[jira] [Commented] (SINGA-162) Overview of features for V1.x

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    [ 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:
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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.



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