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Fixed bug with Zarr using device memory, instead of host memory, for storing metadata when using GPUs. |
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Added new user guide on :ref:`user-guide-gpu`. |
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Added :meth:`zarr.config.enable_gpu` to update Zarr's configuration to use GPUs. |
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Alters the behavior of ``create_array`` to ensure that any groups implied by the array's name are created if they do not already exist. Also simplifies the type signature for any function that takes an ArrayConfig-like object. |
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Add arbitrary `shards` to Hypothesis strategy for generating arrays. |
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.. _user-guide-gpu: | ||
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Using GPUs with Zarr | ||
==================== | ||
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Zarr can use GPUs to accelerate your workload by running | ||
:meth:`zarr.config.enable_gpu`. | ||
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.. note:: | ||
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`zarr-python` currently supports reading the ndarray data into device (GPU) | ||
memory as the final stage of the codec pipeline. Data will still be read into | ||
or copied to host (CPU) memory for encoding and decoding. | ||
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In the future, codecs will be available compressing and decompressing data on | ||
the GPU, avoiding the need to move data between the host and device for | ||
compression and decompression. | ||
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Reading data into device memory | ||
------------------------------- | ||
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:meth:`zarr.config.enable_gpu` configures Zarr to use GPU memory for the data | ||
buffers used internally by Zarr. | ||
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.. code-block:: python | ||
>>> import zarr | ||
>>> import cupy as cp # doctest: +SKIP | ||
>>> zarr.config.enable_gpu() # doctest: +SKIP | ||
>>> store = zarr.storage.MemoryStore() # doctest: +SKIP | ||
>>> z = zarr.create_array( # doctest: +SKIP | ||
... store=store, shape=(100, 100), chunks=(10, 10), dtype="float32", | ||
... ) | ||
>>> type(z[:10, :10]) # doctest: +SKIP | ||
cupy.ndarray | ||
Note that the output type is a ``cupy.ndarray`` rather than a NumPy array. |
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performance | ||
consolidated_metadata | ||
extending | ||
gpu | ||
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.. Coming soon | ||
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