_global_context = _core.Context()
_core.set_global_default_context(_global_context)
# Implements ndarray methods in Python
from chainerx import _ndarray
_ndarray.populate()
# Temporary workaround implementations that fall back to NumPy/CuPy's
# respective functions.
from chainerx import _fallback_workarounds
_fallback_workarounds.populate()
# Dynamically inject docstrings
from chainerx import _docs
_docs.set_docs()
from chainerx import _cuda
# Share memory pool with CuPy.
if bool(int(os.getenv('CHAINERX_CUDA_CUPY_SHARE_ALLOCATOR', '0'))):
_cuda.cupy_share_allocator()
else:
class ndarray(object):
"""Dummy class for type testing."""
def __init__(self, *args, **kwargs):
raise RuntimeError('chainerx is not available.')
def is_available():
_global_context = _core.Context()
_core.set_global_default_context(_global_context)
# Implements ndarray methods in Python
from chainerx import _ndarray
_ndarray.populate()
# Temporary workaround implementations that fall back to NumPy/CuPy's
# respective functions.
from chainerx import _fallback_workarounds
_fallback_workarounds.populate()
# Dynamically inject docstrings
from chainerx import _docs
_docs.set_docs()
from chainerx import _cuda
# Share memory pool with CuPy.
if bool(int(os.getenv('CHAINERX_CUDA_CUPY_SHARE_ALLOCATOR', '0'))):
_cuda.cupy_share_allocator()
else:
class ndarray(object):
"""Dummy class for type testing."""
def __init__(self, *args, **kwargs):
raise RuntimeError('chainerx is not available.')
def is_available():