kedro.io.AbstractDataSet()
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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.AbstractTransformer()
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Transformers will be deprecated in Kedro 0.18.0 in favour of the Dataset Hooks. |
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.DataCatalogWithDefault([data_sets, …])
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A DataCatalog with a default DataSet implementation for any data set which is not registered in the catalog. |
kedro.io.Version(load, save)
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This namedtuple is used to provide load and save versions for versioned data sets. |