gepp | Fully automatic , without any config or question | Continuous Deployment library

by gurayyildirim Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(4)Vulnerabilities Install Support

kandi X-RAY | gepp Summary

gepp is a Python library typically used in Devops, Continuous Deployment, Ansible, Jenkin, Docker, Terraform applications. gepp has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

GEPP takes your app and Dockerize it, sets up a Kubernetes cluster and runs your app in it, configure K8s resources and produce Terraform file for Azure deployments, and more! All are happening in seconds!.

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Quality

Security

License

Reuse

Support

gepp has a low active ecosystem.

It has 71 star(s) with 6 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 1 have been closed. On average issues are closed in 1 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of gepp is current.

Quality

gepp has no bugs reported.

Security

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

License

gepp 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

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

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

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gepp Key Features

No Key Features are available at this moment for gepp.

gepp Examples and Code Snippets

No Code Snippets are available at this moment for gepp.

Community Discussions

Trending Discussions on gepp

convert javascript object into array of objects based on key value array

I am using Python 3 notebook but I am getting the following errors when I run it

Compare the result of Gaussian elimination with the output of numpy.linalg.solve

C++ - Optimized matrix multiply from Kazushige Goto's paper performs worse than naive in O3 flag

QUESTION

convert javascript object into array of objects based on key value array

Asked 2020-Jul-27 at 11:04

I have the following object structure in javascript

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ANSWER

Answered 2020-Jul-27 at 11:04

The esiest way is to map one of the array in object

Source https://stackoverflow.com/questions/63113708

QUESTION

I am using Python 3 notebook but I am getting the following errors when I run it

Asked 2018-Oct-18 at 14:56

I came across the following code of gaussian elimination with no pivoting. I am using Python 3 notebook but I am getting the following errors when I run it and I am looking for some help with troubleshooting these errors, thanks!

here are the errors I am getting, I am not quite sure how to fix these and I am looking for some help with this!

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ANSWER

Answered 2018-Oct-18 at 14:55

xrange is not present in python3. Use range instead.

Source https://stackoverflow.com/questions/52876853

QUESTION

Compare the result of Gaussian elimination with the output of numpy.linalg.solve

Asked 2018-Oct-18 at 00:56

In the following code I have implemented Gaussian elimination with partial pivoting for a general square linear system Ax=b. I have tested my code and it produced the right output. I have used it to solve Ax=b where A is a random 100x100 matrix and b is a random 100x1 vector.**

However now I am looking for some help with comparing my solution against the solution obtained by using numpy.linalg.solve. How to add this comparison to my code?

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ANSWER

Answered 2018-Oct-18 at 00:54

To compare the two solutions, use np.allclose which verifies that two arrays agree, element by element, within some reasonably margins. One should not expect exact equality in floating point arithmetics.

Source https://stackoverflow.com/questions/52864988

QUESTION

C++ - Optimized matrix multiply from Kazushige Goto's paper performs worse than naive in O3 flag

Asked 2018-Sep-18 at 09:04

The relevant paper is here. I am trying to reproduce the seminal paper by Kazushige Goto for fast matrix multiplication by decaying it down to subroutines of gepp (general panel-panel) and gebp(general block-panel) multiplication, which apparently is the fastest building blocks for gemm. I wrote the code below to test it and with -O3 flag, I saw the performance of my code is actually worse than the naive matrix multiplication:

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ANSWER

Answered 2018-Sep-18 at 07:53

Hand-wavy answer: Playing around with block size

I tried playing with gebp's block size, and it seems like for 2048x1024 x 1024 x 2048 matrices, a single float seems to be the best for performance under -O2 and no vectorized instructions. This is a bit strange, since I thought bigger blocks could reside inside the tlb/L1/L2 caches.

Source https://stackoverflow.com/questions/52380155

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

Vulnerabilities

No vulnerabilities reported

Install gepp

You can download it from GitHub.
You can use gepp 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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