sklearnflask | Flask API for training and predicting using scikit learn | Machine Learning library

by amirziai Python Version: Current License: MIT

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

kandi X-RAY | sklearnflask Summary

sklearnflask is a Python library typically used in Artificial Intelligence, Machine Learning, Pytorch, Keras applications. sklearnflask 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.

Flask API for training and predicting using scikit learn models

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Quality

Security

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sklearnflask has a low active ecosystem.

It has 275 star(s) with 138 fork(s). There are 19 watchers for this library.

It had no major release in the last 6 months.

There are 5 open issues and 4 have been closed. On average issues are closed in 109 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of sklearnflask is current.

Quality

sklearnflask has 0 bugs and 0 code smells.

Security

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

sklearnflask code analysis shows 0 unresolved vulnerabilities.

There are 1 security hotspots that need review.

License

sklearnflask 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

sklearnflask 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.

sklearnflask saves you 32 person hours of effort in developing the same functionality from scratch.

It has 88 lines of code, 4 functions and 2 files.

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

Top functions reviewed by kandi - BETA

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

Train the model .
Predict the model .
Remove the model .

Get all kandi verified functions for this library.

sklearnflask Key Features

No Key Features are available at this moment for sklearnflask.

sklearnflask Examples and Code Snippets

No Code Snippets are available at this moment for sklearnflask.

Community Discussions

Trending Discussions on sklearnflask

How to containerize a simple machine learning python application using docker?

QUESTION

How to containerize a simple machine learning python application using docker?

Asked 2018-Aug-20 at 10:08

The code in this repo shows how to create a flask web endpoint to predict probability of 'surviving the titanic disaster'. The trained model is serialized as pickle file using joblib which takes as input age, ticket_class, boarding_location and gender to make the prediction.

Training data - https://www.kaggle.com/c/titanic/data

Architecture of AWS Sagemaker https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-hosting.html

The architecture in the above diagram looks like a good way to containerize and deploy ML application.

Question

How do I use containerize for the ML + flask application in repo on my laptop? My objective is to deploy the container in production environment.
How do I run the above container?

...

ANSWER

Answered 2018-Aug-20 at 10:08

For simplicity, let's create our image based on Ubuntu.

Create a file Dockerfile in an empty directory with the following contents:

Source https://stackoverflow.com/questions/51927474

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

Vulnerabilities

No vulnerabilities reported

Install sklearnflask

You can download it from GitHub.
You can use sklearnflask 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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