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Add Dataset.from_generator to materialize a stream or a Julia iterable - #111
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#109) `from_generator(source)` (also `Dataset.from_generator` and `Dataset(stream)`) writes the examples of an `IterableDataset`, a Julia iterable (or a function returning one, optionally yielding batches of columns with `batched = true`), or a Python generator function to a cached, memory-mapped `Dataset` in the "julia" format. A Julia source is cached under its content fingerprint, as for `map`; a stream is rebuilt on every call, in a temporary directory, unless given a `fingerprint`. The fingerprint is carried by the generator's pickle, so `features` and `split` stay in the cache key. An error in the generator is rethrown instead of `datasets`' uninformative `DatasetGenerationError`, and `num_proc > 1` throws an `ArgumentError`. Require `datasets >= 5.1` and drop the `_consolidate` workaround for older versions. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Write `ds.map`/`ds.filter` (and `dd.`, `stream.`, `ds.shuffle`, ...) instead of a bare `map` or `filter`, which reads as Base's function. Examples use the property calls, docstring headers list `ds.map(f; kws...)` before the `map(f, ds::Dataset)` method, and Python-side names are written `datasets.Dataset.map`. Also drop the obsolete `datasets` < 4.8.3 note from the `RaggedVector` docstring. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
`map` and `filter` are methods of `Base.map`/`Base.filter` on the package's types, so the unqualified `map(f, ds)` is unambiguous and idiomatic Julia. Keeps dropping the obsolete `datasets` < 4.8.3 note from the `RaggedVector` docstring. This reverts commit 5243e07. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
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Closes #109.
Adds
from_generator(source), also reachable asDataset.from_generatorandDataset(stream). It writes the examples to a cached, memory-mappedDatasetin the"julia"format.sourcecan be:IterableDataset, with its Juliamap/filtersteps, iterated in-process (replaces theITERhack). The stream'sfeaturesandsplitcarry over.Dicts/NamedTuples, or a function returning one. Withbatched = trueit yields batches of columns, which are split into rows in Python.How it behaves:
map. A stream can't be fingerprinted, so withoutfingerprintit is rebuilt on every call, indatasets' temporary directory, so the HF cache doesn't fill up with unreusable copies.JuliaFunction), sofeaturesandsplitstay in the cache key.datasets' ownfingerprintkeyword would ignore them.datasets' uninformativeDatasetGenerationError.num_proc > 1: throws anArgumentError.Also raises the floor to
datasets >= 5.1and removes the_consolidateworkaround for older versions.Tested on TinyStories validation (stream + batched Julia
map+fingerprint): the rows are identical to the non-streamingmap, and a new session reuses the cache.🤖 Generated with Claude Code