singa-dev mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From "ASF subversion and git services (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SINGA-162) Overview of features for V1.x
Date Fri, 03 Jun 2016 07:48:59 GMT

    [ https://issues.apache.org/jira/browse/SINGA-162?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15313802#comment-15313802
] 

ASF subversion and git services commented on SINGA-162:
-------------------------------------------------------

Commit 04e23d1a60d5160ff319f63fe89d715feee53b57 in incubator-singa's branch refs/heads/dev
from WANG Sheng
[ https://git-wip-us.apache.org/repos/asf?p=incubator-singa.git;h=04e23d1 ]

SINGA-162 Transfer the codebase for SINGA v1.0 into dev branch

add guard flags in test file to support test without cuda


> Overview of features for V1.x
> -----------------------------
>
>                 Key: SINGA-162
>                 URL: https://issues.apache.org/jira/browse/SINGA-162
>             Project: Singa
>          Issue Type: Wish
>            Reporter: wangwei
>
> This ticket gives an overview of the features to be developed for V1.x.
> First, we will implement a set of core abstractions,
> 1. Tensor, which provides basic linear algebra operations (e.g., addition) and neural
net specific operations (e.g., conv). It is a finer abstraction than Layer in V0.x, and thus
could be able to support a wider range of applications. [Autograd|https://github.com/HIPS/autograd]
would also be implemented.
> 2. Device, which abstract the execution and memory allocation for Tensor using different
hardware/software, including Nvidia GPU (with Cuda/Cudnn) and other GPUs using OpenCL.
> 3. Scheduler, which maximizes the parallelism of executions.
> 4. Memory manager, which manages a memory pool for a device, for garbage collection,
and optimization.
> Second, on top of these core abstractions, we will develop a set of modules specific
for neural networks
> 1. Layer for feature transformation, e.g., conv and pool
> 2. Model for typical models including feed-forward, RNN and energy models.
> 3. Updater for updating parameters on single node or in a distributed environment.
> Third, some utility modules would be implemented for IO/Log/Network.



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

Mime
View raw message