10000 Initial GPU support (#1967) · zarr-developers/zarr-python@2f9cf22 · GitHub
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Initial GPU support (#1967)
* Initial implementation of a GPU version of Buffer and NDBuffer * Adding cupy as an optional dependency * Adding GPU prototype test * Adding GPU memory store implementation * Addressing comments * Making GpuMemoryStore tests conditional on cupy being available * Adding test checking that existing host memory codecs use the gpu_buffer_prototype appropriately * Reducing code and docs duplication * Formatting * Fixing silent rebase conflicts * Reducing code duplication in GpuMemoryStore * Refactoring to an abstract Buffer class and concrete CPU and GPU implementations of those * Templating store tests on Buffer type * Changing imports to prevent circular dependencies * Fixing unsafe calls to Buffer abstract methods in metadata.py and group.py * Preventing calls to abstract classmethods of Buffer and NDBuffer * Fixing some more unsafe usage of Buffer abstract class * Initial testing with cirun based GPU CI * Reverting to basic ubuntu machine image on GCP * Switching to cuda image from the docker registry * Revert "Switching to cuda image from the docker registry" This reverts commit d473a3d. * Revert "Reverting to basic ubuntu machine image on GCP" This reverts commit e5cfd2f. * Revert "Initial testing with cirun based GPU CI" This reverts commit ff40d3c. * Adding pytest mark for GPU tests * Updating GPU memory store test with gpu mark * Adding GPU workflow that only runs GPU tests * Formatting * Fixing mypy errors in buffer code * Fixing errors in test_buffer.py * Fixing errors in test_buffer.py * Fixing store test errors * Fixing stateful store test * Fixing config test * Fixing group tests * Fixing indexing tests * Manually installing cupy in the GPU workflow * Ablating GPU test matrix and adding gpu optional dependencies to the hatch dependency list * Adding some more logging to debug GPU test failures * Adding GA step to install the CUDA toolkit * Adding a separate gputest hatch environment to simplify GPU testing * Fixing error in cuda-toolkit step * Downgrading to CUDA 12.4.1 in cuda-toolkit GA * Trying manual install of the CUDA toolkit * Updating environment variables with CUDA installation * Removing PATH env and setting it only through GITHUB_PATH * Fixing issue from merge conflict --------- Co-authored-by: Davis Bennett <davis.v.bennett@gmail.com>
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‎.github/workflows/gpu_test.yml

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# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
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# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
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name: GPU Test V3
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on:
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push:
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branches: [ v3 ]
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pull_request:
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branches: [ v3 ]
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workflow_dispatch:
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env:
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LD_LIBRARY_PATH: /usr/local/cuda/extras/CUPTI/lib64:/usr/local/cuda/lib64
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concurrency:
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group: ${{ github.workflow }}-${{ github.ref }}
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cancel-in-progress: true
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jobs:
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test:
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name: py=${{ matrix.python-version }}, np=${{ matrix.numpy-version }}, deps=${{ matrix.dependency-set }}
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runs-on: gpu-runner
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strategy:
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matrix:
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python-version: ['3.11']
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numpy-version: ['2.0']
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dependency-set: ["minimal"]
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steps:
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- uses: actions/checkout@v4
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# - name: cuda-toolkit
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# uses: Jimver/cuda-toolkit@v0.2.16
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# id: cuda-toolkit
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# with:
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# cuda: '12.4.1'
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- name: Set up CUDA
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run: |
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wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-keyring_1.1-1_all.deb
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sudo dpkg -i cuda-keyring_1.1-1_all.deb
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sudo apt-get update
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sudo apt-get -y install cuda-toolkit-12-6
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echo "/usr/local/cuda/bin" >> $GITHUB_PATH
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- name: GPU check
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run: |
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nvidia-smi
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echo $PATH
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echo $LD_LIBRARY_PATH
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nvcc -V
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- name: Set up Python
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uses: actions/setup-python@v5
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with:
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python-version: ${{ matrix.python-version }}
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cache: 'pip'
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- name: Install Hatch and CuPy
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run: |
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python -m pip install --upgrade pip
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pip install hatch
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- name: Set Up Hatch Env
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run: |
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hatch env create gputest.py${{ matrix.python-version }}-${{ matrix.numpy-version }}-${{ matrix.dependency-set }}
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hatch env run -e gputest.py${{ matrix.python-version }}-${{ matrix.numpy-version }}-${{ matrix.dependency-set }} list-env
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- name: Run Tests
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run: |
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hatch env run --env gputest.py${{ matrix.python-version }}-${{ matrix.numpy-version }}-${{ matrix.dependency-set }} run-coverage

‎pyproject.toml

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'ipytree>=0.2.2',
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'ipywidgets>=8.0.0',
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]
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gpu = [
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"cupy-cuda12x",
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]
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docs = [
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'sphinx',
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'sphinx-autobuild>=2021.3.14',
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[tool.hatch.envs.test]
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dependencies = [
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"numpy~={matrix:numpy}",
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"universal_pathlib"
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"universal_pathlib",
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]
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features = ["test", "extra"]
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numpy = ["1.24", "1.26", "2.0"]
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features = ["optional"]
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[[tool.hatch.envs.test.matrix]]
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python = ["3.10", "3.11", "3.12"]
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numpy = ["1.24", "1.26", "2.0"]
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features = ["gpu"]
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[tool.hatch.envs.test.scripts]
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run-coverage = "pytest --cov-config=pyproject.toml --cov=pkg --cov=tests"
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run-coverage-gpu = "pip install cupy-cuda12x && pytest -m gpu --cov-config=pyproject.toml --cov=pkg --cov=tests"
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run = "run-coverage --no-cov"
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run-verbose = "run-coverage --verbose"
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run-mypy = "mypy src"
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run-hypothesis = "pytest --hypothesis-profile ci tests/v3/test_properties.py tests/v3/test_store/test_stateful*"
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list-env = "pip list"
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[tool.hatch.envs.gputest]
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dependencies = [
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"numpy~={matrix:numpy}",
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"universal_pathlib",
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]
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features = ["test", "extra", "gpu"]
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[[tool.hatch.envs.gputest.matrix]]
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python = ["3.10", "3.11", "3.12"]
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numpy = ["1.24", "1.26", "2.0"]
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version = ["minimal"]
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[tool.hatch.envs.gputest.scripts]
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run-coverage = "pytest -m gpu --cov-config=pyproject.toml --cov=pkg --cov=tests"
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run = "run-coverage --no-cov"
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run-verbose = "run-coverage --verbose"
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run-mypy = "mypy src"
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"error:::zarr.*",
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"ignore:PY_SSIZE_T_CLEAN will be required.*:DeprecationWarning",
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"ignore:The loop argument is deprecated since Python 3.8.*:DeprecationWarning",
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"ignore:Creating a zarr.buffer.gpu.*:UserWarning",
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]
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markers = [
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"gpu: mark a test as requiring CuPy and GPU"
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]

‎src/zarr/codecs/blosc.py

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from zarr.abc.codec import BytesBytesCodec
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from zarr.core.array_spec import ArraySpec
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from zarr.core.buffer import Buffer, as_numpy_array_wrapper
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from zarr.core.buffer import Buffer
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from zarr.core.buffer.cpu import as_numpy_array_wrapper
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from zarr.core.common import JSON, parse_enum, parse_named_configuration, to_thread
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from zarr.registry import register_codec
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‎src/zarr/codecs/gzip.py

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from zarr.abc.codec import BytesBytesCodec
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from zarr.core.array_spec import ArraySpec
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from zarr.core.buffer import Buffer, as_numpy_array_wrapper
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from zarr.core.buffer import Buffer
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from zarr.core.buffer.cpu import as_numpy_array_wrapper
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from zarr.core.common import JSON, parse_named_configuration, to_thread
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from zarr.registry import register_codec
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‎src/zarr/codecs/zstd.py

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from zarr.abc.codec import BytesBytesCodec
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from zarr.core.array_spec import ArraySpec
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from zarr.core.buffer import Buffer, as_numpy_array_wrapper
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from zarr.core.buffer import Buffer
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from zarr.core.buffer.cpu import as_numpy_array_wrapper
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from zarr.core.common import JSON, parse_named_configuration, to_thread
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from zarr.registry import register_codec
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‎src/zarr/core/array.py

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# check value shape
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if np.isscalar(value):
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value = np.asanyarray(value, dtype=self.metadata.dtype)
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array_like = prototype.buffer.create_zero_length().as_array_like()
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if isinstance(array_like, np._typing._SupportsArrayFunc):
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# TODO: need to handle array types that don't support __array_function__
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# like PyTorch and JAX
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array_like_ = cast(np._typing._SupportsArrayFunc, array_like)
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value = np.asanyarray(value, dtype=self.metadata.dtype, like=array_like_)
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else:
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if not hasattr(value, "shape"):
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value = np.asarray(value, self.metadata.dtype)
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# assert (
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# value.shape == indexer.shape
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# ), f"shape of value doesn't match indexer shape. Expected {indexer.shape}, got {value.shape}"
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if not hasattr(value, "dtype") or value.dtype.name != self.metadata.dtype.name:
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value = np.array(value, dtype=self.metadata.dtype, order="A")
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if hasattr(value, "astype"):
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# Handle things that are already NDArrayLike more efficiently
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value = value.astype(dtype=self.metadata.dtype, order="A")
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else:
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value = np.array(value, dtype=self.metadata.dtype, order="A")
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value = cast(NDArrayLike, value)
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# We accept any ndarray like object from the user and convert it
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# to a NDBuffer (or subclass). From this point onwards, we only pass

‎src/zarr/core/buffer/__init__.py

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from zarr.core.buffer.core import (
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ArrayLike,
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Buffer,
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BufferPrototype,
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NDArrayLike,
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NDBuffer,
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default_buffer_prototype,
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)
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from zarr.core.buffer.cpu import numpy_buffer_prototype
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__all__ = [
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"ArrayLike",
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"Buffer",
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"NDArrayLike",
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"NDBuffer",
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"BufferPrototype",
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"default_buffer_prototype",
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"numpy_buffer_prototype",
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]

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