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Prepare Your Repositories

Iterative Studio creates projects by identifying machine learning models, datasets, metrics and hyperparameters defined in your Git repositories. These values are found in JSON or YAML files in the repository. Additionally, model information may be available as Git tags.

New ML model metadata can be added directly from the Models tab after creating a project. You can also use GTO or MLEM.

Set up datasets, metrics, and hyperparameters

Datasets, metrics, and hyperparameters can be added to a project in two ways:

  1. Set up DVC repositories: You can use DVC and Git to version your code, data and models all within your Git repositories. Data Version Control, or DVC, is a data and ML experiment management tool that takes advantage of the existing engineering toolset that you're already familiar with (Git, CI/CD, etc.). By using DVC, you can be sure not to bloat your repositories with large volumes of data or huge models. These large assets reside in the cloud or other remote storage locations. You will simply track their version info in Git.

    DVC also enables you to store and share your data and model files, create data registries, create data pipelines, connect them with CML for CI/CD in machine learning, and so on. Find more about the features and benefits of DVC here.

    Refer to the DVC documentation to initialize a DVC repository.

    Iterative Studio Model Registry can set up DVC for you when importing model files from cloud locations.

  2. Specify custom files with your metrics and parameters: If you are working with a non-DVC repository, you can add the project provided that the metrics and hyperparameters are stored in JSON or YAML files. For instance, if you have an ML project for which you generate and save metrics either manually or using some ML tracking tools, then you can add this project by specifying the file (within your Git repo) which contains your saved metrics. Refer to the section on project settings to learn how to specify the custom files.

Prepare Your Repositories to Run New Experiments

To run new experiments from Iterative Studio, you should integrate your repositories with a CI/CD setup that includes a model training process. For this, create workflow files (such as GitHub Actions) that get triggered on push or pull request.

You can use the wizard provided by Iterative Studio to automatically generate the workflow configuration, or you can write it on your own.

For more details on how to set up CI/CD pipelines for your ML project, refer to CML.

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