BachelorProject | computer science : Organized Peer

by Baekalfen Rust Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | BachelorProject Summary

BachelorProject is a Rust library typically used in Networking applications. BachelorProject has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

This is my bachelor's project in computer science: Organized Peer-to-Peer Networking

Support

Quality

Security

License

Reuse

Support

BachelorProject has a low active ecosystem.

It has 2 star(s) with 0 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

BachelorProject has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of BachelorProject is current.

Quality

BachelorProject has 0 bugs and 0 code smells.

Security

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

BachelorProject code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

BachelorProject 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

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

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BachelorProject Key Features

No Key Features are available at this moment for BachelorProject.

BachelorProject Examples and Code Snippets

No Code Snippets are available at this moment for BachelorProject.

Community Discussions

Trending Discussions on BachelorProject

Getting same runtime for cpu and gpu on Google Colab

QUESTION

Getting same runtime for cpu and gpu on Google Colab

Asked 2021-May-24 at 08:05

So I've been experiencing with Colab in order to conduct my deep learning project for my Bachelor's. When I run the provided example on colab to test the comparison speed between cpu and gpu it works fine, however when I try with my own code, I get the same run time for both. The task that I was conducting was simply converting 1000 jpg images to RGB values using the PIL.Image package. Shouldn't the runtime when using a gpu be much faster? Or is that only the case when running deep learning models? Please find the code I used below:

...

ANSWER

Answered 2021-May-24 at 08:05

Tensorflow device setting won't affect non-Tensorflow operations.

That said, Tensorflow has now it's own numpy API that can use the device that is set up for Tensorflow in e.g. with tf.device('/device:GPU:0').

The new api can be used similarly as numpy with

Source https://stackoverflow.com/questions/67668232

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

Vulnerabilities

No vulnerabilities reported

Install BachelorProject

You can download it from GitHub.
Rust is installed and managed by the rustup tool. Rust has a 6-week rapid release process and supports a great number of platforms, so there are many builds of Rust available at any time. Please refer rust-lang.org for more information.

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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