Cloudknot: a python library designed to run your existing python code on AWS Batch
A knot is a collective noun for a group of snakes. Cloudknot is a python library designed to run your existing python code on AWS Batch.
Cloudknot takes as input a python function, Dockerizes it for use in an Amazon ECS instance, and creates all the necessary AWS Batch constituent resources to submit jobs. You can then use cloudknot to submit and view jobs for a range of inputs.
To get started using cloudknot, please see the cloudknot documentation
This is the cloudknot development site. You can view the source code, file new issues, and contribute to cloudknot's development. If you are just getting started, you should look at the cloudknot documentation.
We love contributions! Cloudknot is open source, built on open source, and we'd love to have you hang out in our community.
We have developed some guidelines for contributing to cloudknot.
Imposter syndrome disclaimer: We want your help. No, really.
There may be a little voice inside your head that is telling you that you're not ready to be an open source contributor; that your skills aren't nearly good enough to contribute. What could you possibly offer a project like this one?
We assure you - the little voice in your head is wrong. If you can write code at all, you can contribute code to open source. Contributing to open source projects is a fantastic way to advance one's coding skills. Writing perfect code isn't the measure of a good developer (that would disqualify all of us!); it's trying to create something, making mistakes, and learning from those mistakes. That's how we all improve, and we are happy to help others learn.
Being an open source contributor doesn't just mean writing code, either. You can help out by writing documentation, tests, or even giving feedback about the project (and yes - that includes giving feedback about the contribution process). Some of these contributions may be the most valuable to the project as a whole, because you're coming to the project with fresh eyes, so you can see the errors and assumptions that seasoned contributors have glossed over.
If you use cloudknot in a scientific publication, please see our citation instructions.
Cloudknot development is supported through a grant from the Gordon and Betty Moore Foundation and from the Alfred P. Sloan Foundation to the University of Washington eScience Institute, as well as NIH Collaborative Research in Computational Neuroscience grant R01EB027585-01 through the National Institute of Biomedical Imaging and Bioengineering to Eleftherios Garyfallidis (Indiana University) and Ariel Rokem (University of Washington).
This package was created with shablona.
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