{"id":17047606,"url":"https://github.com/residentmario/fahr","last_synced_at":"2025-04-12T15:52:22.499Z","repository":{"id":57428212,"uuid":"106119166","full_name":"ResidentMario/fahr","owner":"ResidentMario","description":"Run remote machine learning model training jobs right from the command line.","archived":false,"fork":false,"pushed_at":"2020-01-03T01:10:59.000Z","size":7423,"stargazers_count":4,"open_issues_count":4,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-04-11T00:42:12.070Z","etag":null,"topics":["aws-sagemaker","cli","machine-learning","model-training","python"],"latest_commit_sha":null,"homepage":"https://residentmario.github.io/fahr/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ResidentMario.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2017-10-07T18:02:17.000Z","updated_at":"2023-11-30T12:23:43.000Z","dependencies_parsed_at":"2022-09-02T18:31:02.436Z","dependency_job_id":null,"html_url":"https://github.com/ResidentMario/fahr","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ResidentMario%2Ffahr","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ResidentMario%2Ffahr/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ResidentMario%2Ffahr/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ResidentMario%2Ffahr/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ResidentMario","download_url":"https://codeload.github.com/ResidentMario/fahr/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248592045,"owners_count":21130164,"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":["aws-sagemaker","cli","machine-learning","model-training","python"],"created_at":"2024-10-14T09:49:49.165Z","updated_at":"2025-04-12T15:52:22.454Z","avatar_url":"https://github.com/ResidentMario.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# fahr ![status beta](https://img.shields.io/badge/status-beta-yellow.svg?style=flat-square) [![PyPi version](https://img.shields.io/pypi/v/fahr.svg?style=flat-square)](https://pypi.python.org/pypi/fahr/) [![docs passing](https://img.shields.io/badge/docs-passing-green.svg?style=flat-square)](https://residentmario.github.io/fahr/index.html)\n\n`fahr` is a command-line tool for building machine learning models on\ncloud hardware with as little overhead as possible.\n\n`fahr` provides a simple unified interface to model training services like AWS SageMaker and Kaggle Kernels. By offloading model training to the cloud, `fahr` aims to make machine learning experimentation easy and fast.\n\n## How it works\n\nFirst, some lingo:\n\n* **training artifact** \u0026mdash; A file (either `.ipynb` or `.py`) which, when executed correctly, produces a model artifact, e.g. a model training script or notebook.\n* **model artifact** \u0026mdash; A file which defines a machine learning model, e.g. a neural weight matrix.\n\n`fahr` turns a training artifact into a model artifact, using the magic of the cloud. Or, specifically, by:\n\n1. Building a Docker image based on your training artifact and uploading it to a container registry.\n2. Executing that Docker image, saving the resulting model artifact somewhere.\n3. Downloading that model artifact to your local machine.\n\nThe current model training drivers supported are:\n\n* `sagemaker` (AWS SageMaker)\n* `kaggle` (Kaggle Kernels)\n\nTo learn more about `fahr` [check out the docs](https://residentmario.github.io/fahr/index.html).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fresidentmario%2Ffahr","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fresidentmario%2Ffahr","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fresidentmario%2Ffahr/lists"}