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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
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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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