{"id":21917117,"url":"https://github.com/shuyib/mawingu-experiments","last_synced_at":"2025-06-19T14:36:39.347Z","repository":{"id":37192900,"uuid":"479682242","full_name":"Shuyib/mawingu-experiments","owner":"Shuyib","description":"Handbook for putting applications in the cloud referencing DS and ML paradigms.","archived":false,"fork":false,"pushed_at":"2025-02-06T05:10:34.000Z","size":766,"stargazers_count":3,"open_issues_count":12,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-19T13:44:24.404Z","etag":null,"topics":["continuous-delivery","data-science","docker","fullstack-datascientist","k8s","kubernetes","ml-engineering-for-production","mlops","mlops-project","mlops-workflow"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"cc0-1.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Shuyib.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2022-04-09T09:43:21.000Z","updated_at":"2025-02-06T05:10:34.000Z","dependencies_parsed_at":"2024-05-03T22:23:26.972Z","dependency_job_id":"decc8455-1def-425a-9d91-3ad03f9c0766","html_url":"https://github.com/Shuyib/mawingu-experiments","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Shuyib/mawingu-experiments","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Shuyib%2Fmawingu-experiments","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Shuyib%2Fmawingu-experiments/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Shuyib%2Fmawingu-experiments/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Shuyib%2Fmawingu-experiments/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Shuyib","download_url":"https://codeload.github.com/Shuyib/mawingu-experiments/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Shuyib%2Fmawingu-experiments/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":260769588,"owners_count":23060139,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["continuous-delivery","data-science","docker","fullstack-datascientist","k8s","kubernetes","ml-engineering-for-production","mlops","mlops-project","mlops-workflow"],"created_at":"2024-11-28T19:23:34.545Z","updated_at":"2025-06-19T14:36:34.335Z","avatar_url":"https://github.com/Shuyib.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"This is a project that has various examples of how you can use kubernetes to run containers in a docker registry in a cloud provider. The examples are mostly pitched on data science and machine learning however, some don't meet that categorization.  \n\n.\n├── data-science-workflows - data science related projects with data loaders, plots, Application programming Interfaces.   \n│   ├── README.md - Explanation of the different use cases an data driven application (dataloader + plotter) \u0026 an API.  \n│   ├── workflow3-data-driven-app  \n│   │   ├── dataloader  \n│   │   │   ├── deployments.yaml  \n│   │   │   ├── Dockerfile  \n│   │   │   ├── main.py  \n│   │   │   ├── Makefile  \n│   │   │   ├── mylib  \n│   │   │   │   ├── dataloader.py  \n│   │   │   │   └── __init__.py  \n│   │   │   ├── Pipfile  \n│   │   │   ├── Pipfile.lock  \n│   │   │   └── test_main.py  \n│   │   ├── README.md  \n│   │   └── timeseries_plot  \n│   │       ├── deployments.yaml  \n│   │       ├── Dockerfile  \n│   │       ├── Makefile  \n│   │       ├── mylib  \n│   │       │   ├── dataloader.py  \n│   │       │   ├── __init__.py    \n│   │       ├── Pipfile  \n│   │       ├── Pipfile.lock  \n│   │       └── plot_timeseries.py  \n│   └── workflow4-data-science-api  \n│       ├── app.py   \n│       ├── deployments.yml  \n│       ├── Dockerfile  \n│       ├── iris-fit-k-nearest-neighbors-pickle-model.ipynb  \n│       ├── iris_knn_model.pkl  \n│       ├── Makefile  \n│       ├── pycaret+gradio.zip  \n│       ├── README.md   \n│       ├── requirements.txt  \n│       ├── service.yaml  \n│       └── test_api_endpoint.ipynb  \n├── getting-stuff-to-cloud.md - a summary of how its done of digital ocean.  \n├── kubernetes-scheduling - example where you run a cronjob every 5 minutes: Here a job that does a dot product on multidimensional arrays made with numpy.  \n│   ├── deployments.yaml - manifest file that specifies instructions that will be given to the kubernetes cluster. It is a cron job meaning that it will run after a certain interval.  \n│   ├── Dockerfile - a file that runs the whole application.  \n│   ├── matmulsched.py - Python script that records the timestamp before the dot product is run and wait for a few minutes and stops.  \n│   ├── Pipfile - contains the requirements of the project as well as the python version.  \n│   ├── Pipfile.lock - Just freezes the requirements for the project.  \n│   └── README.md - Summary of what the project is about and how to run it.  \n├── LICENSE - CC0-1.0 license   \n├── ping-app - a simple flask application that prints out pong if you run a CURL request.    \n│   ├── deployments.yaml - manifest file that specifies instructions that will be given to the kubernetes cluster.  \n│   ├── Dockerfile - a file that packages and runs the application like a zip file.  \n│   ├── ping.py - python file that defines the Flask application and associated methods.  \n│   ├── Pipfile - contains the requirements of the project as well as the python version.  \n│   ├── Pipfile.lock - Just freezes the requirements for the project.  \n│   └── service.yaml - manifest that is passed to the k8s cluster to expose the application to the internet via an IP and host.  \n└── README.md - the file you are reading.   \n\nYou can make these containers made smaller to run more with your limited capacity on digital ocean. Will explore that in the future. Enjoy!  \n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshuyib%2Fmawingu-experiments","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshuyib%2Fmawingu-experiments","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshuyib%2Fmawingu-experiments/lists"}