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https://issues.apache.org/jira/browse/SINGA-423?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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wangwei updated SINGA-423:
--------------------------
Description:
GPU is a fueling factor for deep learning. Most deep learning libraries
(including Singa) are using Nvidia GPUs because
[cuDNN|https://developer.nvidia.com/cudnn] from Nvida provides almost all deep
learning operations, which are also highly optimized.
This ticket is to provide support for AMD GPUs in Singa. AMD has provided a
similar [library |https://rocm.github.io/dl.html] as cuDNN. The task is then to
integrate this library with Singa's programming model, write the documentation
and conduct the corresponding test.
Programming language: C++, CMake, OpenCL or
[HIP|https://gpuopen.com/compute-product/hip-convert-cuda-to-portable-c-code/]
OS: Linux (Ubuntu or CentOS)
Tools: Github, JIRA,
Machine learning (ML) background: basics of ML and DL (deep learning)
was:
GPU is a fueling factor for deep learning. Most deep learning libraries
(including Singa) are using Nvidia GPUs because cuDNN from Nviida provides
almost all deep learning operations, which are also highly optimized.
This ticket is to provide support for AMD GPUs in Singa. AMD has provided a
similar [library |https://rocm.github.io/dl.html] as cuDNN. The task is then to
integrate this library with Singa's programming model, write the documentation
and conduct the corresponding test.
Programming language: C++, CMake, OpenCL or
[HIP|https://gpuopen.com/compute-product/hip-convert-cuda-to-portable-c-code/]
OS: Linux (Ubuntu or CentOS)
Tools: Github, JIRA,
Machine learning (ML) background: basics of ML and DL (deep learning)
Summary: Deep learning over AMD GPUs (was: Implement deep learning
operations over AMD GPUs)
> Deep learning over AMD GPUs
> ---------------------------
>
> Key: SINGA-423
> URL: https://issues.apache.org/jira/browse/SINGA-423
> Project: Singa
> Issue Type: New Feature
> Reporter: wangwei
> Priority: Major
> Labels: gsoc2019
>
> GPU is a fueling factor for deep learning. Most deep learning libraries
> (including Singa) are using Nvidia GPUs because
> [cuDNN|https://developer.nvidia.com/cudnn] from Nvida provides almost all
> deep learning operations, which are also highly optimized.
>
> This ticket is to provide support for AMD GPUs in Singa. AMD has provided a
> similar [library |https://rocm.github.io/dl.html] as cuDNN. The task is then
> to integrate this library with Singa's programming model, write the
> documentation and conduct the corresponding test.
>
> Programming language: C++, CMake, OpenCL or
> [HIP|https://gpuopen.com/compute-product/hip-convert-cuda-to-portable-c-code/]
> OS: Linux (Ubuntu or CentOS)
> Tools: Github, JIRA,
> Machine learning (ML) background: basics of ML and DL (deep learning)
>
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