11.1. Array API support (experimental)¶
Some scikit-learn estimators that primarily rely on NumPy (as opposed to using
Cython) to implement the algorithmic logic of their
transform methods can be configured to accept any Array API compatible input
datastructures and automatically dispatch operations to the underlying namespace
instead of relying on NumPy.
At this stage, this support is considered experimental and must be enabled explicitly as explained in the following.
are known to work with scikit-learn’s estimators.
11.1.1. Example usage¶
>>> from sklearn.datasets import make_classification >>> from sklearn import config_context >>> from sklearn.discriminant_analysis import LinearDiscriminantAnalysis >>> import cupy >>> X_np, y_np = make_classification(random_state=0) >>> X_cu = cupy.asarray(X_np) >>> y_cu = cupy.asarray(y_np) >>> X_cu.device <CUDA Device 0> >>> with config_context(array_api_dispatch=True): ... lda = LinearDiscriminantAnalysis() ... X_trans = lda.fit_transform(X_cu, y_cu) >>> X_trans.device <CUDA Device 0>
After the model is trained, fitted attributes that are arrays will also be
from the same Array API namespace as the training data. For example, if CuPy’s
Array API namespace was used for training, then fitted attributes will be on the
GPU. We provide a experimental
_estimator_with_converted_arrays utility that
transfers an estimator attributes from Array API to a ndarray:
>>> from sklearn.utils._array_api import _estimator_with_converted_arrays >>> cupy_to_ndarray = lambda array : array.get() >>> lda_np = _estimator_with_converted_arrays(lda, cupy_to_ndarray) >>> X_trans = lda_np.transform(X_np) >>> type(X_trans) <class 'numpy.ndarray'>
18.104.22.168. PyTorch Support¶
PyTorch Tensors are supported by setting
array_api_dispatch=True and passing in
the tensors directly:
>>> import torch >>> X_torch = torch.asarray(X_np, device="cuda", dtype=torch.float32) >>> y_torch = torch.asarray(y_np, device="cuda", dtype=torch.float32) >>> with config_context(array_api_dispatch=True): ... lda = LinearDiscriminantAnalysis() ... X_trans = lda.fit_transform(X_torch, y_torch) >>> type(X_trans) <class 'torch.Tensor'> >>> X_trans.device.type 'cuda'
11.1.2. Support for
Array API-compatible inputs¶
Estimators and other tools in scikit-learn that support Array API compatible inputs.
Coverage is expected to grow over time. Please follow the dedicated meta-issue on GitHub to track progress.
11.1.3. Common estimator checks¶
array_api_support tag to an estimator’s set of tags to indicate that
it supports the Array API. This will enable dedicated checks as part of the
common tests to verify that the estimators result’s are the same when using
vanilla NumPy and Array API inputs.
To run these checks you need to install
array_api_compat in your
test environment. To run the full set of checks you need to install both
PyTorch and CuPy and have
a GPU. Checks that can not be executed or have missing dependencies will be
automatically skipped. Therefore it’s important to run the tests with the
-v flag to see which checks are skipped:
pip install array-api-compat # and other libraries as needed pytest -k "array_api" -v
22.214.171.124. Note on MPS device support¶
On macOS, PyTorch can use the Metal Performance Shaders (MPS) to access hardware accelerators (e.g. the internal GPU component of the M1 or M2 chips). However, the MPS device support for PyTorch is incomplete at the time of writing. See the following github issue for more details:
To enable the MPS support in PyTorch, set the environment variable
PYTORCH_ENABLE_MPS_FALLBACK=1 before running the tests:
PYTORCH_ENABLE_MPS_FALLBACK=1 pytest -k "array_api" -v
At the time of writing all scikit-learn tests should pass, however, the computational speed is not necessarily better than with the CPU device.