gpu-training | GPU computing , will involve hands | GPU library

by eth-cscs C++ Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | gpu-training Summary

gpu-training is a C++ library typically used in Hardware, GPU, Deep Learning, Pytorch applications. gpu-training has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

Each section, except for the introduction to GPU computing, will involve hands on practical where participants will get to try out the material covered in the lecture material.

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gpu-training has a low active ecosystem.

It has 7 star(s) with 5 fork(s). There are 7 watchers for this library.

It had no major release in the last 6 months.

gpu-training has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of gpu-training is current.

Quality

gpu-training has no bugs reported.

Security

gpu-training has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

gpu-training does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

gpu-training 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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gpu-training Key Features

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gpu-training Examples and Code Snippets

No Code Snippets are available at this moment for gpu-training.

Community Discussions

Trending Discussions on gpu-training

Trying to generate a Keras model with my own data instead of cifar10

Keras/Tensorflow multi GPU InvalidArgumentError in optimizer

QUESTION

Trying to generate a Keras model with my own data instead of cifar10

Asked 2018-May-17 at 13:05

I have followed this example: https://www.pyimagesearch.com/2017/10/30/how-to-multi-gpu-training-with-keras-python-and-deep-learning/

and had an issue with the following line(line #51):

...

ANSWER

Answered 2018-May-17 at 13:05

Assume you have your images as .jpg format, and your labels as csv format called label.csv, and separated them into 2 folders, train folder and test folder.

Then you can do the following to get the x_train

Source https://stackoverflow.com/questions/50391562

QUESTION

Keras/Tensorflow multi GPU InvalidArgumentError in optimizer

Asked 2017-Sep-28 at 16:35

I want to try multi GPU training in Keras with Tensorflow backend.

I am trying the function make_parallel described here: https://medium.com/@kuza55/transparent-multi-gpu-training-on-tensorflow-with-keras-8b0016fd9012. The code for that is here (updated for Keras 2):

...

ANSWER

Answered 2017-Sep-28 at 16:35

Your issue seems to be similar to the one reported here. It appears that the input data size must be a multiple of the number of GPUs.

From the link:

The number of samples just needs to be a mutiple of the total number of GPUs.

Ex. I had 68531 samples in in my input, and once I shaved that down to 68528 with 8 GPUs, it worked fine.

Source https://stackoverflow.com/questions/46453385

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install gpu-training

and enter password when prompted (username and password are on handout presented at the start of the course). You need to use the scratch file sytem. Set up the environment. Try to compile the first examples. To run the executable you need to get an allocation, then run the executable with aprun.

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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