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# 11.1. Array API support (experimental)

The Array API specification defines a standard API for all array manipulation libraries with a NumPy-like API. Scikit-learn’s Array API support requires array-api-compat to be installed.

Some scikit-learn estimators that primarily rely on NumPy (as opposed to using Cython) to implement the algorithmic logic of their `fit`, `predict` or `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.

Note

Currently, only `cupy.array_api`, `numpy.array_api`, `cupy`, and `PyTorch` are known to work with scikit-learn’s estimators.

## 11.1.1. Example usage

Here is an example code snippet to demonstrate how to use CuPy to run `LinearDiscriminantAnalysis` on a GPU:

    >>> 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'>

### 11.1.1.1. 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. Estimators with support for `Array API`-compatible inputs

- `discriminant_analysis.LinearDiscriminantAnalysis` (with `solver="svd"`)

Coverage for more estimators is expected to grow over time. Please follow the dedicated meta-issue on GitHub to track progress.

## 11.1.3. Common estimator checks

Add the `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.