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        "description": "============\nKubelaunch\n============\n\n.. image:: https://img.shields.io/pypi/v/hugo_jupyter.svg\n        :target: https://pypi.python.org/pypi/kubelaunch\n\n.. image:: https://img.shields.io/travis/knowsuchagency/kubelaunch.svg\n        :target: https://travis-ci.org/knowsuchagency/kubelaunch\n\n.. image:: https://pyup.io/repos/github/knowsuchagency/kubelaunch/shield.svg\n     :target: https://pyup.io/repos/github/knowsuchagency/kubelauch/\n     :alt: Updates\n\n.. image:: https://img.shields.io/github/license/mashape/apistatus.svg\n\n\nMaking analytics-ops on Kubernetes easy.\n\n* Source: https://github.com/kuberlytics/kubelaunch\n\nThis is a work in progress, but the goal is to make\n\nInstallation\n------------\n\n.. code-block:: bash\n\n    pip install kubelaunch\n\nUsage\n-----\nCreate a new empty directory for your cluster.\n\n.. code-block:: bash\n\n    kubel init gcp --jupyter\n\nThis will create .\n\nDocker Image\n-----\nThis is a Jupyter Singleuser container with Google Cloud Platform and Azure CLI installed.\n\nBuild locally:\n```\ndocker build -t kuberlytics/kubelaunch:latest -t kuberlytics/kubelaunch:v0.0.1 .\n```\n### To use Locally for example.\n```\ndocker run -it --rm -p 8888:8888  -v /Users/<yourpath>/launch:/home/jovyan/work kuberlytics/kubelaunch:latest /bin/bash\n```\n### Launch a docker container configured to control.\n```\ndocker run -it --rm -p 8888:8888  kuberlytics/kubelaunch:latest /bin/bash\n```",
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