shiftnet_cuda_v2 | created by Peter Jin

by peterhj C Version: Current License: Non-SPDX

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | shiftnet_cuda_v2 Summary

shiftnet_cuda_v2 is a C library. shiftnet_cuda_v2 has no bugs, it has no vulnerabilities and it has low support. However shiftnet_cuda_v2 has a Non-SPDX License. You can download it from GitHub.

created by Peter Jin.

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Support

shiftnet_cuda_v2 has a low active ecosystem.

It has 22 star(s) with 7 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 4 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of shiftnet_cuda_v2 is current.

Quality

shiftnet_cuda_v2 has no bugs reported.

Security

shiftnet_cuda_v2 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

shiftnet_cuda_v2 has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

shiftnet_cuda_v2 releases are not available. You will need to build from source code and install.

Installation instructions, examples and code snippets are available.

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shiftnet_cuda_v2 Key Features

No Key Features are available at this moment for shiftnet_cuda_v2.

shiftnet_cuda_v2 Examples and Code Snippets

No Code Snippets are available at this moment for shiftnet_cuda_v2.

Community Discussions

No Community Discussions are available at this moment for shiftnet_cuda_v2.Refer to stack overflow page for discussions.

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Vulnerabilities

No vulnerabilities reported

Install shiftnet_cuda_v2

If you have included this shift repository as a submodule in a separate repository, feel free to skip down to step 5. Your prompt should now be prefaced with (shift), as in. Getting invalid_device_function? Update the architecture code in models/shiftnet_cuda_v2/Makefile, currently configured for a Titan X. e.g., A Tesla K80 is sm-30. Your custom CUDA layer is now installed.
If you have not already, setup a virtual environment with Python3, and activate it.
Install pytorch and torchvision. Access pytorch.org, scroll down to the "Getting Started" section, and select the appropriate OS, package manager, Python, and CUDA build. For example, selecting Linux, pip, Python3.5, and CUDA 8 gives the following, as of the time of this writing
Clone this repository.
cd into the root of this repository.
Install the Python requirements for this package.
Compile the Shift Layer implementation in C.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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