#!/usr/bin/env python3
import warnings
from .settings import _feature_flag, _value_context
class _moved_beta_feature(object):
def __init__(self, new_cls, orig_name=None):
self.new_cls = new_cls
self.orig_name = orig_name if orig_name is not None else "gpytorch.settings.{}".format(new_cls.__name__)
def __call__(self, *args, **kwargs):
warnings.warn(
"`{}` has moved to `gpytorch.settings.{}`.".format(self.orig_name, self.new_cls.__name__),
DeprecationWarning,
)
return self.new_cls(*args, **kwargs)
def __getattr__(self, name):
return getattr(self.new_cls, name)
[docs]class checkpoint_kernel(_value_context):
"""
Should the kernel be computed in chunks with checkpointing or not? (Default, no)
If `split_size = 0`:
The kernel is computed explicitly. During training, the kernel matrix is
kept in memory for the backward pass. This is the fastest option but the
most memory intensive.
If `split_size > 0`:
The kernel is never fully computed or stored. Instead, the kernel is only
accessed through matrix multiplication. The matrix multiplication is
computed in `segments` chunks. This is slower, but requires significantly less memory.
Default: 0
"""
_global_value = 0
[docs]class default_preconditioner(_feature_flag):
"""
Add a diagonal correction to scalable inducing point methods
"""
pass
__all__ = ["checkpoint_kernel", "default_preconditioner"]