MLSS | The admin we used at Cambridge for the summer school

by alexksikes Python Version: Current License: AGPL-3.0

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

kandi X-RAY | MLSS Summary

MLSS is a Python library. MLSS has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However MLSS build file is not available. You can download it from GitHub.

This is the system we used in order to process the vast number of applicants for the machine learning summer shcool (MLSS) at Cambridge. This code has been made available open source so it could be re-used for subsequent summer schools. A demo is available here: with instructions: Read INSTALL on how to install and PAYMENTS_INSTRUCTIONS on to how process payments. For questions / suggestion please email Alex Ksikes (ak469 at cam dot ac dot uk).

Support

Quality

Security

License

Reuse

Support

MLSS has a low active ecosystem.

It has 23 star(s) with 11 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

MLSS has no issues reported. There are 3 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of MLSS is current.

Quality

MLSS has no bugs reported.

Security

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

License

MLSS is licensed under the AGPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

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

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

Top functions reviewed by kandi - BETA

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

Submit the application
Clean data from the data
Save a resume
Query the database
Returns a where clause for a given context
Send login information
Render the account page
Render the password
Render the settings
Submit a reference
Add a new application reference
Handle POST requests
Handle POST request
Create a reference form
Default form
Add a score to the database
Update the calculated votes for an applicant
Store values in table
Convert text to html
Capitalize camel case
Checks if the given information is set
Decorator for views that require reviews
Decorator for views that require login
Get a reference to applicant
Gets the counts of applications
Handle GET request

Get all kandi verified functions for this library.

MLSS Key Features

No Key Features are available at this moment for MLSS.

MLSS Examples and Code Snippets

No Code Snippets are available at this moment for MLSS.

Community Discussions

Trending Discussions on MLSS

Running 1000 functions gracefully using python multi-processing

QUESTION

Running 1000 functions gracefully using python multi-processing

Asked 2021-Feb-01 at 15:16

I'm trying to receive stock data for about 1000 stocks, to speed up the process I'm using multiprocessing, unfortunately due to the large amount of stock data I'm trying to receive python as a whole just crashes.

Is there a way to use multiprocessing without python crashing, I understand it would still take some time to do all of the 1000 stocks, but all I need is to do this process as fast as possible.

...

ANSWER

Answered 2021-Jan-31 at 19:18

Ok, here is one way to obtain what you want in about 2min. Some tickers are bad, that's why it crashes.

Here's the code. I use joblib for threading or multiprocess since it doesn't work in my env. But, that's the spirit.

Source https://stackoverflow.com/questions/65980910

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

Vulnerabilities

No vulnerabilities reported

Install MLSS

You can download it from GitHub.
You can use MLSS 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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