SINGA-234
>                 URL: https://issues.apache.org/jira/browse/SINGA-234
>             Project: Singa
>          Issue Type: Improvement
>            Reporter: wangwei
>
> For most layers, we would have multiple implementations, e.g., using cudnn for nvidia gpu, using cpp for cpu and using opencl for other gpus.
> These layers have different classes. They are registered with different identifiers. This ticket would unify the layer identifiers for each engine:
> 1. cudnn layers are registered with identifier = cudnn_xxx, e.g., cudnn_convolution for the CudnnConvolution layer.
> 2. singa layers are registered with identifier = singa_xxx, e.g., singa_convolution for the Convolution layer.
> cudnn engine must run on cuda devices. and singa engine could run on cuda-gpu device or cpp-cpu device depending on the layer type. For instance, the Convolution layer must run on cpp-cpu device, and Dense layer can run on both devices and would select the correct device automatically.
> Users need to make sure the engine and the device of the tensors.
> Both CPP and Python code is updated. Users have to compose the layer identifier manually for CPP version. For Python version, users can set layer.engine='cudnn' or 'singa'. 



--
This message was sent by Atlassian JIRA
(v6.3.4#6332)