The model run config file is automatically generated whenever you create a model run in your experiment. The model run config logs the hyperparameters and any training-related configurations for each model run.

It is common for ML teams to kick off multiple model runs with different sets of hyperparameters so they can compare their performance. Having the model configs logged is crucial for troubleshooting a model, sharing best practices, and reproducing model results.

Manage your model run configs

On a model run page, click the Settings icon and select Model run config.

If you have not created a model run config for this model run before, there will be a pre-populated template with common hyperparameters and their default values of None. You can modify the values (must be booleans, strings, or numbers) and save them. The format of the model run config must be a valid JSON.

To load the previous model run's config, toggle on Use another model run's config as a template and modify the template.

Click the trash icon to reset all content and the copy icon to copy the content of the model run config in your clipboard.

To export and download the model run config, click the download icon.