allennlp.models.archival

Helper functions for archiving models and restoring archived models.

class allennlp.models.archival.Archive[source]

Bases: tuple

An archive comprises a Model and its experimental config

config

Alias for field number 1

extract_module(path: str, freeze: bool = True) → torch.nn.modules.module.Module[source]

This method can be used to load a module from the pretrained model archive.

It is also used implicitly in FromParams based construction. So instead of using standard params to construct a module, you can instead load a pretrained module from the model archive directly. For eg, instead of using params like {“type”: “module_type”, ...}, you can use the following template:

{
    "_pretrained": {
        "archive_file": "../path/to/model.tar.gz",
        "path": "path.to.module.in.model",
        "freeze": False
    }
}

If you use this feature with FromParams, take care of the following caveat: Call to initializer(self) at end of model initializer can potentially wipe the transferred parameters by reinitializing them. This can happen if you have setup initializer regex that also matches parameters of the transferred module. To safe-guard against this, you can either update your initializer regex to prevent conflicting match or add extra initializer:

[
    [".*transferred_module_name.*", "prevent"]]
]
Parameters:
path : str, required

Path of target module to be loaded from the model. Eg. “_textfield_embedder.token_embedder_tokens”

freeze : bool, optional (default=True)

Whether to freeze the module parameters or not.

model

Alias for field number 0

allennlp.models.archival.archive_model(serialization_dir: str, weights: str = 'best.th', files_to_archive: typing.Dict[str, str] = None) → None[source]

Archive the model weights, its training configuration, and its vocabulary to model.tar.gz. Include the additional files_to_archive if provided.

Parameters:
serialization_dir: ``str``

The directory where the weights and vocabulary are written out.

weights: ``str``, optional (default=_DEFAULT_WEIGHTS)

Which weights file to include in the archive. The default is best.th.

files_to_archive: ``Dict[str, str]``, optional (default=None)

A mapping {flattened_key -> filename} of supplementary files to include in the archive. That is, if you wanted to include params['model']['weights'] then you would specify the key as “model.weights”.

allennlp.models.archival.load_archive(archive_file: str, cuda_device: int = -1, overrides: str = '', weights_file: str = None) → allennlp.models.archival.Archive[source]

Instantiates an Archive from an archived tar.gz file.

Parameters:
archive_file: ``str``

The archive file to load the model from.

weights_file: ``str``, optional (default = None)

The weights file to use. If unspecified, weights.th in the archive_file will be used.

cuda_device: ``int``, optional (default = -1)

If cuda_device is >= 0, the model will be loaded onto the corresponding GPU. Otherwise it will be loaded onto the CPU.

overrides: ``str``, optional (default = “”)

JSON overrides to apply to the unarchived Params object.