Using GPUs with Zarr

Zarr can use GPUs to accelerate your workload by running zarr.config.enable_gpu().

Note

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.

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.

Reading data into device memory

zarr.config.enable_gpu() configures Zarr to use GPU memory for the data buffers used internally by Zarr.

>>> import zarr
>>> import cupy as cp  
>>> zarr.config.enable_gpu()  
>>> store = zarr.storage.MemoryStore()  
>>> z = zarr.create_array(  
...     store=store, shape=(100, 100), chunks=(10, 10), dtype="float32",
... )
>>> type(z[:10, :10])  
cupy.ndarray

Note that the output type is a cupy.ndarray rather than a NumPy array.