def test_save_hdf5(self):
# HDF5 support is optional
if not ht.io.supports_hdf5():
return
# local unsplit data
local_data = ht.arange(100)
ht.save_hdf5(local_data,
self.HDF5_OUT_PATH,
self.HDF5_DATASET,
dtype=local_data.dtype.char())
if local_data.comm.rank == 0:
with ht.io.h5py.File(self.HDF5_OUT_PATH, "r") as handle:
comparison = torch.tensor(handle[self.HDF5_DATASET],
dtype=torch.int32,
device=self.device.torch_device)
self.assertTrue((local_data.larray == comparison).all())
# distributed data range
split_data = ht.arange(100, split=0)
ht.save_hdf5(split_data,
self.HDF5_OUT_PATH,
self.HDF5_DATASET,
dtype=split_data.dtype.char())
if split_data.comm.rank == 0:
with ht.io.h5py.File(self.HDF5_OUT_PATH, "r") as handle:
comparison = torch.tensor(handle[self.HDF5_DATASET],
dtype=torch.int32,
device=self.device.torch_device)
self.assertTrue((local_data.larray == comparison).all())
def test_save_hdf5_exception(self):
# HDF5 support is optional
if not ht.io.supports_hdf5():
return
# dummy data
data = ht.arange(1)
with self.assertRaises(TypeError):
ht.save_hdf5(1, self.HDF5_OUT_PATH, self.HDF5_DATASET)
with self.assertRaises(TypeError):
ht.save_hdf5(data, 1, self.HDF5_DATASET)
with self.assertRaises(TypeError):
ht.save_hdf5(data, self.HDF5_OUT_PATH, 1)