cnn-benchmarks | Benchmarks for popular CNN models | Machine Learning library

by jcjohnson Python Version: Current License: MIT

X-Ray Key Features Code Snippets(6)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | cnn-benchmarks Summary

cnn-benchmarks is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. cnn-benchmarks has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However cnn-benchmarks build file is not available. You can download it from GitHub.

Benchmarks for popular CNN models

Support

Quality

Security

License

Reuse

Support

cnn-benchmarks has a medium active ecosystem.

It has 2484 star(s) with 411 fork(s). There are 164 watchers for this library.

It had no major release in the last 6 months.

There are 20 open issues and 11 have been closed. On average issues are closed in 40 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of cnn-benchmarks is current.

Quality

cnn-benchmarks has 0 bugs and 0 code smells.

Security

cnn-benchmarks has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

cnn-benchmarks code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

cnn-benchmarks is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

cnn-benchmarks releases are not available. You will need to build from source code and install.

cnn-benchmarks has no build file. You will be need to create the build yourself to build the component from source.

cnn-benchmarks saves you 54 person hours of effort in developing the same functionality from scratch.

It has 141 lines of code, 4 functions and 2 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed cnn-benchmarks and discovered the below as its top functions. This is intended to give you an instant insight into cnn-benchmarks implemented functionality, and help decide if they suit your requirements.

Entry point for running the results .
Returns the standard deviation of values .
Returns the mean value of the vector

Get all kandi verified functions for this library.

cnn-benchmarks Key Features

No Key Features are available at this moment for cnn-benchmarks.

cnn-benchmarks Examples and Code Snippets

Benchmarks.,Combinations benchmarks

C++

Lines of Code : 24

License : Permissive (MIT)

Copy

gsl_combination * c;

c = gsl_combination_calloc (n, n/2);
do
{
    DoNotOptimize(*c);
}
while (gsl_combination_next (c) == GSL_SUCCESS);
gsl_combination_free (c);    

auto end = combination_iterator();

for (auto it = combination_iterator(n, n/2);

Benchmark

Python

Lines of Code : 24

License : Permissive (MIT)

Copy

pipe
128             512           1024          4096
1319Mb/s        5110Mb/s      8932Mb/s      20297Mb/s
1288233msg/s    1247449msg/s  1090370msg/s  619407msg/s

fifo
128             512           1024          4096
1358Mb/s        5491Mb/s      8

Benchmarks,Remote benchmarks

Java

Lines of Code : 16

License : Permissive (Apache-2.0)

Copy

$ ./benchmark-runner --output-file "echo-test" --messages "1000, 5000" --burst-size "1, 10" --message-length "32, 224, 1376" "aeron/echo-client"

results
├── echo-test_1000_1_32_c7a083c84b45f77fdee5cedc272d898d44b6e18deaf963b3e2b2c074006b0b10-0.hdr
├

Performs a benchmark of the benchmark .

java

Lines of Code : 4

License : Permissive (MIT License)

Copy

@Benchmark
    public int benchmarkStringCompareTo() {
        return longString.compareTo(baeldung);
    }

Performs a benchmark of the benchmark of the benchmark .

java

Lines of Code : 4

License : Permissive (MIT License)

Copy

@Benchmark
    public String benchmarkStringValueOf() {
        return String.valueOf(sampleNumber);
    }

Performs a benchmark of the benchmark .

java

Lines of Code : 4

License : Permissive (MIT License)

Copy

@Benchmark
    public String benchmarkStringIntern() {
        return baeldung.intern();
    }

Community Discussions

Trending Discussions on cnn-benchmarks

tensorflow 2.5x slower than pytorch on vgg16 architecture

QUESTION

tensorflow 2.5x slower than pytorch on vgg16 architecture

Asked 2018-Sep-07 at 14:53

So I'm trying to get into tensorflow and liking it so far.

Today I upgraded to cuda 8, cudnn 5.1 and tensorflow 0.12.1. Using a Maxwell Titan X GPU.

Using the following short code of loading the pretrained vgg16:

...

ANSWER

Answered 2018-Sep-07 at 14:53

Tested recently on cuda 9.0, tensorflow 1.9 and pytorch 0.4.1, the differences are now negligible for the same operations.

See the proper timing here.

Source https://stackoverflow.com/questions/41832779

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

Vulnerabilities

No vulnerabilities reported

Install cnn-benchmarks

You can download it from GitHub.
You can use cnn-benchmarks like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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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