{"id":13570651,"url":"https://github.com/equinor/gordo","last_synced_at":"2025-05-01T17:33:36.400Z","repository":{"id":38429960,"uuid":"152386408","full_name":"equinor/gordo","owner":"equinor","description":"An API-first distributed deployment system of deep learning models using timeseries data to predict the behaviour of systems","archived":true,"fork":false,"pushed_at":"2025-03-26T12:52:10.000Z","size":3157,"stargazers_count":26,"open_issues_count":41,"forks_count":23,"subscribers_count":17,"default_branch":"master","last_synced_at":"2025-04-27T10:44:17.133Z","etag":null,"topics":["distributed-computing","end-to-end","machine-learning","out-of-the-box","scalable"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"agpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/equinor.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":"SECURITY.md","support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2018-10-10T08:09:43.000Z","updated_at":"2025-03-26T12:52:49.000Z","dependencies_parsed_at":"2023-10-18T23:31:04.252Z","dependency_job_id":"54c834f1-5799-47a9-8bb5-2e56f93bc436","html_url":"https://github.com/equinor/gordo","commit_stats":{"total_commits":831,"total_committers":26,"mean_commits":31.96153846153846,"dds":0.6341756919374248,"last_synced_commit":"4b54d1f0c846e273bc2cb936e2c1e0ce48f5589a"},"previous_names":["equinor/gordo-components"],"tags_count":221,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/equinor%2Fgordo","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/equinor%2Fgordo/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/equinor%2Fgordo/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/equinor%2Fgordo/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/equinor","download_url":"https://codeload.github.com/equinor/gordo/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251914972,"owners_count":21664457,"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":["distributed-computing","end-to-end","machine-learning","out-of-the-box","scalable"],"created_at":"2024-08-01T14:00:54.003Z","updated_at":"2025-05-01T17:33:36.325Z","avatar_url":"https://github.com/equinor.png","language":"Python","funding_links":[],"categories":["📦 Packages"],"sub_categories":["Python"],"readme":"\u003ch1 align=\"center\"\u003eGordo\u003c/h1\u003e\n\u003cdiv align=\"center\"\u003e\n \u003c!-- Uncomment line below once we decided on 'logo.png' --\u003e\n \u003c!--\u003cimg align=\"center\" src=\"logo.png\" width=\"250\" height=\"250\"\u003e--\u003e\n \u003cbr /\u003e\n \u003cstrong\u003e\n   Building thousands of models with timeseries data to monitor systems.\n \u003c/strong\u003e\n\u003c/div\u003e\n\n\u003cbr /\u003e\n\n\u003cdiv align=\"center\"\u003e\n  \u003ca href=\"https://scm-compliance-api.radix.equinor.com/repos/equinor/527971a2-15b2-4466-8c4d-0e718804f93d/badge\"\u003e\n    \u003cimg src=\"https://scm-compliance-api.radix.equinor.com/repos/equinor/527971a2-15b2-4466-8c4d-0e718804f93d/badge\" alt=\"SCM Compliance\"/\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://github.com/equinor/gordo/actions?query=branch=master\"\u003e\n    \u003cimg src=\"https://github.com/equinor/gordo/workflows/CI/badge.svg?branch=master\" alt=\"Build Status\"/\u003e\n  \u003c/a\u003e\n\u003c/div\u003e\n\n---\n# Archival Notice\n\n**Date**: 26.03.2025\n\n**Reason**: This repository has been archived due to changes in the project's architecture. The current implementation is no longer maintained and developed.\n\n\n\n\n# Table of Contents\n* [About](#About)\n* [Components](#Components)\n* [Install](#Install)\n\t* [Python package ](#Python-package)\n* [Developer manual](#Developer-manual)\n\t* [Setup](#Setup)\n\t\t* [How to update packages](#How-to-update-packages)\n\t* [Examples](#Examples)\n\t* [How to run tests locally](#How-to-run-tests-locally)\n\t* [Build the documentation](#Build-the-documentation)\n* [Contributing](#Contributing)\n\n---\n\n## About\n\nGordo fulfills the role of inhaling config files and supplying components to the pipeline of:\n\n1. Fetching data\n2. Training model\n3. Serving model\n\n## Components\n\n* [gordo-controller](https://github.com/equinor/gordo-controller/) - Kubernetes controller for the Gordo CRDs.\n* [gordo-core](https://github.com/equinor/gordo-core/) - Gordo core library.\n* [gordo-client](https://github.com/equinor/gordo-client/) - Gordo server's client. It can make predictions from deployed models.\n\n---\n\n[Documentation is available on Read the Docs](https://gordo1.readthedocs.io/)\n\n---\n## Install\n\n[gordo-helm](https://github.com/equinor/gordo-helm) - you can use [gordo](https://github.com/equinor/gordo-helm/tree/main/charts/gordo) helm chart from this repository to deploy gordo infrastructure to your Kubernetes cluster. \n\n### Python package \n\n`pip install --upgrade gordo`  \n\nWith additional extras:\n`pip install gordo[postgres,mlflow]`  \n\nBleeding edge:  \n`pip install git+https://github.com/equinor/gordo.git`\n\n\n## Developer manual\n\nThis section will explain how to start development of Gordo.\n\n### Setup\n\nCreate and activate a virtual environment first. As a default option, it can be [venv](https://docs.python.org/3/library/venv.html) module.\n\nInstall pip-tools\n```\npip install --upgrade pip\npip install --upgrade pip-tools\n```\n\nInstall requirements\n```\npip install -r requirements/full_requirements.txt\npip install -r requirements/test_requirements.txt\n```\n\nInstall package:\n```\npython3 setup.py install\n```\n\n#### How to update packages\n\nNote: you have to install `pip-tools` version higher then `6` for requirements to have same multi-line output format.\n\nTo update some package in `full_requirements.txt`:\n- Change its version in `requirements.in` file;\n- Compile and upgrade requirements:\n```shell\npip-compile --upgrade --output-file=full_requirements.txt mlflow_requirements.in postgres_requirements.in requirements.in  \n```\n\n### Examples\n\nSee our [example](./examples) notebooks for how to develop with `gordo` locally.\n\n### How to run tests locally\n\nList of commands to run tests can be found [here](/setup.cfg).\nRunning of tests takes some time, so it's faster to run tests in parallel:\n```\npytest -n auto -m 'not dockertest' --ignore benchmarks\n```\nRun docker-related tests:\n```\npytest -m 'dockertest'\n```\n\n\u003e **_NOTE:_**  To run tests it's required for your system to has (note: commands might differ from your OS):\n\u003e - Running docker daemon.\n\u003e - Available 5432 port for `postgres` container.\n\n\u003e **_NOTE:_** this example is for Pycharm IDE to use `breakpoints` in the code of the tests.  \n\u003e On the configuration setup for test running add to `Additional arguments:` in `pytest` \n\u003e section following string: `--ignore benchmarks --cov-report= --no-cov ` \n\n### Build the documentation\n\nThis command will run the local documentation server:\n\n```console\n\u003e cd docs/\n\u003e make watch\n```\n\n## Contributing\nWe welcome contributions to this project! To get started, please follow these steps:\n\n1. Fork this repository to your own GitHub account and then clone it to your local device.\n\n```\ngit clone https://github.com/your-account/your-project.git\n```\n\n2. Create a new branch for your feature or bug fix.\n\n```\ngit checkout -b your-feature-or-bugfix-branch\n```\n\n3. Make your changes and commit them with a descriptive message.\n\n```\ngit commit -m \"Add a new feature\" -a\n```\n\n4. Push your changes to your forked repository.\n\n```\ngit push origin your-feature-or-bugfix-branch\n```\n\n5. Open a pull request in this repository and describe the changes you made.\n\nWe'll review your changes and work with you to get them merged into the main branch of the project.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fequinor%2Fgordo","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fequinor%2Fgordo","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fequinor%2Fgordo/lists"}