Metadata-Version: 2.1
Name: eigensheep
Version: 0.0.2
Summary: Run Jupyter cells in AWS Lambda for massively parallel experimentation
Home-page: https://github.com/antimatter15/eigensheep
Author: Kevin Kwok
Author-email: antimatter15@gmail.com
License: UNKNOWN
Description: <img src="https://raw.githubusercontent.com/antimatter15/lambdu/master/images/logo.png" alt="eigensheep" width="500"/>
        
        ![PyPI](https://img.shields.io/pypi/v/eigensheep.svg)
        ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/eigensheep.svg)
        ![PyPI - License](https://img.shields.io/pypi/l/eigensheep.svg)
        
        Eigensheep lets you easily run cells in Jupyter Notebooks on AWS Lambda with massive parallelism. You can instantly provision and run your code on 1000 different tiny VMs by simply prefixing a cell with `%%eigensheep -n 1000`. 
        
        ## Getting Started
        
        Open up your Terminal and install `eigensheep` with `pip`
        
            pip3 install eigensheep
        
        Open a Jupyter notebook with `jupyter notebook` and create a new Python 3 notebook. Run the following code in a cell:
        
            import eigensheep
        
        Follow the on-screen instructions to configure AWS credentials. Eigensheep uses AWS CloudFormation so you only need to a few clicks to get started. 
        
        <img src="https://raw.githubusercontent.com/antimatter15/lambdu/master/images/setup.png" alt="eigensheep setup" width="500" />
        
        Once Eigensheep is set up, you can run any code on Lambda by prefixing the cell with `%%eigensheep`. You can include dependencies from `pip` by typing `%%eigensheep requests numpy`. You can invoke a cell multiple times concurrently with `%%eigensheep -n 100`. 
        
        <img src="https://raw.githubusercontent.com/antimatter15/lambdu/master/images/parallel.gif" alt="eigensheep usage" width="500"  />
        
        ## Acknowledgements
        
        This library was written by [Kevin Kwok](https://twitter.com/antimatter15) and [Guillermo Webster](https://twitter.com/biject). It is based on Jupyter/IPython, `tqdm`, `boto3`, and countless Stackoverflow answers.
        
        If you're interested in this project, you should also check out [PyWren](http://pywren.io/) by Eric Jonas, and [ExCamera](https://www.usenix.org/system/files/conference/nsdi17/nsdi17-fouladi.pdf) from Sadjad Fouladi, et al. 
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 2
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 3 - Alpha
Classifier: Framework :: Jupyter
Classifier: Topic :: Scientific/Engineering
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Description-Content-Type: text/markdown
