semvec/pypi-packages huggingface dataset:
factryengine 0.3.3
production / job shop / resource scheduler for Python
License: MIT
Requires Python: <4.0,>=3.11

# 📅 factryengine

`factryengine` is a high-speed Python package for effortless and efficient task scheduling, specifically tailored for production scheduling. Built with `numpy`, it ensures tasks are executed in the correct order while considering their priorities, resources, and dependencies.

## 🛠 Installation

Install `factryengine` with a simple pip command:

```bash
pip install factryengine
```

## 🌟 Features

- ⚡ **Fast Performance**: Built with `numpy` for high-speed task scheduling.
- 🏭 **Production Scheduling**: Specifically designed for seamless production scheduling.
- 📝 **Simple Task Creation**: Easily define tasks with attributes like duration, priority, and resources.
- 🛠️ **Resource Management**: Assign resources with availability windows to tasks.
- 🔄 **Task Dependencies**: Ensure tasks that depend on others are scheduled in the correct order.
- 📅 **Efficient Scheduling**: Automatically schedule tasks while considering their priorities and dependencies.

## 🚀 Quick Start

Get started with `factryengine` with this basic example:

```python
from factryengine import Task, Resource, Scheduler

# Creating a Resource object
resource = Resource(id=1, available_windows=[(0,10)])

# Creating Task objects
task1 = Task(id=1, duration=3, priority=2, constraints=[resource])
task2 = Task(id=2, duration=5, priority=1, constraints=[resource], predecessor_ids=[1])

# Creating a Scheduler object and scheduling the tasks
scheduler = Scheduler(tasks=[task1, task2], resources=[resource])
scheduler_result = scheduler.schedule()
```

In this example, `task1` is scheduled before `task2` as `task2` depends on `task1`, despite its lower priority.

## 📖 Documentation

For more detailed information, check out the [documentation](https://yacobolo.github.io/factryengine/).

## 🤝 Contributing

Contributions, issues, and feature requests are welcome!

## 📝 License

This project is [MIT](../LICENSE) licensed.
meshai 0.1.7
MeshAI SDK for decentralized AI model development
License: MIT
Requires Python: >=3.7

# MeshAI SDK
<picture align="center">
    <source media="(prefers-color-scheme: dark)" srcset="./assets/meshai-high-resolution-logo.png">
    <img alt="MeshAI Logo" src="./assets/meshai-high-resolution-logo.png">
</picture>

## What is it?

MeshAI SDK is an open-source toolkit designed to decentralize artificial intelligence within organizations, aligning with the data mesh paradigm. It empowers individual teams to develop, train, and maintain their own AI models, enhancing data integration, autonomy, scalability, and flexibility.

## Features

### Currently Implemented

- **Domain-Oriented AI Ownership**: Assign AI agents to specific business domains using the `DomainManager`.
- **Governance and Compliance**: Ensure adherence to standards with integrated logging and audit trails.
- **Support for Multiple Data Types**: Handle text, images, numerical data, and more.
- **Pre-trained Model Integration**: Easily load and fine-tune pre-trained models from popular libraries like Hugging Face Transformers and torchvision.
- **Custom Model Support**: Allow users to integrate their own models and architectures.
- **Extensibility**: Modular design allows for easy addition of new features and data handlers.

### Upcoming Features

- **Data Proximity**: Deploy AI agents close to data sources with provided deployment tools and guidelines.
- **Interoperability**: Facilitate communication between AI agents across domains.
- **Scalability and Flexibility**: Enable scalable AI development with distributed training capabilities.

## Installation

You can install the MeshAI SDK from PyPI:

```bash
pip install meshai