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[](https://autoray.readthedocs.io)
[](https://pypi.org/project/autoray/)
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[`autoray`](https://autoray.readthedocs.io/en/latest) is a lightweight python AUTOmatic-arRAY library for
abstracting your tensor operations. Primarily it provides an
[*automatic* dispatch mechanism](https://autoray.readthedocs.io/en/latest/automatic_dispatch.html#)
that means you can write backend agnostic code that works for:
* [numpy](https://github.com/numpy/numpy)
* [pytorch](https://pytorch.org/)
* [jax](https://github.com/google/jax)
* [cupy](https://github.com/cupy/cupy)
* [dask](https://github.com/dask/dask)
* [autograd](https://github.com/HIPS/autograd)
* [tensorflow](https://github.com/tensorflow/tensorflow)
* [sparse](https://sparse.pydata.org/)
* [mars](https://github.com/mars-project/mars)
* ... and indeed **any** library that provides a numpy-*ish* api, even if it
knows nothing about `autoray`.
Beyond that, abstracting the array interface allows you to:
* *swap [custom versions of functions](https://autoray.readthedocs.io/en/latest/automatic_dispatch.html#functions)
for specific backends*
* *trace through computations [lazily](https://autoray.readthedocs.io/en/latest/lazy_computation.html) without actually
running them*
* *automatically [share intermediates and fold constants](https://autoray.readthedocs.io/en/latest/lazy_computation.html#sharing-intermediates)
in computations*
* *compile functions with a [unified interface](https://autoray.readthedocs.io/en/latest/compilation.html) for different
backends*
## Basic usage
The main function of `autoray` is
[`do`](https://autoray.readthedocs.io/en/latest/autoapi/autoray/autoray/index.html#autoray.autoray.do),
which takes a function
name followed by `*args` and `**kwargs`, and automatically looks up (and
caches) the correct function to match the equivalent numpy call:
```python
from autoray as ar
def noised_svd(x):
# automatic dispatch based on supplied array
U, s, VH = ar.do('linalg.svd', x)
# automatic dispatch based on different array
sn = s + 0.1 * ar.do('random.normal', size=ar.shape(s), like=s)
# automatic dispatch for multiple arrays for certain functions
return ar.do('einsum', 'ij,j,jk->ik', U, sn, VH)
# explicit backend given by string
x = ar.do('random.uniform', size=(100, 100), like="torch")
# this function now works for any backend
y = noised_svd(x)
# explicit inference of backend from array
ar.infer_backend(y)
# 'torch'
```
If you don't like the explicit `do` syntax, or simply want a
drop-in replacement for existing code, you can also import the `autoray.numpy`
module:
```python
from autoray import numpy as np
# set a temporary default backend
with ar.backend_like('cupy'):
z = np.ones((3, 4), dtype='float32')
np.exp(z)
# array([[2.7182817, 2.7182817, 2.7182817, 2.7182817],
# [2.7182817, 2.7182817, 2.7182817, 2.7182817],
# [2.7182817, 2.7182817, 2.7182817, 2.7182817]], dtype=float32)
```
Custom backends and functions can be dynamically registered with:
* [`register_backend`](https://autoray.readthedocs.io/en/latest/autoapi/autoray/autoray/index.html#autoray.autoray.register_backend)
* [`register_function`](https://autoray.readthedocs.io/en/latest/autoapi/autoray/autoray/index.html#autoray.autoray.register_function)
The main documentation is available at [autoray.readthedocs.io](https://autoray.readthedocs.io/en/latest/).
Raw data
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