def export_mxnet(model: gluon.HybridBlock,
sample_input: List[mx.ndarray.NDArray]):
model.hybridize()
model(*sample_input)
directory = "build"
if os.path.exists(directory) and os.path.isdir(directory):
shutil.rmtree(directory)
os.mkdir(directory)
model_path = os.path.join(directory, "mxmodel")
# Create and stored the model
model.export(model_path)
zip_files(directory, model_path)
# Start creating template
array = [ndarray_to_numpy(x) for x in sample_input]
dest = get_djl_template("mxmodel.zip", array, add_deps())
shutil.copy(model_path + ".zip", dest)
def save(
name: str,
model: gluon.HybridBlock,
*,
labels: t.Optional[t.Dict[str, str]] = None,
custom_objects: t.Optional[t.Dict[str, t.Any]] = None,
metadata: t.Optional[t.Dict[str, t.Any]] = None,
model_store: "ModelStore" = Provide[BentoMLContainer.model_store],
) -> Tag:
"""
Save a model instance to BentoML modelstore.
Args:
name (:code:`str`):
Name for given model instance. This should pass Python identifier check.
model (`mxnet.gluon.HybridBlock`):
Instance of gluon.HybridBlock model to be saved.
labels (:code:`Dict[str, str]`, `optional`, default to :code:`None`):
user-defined labels for managing models, e.g. team=nlp, stage=dev
custom_objects (:code:`Dict[str, Any]]`, `optional`, default to :code:`None`):
user-defined additional python objects to be saved alongside the model,
e.g. a tokenizer instance, preprocessor function, model configuration json
metadata (:code:`Dict[str, Any]`, `optional`, default to :code:`None`):
Custom metadata for given model.
model_store (:mod:`~bentoml._internal.models.store.ModelStore`, default to :mod:`BentoMLContainer.model_store`):
BentoML modelstore, provided by DI Container.
Returns:
:obj:`~bentoml.Tag`: A :obj:`tag` with a format `name:version` where `name` is the user-defined model's name, and a generated `version` by BentoML.
Examples:
.. code-block:: python
import mxnet
import mxnet.gluon as gluon
import bentoml
def train_gluon_classifier() -> gluon.nn.HybridSequential:
net = mxnet.gluon.nn.HybridSequential()
net.hybridize()
net.forward(mxnet.nd.array(0))
return net
model = train_gluon_classifier()
tag = bentoml.gluon.save("gluon_block", model)
""" # noqa
context: t.Dict[str, t.Any] = {
"framework_name": "gluon",
"pip_dependencies": [f"mxnet=={get_pkg_version('mxnet')}"],
}
options: t.Dict[str, t.Any] = dict()
with bentoml.models.create(
name,
module=MODULE_NAME,
labels=labels,
custom_objects=custom_objects,
options=options,
context=context,
metadata=metadata,
) as _model:
model.export(_model.path_of(SAVE_NAMESPACE))
return _model.tag
