Metadata-Version: 2.1
Name: gibbs
Version: 0.1.0
Summary: Easily scale your ML models
Home-page: https://github.com/astariul/gibbs
Author: Nicolas REMOND
Author-email: remondnicola@gmail.com
License: UNKNOWN
Description: <h1 align="center">gibbs</h1>
        <p align="center">
        Scale your ML workers asynchronously across processes and machines
        </p>
        
        <p align="center">
            <a href="https://github.com/astariul/gibbs/releases"><img src="https://img.shields.io/github/release/astariul/gibbs.svg" alt="GitHub release" /></a>
            <a href="https://github.com/astariul/gibbs/actions/workflows/pytest.yaml"><img src="https://github.com/astariul/gibbs/actions/workflows/pytest.yaml/badge.svg" alt="Test status" /></a>
            <a href="https://github.com/astariul/gibbs/actions/workflows/lint.yaml"><img src="https://github.com/astariul/gibbs/actions/workflows/lint.yaml/badge.svg" alt="Lint status" /></a>
            <img src=".github/badges/coverage.svg" alt="Coverage status" />
            <a href="https://astariul.github.io/gibbs"><img src="https://img.shields.io/website?down_message=failing&label=docs&up_color=green&up_message=passing&url=https%3A%2F%2Fastariul.github.io%2Fgibbs" alt="Docs" /></a>
            <a href="https://github.com/astariul/gibbs/blob/main/LICENSE"><img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="licence" /></a>
        </p>
        
        <p align="center">
          <a href="#description">Description</a> •
          <a href="#install">Install</a> •
          <a href="#usage">Usage</a> •
          <a href="#faq">FAQ</a> •
          <a href="#contribute">Contribute</a>
          <br>
          <a href="https://astariul.github.io/gibbs/" target="_blank">Documentation</a>
        </p>
        
        
        <h2 align="center">Description</h2>
        
        **`gibbs`** is a python package that helps you scale your ML workers (or any python code) across processes and machines, asynchronously.
        
        `gibbs` is :
        
        * ⚡️ Highly performant
        * 🔀 Asynchronous
        * 🐥 Easy-to-use
        
        
        <h2 align="center">Install</h2>
        
        Install `gibbs` by running :
        
        
        ```
        pip install gibbs
        ```
        
        
        <h2 align="center">Usage</h2>
        
        After defining your awesome model :
        
        ```python
        import time
        
        class MyAwesomeModel:
            def __init__(self, wait_time=0.25):
                super().__init__()
                self.w = wait_time
        
            def __call__(self, x):
                time.sleep(self.w)
                return x**2
        ```
        
        You can simply start a few workers serving the model :
        
        ```python
        from gibbs import Worker
        
        for _ in range(4):
            Worker(MyAwesomeModel).start()
        ```
        
        And send requests through the Hub :
        
        ```python
        from gibbs import Hub
        
        hub = Hub()
        
        # In an async function
        await hub.request(34)
        ```
        
        And that's it !
        
        ---
        
        Make sure to check the [documentation](https://astariul.github.io/gibbs/usage/) for a more detailed explanation.
        
        Or you can run some examples from the `examples/` folder.
        
        
        <h2 align="center">FAQ</h2>
        
        #### ❓ **How `gibbs` works ?**
        
        `gibbs` simply run your model code in separate processes, and send requests to the right process to ensure requests are treated in parallel.
        
        `gibbs` uses a modified form of [the Paranoid Pirate Pattern from the zmq guide](https://zguide.zeromq.org/docs/chapter4/#Robust-Reliable-Queuing-Paranoid-Pirate-Pattern).
        
        #### ❓ **Why the name "gibbs" ?**
        
        Joshamee Gibbs is the devoted first mate of Captain Jack Sparrow.  
        Since we are using the Paranoid Pirate Pattern, we needed a pirate name !
        
        <h2 align="center">Contribute</h2>
        
        To contribute, install the package locally, create your own branch, add your code (and tests, and documentation), and open a PR !
        
        ### Pre-commit hooks
        
        Pre-commit hooks are set to check the code added whenever you commit something.
        
        If you never ran the hooks before, install it with :
        
        ```bash
        pre-commit install
        ```
        
        ---
        
        Then you can just try to commit your code. If anything fails (linters, tests, etc...), your code will not be committed. You can just fix your code and try to commit again !
        
        ### Documentation
        
        The documentation should be kept up-to-date. You can visualize the documentation locally by running :
        
        ```bash
        mkdocs serve
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.7
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: test
Provides-Extra: hook
Provides-Extra: lint
Provides-Extra: docs
Provides-Extra: ex
Provides-Extra: all
Provides-Extra: dev
