@misc {26189,
	title = {Deploying a cloud-based JupyterHub for students and researchers},
	howpublished = {JupyterCon, New York, NY, USA},
	year = {2018},
	abstract = {Tutorial to demonstrate how to provide easy access to Jupyter notebooks and JupyterLab without requiring users to install anything on their computers. You{\textquoteright}ll learn how to configure and deploy a cloud-based JupyterHub using Kubernetes and how to customize and extend it for your needs.},
	keywords = {jupyter, jupyterhub},
	author = {Willing, Carol and Ragan-Kelley, Benjamin and Sundell, Erik}
}
@inproceedings {26122,
	title = {Reproducible Research Environments with Repo2Docker},
	journal = {ICML 2018 Reproducible Machine Learning},
	year = {2018},
	month = {07/2018},
	publisher = {ICML},
	abstract = {Reproducibility challenges in machine learning often center on questions of software engineering practices. Researchers struggle to reproduce another scientist{\textquoteright}s work because they cannot translate a paper into code with similar results or run an author{\textquoteright}s code. repo2docker provides a simple tool for checking the minimum requirements to reproduce a paper by building a Docker image based on a repository path or URL.  Its goal is to minimize the effort needed to convert a static repository into a working software environment. By inspecting a repository for standard configuration files used in contemporary software engineering and leveraging containerization methods, repo2docker deterministically reproduces the environment of the author so the researcher can reproduce the author{\textquoteright}s experiments.},
	keywords = {docker, jupyter, python, reproducibility},
	url = {https://openreview.net/forum?id=B1lYOwuoxm},
	author = {Forde, Jessica and Head, Tim and Holdgraf, Chris and Panda, Yuvi and Nalvarte, Gladys and Pacer, M and Perez, Fernando and Ragan-Kelley, Benjamin and Sundell, Erik}
}
