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 management
Reproducibility (dataset training snapshot)
Experimentation (creating subsets from different kinds)
Task/Assignment management
Data 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.

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_id = 'my-dataset-id'
clone_name = 'clone-name'
dataset = project.datasets.get(dataset_id=dataset_id)
dataset_clone = dataset.clone(clone_name=clone_name,
                              filters=dl.Filters(field='dir', values='/only-this-folder'),

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"]
merge_name = 'my_dataset-merge'
dataset_merge = dl.datasets.merge(merge_name=merge_name,