kedro.io.AbstractDataSet (*args, **kwds)
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AbstractDataSet is the base class for all data set implementations. All data set implementations should extend this abstract class and implement the methods marked as abstract. If a specific dataset implementation cannot be used in conjunction with the ParallelRunner , such user-defined dataset should have the attribute _SINGLE_PROCESS = True. Example: ::.
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kedro.io.AbstractVersionedDataSet (filepath, ...)
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AbstractVersionedDataSet is the base class for all versioned data set implementations.
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kedro.io.DataCatalog ([data_sets, feed_dict, ...])
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DataCatalog stores instances of AbstractDataSet implementations to provide load and save capabilities from anywhere in the program.
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kedro.io.LambdaDataSet (load, save[, exists, ...])
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LambdaDataSet loads and saves data to a data set.
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kedro.io.MemoryDataSet ([data, copy_mode, ...])
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MemoryDataSet loads and saves data from/to an in-memory Python object.
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kedro.io.PartitionedDataSet (path, dataset[, ...])
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PartitionedDataSet loads and saves partitioned file-like data using the underlying dataset definition.
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kedro.io.IncrementalDataSet (path, dataset[, ...])
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IncrementalDataSet inherits from PartitionedDataSet , which loads and saves partitioned file-like data using the underlying dataset definition.
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kedro.io.CachedDataSet (dataset[, version, ...])
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CachedDataSet is a dataset wrapper which caches in memory the data saved, so that the user avoids io operations with slow storage media.
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kedro.io.Version (load, save)
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This namedtuple is used to provide load and save versions for versioned data sets. |