cloudyr / cloudyr/cloudyr.github.io
docker-machine wrapper
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- Dominant language
- CSS
- Stars
- 29
- Forks
- 3
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Description
Hi @leeper , Cloudyr team,
Would you be interested in a package wrapping the docker-machine interface?
docker-machine is essentially just a simple wrapper around the APIs of many common cloud computing platforms (listed below), giving you a more-or-less consistent interface for launching, controlling & stopping an instance with particular specifications on that platform with Docker running on it. Combined with @wch 's harbor package & rocker images this would let users deploy RStudio, Shiny, or long-running Rscripts on a desired platform with one or two lines of code.
A compelling use case for this would be the ability to deploy long-running code on any of these environments in a way that the machine will just shut down once the code has finished running and the results have been pushed to the appropriate place (e.g. S3, github, or similar), avoiding the need to monitor for when a long-running job finishes or incurring use charges for idle compute. (If you know of a better way to do that, I'm all ears).
Here's the platform list supported already by docker-machine:
Amazon Web Services
Microsoft Azure
Digital Ocean
Exoscale
Google Compute Engine
Generic
Microsoft Hyper-V
OpenStack
Rackspace
IBM Softlayer
Oracle VirtualBox
VMware vCloud Air
VMware Fusion
VMware vSphere
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
No files, tests, or entry points are mentioned. Start by reviewing the proposed docker-machine interface and its listed cloud platforms, then define the package scope around launching, controlling, stopping, and shutting down machines after jobs complete. Done criteria are not specified in the issue and would need agreement before implementation.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- docker, r
- Domain
- cloud, devops
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100