Comments (4)
jbms
commented on May 15, 2024
3
zarr vs n5 performance will be very similar. In general I would recommend zarr over n5 since zarr is becoming more standard and offers a more functionality.
Compared to zarr-python, tensorstore is better able to take advantage of multiple CPU cores, and multiple concurrent IO operations, without relying on additional layers like dask for parallelism. In general with zarr-python it is usually necessary to use very large chunk sizes to get reasonable performance; that is not generally the case with tensorstore.
Of course actual benchmarks would be better than these anecdotes, but we haven't run anything systematic yet.
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harpone
commented on May 15, 2024
+1 for this. Also would be nice to have something about pros/cons of e.g. zarr vs n5 drivers...
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Axel-Jacobsen
commented on May 15, 2024
+1 - I am curious what performance impact one could expect
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JackKelly
commented on May 15, 2024
This is helpful, thank you!
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Related Issues (20)
- Where is the change log? HOT 2
- Registry Check fails in external package using Tensorstore as a dependency - Windows Python Wheel HOT 3
- Concatenating multiple archives HOT 8
- tensorstore cannot open vlen UTF8 string written with Zarr-Python HOT 1
- Bad Request error to access H01 dataset on a local machine HOT 2
- `zarr` driver fails to load quoted floating point data for `fill_value` HOT 1
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- TensorStore does not compile with latest Visual Studio HOT 19
- Master does not compile on Linux HOT 11
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- Converting c-order array to fortran-order array HOT 1
- Updated `bazel_to_cmake` causes trouble HOT 6
- Reading data from neuroglancer in the correct order HOT 3
- Generate `.pyi` files for type inference compatibility
- windows build failing in riegeli::EstimatedAllocatedSize HOT 16
- Writing to new Neuroglancer dataset in C++ HOT 4
- Replace deprecated `set-output` command with environment file HOT 1
- Any plans to implement ZEP0002 - Sharding codec? HOT 4
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