https://github.com/residentmario/fahr

Run remote machine learning model training jobs right from the command line.
https://github.com/residentmario/fahr

aws-sagemaker cli machine-learning model-training python

Last synced: 6 months ago
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Run remote machine learning model training jobs right from the command line.

• Host: GitHub
• URL: https://github.com/residentmario/fahr
• Owner: ResidentMario
• License: mit
• Created: 2017-10-07T18:02:17.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2020-01-03T01:10:59.000Z (almost 6 years ago)
• Last Synced: 2025-04-11T00:42:12.070Z (6 months ago)
• Topics: aws-sagemaker, cli, machine-learning, model-training, python
• Language: Python
• Homepage: https://residentmario.github.io/fahr/
• Size: 7.08 MB
• Stars: 4
• Watchers: 2
• Forks: 0
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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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)

`fahr` is a command-line tool for building machine learning models on

cloud hardware with as little overhead as possible.

`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.

## How it works

First, some lingo:

* **training artifact** — A file (either `.ipynb` or `.py`) which, when executed correctly, produces a model artifact, e.g. a model training script or notebook.

* **model artifact** — A file which defines a machine learning model, e.g. a neural weight matrix.

`fahr` turns a training artifact into a model artifact, using the magic of the cloud. Or, specifically, by:

1. Building a Docker image based on your training artifact and uploading it to a container registry.

2. Executing that Docker image, saving the resulting model artifact somewhere.

3. Downloading that model artifact to your local machine.

The current model training drivers supported are:

* `sagemaker` (AWS SageMaker)

* `kaggle` (Kaggle Kernels)

To learn more about `fahr` [check out the docs](https://residentmario.github.io/fahr/index.html).