Search Results for "mac" - Page 54
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Showing 11365 open source projects for "mac"
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1
Testcontainers Python
Testcontainers is a Python library that providing a friendly API
Testcontainers is a Python library that provides a friendly API to run a Docker container. It is designed to create a runtime environment to use during your automatic tests. -
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TinyStatus
Tiny status page generated by a Python script
TinyStatus is a simple, customizable status page generator that allows you to monitor the status of various services and display them on a clean, responsive web page. -
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supabase-py
Python Client for Supabase. Query Postgres from Flask, Django
Python Client for Supabase. Query Postgres from Flask, Django, FastAPI. Python user authentication, security policies, edge functions, file storage, and realtime data streaming. Good first issue. -
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Feature-engine
Feature engineering package with sklearn like functionality
Feature-engine is a Python library with multiple transformers to engineer and select features for use in machine learning models. Feature-engine's transformers follow Scikit-learn's functionality with fit() and transform() methods to learn the transforming parameters from the data and then transform it. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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POT
Python Optimal Transport
This open source Python library provides several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning. -
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Lazy Predict
Lazy Predict help build a lot of basic models without much code
Lazy Predict helps build a lot of basic models without much code and helps understand which models work better without any parameter tuning. -
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tslearn
The machine learning toolkit for time series analysis in Python
The machine learning toolkit for time series analysis in Python. tslearn expects a time series dataset to be formatted as a 3D numpy array. The three dimensions correspond to the number of time series, the number of measurements per time series and the number of dimensions respectively (n_ts, max_sz, d). In order to get the data in the right format. -
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River ML
Online machine learning in Python
River is a Python library for online machine learning. It aims to be the most user-friendly library for doing machine learning on streaming data. River is the result of a merger between creme and scikit-multiflow. -
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kapture
Tools for manipulating datasets
Kapture is a pivot file format, based on text and binary files, used to describe SfM (Structure From Motion) and more generally sensor-acquired data. -
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10
FeelUOwn
Trying to be a robust, user-friendly and hackable music player
FeelUOwn is a user-friendly, and hackable music player. -
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pyTermTk
Python Terminal Toolkit - a Spiced Up TUI Library
pyTermTk is a Text-based user interface library (TUI). Evolved from the discontinued project pyCuT and inspired by a mix of Qt5, GTK, and tkinter API definition with a touch of personal interpretation. -
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nbgrader
A system for assigning and grading notebooks
nbgrader is a tool that facilitates creating and grading assignments in the Jupyter notebook. It allows instructors to easily create notebook-based assignments that include both coding exercises and written free responses. nbgrader then also provides a streamlined interface for quickly grading completed assignments. -
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Recommenders 2023
Best Practices on Recommendation Systems
Recommenders objective is to assist researchers, developers and enthusiasts in prototyping, experimenting with and bringing to production a range of classic and state-of-the-art recommendation systems. Recommenders is a project under the Linux Foundation of AI and Data. -
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bert4torch
An elegent pytorch implement of transformers
An elegant PyTorch implement of transformers. -
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autoresearch-mlx
Apple Silicon (MLX) port of Karpathy's autoresearch
...It maintains the core autoresearch structure, where an AI agent iteratively edits a training script, executes experiments under a fixed time budget, and evaluates results based on a defined metric such as validation bits per byte. The system is tailored for Apple hardware, leveraging unified memory and MLX capabilities to achieve efficient training on Mac devices. It includes a minimal and focused project structure consisting of data preparation utilities, a modifiable training file, and a program specification that governs the agent’s behavior. The framework logs experiment results and supports continuous iteration, enabling long-running optimization cycles that can reveal hardware-specific performance patterns. -
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SageMaker Python SDK
Training and deploying machine learning models on Amazon SageMaker
SageMaker Python SDK is an open source library for training and deploying machine learning models on Amazon SageMaker. With the SDK, you can train and deploy models using popular deep learning frameworks Apache MXNet and TensorFlow. You can also train and deploy models with Amazon algorithms, which are scalable implementations of core machine learning algorithms that are optimized for SageMaker and GPU training. If you have your own algorithms built into SageMaker-compatible Docker... -
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FL4Health
Library to facilitate federated learning research
FL4Health is a Vector Institute toolkit for building modular, clinically-focused FL pipelines. Tailored for healthcare, it supports privacy-preserving FL, heterogeneous data settings, integrated reporting, and clear API design. -
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Enferno
Modern Flask framework optimized for AI-assisted development
Enferno is a framework for building developer-first cloud backends using PostgreSQL and TypeScript. It offers primitives for defining data models, APIs, and access rules directly in code, enabling quick iteration and deployment. Enferno is designed to accelerate SaaS and internal tool development by combining the benefits of traditional backends with developer ergonomics. -
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pg_probackup
Backup and recovery manager for PostgreSQL
pg_probackup is an advanced backup and recovery tool for PostgreSQL developed by PostgresPro. It supports full, incremental, and differential backups with compression and encryption, and provides fine-grained control over backup and restore processes. It’s designed for enterprise-grade backup strategies in PostgreSQL environments. -
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Pyparsing
Python library for creating PEG parsers
pyparsing is a Python library that facilitates the creation of parsers using a parsing expression grammar (PEG) approach. It allows developers to construct grammars directly in Python code, offering an alternative to traditional parsing methods. -
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MCP Agent
Build effective agents using Model Context Protocol
The MCP Agent is a framework that enables the construction of effective AI agents using the Model Context Protocol. It focuses on simple, composable patterns to build production-ready AI agents, facilitating seamless integration with various tools and services to enhance AI capabilities. -
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MCP Server OpenDAL
Model Context Protocol Server for Apache OpenDAL™
Model Context Protocol Server for Apache OpenDAL™ is an MCP server implementation that provides access to various storage services via Apache OpenDAL. It enables seamless interactions with multiple storage backends through a unified interface. -
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MCP Neo4j
Model Context Protocol with Neo4j
An implementation of the Model Context Protocol with Neo4j, enabling natural language interactions with Neo4j databases and facilitating operations such as schema retrieval and Cypher query execution. -
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MCP Server DuckDB
A Model Context Protocol (MCP) server implementation for DuckDB
An MCP server implementation for DuckDB, providing database interaction capabilities through MCP tools, allowing operations like querying, table creation, and schema inspection. -
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MCP Timeplus
Execute SQL queries and manage databases seamlessly with Timeplus
An MCP server designed for integration with Timeplus, enabling real-time data streaming and analytics through natural language interactions.
