Querying Datasets
Activeloop Platform offer a highly-performant SQL-style query engine for filtering your data
How to query machine learning datasets using Activeloop's query engine
Querying datasets is a critical aspect of data science workflows that enables users to filter datasets and focus their work on the most relevant data at hand. Activeloop offers a highly-performant dataset query engine built in C++ and optimized for Deep Lake datasets.
Dataset Query Summary
Saving and utilizing dataset query results
The query results (Dataset Views
) can be saved in the UI as shown above, or if the view is generated in Python, it can be saved using the Python API below. Full details are available here.
In order to maintain data lineage, Dataset Views
are immutable and are connected to specific commits. Therefore, views can only be saved if the dataset has a commit and there are no uncommitted changes in the HEAD
.
Dataset Views
can be loaded in the python API and they can passed to ML frameworks just like regular datasets:
The optimize
parameter in ds.load_view(...,
optimize = True
)
materializes the Dataset View
into a new sub-dataset that is optimized for streaming. If the original dataset uses linked tensors, the data will be copied to Deep Lake format.
Optimizing the Dataset View
is critical for achieving rapid streaming.
If the saved Dataset View
is no longer needed, it can be deleted using:
Dataset Query Syntax
CONTAINS and ==
Any special characters in tensor or group names should be wrapped with double-quotes:
SHAPE
LIMIT
AND, OR, NOT
UNION and INTERSECT
ORDER BY
ANY, ALL, and ALL_STRICT
all
adheres to NumPy and list logic where all(empty_sample)
returns True
all_strict
is more intuitive for queries so all_strict(empty_sample)
returns False
LOGICAL_AND and LOGICAL_OR
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