Serialization¶
- metatensor.torch.save(path: str, data: TensorMap | TensorBlock | Labels)[source]¶
Save the given data (either
TensorMap
,TensorBlock
, orLabels
) to the given file at the givenpath
.If the file already exists, it is overwritten. When saving a
TensorMap
orTensorBlock
, the file extension should be.npz
; and when savingLabels
it should be.npy
- Parameters:
path (str) – path of the file where to save the data
data (TensorMap | TensorBlock | Labels) – data to serialize and save
- metatensor.torch.save_buffer(data: TensorMap | TensorBlock | Labels) Tensor [source]¶
Save the given data (either
TensorMap
,TensorBlock
, orLabels
) to an in-memory buffer, represented as 1-dimensionaltorch.Tensor
ofuint8
.- Parameters:
data (TensorMap | TensorBlock | Labels) – data to serialize and save
- Return type:
- metatensor.torch.load(path: str) TensorMap [source]¶
Load a previously saved
TensorMap
from the given path.TensorMap
are serialized using numpy’s.npz
format, i.e. a ZIP file without compression (storage method isSTORED
), where each file is stored as a.npy
array. See the C API documentation for more information on the format.
- metatensor.torch.load_block(path: str) TensorBlock [source]¶
Load previously saved
TensorBlock
from the given file.- Parameters:
path (str) – path of the file to load
- Return type:
- metatensor.torch.load_labels(path: str) Labels [source]¶
Load previously saved
Labels
from the given file.
- metatensor.torch.load_buffer(buffer: Tensor) TensorMap [source]¶
Load a previously saved
TensorMap
from an in-memory buffer, stored inside a 1-dimensionaltorch.Tensor
ofuint8
.
- metatensor.torch.load_block_buffer(buffer: Tensor) TensorBlock [source]¶
Load a previously saved
TensorBlock
from an in-memory buffer, stored inside a 1-dimensionaltorch.Tensor
ofuint8
.- Parameters:
buffer (Tensor) – CPU tensor of
uint8
representing a in-memory buffer- Return type: