pyhf.set_backend¶
- pyhf.set_backend(backend, custom_optimizer=None, precision=None)[source]¶
Set the backend and the associated optimizer
Example
>>> import pyhf >>> pyhf.set_backend("tensorflow") >>> pyhf.tensorlib.name 'tensorflow' >>> pyhf.tensorlib.precision '64b' >>> pyhf.set_backend(b"pytorch", precision="32b") >>> pyhf.tensorlib.name 'pytorch' >>> pyhf.tensorlib.precision '32b' >>> pyhf.set_backend(pyhf.tensor.numpy_backend()) >>> pyhf.tensorlib.name 'numpy' >>> pyhf.tensorlib.precision '64b'
- Parameters
backend (
str
or pyhf.tensor backend) – One of the supported pyhf backends: NumPy, TensorFlow, PyTorch, and JAXcustom_optimizer (pyhf.optimize optimizer) – Optional custom optimizer defined by the user
precision (
str
) – Floating point precision to use in the backend:64b
or32b
. Default is backend dependent.
- Returns
None