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Best Practices


This page covers clearml-data, ClearML's file-based data management solution. See Hyper-Datasets for ClearML's advanced queryable dataset management solution.

The following are some recommendations for using ClearML Data.

Versioning Datasets#

Use ClearML Data to version your datasets. Once a dataset is finalized, it can no longer be modified. This makes clear which version of the dataset was used with which task, enabling the accurate reproduction of your experiments.

Once you need to change the dataset's contents, you can create a new version of the dataset by specifying the previous dataset as a parent. This makes the new dataset version inherit the previous version's contents, with the dataset's new version contents ready to be updated.

Organize Datasets for Easier Access#

Organize the datasets according to use-cases and use tags. This makes managing multiple datasets and accessing the most updated datasets for different use-cases easier.

Like any ClearML tasks, datasets can be organized into projects (and sub-projects). Additionally, when creating a dataset, tags can be applied to the dataset, which will make searching for the dataset easier.

Organizing your datasets into projects by use-case makes it easier to access the most recent dataset version for that use-case. If only a project is specified when using Dataset.get, the method returns the most recent dataset in a project. The same is true with tags; if a tag is specified, the method will return the most recent dataset that is labeled with that tag.

Document your Datasets#

Attach informative metrics or debug samples to the Dataset itself. Use the get_logger method to access the dataset's logger object, then add any additional information to the dataset, using the methods available with a logger object.

You can add some dataset summaries (like table reporting) to create a preview of the data stored for better visibility, or attach any statistics generated by the data ingestion process.

Periodically Update Your Dataset#

Your data probably changes from time to time. If the data is updated into the same local / network folder structure, which serves as a dataset's single point of truth, you can schedule a script which uses the dataset sync functionality which will update the dataset based on the modifications made to the folder. This way, there is no need to manually modify a dataset. This functionality will also track the modifications made to a folder.

See the sync function with the CLI or SDK interface.