# Welcome to autoray’s documentation!
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[autoray](autoray) is a lightweight python AUTOmatic-arRAY library for abstracting your tensor operations. Primarily it provides an [automatic dispatch mechanism](automatic_dispatch) that means you can write backend agnostic code that works for:
[numpy](https://github.com/numpy/numpy)
[pytorch](https://pytorch.org/)
[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/)
… 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](automatic_dispatch.md#functions) for specific backends
trace through computations [lazily](lazy_computation) without actually running them
automatically [share intermediates and fold constants](lazy_computation) in computations
compile functions with a [unified interface](compilation) for different backends
## Basic usage
The main function of autoray is [do](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:
- 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](autoray.do) syntax, or simply want a drop-in replacement for existing code, you can also import the autoray.numpy module:
```{code-block} 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](autoray.register_backend)
[register_function](autoray.register_function)
—
## Advanced details
```{toctree} :caption: Guides :maxdepth: 2
installation.md automatic_dispatch.md lazy_computation.ipynb compilation.ipynb development.md ```
```{toctree} :caption: Links :hidden:
GitHub Repository <https://github.com/jcmgray/autoray> ```