3. Data Versioning

Dataloop’s powerful data versioning provides you with unique tools for data management - clone, merge, slice & dice your files, to create multiple versions for various applications. Sample use cases include:Golden training sets managementReproducibility (dataset training snapshot)Experimentation (creating subsets from different kinds)Task/Assignment managementData Version “Snapshot” - Use our versioning feature as a way to save data (items, annotations, metadata) before any major process. For example, a snapshot can serve as a roll-back mechanism to original datasets in case of any error without losing the data.

3.1. Clone Datasets

Cloning a dataset creates a new dataset with the same files as the original. Files are actually a reference to the original binary and not a new copy of the original, so your cloud data remains safe and protected. When cloning a dataset, you can add a destination dataset, remote file path, and more…

dataset = project.datasets.get(dataset_id='my-dataset-id')

3.2. Merge Datasets

Dataset merging outcome depends on how similar or different the datasets are.

  • Cloned Datasets - items, annotations, and metadata will be merged. This means that you will see annotations from different datasets on the same item.

  • Different datasets (not clones) with similar recipes - items will be summed up, which will cause duplication of similar items.

  • Datasets with different recipes - Datasets with different default recipes cannot be merged. Use the ‘Switch recipe’ option on dataset level (3-dots action button) to match recipes between datasets and be able to merge them.

dataset_ids = ["dataset-1-id", "dataset-2-id"]
project_ids = ["dataset-1-project-id", "dataset-2-project-id"]
dataset_merge = dl.datasets.merge(merge_name="my_dataset-merge",