Scaling Many Model Training with Ray Tune

Template Specification	Description
Summary	This template demonstrates how to parallelize the training of hundreds of time-series forecasting models with Ray Tune. The template uses the statsforecast library to fit models to partitions of the M4 forecasting competition dataset.
Time to Run	Around 5 minutes to train all models.
Minimum Compute Requirements	No hard requirements. The default is 8 nodes with 8 CPUs each.
Cluster Environment	This template uses the latest Anyscale-provided Ray ML image using Python 3.9: anyscale/ray-ml:latest-py39-gpu, with some extra requirements from requirements.txt installed on top. If you want to change to a different cluster environment, make sure that it is based off of this image and includes all packages listed in the requirements.txt file.

Getting Started

When the workspace is up and running, start coding by clicking on the Jupyter or VSCode icon above. Open the start.ipynb file and follow the instructions there.

The end result of the template is fitting multiple models on each dataset partition, then determining the best model based on cross-validation metrics. Then, using the best model, we can generate forecasts like the ones shown below: