code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
def _search_traces():
"""
A request handler for `GET /mlflow/traces` to search for TraceInfo records in tracking store.
"""
request_message = _get_request_message(
SearchTraces(),
schema={
"experiment_ids": [
_assert_array,
_assert_item_type_st... |
A request handler for `GET /mlflow/traces` to search for TraceInfo records in tracking store.
| _search_traces | python | mlflow/mlflow | mlflow/server/handlers.py | https://github.com/mlflow/mlflow/blob/master/mlflow/server/handlers.py | Apache-2.0 |
def _delete_traces():
"""
A request handler for `POST /mlflow/traces/delete-traces` to delete TraceInfo records
from tracking store.
"""
request_message = _get_request_message(
DeleteTraces(),
schema={
"experiment_id": [_assert_string, _assert_required],
"max_... |
A request handler for `POST /mlflow/traces/delete-traces` to delete TraceInfo records
from tracking store.
| _delete_traces | python | mlflow/mlflow | mlflow/server/handlers.py | https://github.com/mlflow/mlflow/blob/master/mlflow/server/handlers.py | Apache-2.0 |
def _set_trace_tag(request_id):
"""
A request handler for `PATCH /mlflow/traces/{request_id}/tags` to set tags on a TraceInfo record
"""
request_message = _get_request_message(
SetTraceTag(),
schema={
"key": [_assert_string, _assert_required],
"value": [_assert_st... |
A request handler for `PATCH /mlflow/traces/{request_id}/tags` to set tags on a TraceInfo record
| _set_trace_tag | python | mlflow/mlflow | mlflow/server/handlers.py | https://github.com/mlflow/mlflow/blob/master/mlflow/server/handlers.py | Apache-2.0 |
def _delete_trace_tag(request_id):
"""
A request handler for `DELETE /mlflow/traces/{request_id}/tags` to delete tags from a TraceInfo
record.
"""
request_message = _get_request_message(
DeleteTraceTag(),
schema={
"key": [_assert_string, _assert_required],
},
... |
A request handler for `DELETE /mlflow/traces/{request_id}/tags` to delete tags from a TraceInfo
record.
| _delete_trace_tag | python | mlflow/mlflow | mlflow/server/handlers.py | https://github.com/mlflow/mlflow/blob/master/mlflow/server/handlers.py | Apache-2.0 |
def _get_paths(base_path):
"""
A service endpoints base path is typically something like /mlflow/experiment.
We should register paths like /api/2.0/mlflow/experiment and
/ajax-api/2.0/mlflow/experiment in the Flask router.
"""
base_path = _convert_path_parameter_to_flask_format(base_path)
re... |
A service endpoints base path is typically something like /mlflow/experiment.
We should register paths like /api/2.0/mlflow/experiment and
/ajax-api/2.0/mlflow/experiment in the Flask router.
| _get_paths | python | mlflow/mlflow | mlflow/server/handlers.py | https://github.com/mlflow/mlflow/blob/master/mlflow/server/handlers.py | Apache-2.0 |
def get_endpoints(get_handler=get_handler):
"""
Returns:
List of tuples (path, handler, methods)
"""
return (
get_service_endpoints(MlflowService, get_handler)
+ get_service_endpoints(ModelRegistryService, get_handler)
+ get_service_endpoints(MlflowArtifactsService, get_h... |
Returns:
List of tuples (path, handler, methods)
| get_endpoints | python | mlflow/mlflow | mlflow/server/handlers.py | https://github.com/mlflow/mlflow/blob/master/mlflow/server/handlers.py | Apache-2.0 |
def _validate_content_type(flask_request, allowed_content_types: list[str]):
"""
Validates that the request content type is one of the allowed content types.
Args:
flask_request: Flask request object (flask.request)
allowed_content_types: A list of allowed content types
"""
if flask... |
Validates that the request content type is one of the allowed content types.
Args:
flask_request: Flask request object (flask.request)
allowed_content_types: A list of allowed content types
| _validate_content_type | python | mlflow/mlflow | mlflow/server/validation.py | https://github.com/mlflow/mlflow/blob/master/mlflow/server/validation.py | Apache-2.0 |
def _is_factory(app: str) -> bool:
"""
Returns True if the given app is a factory function, False otherwise.
Args:
app: The app to check, e.g. "mlflow.server.app:app
"""
module, obj_name = app.rsplit(":", 1)
mod = importlib.import_module(module)
obj = getattr(mod, obj_name)
retu... |
Returns True if the given app is a factory function, False otherwise.
Args:
app: The app to check, e.g. "mlflow.server.app:app
| _is_factory | python | mlflow/mlflow | mlflow/server/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/server/__init__.py | Apache-2.0 |
def get_app_client(app_name: str, *args, **kwargs):
"""
Instantiate a client provided by an app.
Args:
app_name: The app name defined in `setup.py`, e.g., "basic-auth".
args: Additional arguments passed to the app client constructor.
kwargs: Additional keyword arguments passed to th... |
Instantiate a client provided by an app.
Args:
app_name: The app name defined in `setup.py`, e.g., "basic-auth".
args: Additional arguments passed to the app client constructor.
kwargs: Additional keyword arguments passed to the app client constructor.
Returns:
An app clie... | get_app_client | python | mlflow/mlflow | mlflow/server/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/server/__init__.py | Apache-2.0 |
def _run_server( # noqa: D417
file_store_path,
registry_store_uri,
default_artifact_root,
serve_artifacts,
artifacts_only,
artifacts_destination,
host,
port,
static_prefix=None,
workers=None,
gunicorn_opts=None,
waitress_opts=None,
expose_prometheus=None,
app_nam... |
Run the MLflow server, wrapping it in gunicorn or waitress on windows
Args:
static_prefix: If set, the index.html asset will be served from the path static_prefix.
If left None, the index.html asset will be served from the root path.
Returns:
None
| _run_server | python | mlflow/mlflow | mlflow/server/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/server/__init__.py | Apache-2.0 |
def create_user(self, username: str, password: str):
"""
Create a new user.
Args:
username: The username.
password: The user's password. Must not be empty string.
Raises:
mlflow.exceptions.RestException: if the username is already taken.
Ret... |
Create a new user.
Args:
username: The username.
password: The user's password. Must not be empty string.
Raises:
mlflow.exceptions.RestException: if the username is already taken.
Returns:
A single :py:class:`mlflow.server.auth.entitie... | create_user | python | mlflow/mlflow | mlflow/server/auth/client.py | https://github.com/mlflow/mlflow/blob/master/mlflow/server/auth/client.py | Apache-2.0 |
def get_user(self, username: str):
"""
Get a user with a specific username.
Args:
username: The username.
Raises:
mlflow.exceptions.RestException: if the user does not exist
Returns:
A single :py:class:`mlflow.server.auth.entities.User` obje... |
Get a user with a specific username.
Args:
username: The username.
Raises:
mlflow.exceptions.RestException: if the user does not exist
Returns:
A single :py:class:`mlflow.server.auth.entities.User` object.
.. code-block:: bash
... | get_user | python | mlflow/mlflow | mlflow/server/auth/client.py | https://github.com/mlflow/mlflow/blob/master/mlflow/server/auth/client.py | Apache-2.0 |
def update_user_password(self, username: str, password: str):
"""
Update the password of a specific user.
Args:
username: The username.
password: The new password.
Raises:
mlflow.exceptions.RestException: if the user does not exist
.. code-b... |
Update the password of a specific user.
Args:
username: The username.
password: The new password.
Raises:
mlflow.exceptions.RestException: if the user does not exist
.. code-block:: bash
:caption: Example
export MLFLOW_TRAC... | update_user_password | python | mlflow/mlflow | mlflow/server/auth/client.py | https://github.com/mlflow/mlflow/blob/master/mlflow/server/auth/client.py | Apache-2.0 |
def update_user_admin(self, username: str, is_admin: bool):
"""
Update the admin status of a specific user.
Args:
username: The username.
is_admin: The new admin status.
Raises:
mlflow.exceptions.RestException: if the user does not exist
.. ... |
Update the admin status of a specific user.
Args:
username: The username.
is_admin: The new admin status.
Raises:
mlflow.exceptions.RestException: if the user does not exist
.. code-block:: bash
:caption: Example
export MLF... | update_user_admin | python | mlflow/mlflow | mlflow/server/auth/client.py | https://github.com/mlflow/mlflow/blob/master/mlflow/server/auth/client.py | Apache-2.0 |
def delete_user(self, username: str):
"""
Delete a specific user.
Args:
username: The username.
Raises:
mlflow.exceptions.RestException: if the user does not exist
.. code-block:: bash
:caption: Example
export MLFLOW_TRACKING_US... |
Delete a specific user.
Args:
username: The username.
Raises:
mlflow.exceptions.RestException: if the user does not exist
.. code-block:: bash
:caption: Example
export MLFLOW_TRACKING_USERNAME=admin
export MLFLOW_TRACKING_P... | delete_user | python | mlflow/mlflow | mlflow/server/auth/client.py | https://github.com/mlflow/mlflow/blob/master/mlflow/server/auth/client.py | Apache-2.0 |
def create_experiment_permission(self, experiment_id: str, username: str, permission: str):
"""
Create a permission on an experiment for a user.
Args:
experiment_id: The id of the experiment.
username: The username.
permission: Permission to grant. Must be on... |
Create a permission on an experiment for a user.
Args:
experiment_id: The id of the experiment.
username: The username.
permission: Permission to grant. Must be one of "READ", "EDIT", "MANAGE" and
"NO_PERMISSIONS".
Raises:
mlflow... | create_experiment_permission | python | mlflow/mlflow | mlflow/server/auth/client.py | https://github.com/mlflow/mlflow/blob/master/mlflow/server/auth/client.py | Apache-2.0 |
def get_experiment_permission(self, experiment_id: str, username: str):
"""
Get an experiment permission for a user.
Args:
experiment_id: The id of the experiment.
username: The username.
Raises:
mlflow.exceptions.RestException: if the user does not ... |
Get an experiment permission for a user.
Args:
experiment_id: The id of the experiment.
username: The username.
Raises:
mlflow.exceptions.RestException: if the user does not exist,
or no permission exists for this experiment user pair.
... | get_experiment_permission | python | mlflow/mlflow | mlflow/server/auth/client.py | https://github.com/mlflow/mlflow/blob/master/mlflow/server/auth/client.py | Apache-2.0 |
def update_experiment_permission(self, experiment_id: str, username: str, permission: str):
"""
Update an existing experiment permission for a user.
Args:
experiment_id: The id of the experiment.
username: The username.
permission: New permission to grant. Mu... |
Update an existing experiment permission for a user.
Args:
experiment_id: The id of the experiment.
username: The username.
permission: New permission to grant. Must be one of "READ", "EDIT", "MANAGE" and
"NO_PERMISSIONS".
Raises:
... | update_experiment_permission | python | mlflow/mlflow | mlflow/server/auth/client.py | https://github.com/mlflow/mlflow/blob/master/mlflow/server/auth/client.py | Apache-2.0 |
def delete_experiment_permission(self, experiment_id: str, username: str):
"""
Delete an existing experiment permission for a user.
Args:
experiment_id: The id of the experiment.
username: The username.
Raises:
mlflow.exceptions.RestException: if the... |
Delete an existing experiment permission for a user.
Args:
experiment_id: The id of the experiment.
username: The username.
Raises:
mlflow.exceptions.RestException: if the user does not exist, or no permission exists for
this experiment user... | delete_experiment_permission | python | mlflow/mlflow | mlflow/server/auth/client.py | https://github.com/mlflow/mlflow/blob/master/mlflow/server/auth/client.py | Apache-2.0 |
def create_registered_model_permission(self, name: str, username: str, permission: str):
"""
Create a permission on an registered model for a user.
Args:
name: The name of the registered model.
username: The username.
permission: Permission to grant. Must be ... |
Create a permission on an registered model for a user.
Args:
name: The name of the registered model.
username: The username.
permission: Permission to grant. Must be one of "READ", "EDIT", "MANAGE" and
"NO_PERMISSIONS".
Raises:
m... | create_registered_model_permission | python | mlflow/mlflow | mlflow/server/auth/client.py | https://github.com/mlflow/mlflow/blob/master/mlflow/server/auth/client.py | Apache-2.0 |
def get_registered_model_permission(self, name: str, username: str):
"""
Get an registered model permission for a user.
Args:
name: The name of the registered model.
username: The username.
Raises:
mlflow.exceptions.RestException: if the user does no... |
Get an registered model permission for a user.
Args:
name: The name of the registered model.
username: The username.
Raises:
mlflow.exceptions.RestException: if the user does not
exist, or no permission exists for this registered model user ... | get_registered_model_permission | python | mlflow/mlflow | mlflow/server/auth/client.py | https://github.com/mlflow/mlflow/blob/master/mlflow/server/auth/client.py | Apache-2.0 |
def update_registered_model_permission(self, name: str, username: str, permission: str):
"""
Update an existing registered model permission for a user.
Args:
name: The name of the registered model.
username: The username.
permission: New permission to grant. ... |
Update an existing registered model permission for a user.
Args:
name: The name of the registered model.
username: The username.
permission: New permission to grant. Must be one of "READ", "EDIT", "MANAGE" and
"NO_PERMISSIONS".
Raises:
... | update_registered_model_permission | python | mlflow/mlflow | mlflow/server/auth/client.py | https://github.com/mlflow/mlflow/blob/master/mlflow/server/auth/client.py | Apache-2.0 |
def delete_registered_model_permission(self, name: str, username: str):
"""
Delete an existing registered model permission for a user.
Args:
name: The name of the registered model.
username: The username.
Raises:
mlflow.exceptions.RestException: if t... |
Delete an existing registered model permission for a user.
Args:
name: The name of the registered model.
username: The username.
Raises:
mlflow.exceptions.RestException: if the user does not exist,
or no permission exists for this registered... | delete_registered_model_permission | python | mlflow/mlflow | mlflow/server/auth/client.py | https://github.com/mlflow/mlflow/blob/master/mlflow/server/auth/client.py | Apache-2.0 |
def _get_permission_from_store_or_default(store_permission_func: Callable[[], str]) -> Permission:
"""
Attempts to get permission from store,
and returns default permission if no record is found.
"""
try:
perm = store_permission_func()
except MlflowException as e:
if e.error_code... |
Attempts to get permission from store,
and returns default permission if no record is found.
| _get_permission_from_store_or_default | python | mlflow/mlflow | mlflow/server/auth/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/server/auth/__init__.py | Apache-2.0 |
def username_is_sender():
"""Validate if the request username is the sender"""
username = _get_request_param("username")
sender = authenticate_request().username
return username == sender | Validate if the request username is the sender | username_is_sender | python | mlflow/mlflow | mlflow/server/auth/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/server/auth/__init__.py | Apache-2.0 |
def get_auth_func(authorization_function: str) -> Callable[[], Union[Authorization, Response]]:
"""
Import and return the specified authorization function.
Args:
authorization_function: A string of the form "module.submodule:auth_func"
"""
mod_name, fn_name = authorization_function.split(":... |
Import and return the specified authorization function.
Args:
authorization_function: A string of the form "module.submodule:auth_func"
| get_auth_func | python | mlflow/mlflow | mlflow/server/auth/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/server/auth/__init__.py | Apache-2.0 |
def authenticate_request_basic_auth() -> Union[Authorization, Response]:
"""Authenticate the request using basic auth."""
if request.authorization is None:
return make_basic_auth_response()
username = request.authorization.username
password = request.authorization.password
if store.authenti... | Authenticate the request using basic auth. | authenticate_request_basic_auth | python | mlflow/mlflow | mlflow/server/auth/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/server/auth/__init__.py | Apache-2.0 |
def _find_validator(req: Request) -> Optional[Callable[[], bool]]:
"""
Finds the validator matching the request path and method.
"""
if "/mlflow/logged-models" in req.path:
# logged model routes are not registered in the app
# so we need to check them manually
return next(
... |
Finds the validator matching the request path and method.
| _find_validator | python | mlflow/mlflow | mlflow/server/auth/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/server/auth/__init__.py | Apache-2.0 |
def delete_can_manage_registered_model_permission(resp: Response):
"""
Delete registered model permission when the model is deleted.
We need to do this because the primary key of the registered model is the name,
unlike the experiment where the primary key is experiment_id (UUID). Therefore,
we hav... |
Delete registered model permission when the model is deleted.
We need to do this because the primary key of the registered model is the name,
unlike the experiment where the primary key is experiment_id (UUID). Therefore,
we have to delete the permission record when the model is deleted otherwise it
... | delete_can_manage_registered_model_permission | python | mlflow/mlflow | mlflow/server/auth/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/server/auth/__init__.py | Apache-2.0 |
def filter_search_logged_models(resp: Response) -> None:
"""
Filter out unreadable logged models from the search results.
"""
from mlflow.utils.search_utils import SearchLoggedModelsPaginationToken as Token
if sender_is_admin():
return
response_proto = SearchLoggedModels.Response()
... |
Filter out unreadable logged models from the search results.
| filter_search_logged_models | python | mlflow/mlflow | mlflow/server/auth/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/server/auth/__init__.py | Apache-2.0 |
def rename_registered_model_permission(resp: Response):
"""
A model registry can be assigned to multiple users with different permissions.
Changing the model registry name must be propagated to all users.
"""
# get registry model name before update
data = request.get_json(force=True, silent=Tru... |
A model registry can be assigned to multiple users with different permissions.
Changing the model registry name must be propagated to all users.
| rename_registered_model_permission | python | mlflow/mlflow | mlflow/server/auth/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/server/auth/__init__.py | Apache-2.0 |
def create_app(app: Flask = app):
"""
A factory to enable authentication and authorization for the MLflow server.
Args:
app: The Flask app to enable authentication and authorization for.
Returns:
The app with authentication and authorization enabled.
"""
_logger.warning(
... |
A factory to enable authentication and authorization for the MLflow server.
Args:
app: The Flask app to enable authentication and authorization for.
Returns:
The app with authentication and authorization enabled.
| create_app | python | mlflow/mlflow | mlflow/server/auth/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/server/auth/__init__.py | Apache-2.0 |
def get_underlying_model_flavor(model):
"""
Find the underlying models flavor.
Args:
model: underlying model of the explainer.
"""
# checking if underlying model is wrapped
if hasattr(model, "inner_model"):
unwrapped_model = model.inner_model
# check if passed model i... |
Find the underlying models flavor.
Args:
model: underlying model of the explainer.
| get_underlying_model_flavor | python | mlflow/mlflow | mlflow/shap/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/shap/__init__.py | Apache-2.0 |
def _log_artifact_contextmanager(out_file, artifact_path=None):
"""
A context manager to make it easier to log an artifact.
"""
with tempfile.TemporaryDirectory() as tmp_dir:
tmp_path = os.path.join(tmp_dir, out_file)
yield tmp_path
mlflow.log_artifact(tmp_path, artifact_path) |
A context manager to make it easier to log an artifact.
| _log_artifact_contextmanager | python | mlflow/mlflow | mlflow/shap/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/shap/__init__.py | Apache-2.0 |
def _get_conda_and_pip_dependencies(conda_env):
"""
Extract conda and pip dependencies from conda environments
Args:
conda_env: Conda environment
"""
conda_deps = []
# NB: Set operations are required in case there are multiple references of MLflow as a
# dependency to ensure that d... |
Extract conda and pip dependencies from conda environments
Args:
conda_env: Conda environment
| _get_conda_and_pip_dependencies | python | mlflow/mlflow | mlflow/shap/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/shap/__init__.py | Apache-2.0 |
def _merge_environments(shap_environment, model_environment):
"""
Merge conda environments of underlying model and shap.
Args:
shap_environment: SHAP conda environment.
model_environment: Underlying model conda environment.
"""
# merge the channels from the two environments and remo... |
Merge conda environments of underlying model and shap.
Args:
shap_environment: SHAP conda environment.
model_environment: Underlying model conda environment.
| _merge_environments | python | mlflow/mlflow | mlflow/shap/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/shap/__init__.py | Apache-2.0 |
def load_explainer(model_uri):
"""
Load a SHAP explainer from a local file or a run.
Args:
model_uri: The location, in URI format, of the MLflow model. For example:
- ``/Users/me/path/to/local/model``
- ``relative/path/to/local/model``
- ``s3://my_bucket/path/to... |
Load a SHAP explainer from a local file or a run.
Args:
model_uri: The location, in URI format, of the MLflow model. For example:
- ``/Users/me/path/to/local/model``
- ``relative/path/to/local/model``
- ``s3://my_bucket/path/to/model``
- ``runs:/<mlflow... | load_explainer | python | mlflow/mlflow | mlflow/shap/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/shap/__init__.py | Apache-2.0 |
def _load_explainer(explainer_file, model=None):
"""
Load a SHAP explainer saved as an MLflow artifact on the local file system.
Args:
explainer_file: Local filesystem path to the MLflow Model saved with the ``shap`` flavor.
model: Model to override underlying explainer model.
"""
... |
Load a SHAP explainer saved as an MLflow artifact on the local file system.
Args:
explainer_file: Local filesystem path to the MLflow Model saved with the ``shap`` flavor.
model: Model to override underlying explainer model.
| _load_explainer | python | mlflow/mlflow | mlflow/shap/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/shap/__init__.py | Apache-2.0 |
def _get_estimator_info_tags(estimator):
"""
Returns:
A dictionary of MLflow run tag keys and values describing the specified estimator.
"""
return {
"estimator_name": estimator.__class__.__name__,
"estimator_class": (estimator.__class__.__module__ + "." + estimator.__class__.__n... |
Returns:
A dictionary of MLflow run tag keys and values describing the specified estimator.
| _get_estimator_info_tags | python | mlflow/mlflow | mlflow/sklearn/utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/sklearn/utils.py | Apache-2.0 |
def _get_X_y_and_sample_weight(fit_func, fit_args, fit_kwargs):
"""
Get a tuple of (X, y, sample_weight) in the following steps.
1. Extract X and y from fit_args and fit_kwargs.
2. If the sample_weight argument exists in fit_func,
extract it from fit_args or fit_kwargs and return (X, y, sample_w... |
Get a tuple of (X, y, sample_weight) in the following steps.
1. Extract X and y from fit_args and fit_kwargs.
2. If the sample_weight argument exists in fit_func,
extract it from fit_args or fit_kwargs and return (X, y, sample_weight),
otherwise return (X, y)
Args:
fit_func: A f... | _get_X_y_and_sample_weight | python | mlflow/mlflow | mlflow/sklearn/utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/sklearn/utils.py | Apache-2.0 |
def _get_classifier_metrics(fitted_estimator, prefix, X, y_true, sample_weight, pos_label): # noqa: D417
"""
Compute and record various common metrics for classifiers
For (1) precision score:
https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html
(2) recall score:
... |
Compute and record various common metrics for classifiers
For (1) precision score:
https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html
(2) recall score:
https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html
(3) f1_score:
http... | _get_classifier_metrics | python | mlflow/mlflow | mlflow/sklearn/utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/sklearn/utils.py | Apache-2.0 |
def _get_classifier_artifacts(fitted_estimator, prefix, X, y_true, sample_weight): # noqa: D417
"""
Draw and record various common artifacts for classifier
For all classifiers, we always log:
(1) confusion matrix:
https://scikit-learn.org/stable/modules/generated/sklearn.metrics.plot_confusion_mat... |
Draw and record various common artifacts for classifier
For all classifiers, we always log:
(1) confusion matrix:
https://scikit-learn.org/stable/modules/generated/sklearn.metrics.plot_confusion_matrix.html
For only binary classifiers, we will log:
(2) precision recall curve:
https://scik... | _get_classifier_artifacts | python | mlflow/mlflow | mlflow/sklearn/utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/sklearn/utils.py | Apache-2.0 |
def _get_regressor_metrics(fitted_estimator, prefix, X, y_true, sample_weight): # noqa: D417
"""
Compute and record various common metrics for regressors
For (1) (root) mean squared error:
https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html
(2) mean absolute e... |
Compute and record various common metrics for regressors
For (1) (root) mean squared error:
https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html
(2) mean absolute error:
https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html
... | _get_regressor_metrics | python | mlflow/mlflow | mlflow/sklearn/utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/sklearn/utils.py | Apache-2.0 |
def _get_meta_estimators_for_autologging():
"""
Returns:
A list of meta estimator class definitions
(e.g., `sklearn.model_selection.GridSearchCV`) that should be included
when patching training functions for autologging
"""
from sklearn.model_selection import GridSearchCV, Random... |
Returns:
A list of meta estimator class definitions
(e.g., `sklearn.model_selection.GridSearchCV`) that should be included
when patching training functions for autologging
| _get_meta_estimators_for_autologging | python | mlflow/mlflow | mlflow/sklearn/utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/sklearn/utils.py | Apache-2.0 |
def _is_parameter_search_estimator(estimator):
"""
Returns:
`True` if the specified scikit-learn estimator is a parameter search estimator,
such as `GridSearchCV`. `False` otherwise.
"""
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
parameter_search_estimators... |
Returns:
`True` if the specified scikit-learn estimator is a parameter search estimator,
such as `GridSearchCV`. `False` otherwise.
| _is_parameter_search_estimator | python | mlflow/mlflow | mlflow/sklearn/utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/sklearn/utils.py | Apache-2.0 |
def _log_parameter_search_results_as_artifact(cv_results_df, run_id):
"""
Records a collection of parameter search results as an MLflow artifact
for the specified run.
Args:
cv_results_df: A Pandas DataFrame containing the results of a parameter search
training session, which may be... |
Records a collection of parameter search results as an MLflow artifact
for the specified run.
Args:
cv_results_df: A Pandas DataFrame containing the results of a parameter search
training session, which may be obtained by parsing the `cv_results_`
attribute of a trained par... | _log_parameter_search_results_as_artifact | python | mlflow/mlflow | mlflow/sklearn/utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/sklearn/utils.py | Apache-2.0 |
def _create_child_runs_for_parameter_search( # noqa: D417
autologging_client,
cv_estimator,
parent_run,
max_tuning_runs,
child_tags=None,
dataset=None,
best_estimator_params=None,
best_estimator_model_id=None,
):
"""
Creates a collection of child runs for a parameter search trai... |
Creates a collection of child runs for a parameter search training session.
Runs are reconstructed from the `cv_results_` attribute of the specified trained
parameter search estimator - `cv_estimator`, which provides relevant performance
metrics for each point in the parameter search space. One child r... | _create_child_runs_for_parameter_search | python | mlflow/mlflow | mlflow/sklearn/utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/sklearn/utils.py | Apache-2.0 |
def save_model(
sk_model,
path,
conda_env=None,
code_paths=None,
mlflow_model=None,
serialization_format=SERIALIZATION_FORMAT_CLOUDPICKLE,
signature: ModelSignature = None,
input_example: ModelInputExample = None,
pip_requirements=None,
extra_pip_requirements=None,
pyfunc_pre... |
Save a scikit-learn model to a path on the local file system. Produces a MLflow Model
containing the following flavors:
- :py:mod:`mlflow.sklearn`
- :py:mod:`mlflow.pyfunc`. NOTE: This flavor is only included for scikit-learn models
that define `predict()`, since `predict()` is requi... | save_model | python | mlflow/mlflow | mlflow/sklearn/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/sklearn/__init__.py | Apache-2.0 |
def log_model(
sk_model,
artifact_path: Optional[str] = None,
conda_env=None,
code_paths=None,
serialization_format=SERIALIZATION_FORMAT_CLOUDPICKLE,
registered_model_name=None,
signature: ModelSignature = None,
input_example: ModelInputExample = None,
await_registration_for=DEFAULT_... |
Log a scikit-learn model as an MLflow artifact for the current run. Produces an MLflow Model
containing the following flavors:
- :py:mod:`mlflow.sklearn`
- :py:mod:`mlflow.pyfunc`. NOTE: This flavor is only included for scikit-learn models
that define `predict()`, since `predict()` i... | log_model | python | mlflow/mlflow | mlflow/sklearn/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/sklearn/__init__.py | Apache-2.0 |
def _load_model_from_local_file(path, serialization_format):
"""Load a scikit-learn model saved as an MLflow artifact on the local file system.
Args:
path: Local filesystem path to the MLflow Model saved with the ``sklearn`` flavor
serialization_format: The format in which the model was seriali... | Load a scikit-learn model saved as an MLflow artifact on the local file system.
Args:
path: Local filesystem path to the MLflow Model saved with the ``sklearn`` flavor
serialization_format: The format in which the model was serialized. This should be one of
the following: ``mlflow.sklea... | _load_model_from_local_file | python | mlflow/mlflow | mlflow/sklearn/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/sklearn/__init__.py | Apache-2.0 |
def _load_pyfunc(path):
"""
Load PyFunc implementation. Called by ``pyfunc.load_model``.
Args:
path: Local filesystem path to the MLflow Model with the ``sklearn`` flavor.
"""
if os.path.isfile(path):
# Scikit-learn models saved in older versions of MLflow (<= 1.9.1) specify the ``d... |
Load PyFunc implementation. Called by ``pyfunc.load_model``.
Args:
path: Local filesystem path to the MLflow Model with the ``sklearn`` flavor.
| _load_pyfunc | python | mlflow/mlflow | mlflow/sklearn/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/sklearn/__init__.py | Apache-2.0 |
def __init__(self, sk_model, original_exception):
"""
Args:
sk_model: The custom sklearn model to be pickled
original_exception: The original exception raised
"""
super().__init__(
f"Pickling custom sklearn model {sk_model.__class__.__name__} failed "
... |
Args:
sk_model: The custom sklearn model to be pickled
original_exception: The original exception raised
| __init__ | python | mlflow/mlflow | mlflow/sklearn/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/sklearn/__init__.py | Apache-2.0 |
def _save_model(sk_model, output_path, serialization_format):
"""
Args:
sk_model: The scikit-learn model to serialize.
output_path: The file path to which to write the serialized model.
serialization_format: The format in which to serialize the model. This should be one of
th... |
Args:
sk_model: The scikit-learn model to serialize.
output_path: The file path to which to write the serialized model.
serialization_format: The format in which to serialize the model. This should be one of
the following: ``mlflow.sklearn.SERIALIZATION_FORMAT_PICKLE`` or
... | _save_model | python | mlflow/mlflow | mlflow/sklearn/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/sklearn/__init__.py | Apache-2.0 |
def load_model(model_uri, dst_path=None):
"""
Load a scikit-learn model from a local file or a run.
Args:
model_uri: The location, in URI format, of the MLflow model, for example:
- ``/Users/me/path/to/local/model``
- ``relative/path/to/local/model``
- ``s3://my... |
Load a scikit-learn model from a local file or a run.
Args:
model_uri: The location, in URI format, of the MLflow model, for example:
- ``/Users/me/path/to/local/model``
- ``relative/path/to/local/model``
- ``s3://my_bucket/path/to/model``
- ``runs:/<ml... | load_model | python | mlflow/mlflow | mlflow/sklearn/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/sklearn/__init__.py | Apache-2.0 |
def is_metric_value_loggable(metric_value):
"""
Check whether the specified `metric_value` is a numeric value which can be logged
as an MLflow metric.
"""
return isinstance(metric_value, (int, float, np.number)) and not isinstance(
metric_value, bool
) |
Check whether the specified `metric_value` is a numeric value which can be logged
as an MLflow metric.
| is_metric_value_loggable | python | mlflow/mlflow | mlflow/sklearn/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/sklearn/__init__.py | Apache-2.0 |
def register_prediction_result(self, run_id, eval_dataset_name, predict_result, model_id=None):
"""
Register the relationship
id(prediction_result) --> (eval_dataset_name, run_id, model_id)
into map `_pred_result_id_mapping`
"""
value = (eval_dataset_name, run_id, model_... |
Register the relationship
id(prediction_result) --> (eval_dataset_name, run_id, model_id)
into map `_pred_result_id_mapping`
| register_prediction_result | python | mlflow/mlflow | mlflow/sklearn/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/sklearn/__init__.py | Apache-2.0 |
def gen_metric_call_command(self_obj, metric_fn, *call_pos_args, **call_kwargs):
"""
Generate metric function call command string like `metric_fn(arg1, arg2, ...)`
Note: this method include inspecting argument variable name.
So should be called directly from the "patched method", to ens... |
Generate metric function call command string like `metric_fn(arg1, arg2, ...)`
Note: this method include inspecting argument variable name.
So should be called directly from the "patched method", to ensure it capture
correct argument variable name.
Args:
self_obj:... | gen_metric_call_command | python | mlflow/mlflow | mlflow/sklearn/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/sklearn/__init__.py | Apache-2.0 |
def register_metric_api_call(self, run_id, metric_name, dataset_name, call_command):
"""
This method will do:
(1) Generate and return metric key, format is:
{metric_name}[-{call_index}]_{eval_dataset_name}
metric_name is generated by metric function name, if multiple calls on... |
This method will do:
(1) Generate and return metric key, format is:
{metric_name}[-{call_index}]_{eval_dataset_name}
metric_name is generated by metric function name, if multiple calls on the same
metric API happen, the following calls will be assigned with an increasing "... | register_metric_api_call | python | mlflow/mlflow | mlflow/sklearn/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/sklearn/__init__.py | Apache-2.0 |
def get_info_for_metric_api_call(self, call_pos_args, call_kwargs):
"""
Given a metric api call (include the called metric function, and call arguments)
Register the call information (arguments dict) into the `metric_api_call_arg_dict_list_map`
and return a tuple of (run_id, eval_dataset... |
Given a metric api call (include the called metric function, and call arguments)
Register the call information (arguments dict) into the `metric_api_call_arg_dict_list_map`
and return a tuple of (run_id, eval_dataset_name, model_id)
| get_info_for_metric_api_call | python | mlflow/mlflow | mlflow/sklearn/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/sklearn/__init__.py | Apache-2.0 |
def log_post_training_metric(self, run_id, key, value, model_id=None):
"""
Log the metric into the specified mlflow run.
and it will also update the metric_info artifact if needed.
If model_id is not None, metrics are logged into the model as well.
"""
# Note: if the case... |
Log the metric into the specified mlflow run.
and it will also update the metric_info artifact if needed.
If model_id is not None, metrics are logged into the model as well.
| log_post_training_metric | python | mlflow/mlflow | mlflow/sklearn/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/sklearn/__init__.py | Apache-2.0 |
def _get_metric_name_list():
"""
Return metric function name list in `sklearn.metrics` module
"""
from sklearn import metrics
metric_list = []
for metric_method_name in metrics.__all__:
# excludes plot_* methods
# exclude class (e.g. metrics.ConfusionMatrixDisplay)
metri... |
Return metric function name list in `sklearn.metrics` module
| _get_metric_name_list | python | mlflow/mlflow | mlflow/sklearn/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/sklearn/__init__.py | Apache-2.0 |
def _autolog( # noqa: D417
flavor_name=FLAVOR_NAME,
log_input_examples=False,
log_model_signatures=True,
log_models=True,
log_datasets=True,
disable=False,
exclusive=False,
disable_for_unsupported_versions=False,
silent=False,
max_tuning_runs=5,
log_post_training_metrics=Tru... |
Internal autologging function for scikit-learn models.
Args:
flavor_name: A string value. Enable a ``mlflow.sklearn`` autologging routine
for a flavor. By default it enables autologging for original
scikit-learn models, as ``mlflow.sklearn.autolog()`` does. If
the a... | _autolog | python | mlflow/mlflow | mlflow/sklearn/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/sklearn/__init__.py | Apache-2.0 |
def fit_mlflow_xgboost_and_lightgbm(original, self, *args, **kwargs):
"""
Autologging function for XGBoost and LightGBM scikit-learn models
"""
# Obtain a copy of a model input example from the training dataset prior to model training
# for subsequent use during model logging, en... |
Autologging function for XGBoost and LightGBM scikit-learn models
| fit_mlflow_xgboost_and_lightgbm | python | mlflow/mlflow | mlflow/sklearn/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/sklearn/__init__.py | Apache-2.0 |
def fit_mlflow(original, self, *args, **kwargs):
"""
Autologging function that performs model training by executing the training method
referred to be `func_name` on the instance of `clazz` referred to by `self` & records
MLflow parameters, metrics, tags, and artifacts to a corresponding... |
Autologging function that performs model training by executing the training method
referred to be `func_name` on the instance of `clazz` referred to by `self` & records
MLflow parameters, metrics, tags, and artifacts to a corresponding MLflow Run.
| fit_mlflow | python | mlflow/mlflow | mlflow/sklearn/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/sklearn/__init__.py | Apache-2.0 |
def _log_pretraining_metadata(autologging_client, estimator, X, y): # noqa: D417
"""
Records metadata (e.g., params and tags) for a scikit-learn estimator prior to training.
This is intended to be invoked within a patched scikit-learn training routine
(e.g., `fit()`, `fit_transform()`, ... |
Records metadata (e.g., params and tags) for a scikit-learn estimator prior to training.
This is intended to be invoked within a patched scikit-learn training routine
(e.g., `fit()`, `fit_transform()`, ...) and assumes the existence of an active
MLflow run that can be referenced via the... | _log_pretraining_metadata | python | mlflow/mlflow | mlflow/sklearn/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/sklearn/__init__.py | Apache-2.0 |
def _log_posttraining_metadata(autologging_client, estimator, X, y, sample_weight):
"""
Records metadata for a scikit-learn estimator after training has completed.
This is intended to be invoked within a patched scikit-learn training routine
(e.g., `fit()`, `fit_transform()`, ...) and as... |
Records metadata for a scikit-learn estimator after training has completed.
This is intended to be invoked within a patched scikit-learn training routine
(e.g., `fit()`, `fit_transform()`, ...) and assumes the existence of an active
MLflow run that can be referenced via the fluent Track... | _log_posttraining_metadata | python | mlflow/mlflow | mlflow/sklearn/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/sklearn/__init__.py | Apache-2.0 |
def patched_fit(fit_impl, allow_children_patch, original, self, *args, **kwargs):
"""
Autologging patch function to be applied to a sklearn model class that defines a `fit`
method and inherits from `BaseEstimator` (thereby defining the `get_params()` method)
Args:
fit_impl: ... |
Autologging patch function to be applied to a sklearn model class that defines a `fit`
method and inherits from `BaseEstimator` (thereby defining the `get_params()` method)
Args:
fit_impl: The patched fit function implementation, the function should be defined as
`f... | patched_fit | python | mlflow/mlflow | mlflow/sklearn/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/sklearn/__init__.py | Apache-2.0 |
def patched_predict(original, self, *args, **kwargs):
"""
In `patched_predict`, register the prediction result instance with the run id and
eval dataset name. e.g.
```
prediction_result = model_1.predict(eval_X)
```
then we need register the following relationshi... |
In `patched_predict`, register the prediction result instance with the run id and
eval dataset name. e.g.
```
prediction_result = model_1.predict(eval_X)
```
then we need register the following relationship into the `_AUTOLOGGING_METRICS_MANAGER`:
id(prediction_... | patched_predict | python | mlflow/mlflow | mlflow/sklearn/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/sklearn/__init__.py | Apache-2.0 |
def patched_IffHasAttrDescriptor__get__(self, obj, type=None):
"""
For sklearn version <= 0.24.2, `_IffHasAttrDescriptor.__get__` method does not
support unbound method call.
See https://github.com/scikit-learn/scikit-learn/issues/20614
Thi... |
For sklearn version <= 0.24.2, `_IffHasAttrDescriptor.__get__` method does not
support unbound method call.
See https://github.com/scikit-learn/scikit-learn/issues/20614
This patched function is for hot patch.
| patched_IffHasAttrDescriptor__get__ | python | mlflow/mlflow | mlflow/sklearn/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/sklearn/__init__.py | Apache-2.0 |
def set_span_chat_attributes(span: LiveSpan, messages: list[dict[str, Any]], output: Any):
"""
This method logs chat messages to a span when LLM class is called.
Args:
span: A span object to record the chat messages.
messages: Input messages for the LLM call.
output: Response from L... |
This method logs chat messages to a span when LLM class is called.
Args:
span: A span object to record the chat messages.
messages: Input messages for the LLM call.
output: Response from LLM. Though the signature is str, tool response might be returned.
| set_span_chat_attributes | python | mlflow/mlflow | mlflow/smolagents/chat.py | https://github.com/mlflow/mlflow/blob/master/mlflow/smolagents/chat.py | Apache-2.0 |
def autolog(
log_traces: bool = True,
disable: bool = False,
silent: bool = False,
):
"""
Enables (or disables) and configures autologging from Smolagents to MLflow.
Note that asynchnorous APIs and Tool calling are not recorded now.
Args:
log_traces: If ``True``, traces are logged f... |
Enables (or disables) and configures autologging from Smolagents to MLflow.
Note that asynchnorous APIs and Tool calling are not recorded now.
Args:
log_traces: If ``True``, traces are logged for Smolagents agents.
If ``False``, no traces are collected during inference. Default to ``Tr... | autolog | python | mlflow/mlflow | mlflow/smolagents/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/smolagents/__init__.py | Apache-2.0 |
def predict(
self,
dataframe,
params: Optional[dict[str, Any]] = None,
):
"""Only works for predicting using text categorizer.
Not suitable for other pipeline components (e.g: parser)
Args:
dataframe: pandas dataframe containing texts to be categorized
... | Only works for predicting using text categorizer.
Not suitable for other pipeline components (e.g: parser)
Args:
dataframe: pandas dataframe containing texts to be categorized
expected shape is (n_rows,1 column)
params: Additional parameters to pass to the... | predict | python | mlflow/mlflow | mlflow/spacy/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/spacy/__init__.py | Apache-2.0 |
def load_model(model_uri, dst_path=None):
"""Load a spaCy model from a local file (if ``run_id`` is ``None``) or a run.
Args:
model_uri: The location, in URI format, of the MLflow model. For example:
- ``/Users/me/path/to/local/model``
- ``relative/path/to/l... | Load a spaCy model from a local file (if ``run_id`` is ``None``) or a run.
Args:
model_uri: The location, in URI format, of the MLflow model. For example:
- ``/Users/me/path/to/local/model``
- ``relative/path/to/local/model``
- ``s3://my_bu... | load_model | python | mlflow/mlflow | mlflow/spacy/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/spacy/__init__.py | Apache-2.0 |
def clear_table_infos():
"""Clear the table info accumulated SparkAutologgingContext.
This is currently only used in unit tests.
"""
with _lock:
global _table_infos
_table_infos = [] | Clear the table info accumulated SparkAutologgingContext.
This is currently only used in unit tests.
| clear_table_infos | python | mlflow/mlflow | mlflow/spark/autologging.py | https://github.com/mlflow/mlflow/blob/master/mlflow/spark/autologging.py | Apache-2.0 |
def _get_jvm_event_publisher(spark_context):
"""
Get JVM-side object implementing the following methods:
- init() for initializing JVM state needed for autologging (e.g. attaching a SparkListener
to watch for datasource reads)
- register(subscriber) for registering subscribers to receive datasourc... |
Get JVM-side object implementing the following methods:
- init() for initializing JVM state needed for autologging (e.g. attaching a SparkListener
to watch for datasource reads)
- register(subscriber) for registering subscribers to receive datasource events
| _get_jvm_event_publisher | python | mlflow/mlflow | mlflow/spark/autologging.py | https://github.com/mlflow/mlflow/blob/master/mlflow/spark/autologging.py | Apache-2.0 |
def _get_repl_id():
"""
Get a unique REPL ID for a PythonSubscriber instance. This is used to distinguish between
REPLs in multitenant, REPL-aware environments where multiple Python processes may share the
same Spark JVM (e.g. in Databricks). In such environments, we pull the REPL ID from Spark
loca... |
Get a unique REPL ID for a PythonSubscriber instance. This is used to distinguish between
REPLs in multitenant, REPL-aware environments where multiple Python processes may share the
same Spark JVM (e.g. in Databricks). In such environments, we pull the REPL ID from Spark
local properties, and expect th... | _get_repl_id | python | mlflow/mlflow | mlflow/spark/autologging.py | https://github.com/mlflow/mlflow/blob/master/mlflow/spark/autologging.py | Apache-2.0 |
def _notify(self, path, version, data_format):
"""
Method called by Scala SparkListener to propagate datasource read events to the current
Python process
"""
if autologging_is_disabled(FLAVOR_NAME):
return
# If there are active runs, simply set the tag on the ... |
Method called by Scala SparkListener to propagate datasource read events to the current
Python process
| _notify | python | mlflow/mlflow | mlflow/spark/autologging.py | https://github.com/mlflow/mlflow/blob/master/mlflow/spark/autologging.py | Apache-2.0 |
def get_default_conda_env(is_spark_connect_model=False):
"""
Returns:
The default Conda environment for MLflow Models produced by calls to
:func:`save_model()` and :func:`log_model()`. This Conda environment
contains the current version of PySpark that is installed on the caller's
... |
Returns:
The default Conda environment for MLflow Models produced by calls to
:func:`save_model()` and :func:`log_model()`. This Conda environment
contains the current version of PySpark that is installed on the caller's
system. ``dev`` versions of PySpark are replaced with stable v... | get_default_conda_env | python | mlflow/mlflow | mlflow/spark/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/spark/__init__.py | Apache-2.0 |
def log_model(
spark_model,
artifact_path,
conda_env=None,
code_paths=None,
dfs_tmpdir=None,
registered_model_name=None,
signature: ModelSignature = None,
input_example: ModelInputExample = None,
await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS,
pip_requirements=None,
e... |
Log a Spark MLlib model as an MLflow artifact for the current run. This uses the
MLlib persistence format and produces an MLflow Model with the Spark flavor.
Note: If no run is active, it will instantiate a run to obtain a run_id.
Args:
spark_model: Spark model to be saved - MLflow can only s... | log_model | python | mlflow/mlflow | mlflow/spark/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/spark/__init__.py | Apache-2.0 |
def maybe_copy_from_local_file(cls, src, dst):
"""
Conditionally copy the file to the Hadoop DFS.
The file is copied iff the configuration has distributed filesystem.
Returns:
If copied, return new target location, otherwise return (absolute) source path.
"""
... |
Conditionally copy the file to the Hadoop DFS.
The file is copied iff the configuration has distributed filesystem.
Returns:
If copied, return new target location, otherwise return (absolute) source path.
| maybe_copy_from_local_file | python | mlflow/mlflow | mlflow/spark/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/spark/__init__.py | Apache-2.0 |
def maybe_copy_from_uri(cls, src_uri, dst_path, local_model_path=None):
"""
Conditionally copy the file to the Hadoop DFS from the source uri.
In case the file is already on the Hadoop DFS do nothing.
Returns:
If copied, return new target location, otherwise return source ur... |
Conditionally copy the file to the Hadoop DFS from the source uri.
In case the file is already on the Hadoop DFS do nothing.
Returns:
If copied, return new target location, otherwise return source uri.
| maybe_copy_from_uri | python | mlflow/mlflow | mlflow/spark/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/spark/__init__.py | Apache-2.0 |
def _save_model_metadata(
dst_dir,
spark_model,
mlflow_model,
conda_env,
code_paths,
signature=None,
input_example=None,
pip_requirements=None,
extra_pip_requirements=None,
remote_model_path=None,
):
"""
Saves model metadata into the passed-in directory.
If mlflowdbfs... |
Saves model metadata into the passed-in directory.
If mlflowdbfs is not used, the persisted metadata assumes that a model can be
loaded from a relative path to the metadata file (currently hard-coded to "sparkml").
If mlflowdbfs is used, remote_model_path should be provided, and the model needs to
... | _save_model_metadata | python | mlflow/mlflow | mlflow/spark/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/spark/__init__.py | Apache-2.0 |
def _is_spark_connect_model(spark_model):
"""
Return whether the spark model is spark connect ML model
"""
try:
from pyspark.ml.connect import Model as ConnectModel
return isinstance(spark_model, ConnectModel)
except ImportError:
# pyspark < 3.5 does not support Spark connec... |
Return whether the spark model is spark connect ML model
| _is_spark_connect_model | python | mlflow/mlflow | mlflow/spark/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/spark/__init__.py | Apache-2.0 |
def save_model(
spark_model,
path,
mlflow_model=None,
conda_env=None,
code_paths=None,
dfs_tmpdir=None,
signature: ModelSignature = None,
input_example: ModelInputExample = None,
pip_requirements=None,
extra_pip_requirements=None,
metadata=None,
):
"""
Save a Spark ML... |
Save a Spark MLlib Model to a local path.
By default, this function saves models using the Spark MLlib persistence mechanism.
Args:
spark_model: Spark model to be saved - MLflow can only save descendants of
pyspark.ml.Model or pyspark.ml.Transformer which implement
MLReada... | save_model | python | mlflow/mlflow | mlflow/spark/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/spark/__init__.py | Apache-2.0 |
def _load_pyfunc(path):
"""
Load PyFunc implementation. Called by ``pyfunc.load_model``.
Args:
path: Local filesystem path to the MLflow Model with the ``spark`` flavor.
"""
from mlflow.utils._spark_utils import (
_create_local_spark_session_for_loading_spark_model,
_get_act... |
Load PyFunc implementation. Called by ``pyfunc.load_model``.
Args:
path: Local filesystem path to the MLflow Model with the ``spark`` flavor.
| _load_pyfunc | python | mlflow/mlflow | mlflow/spark/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/spark/__init__.py | Apache-2.0 |
def _find_and_set_features_col_as_vector_if_needed(spark_df, spark_model):
"""
Finds the `featuresCol` column in spark_model and
then tries to cast that column to `vector` type.
This method is noop if the `featuresCol` is already of type `vector`
or if it can't be cast to `vector` type
Note:
... |
Finds the `featuresCol` column in spark_model and
then tries to cast that column to `vector` type.
This method is noop if the `featuresCol` is already of type `vector`
or if it can't be cast to `vector` type
Note:
If a spark ML pipeline contains a single Estimator stage, it requires
the inp... | _find_and_set_features_col_as_vector_if_needed | python | mlflow/mlflow | mlflow/spark/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/spark/__init__.py | Apache-2.0 |
def predict(
self,
pandas_df,
params: Optional[dict[str, Any]] = None,
):
"""
Generate predictions given input data in a pandas DataFrame.
Args:
pandas_df: pandas DataFrame containing input data.
params: Additional parameters to pass to the mo... |
Generate predictions given input data in a pandas DataFrame.
Args:
pandas_df: pandas DataFrame containing input data.
params: Additional parameters to pass to the model for inference.
Returns:
List with model predictions.
| predict | python | mlflow/mlflow | mlflow/spark/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/spark/__init__.py | Apache-2.0 |
def save_model(
statsmodels_model,
path,
conda_env=None,
code_paths=None,
mlflow_model=None,
remove_data: bool = False,
signature: ModelSignature = None,
input_example: ModelInputExample = None,
pip_requirements=None,
extra_pip_requirements=None,
metadata=None,
):
"""
... |
Save a statsmodels model to a path on the local file system.
Args:
statsmodels_model: statsmodels model (an instance of `statsmodels.base.model.Results`_) to
be saved.
path: Local path where the model is to be saved.
conda_env: {{ conda_env }}
code_paths: {{ code_pa... | save_model | python | mlflow/mlflow | mlflow/statsmodels/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/statsmodels/__init__.py | Apache-2.0 |
def log_model(
statsmodels_model,
artifact_path: Optional[str] = None,
conda_env=None,
code_paths=None,
registered_model_name=None,
remove_data: bool = False,
signature: ModelSignature = None,
input_example: ModelInputExample = None,
await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SEC... |
Log a statsmodels model as an MLflow artifact for the current run.
Args:
statsmodels_model: statsmodels model (an instance of `statsmodels.base.model.Results`_) to
be saved.
artifact_path: Deprecated. Use `name` instead.
conda_env: {{ conda_env }}
code_paths: {{ cod... | log_model | python | mlflow/mlflow | mlflow/statsmodels/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/statsmodels/__init__.py | Apache-2.0 |
def load_model(model_uri, dst_path=None):
"""
Load a statsmodels model from a local file or a run.
Args:
model_uri: The location, in URI format, of the MLflow model. For example:
- ``/Users/me/path/to/local/model``
- ``relative/path/to/local/model``
- ``s3://my_... |
Load a statsmodels model from a local file or a run.
Args:
model_uri: The location, in URI format, of the MLflow model. For example:
- ``/Users/me/path/to/local/model``
- ``relative/path/to/local/model``
- ``s3://my_bucket/path/to/model``
- ``runs:/<mlf... | load_model | python | mlflow/mlflow | mlflow/statsmodels/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/statsmodels/__init__.py | Apache-2.0 |
def find_subclasses(klass):
"""
Recursively return a (non-nested) list of the class object and all its subclasses.
Args:
klass: The class whose class subtree we want to retrieve.
Returns:
A list of classes that includes the argument in the first position.
... |
Recursively return a (non-nested) list of the class object and all its subclasses.
Args:
klass: The class whose class subtree we want to retrieve.
Returns:
A list of classes that includes the argument in the first position.
| find_subclasses | python | mlflow/mlflow | mlflow/statsmodels/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/statsmodels/__init__.py | Apache-2.0 |
def overrides(klass, function_name):
"""
Returns True when the class passed as first argument overrides the function_name
Based on https://stackoverflow.com/a/62303206/5726057
Args:
klass: The class we are inspecting.
function_name: A string with the name of the ... |
Returns True when the class passed as first argument overrides the function_name
Based on https://stackoverflow.com/a/62303206/5726057
Args:
klass: The class we are inspecting.
function_name: A string with the name of the method we want to check overriding.
Ret... | overrides | python | mlflow/mlflow | mlflow/statsmodels/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/statsmodels/__init__.py | Apache-2.0 |
def patch_class_tree(klass):
"""
Patches all subclasses that override any auto-loggable method via monkey patching using
the gorilla package, taking the argument as the tree root in the class hierarchy. Every
auto-loggable method found in any of the subclasses is replaced by the patched ... |
Patches all subclasses that override any auto-loggable method via monkey patching using
the gorilla package, taking the argument as the tree root in the class hierarchy. Every
auto-loggable method found in any of the subclasses is replaced by the patched version.
Args:
klas... | patch_class_tree | python | mlflow/mlflow | mlflow/statsmodels/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/statsmodels/__init__.py | Apache-2.0 |
def _retry_with_new_creds(try_func, creds_func, orig_creds=None):
"""
Attempt the try_func with the original credentials (og_creds) if provided, or by generating the
credentials using creds_func. If the try_func throws, then try again with new credentials
provided by creds_func.
"""
try:
... |
Attempt the try_func with the original credentials (og_creds) if provided, or by generating the
credentials using creds_func. If the try_func throws, then try again with new credentials
provided by creds_func.
| _retry_with_new_creds | python | mlflow/mlflow | mlflow/store/artifact/artifact_repo.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/artifact/artifact_repo.py | Apache-2.0 |
def log_artifact(self, local_file, artifact_path=None):
"""
Log a local file as an artifact, optionally taking an ``artifact_path`` to place it in
within the run's artifacts. Run artifacts can be organized into directories, so you can
place the artifact in a directory this way.
... |
Log a local file as an artifact, optionally taking an ``artifact_path`` to place it in
within the run's artifacts. Run artifacts can be organized into directories, so you can
place the artifact in a directory this way.
Args:
local_file: Path to artifact to log.
... | log_artifact | python | mlflow/mlflow | mlflow/store/artifact/artifact_repo.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/artifact/artifact_repo.py | Apache-2.0 |
def _log_artifact_async(self, filename, artifact_path=None, artifact=None):
"""
Asynchronously log a local file as an artifact, optionally taking an ``artifact_path`` to
place it within the run's artifacts. Run artifacts can be organized into directory, so you
can place the artifact in t... |
Asynchronously log a local file as an artifact, optionally taking an ``artifact_path`` to
place it within the run's artifacts. Run artifacts can be organized into directory, so you
can place the artifact in the directory this way. Cleanup tells the function whether to
cleanup the local_... | _log_artifact_async | python | mlflow/mlflow | mlflow/store/artifact/artifact_repo.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/artifact/artifact_repo.py | Apache-2.0 |
def log_artifacts(self, local_dir, artifact_path=None):
"""
Log the files in the specified local directory as artifacts, optionally taking
an ``artifact_path`` to place them in within the run's artifacts.
Args:
local_dir: Directory of local artifacts to log.
arti... |
Log the files in the specified local directory as artifacts, optionally taking
an ``artifact_path`` to place them in within the run's artifacts.
Args:
local_dir: Directory of local artifacts to log.
artifact_path: Directory within the run's artifact directory in which t... | log_artifacts | python | mlflow/mlflow | mlflow/store/artifact/artifact_repo.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/artifact/artifact_repo.py | Apache-2.0 |
def list_artifacts(self, path: Optional[str] = None) -> list[FileInfo]:
"""
Return all the artifacts for this run_id directly under path. If path is a file, returns
an empty list. Will error if path is neither a file nor directory.
Args:
path: Relative source path that conta... |
Return all the artifacts for this run_id directly under path. If path is a file, returns
an empty list. Will error if path is neither a file nor directory.
Args:
path: Relative source path that contains desired artifacts.
Returns:
List of artifacts as FileInfo ... | list_artifacts | python | mlflow/mlflow | mlflow/store/artifact/artifact_repo.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/artifact/artifact_repo.py | Apache-2.0 |
def _create_download_destination(self, src_artifact_path, dst_local_dir_path=None):
"""
Creates a local filesystem location to be used as a destination for downloading the artifact
specified by `src_artifact_path`. The destination location is a subdirectory of the
specified `dst_local_di... |
Creates a local filesystem location to be used as a destination for downloading the artifact
specified by `src_artifact_path`. The destination location is a subdirectory of the
specified `dst_local_dir_path`, which is determined according to the structure of
`src_artifact_path`. For exa... | _create_download_destination | python | mlflow/mlflow | mlflow/store/artifact/artifact_repo.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/artifact/artifact_repo.py | Apache-2.0 |
def download_artifacts(self, artifact_path, dst_path=None):
"""
Download an artifact file or directory to a local directory if applicable, and return a
local path for it.
The caller is responsible for managing the lifecycle of the downloaded artifacts.
Args:
artifact... |
Download an artifact file or directory to a local directory if applicable, and return a
local path for it.
The caller is responsible for managing the lifecycle of the downloaded artifacts.
Args:
artifact_path: Relative source path to the desired artifacts.
dst_p... | download_artifacts | python | mlflow/mlflow | mlflow/store/artifact/artifact_repo.py | https://github.com/mlflow/mlflow/blob/master/mlflow/store/artifact/artifact_repo.py | Apache-2.0 |
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