deploy-ml-fastapi-redis-docker | Deploy and scale machine learning models with FastAPI | Continuous Deployment library

by shanesoh Python Version: Current License: No License

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

kandi X-RAY | deploy-ml-fastapi-redis-docker Summary

deploy-ml-fastapi-redis-docker is a Python library typically used in Manufacturing, Utilities, Machinery, Process, Devops, Continuous Deployment, Fastapi, Docker applications. deploy-ml-fastapi-redis-docker has no bugs, it has no vulnerabilities and it has low support. However deploy-ml-fastapi-redis-docker build file is not available. You can download it from GitHub.

Deploy and scale machine learning models with FastAPI, Redis and Docker

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deploy-ml-fastapi-redis-docker has a low active ecosystem.

It has 79 star(s) with 33 fork(s). There are 7 watchers for this library.

It had no major release in the last 6 months.

deploy-ml-fastapi-redis-docker has no issues reported. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of deploy-ml-fastapi-redis-docker is current.

Quality

deploy-ml-fastapi-redis-docker has 0 bugs and 0 code smells.

Security

deploy-ml-fastapi-redis-docker has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

deploy-ml-fastapi-redis-docker code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

deploy-ml-fastapi-redis-docker 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.

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deploy-ml-fastapi-redis-docker releases are not available. You will need to build from source code and install.

deploy-ml-fastapi-redis-docker has no build file. You will be need to create the build yourself to build the component from source.

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

deploy-ml-fastapi-redis-docker saves you 43 person hours of effort in developing the same functionality from scratch.

It has 116 lines of code, 6 functions and 3 files.

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

Top functions reviewed by kandi - BETA

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

Classify images from Redis
Decode a base64 image
Predict class
Preprocess an image

Get all kandi verified functions for this library.

deploy-ml-fastapi-redis-docker Key Features

No Key Features are available at this moment for deploy-ml-fastapi-redis-docker.

deploy-ml-fastapi-redis-docker Examples and Code Snippets

No Code Snippets are available at this moment for deploy-ml-fastapi-redis-docker.

Community Discussions

Trending Discussions on deploy-ml-fastapi-redis-docker

Poll SQL table using Python?

QUESTION

Poll SQL table using Python?

Asked 2020-Nov-26 at 16:34

I want to implement something similar to this, where the custom function classify_process() continuously asks a Redis db for new entries.

Can this be done a regular SQL table? The db.pipeline() seems to be Redis-specific, so I'm not sure how to imitate this functionality on a SQL db.

My goal is to check one table for new rows, and if there's a new row, run a prediction using a ML model.

QUESTION: How can I continously check if there are any new records in a MS SQL table (which has an interger ID column that automatically increments) and then trigger a function?

My idea of what has to happen using sqlalchemy:

...

ANSWER

Answered 2020-Nov-26 at 16:34

With an auto-incrementing integer (IDENTITY(1,1)) primary key you would check for new rows by looking up the largest PK value …

Source https://stackoverflow.com/questions/65004791

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

Vulnerabilities

No vulnerabilities reported

Install deploy-ml-fastapi-redis-docker

You can download it from GitHub.
You can use deploy-ml-fastapi-redis-docker 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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