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.

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.