Best-practices Libraries

11 Components & Libraries

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Best-practices Libraries

Modest and essential collection of software engineering practices.

Collective Mind (CM) is a collection of portable and extensible automation recipes with a unified and human-friendly interface and minimal dependencies to make it easier to compose, benchmark and optimize AI, ML and other applications and systems across diverse and continuously changing models, data sets, software and hardware (cloud/edge)

For example, you should be able to run the MLPerf inference benchmark on Linux, Windows and MacOS using a few CM commands: You can also run the same commands using a unified CM Python API:

This repository provides a working, deployable, open source-based, serverless service template with an AWS Lambda function and AWS CDK Python code with all the best practices and a complete CI/CD pipeline.

This project provides a working, open source based, AWS Lambda handler skeleton Python code including DEPLOYMENT code with CDK and a pipeline. This project can serve as a template for new Serverless …

A comprehensive LaTeX template with examples for theses, books and more, employing the 'latest and greatest' (UTF8, glossaries, fonts, ...). The PDF artifact is built using CI/CD, with a Python testing framework.

Bash: PowerShell: comprehensive support for: structured and commented source code, explaining rationales and providing context showcasing plotting and data display (floats): typesetting more complex …

Modest and essential collection of software engineering practices.

💻 Microservice lib designed to ease service building using Python and asyncio, with ready to use support for HTTP + WS, AWS SNS+SQS, RabbitMQ / AMQP, middlewares, envelopes, logging, lifecycles. Extend to GraphQL, protobuf, etc.

Includes ready implementations to support handlers built for HTTP requests, websockets, AWS SNS+SQS and RabbitMQ / AMQP for 🚀 event based messaging, 🔗 intra-service communication and 🐶 watchdog handl…

A best-practices first project template that allows you to get started on a new machine learning project.

The overall objective of this toolkit is to provide and offer a free collection of data analysis and machine learning that is specifically suited for doing data science. Its purpose is to get you started in a matter of minutes. You can run this collections either in Jupyter notebook or python alone.

The overall objective of this toolkit is to provide and offer a free collection of data analysis and machine learning that is specifically suited for doing data science. Its purpose is to get you sta…

This project focuses on understanding the language ecosystem

This project focuses on understanding the language ecosystem, not getting into programming details. This topic describes how to set up the environment for Python development. This topic describes fea…

The goal of this repository is to enable data scientists and ML engineers to develop data science use cases and making it ready for production use. This means focusing on the versioning, scalability, monitoring and engineering of the solution.

`mllint` is a command-line utility to evaluate the technical quality of Python Machine Learning (ML) projects by means of static analysis of the project's repository.

For those interested in the research output produced in my MSc thesis: Or if your project uses Pipenv: In CI scripts, such raw markdown output (whether as a file or printed to the standard output) ca…

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docker-web-management