def log_model(self, key, body=None, tag='', model_dir=None, model_file=None,
metrics=None, parameters=None, artifact_path=None,
upload=True, labels=None, inputs=None, outputs=None,
extra_data=None, db_key=None):
"""log a model artifact and optionally upload it"""
model = ModelArtifact(key, body, model_file=model_file,
metrics=metrics, parameters=parameters,
inputs=inputs, outputs=outputs, extra_data=extra_data)
item = self._artifacts_manager.log_artifact(self, model, local_path=model_dir,
artifact_path=artifact_path,
tag=tag,
upload=upload,
db_key=db_key,
labels=labels)
self._update_db()
return item
def log_model(
self,
key,
body=None,
framework="",
tag="",
model_dir=None,
model_file=None,
algorithm=None,
metrics=None,
parameters=None,
artifact_path=None,
upload=True,
labels=None,
inputs: List[Feature] = None,
outputs: List[Feature] = None,
feature_vector: str = None,
feature_weights: list = None,
training_set=None,
label_column: Union[str, list] = None,
extra_data=None,
db_key=None,
**kwargs,
):
"""log a model artifact and optionally upload it to datastore
example::
context.log_model("model", body=dumps(model),
model_file="model.pkl",
metrics=context.results,
training_set=training_df,
label_column='label',
feature_vector=feature_vector_uri,
labels={"app": "fraud"})
:param key: artifact key or artifact class ()
:param body: will use the body as the artifact content
:param model_file: path to the local model file we upload (see also model_dir)
:param model_dir: path to the local dir holding the model file and extra files
:param artifact_path: target artifact path (when not using the default)
to define a subpath under the default location use:
`artifact_path=context.artifact_subpath('data')`
:param framework: name of the ML framework
:param algorithm: training algorithm name
:param tag: version tag
:param metrics: key/value dict of model metrics
:param parameters: key/value dict of model parameters
:param inputs: ordered list of model input features (name, type, ..)
:param outputs: ordered list of model output/result elements (name, type, ..)
:param upload: upload to datastore (default is True)
:param labels: a set of key/value labels to tag the artifact with
:param feature_vector: feature store feature vector uri (store://feature-vectors/<project>/<name>[:tag])
:param feature_weights: list of feature weights, one per input column
:param training_set: training set dataframe, used to infer inputs & outputs
:param label_column: which columns in the training set are the label (target) columns
:param extra_data: key/value list of extra files/charts to link with this dataset
value can be abs/relative path string | bytes | artifact object
:param db_key: the key to use in the artifact DB table, by default
its run name + '_' + key
db_key=False will not register it in the artifacts table
:returns: artifact object
"""
if training_set is not None and inputs:
raise MLRunInvalidArgumentError(
"cannot specify inputs and training set together")
model = ModelArtifact(
key,
body,
model_file=model_file,
metrics=metrics,
parameters=parameters,
inputs=inputs,
outputs=outputs,
framework=framework,
algorithm=algorithm,
feature_vector=feature_vector,
feature_weights=feature_weights,
extra_data=extra_data,
**kwargs,
)
if training_set is not None:
model.infer_from_df(training_set, label_column)
item = self._artifacts_manager.log_artifact(
self,
model,
local_path=model_dir,
artifact_path=extend_artifact_path(artifact_path,
self.artifact_path),
tag=tag,
upload=upload,
db_key=db_key,
labels=labels,
)
self._update_db()
return item
def log_model(
self,
key,
body=None,
framework="",
tag="",
model_dir=None,
model_file=None,
metrics=None,
parameters=None,
artifact_path=None,
upload=True,
labels=None,
inputs=None,
outputs=None,
extra_data=None,
db_key=None,
):
"""log a model artifact and optionally upload it to datastore
:param key: artifact key or artifact class ()
:param body: will use the body as the artifact content
:param model_file: path to the local model file we upload (seel also model_dir)
:param model_dir: path to the local dir holding the model file and extra files
:param artifact_path: target artifact path (when not using the default)
to define a subpath under the default location use:
artifact_path=context.artifact_subpath('data')
:param framework: name of the ML framework
:param tag: version tag
:param metrics: key/value dict of model metrics
:param parameters: key/value dict of model parameters
:param inputs: ordered list of model input features (name, type, ..)
:param outputs: ordered list of model output/result elements (name, type, ..)
:param upload: upload to datastore (default is True)
:param labels: a set of key/value labels to tag the artifact with
:param extra_data: key/value list of extra files/charts to link with this dataset
value can be abs/relative path string | bytes | artifact object
:param db_key: the key to use in the artifact DB table, by default
its run name + '_' + key
db_key=False will not register it in the artifacts table
:returns: artifact object
"""
model = ModelArtifact(
key,
body,
model_file=model_file,
metrics=metrics,
parameters=parameters,
inputs=inputs,
outputs=outputs,
framework=framework,
extra_data=extra_data,
)
item = self._artifacts_manager.log_artifact(
self,
model,
local_path=model_dir,
artifact_path=artifact_path or self.artifact_path,
tag=tag,
upload=upload,
db_key=db_key,
labels=labels,
)
self._update_db()
return item