benchmarks: benchmark dask
Nobody has claimed this yet.
Assessment
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Newbie friendliness
- 35/100
- Issue type
- Feature
- Clarity
- Mostly clear
- Activity status
- Stale
- Tech stack
- python
- Domain
- data, performance
Research direction
Start with the linked pull request discussion to understand the requested benchmark and inspect the repository for its existing benchmark entry point. Add coverage for randomly generated Dask arrays using the shape, dtype, chunks, and mapping approach shown in the issue; done means the Dask benchmark runs alongside the existing benchmarks.
Written by the indexing model from the issue text.
Description
See https://github.com/scverse/fast-array-utils/pull/30#discussion_r1965881913
We could create random dask arrays something like this:
import dask.array as da
arr = da.zeros(shape, dtype=dtype, chunks=_half_chunk_size(shape))
return arr.map_blocks(
lambda x: self.random(x.shape, dtype=x.dtype, gen=gen), dtype=dtype
)
cc @ilan-gold
- Dominant language
- Python
- Stars
- 15
- Forks
- 5
- Avg merge
- 10h 33m
- Merged PRs (30d)
- 9
Contributor guide
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First steps
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- Open a pull request that references the issue number.
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