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Why use a Heat Generator Service 

The modern technology stack used by data scientists can be large and complex.  The amount of time spent creating servers and configuring software can be considerable. Openstack Heat implements an orchestration engine to launch cloud applications and resources. Openstack Heat orchestration requires a heat template (YAML) file as an input to describe the openstack resources to create. This template may also include provisioning scripts to setup environments and install software. Heat templates can be quite complicated and can be authored in text editors, but this application allows quick and easy template generation from a high level.

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Select a template from the dropdown.  The templates provide code and configuration to build a software stack for data scientists. Each template creates a unique environment. Currently there are two templates that a data scientist can choose.

1. The Jupyterlab_local template is coded to create a Jupyter lab environment where the code and supporting libs are installed unto the local OS.
2. The Jupyterlab_docker template is coded to create a Jupyter lab environment where the code and supporting libs are installed onto a docker image.

Enter a Github repo to download.  HGS will replace "<REPO>" in the selected templateHeat templates have provision code to download a specific Github repo that has the install and configuration scripts.

Enter a volume size.    HGS will replace "<SIZE>" in the selected template. Heat templates have provision code to create and mount a volume to store large data sets. (Maximum 250GB)

Select image name.  HGS will replace "<IMAGE>" in the selected templateThe Heat template will be given this name.

Select a flavor.  HGS will replace "<FLAVOR>" in the selected templateFlavors range from tiny to xlarge. The flavor determines the amount of RAM, VCPUs, root disk size and swap size the instance will use.

Click down load button to download the template file.

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