Backends

Supported backends

The project currently supports the following ML backends:

Backend Enum

We provide a Backend Enum class to help you choose the backend you want to use or for type hinting purposes.

from tensorshare import Backend

flax_backend = Backend.FLAX
>>> <Backend.FLAX: "flax">

numpy_backend = Backend.NUMPY
>>> <Backend.NUMPY: "numpy">

paddlepaddle_backend = Backend.PADDLEPADDLE
>>> <Backend.PADDLEPADDLE: "paddlepaddle">

tensorflow_backend = Backend.TENSORFLOW
>>> <Backend.TENSORFLOW: "tensorflow">

backend = Backend.TORCH
>>> <Backend.TORCH: "torch">

Tip

When a method requires you to specify a backend you can choose to use the Backend Enum class or the string representation of the backend. For example, the following two lines are equivalent:

ts = TensorShare(...)

tensors = ts.to_tensors(backend=Backend.FLAX)
tensors = ts.to_tensors(backend="flax")

One is prone to typos. The other enables type hinting and IDE autocompletion.

The choice is yours. 😉

TensorType Enum

We also provide a TensorType Enum class to help you choose the tensor type you want to use or for type hinting purposes.

from tensorshare import TensorType

flax_tensor = TensorType.FLAX
>>> <TensorType.FLAX: "jaxlib.xla_extension.ArrayImpl">

numpy_tensor = TensorType.NUMPY
>>> <TensorType.NUMPY: "numpy.ndarray">

paddlepaddle_tensor = TensorType.PADDLEPADDLE
>>> <TensorType.PADDLEPADDLE: "paddle.Tensor">

tensorflow_tensor = TensorType.TENSORFLOW
>>> <TensorType.TENSORFLOW: "tensorflow.Tensor">

torch_tensor = TensorType.TORCH
>>> <TensorType.TORCH: "torch.Tensor">

Last update: 2023-08-20
Created: 2023-08-20