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 |
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def _download_wheels(cls, pip_requirements_path, dst_path):
"""
Downloads all the wheels of the dependencies specified in the requirements.txt file.
The pip wheel download_command defaults to downloading only binary packages using
the `--only-binary=:all:` option. This behavior can be ov... |
Downloads all the wheels of the dependencies specified in the requirements.txt file.
The pip wheel download_command defaults to downloading only binary packages using
the `--only-binary=:all:` option. This behavior can be overridden using an
environment variable `MLFLOW_WHEELED_MODEL_PI... | _download_wheels | python | mlflow/mlflow | mlflow/models/wheeled_model.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/wheeled_model.py | Apache-2.0 |
def _overwrite_pip_requirements_with_wheels(self, pip_requirements_path, wheels_dir):
"""
Overwrites the requirements.txt with the wheels of the required dependencies.
Args:
pip_requirements_path: Path to requirements.txt in the model directory.
wheels_dir: Path to direc... |
Overwrites the requirements.txt with the wheels of the required dependencies.
Args:
pip_requirements_path: Path to requirements.txt in the model directory.
wheels_dir: Path to directory where wheels are stored.
| _overwrite_pip_requirements_with_wheels | python | mlflow/mlflow | mlflow/models/wheeled_model.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/wheeled_model.py | Apache-2.0 |
def _create_pip_requirement(self, conda_env_path, pip_requirements_path):
"""
This method creates a requirements.txt file for the model dependencies if the file does not
already exist. It uses the pip dependencies found in the conda.yaml env file.
Args:
conda_env_path: Path ... |
This method creates a requirements.txt file for the model dependencies if the file does not
already exist. It uses the pip dependencies found in the conda.yaml env file.
Args:
conda_env_path: Path to conda.yaml env file which contains the required pip
dependencies
... | _create_pip_requirement | python | mlflow/mlflow | mlflow/models/wheeled_model.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/wheeled_model.py | Apache-2.0 |
def _init(cmd, env_manager): # noqa: D417
"""
Initialize the container and execute command.
Args:
cmd: Command param passed by Sagemaker. Can be "serve" or "train" (unimplemented).
"""
if cmd == "serve":
_serve(env_manager)
elif cmd == "train":
_train()
else:
... |
Initialize the container and execute command.
Args:
cmd: Command param passed by Sagemaker. Can be "serve" or "train" (unimplemented).
| _init | python | mlflow/mlflow | mlflow/models/container/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/container/__init__.py | Apache-2.0 |
def _serve(env_manager):
"""
Serve the model.
Read the MLmodel config, initialize the Conda environment if needed and start python server.
"""
model_config_path = os.path.join(MODEL_PATH, MLMODEL_FILE_NAME)
m = Model.load(model_config_path)
if pyfunc.FLAVOR_NAME in m.flavors:
_serv... |
Serve the model.
Read the MLmodel config, initialize the Conda environment if needed and start python server.
| _serve | python | mlflow/mlflow | mlflow/models/container/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/container/__init__.py | Apache-2.0 |
def _install_pyfunc_deps(
model_path=None, install_mlflow=False, enable_mlserver=False, env_manager=em.VIRTUALENV
):
"""
Creates a conda env for serving the model at the specified path and installs almost all serving
dependencies into the environment - MLflow is not installed as it's not available via c... |
Creates a conda env for serving the model at the specified path and installs almost all serving
dependencies into the environment - MLflow is not installed as it's not available via conda.
| _install_pyfunc_deps | python | mlflow/mlflow | mlflow/models/container/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/container/__init__.py | Apache-2.0 |
def _install_model_dependencies_to_env(model_path, env_manager) -> list[str]:
""":
Installs model dependencies to the specified environment, which can be either a local
environment, a conda environment, or a virtualenv.
Returns:
Empty list if local environment, otherwise a list of bash commands... | :
Installs model dependencies to the specified environment, which can be either a local
environment, a conda environment, or a virtualenv.
Returns:
Empty list if local environment, otherwise a list of bash commands to activate the
virtualenv or conda environment.
| _install_model_dependencies_to_env | python | mlflow/mlflow | mlflow/models/container/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/container/__init__.py | Apache-2.0 |
def _sigterm_handler(pids):
"""
Cleanup when terminating.
Attempt to kill all launched processes and exit.
"""
_logger.info("Got sigterm signal, exiting.")
for pid in pids:
try:
os.kill(pid, signal.SIGTERM)
except OSError:
pass
sys.exit(0) |
Cleanup when terminating.
Attempt to kill all launched processes and exit.
| _sigterm_handler | python | mlflow/mlflow | mlflow/models/container/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/container/__init__.py | Apache-2.0 |
def _infer_artifact_type_and_ext(artifact_name, raw_artifact, custom_metric_tuple):
"""
This function performs type and file extension inference on the provided artifact
Args:
artifact_name: The name of the provided artifact
raw_artifact: The artifact object
custom_metric_tuple: Con... |
This function performs type and file extension inference on the provided artifact
Args:
artifact_name: The name of the provided artifact
raw_artifact: The artifact object
custom_metric_tuple: Containing a user provided function and its index in the
``custom_metrics`` parame... | _infer_artifact_type_and_ext | python | mlflow/mlflow | mlflow/models/evaluation/artifacts.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/artifacts.py | Apache-2.0 |
def _generate_eval_metric_class(eval_fn, require_strict_signature=False):
"""
Dynamically generate a GenAIEvaluationMetric class that can be used to evaluate the metric
on the given input data. The generated class is callable with a __call__ method that
takes the arguments specified in the signature of ... |
Dynamically generate a GenAIEvaluationMetric class that can be used to evaluate the metric
on the given input data. The generated class is callable with a __call__ method that
takes the arguments specified in the signature of the eval_fn function.
Args:
eval_fn: the evaluation function of the ... | _generate_eval_metric_class | python | mlflow/mlflow | mlflow/models/evaluation/base.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/base.py | Apache-2.0 |
def make_metric(
*,
eval_fn,
greater_is_better,
name=None,
long_name=None,
version=None,
metric_details=None,
metric_metadata=None,
genai_metric_args=None,
):
'''
A factory function to create an :py:class:`EvaluationMetric` object.
Args:
eval_fn: A function that ... |
A factory function to create an :py:class:`EvaluationMetric` object.
Args:
eval_fn: A function that computes the metric with the following signature:
.. code-block:: python
def eval_fn(
predictions: pandas.Series,
targets: pandas.Se... | make_metric | python | mlflow/mlflow | mlflow/models/evaluation/base.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/base.py | Apache-2.0 |
def _make_metric(
*,
eval_fn,
greater_is_better,
name=None,
long_name=None,
version=None,
metric_details=None,
metric_metadata=None,
genai_metric_args=None,
require_strict_signature=False,
):
'''
A factory function to create an :py:class:`EvaluationMetric` object.
Ar... |
A factory function to create an :py:class:`EvaluationMetric` object.
Args:
eval_fn: A function that computes the metric with the following signature:
.. code-block:: python
def eval_fn(
predictions: pandas.Series,
targets: pandas.Se... | _make_metric | python | mlflow/mlflow | mlflow/models/evaluation/base.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/base.py | Apache-2.0 |
def _load_content_from_file(self, local_artifact_path):
"""
Abstract interface to load the content from local artifact file path,
and return the loaded content.
""" |
Abstract interface to load the content from local artifact file path,
and return the loaded content.
| _load_content_from_file | python | mlflow/mlflow | mlflow/models/evaluation/base.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/base.py | Apache-2.0 |
def _load(self, local_artifact_path=None):
"""
If ``local_artifact_path`` is ``None``, download artifact from the artifact uri.
Otherwise, load artifact content from the specified path. Assign the loaded content to
``self._content``, and return the loaded content.
"""
if ... |
If ``local_artifact_path`` is ``None``, download artifact from the artifact uri.
Otherwise, load artifact content from the specified path. Assign the loaded content to
``self._content``, and return the loaded content.
| _load | python | mlflow/mlflow | mlflow/models/evaluation/base.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/base.py | Apache-2.0 |
def content(self):
"""
The content of the artifact (representation varies)
"""
if self._content is None:
self._load()
return self._content |
The content of the artifact (representation varies)
| content | python | mlflow/mlflow | mlflow/models/evaluation/base.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/base.py | Apache-2.0 |
def load(cls, path):
"""Load the evaluation results from the specified local filesystem path"""
with open(os.path.join(path, "metrics.json")) as fp:
metrics = json.load(fp)
with open(os.path.join(path, "artifacts_metadata.json")) as fp:
artifacts_metadata = json.load(fp)... | Load the evaluation results from the specified local filesystem path | load | python | mlflow/mlflow | mlflow/models/evaluation/base.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/base.py | Apache-2.0 |
def save(self, path):
"""Write the evaluation results to the specified local filesystem path"""
os.makedirs(path, exist_ok=True)
with open(os.path.join(path, "metrics.json"), "w") as fp:
json.dump(self.metrics, fp, cls=NumpyEncoder)
artifacts_metadata = {
artifac... | Write the evaluation results to the specified local filesystem path | save | python | mlflow/mlflow | mlflow/models/evaluation/base.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/base.py | Apache-2.0 |
def tables(self) -> dict[str, "pd.DataFrame"]:
"""
A dictionary mapping standardized artifact names (e.g. "eval_results_table") to
corresponding table content as pandas DataFrame.
"""
eval_tables = {}
if self._run_id is None:
_logger.warning("Cannot load eval_... |
A dictionary mapping standardized artifact names (e.g. "eval_results_table") to
corresponding table content as pandas DataFrame.
| tables | python | mlflow/mlflow | mlflow/models/evaluation/base.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/base.py | Apache-2.0 |
def list_evaluators():
"""
Return a name list for all available Evaluators.
"""
# import _model_evaluation_registry inside function to avoid circuit importing
from mlflow.models.evaluation.evaluator_registry import _model_evaluation_registry
return list(_model_evaluation_registry._registry.keys... |
Return a name list for all available Evaluators.
| list_evaluators | python | mlflow/mlflow | mlflow/models/evaluation/base.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/base.py | Apache-2.0 |
def _start_run_or_reuse_active_run():
"""
A manager context return:
- If there's an active run, return the active run id.
- otherwise start a mflow run with the specified run_id,
if specified run_id is None, start a new run.
"""
active_run = mlflow.active_run()
if not active_run:
... |
A manager context return:
- If there's an active run, return the active run id.
- otherwise start a mflow run with the specified run_id,
if specified run_id is None, start a new run.
| _start_run_or_reuse_active_run | python | mlflow/mlflow | mlflow/models/evaluation/base.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/base.py | Apache-2.0 |
def _model_validation_contains_model_comparison(validation_thresholds):
"""
Helper function for determining if validation_thresholds contains
thresholds for model comparison: either min_relative_change or min_absolute_change
"""
if not validation_thresholds:
return False
thresholds = val... |
Helper function for determining if validation_thresholds contains
thresholds for model comparison: either min_relative_change or min_absolute_change
| _model_validation_contains_model_comparison | python | mlflow/mlflow | mlflow/models/evaluation/base.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/base.py | Apache-2.0 |
def _evaluate(
*,
model,
model_type,
model_id,
dataset,
run_id,
# The `evaluator_name_list` and `evaluator_name_to_conf_map` are not used by MLflow at all,
# but we need to keep these for backward compatibility.
evaluator_name_list,
evaluator_name_to_conf_map,
extra_metrics,
... |
The public API "evaluate" will verify argument first, and then pass normalized arguments
to the _evaluate method.
| _evaluate | python | mlflow/mlflow | mlflow/models/evaluation/base.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/base.py | Apache-2.0 |
def make_multi_class_calibration_plot(
n_classes, y_true, y_probs, calibration_config, label_list
) -> Figure:
"""Generate one calibration plot for all classes of a multi-class classifier
Args:
n_classes (int): number of classes in multi-class prediction
y_true (array-like, shape (n_samples... | Generate one calibration plot for all classes of a multi-class classifier
Args:
n_classes (int): number of classes in multi-class prediction
y_true (array-like, shape (n_samples)):
Ground truth (correct) target values.
y_probs (array-like, shape (n_samples, n_classes)):
... | make_multi_class_calibration_plot | python | mlflow/mlflow | mlflow/models/evaluation/calibration_curve.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/calibration_curve.py | Apache-2.0 |
def plot_calibration_curve(y_true, y_probs, pos_label, calibration_config, label_list) -> Figure:
"""Generate a calibration curve for a trained classifier
Args:
y_true (array-like, shape (n_samples)):
Ground truth (correct) target values.
y_probs (array-like, shape (n_samples, n_cl... | Generate a calibration curve for a trained classifier
Args:
y_true (array-like, shape (n_samples)):
Ground truth (correct) target values.
y_probs (array-like, shape (n_samples, n_classes)):
Prediction probabilities for each class returned by a classifier.
pos_label... | plot_calibration_curve | python | mlflow/mlflow | mlflow/models/evaluation/calibration_curve.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/calibration_curve.py | Apache-2.0 |
def _extract_predict_fn(model: Any) -> Optional[Callable]:
"""
Extracts the predict function from the given model or raw_model.
Precedence order:
1. If raw_model is specified, its predict function is used.
2. If model is specified, its predict function is used.
3. If none of the above, predict ... |
Extracts the predict function from the given model or raw_model.
Precedence order:
1. If raw_model is specified, its predict function is used.
2. If model is specified, its predict function is used.
3. If none of the above, predict function is None.
Args:
model: A model object that ha... | _extract_predict_fn | python | mlflow/mlflow | mlflow/models/evaluation/default_evaluator.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/default_evaluator.py | Apache-2.0 |
def _get_dataframe_with_renamed_columns(x, new_column_names):
"""
Downstream inference functions may expect a pd.DataFrame to be created from x. However,
if x is already a pd.DataFrame, and new_column_names != x.columns, we cannot simply call
pd.DataFrame(x, columns=new_column_names) because the resulti... |
Downstream inference functions may expect a pd.DataFrame to be created from x. However,
if x is already a pd.DataFrame, and new_column_names != x.columns, we cannot simply call
pd.DataFrame(x, columns=new_column_names) because the resulting pd.DataFrame will contain
NaNs for every column in new_column_... | _get_dataframe_with_renamed_columns | python | mlflow/mlflow | mlflow/models/evaluation/default_evaluator.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/default_evaluator.py | Apache-2.0 |
def _evaluate_custom_artifacts(custom_artifact_tuple, eval_df, builtin_metrics):
"""
This function calls the `custom_artifact` function and performs validations on the returned
result to ensure that they are in the expected format. It will raise a MlflowException if
the result is not in the expected for... |
This function calls the `custom_artifact` function and performs validations on the returned
result to ensure that they are in the expected format. It will raise a MlflowException if
the result is not in the expected format.
Args:
custom_artifact_tuple: Containing a user provided function and i... | _evaluate_custom_artifacts | python | mlflow/mlflow | mlflow/models/evaluation/default_evaluator.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/default_evaluator.py | Apache-2.0 |
def _evaluate(
self,
model: Optional["mlflow.pyfunc.PyFuncModel"],
extra_metrics: list[EvaluationMetric],
custom_artifacts=None,
**kwargs,
) -> Optional[EvaluationResult]:
"""Implement the evaluation logic for each evaluator.""" | Implement the evaluation logic for each evaluator. | _evaluate | python | mlflow/mlflow | mlflow/models/evaluation/default_evaluator.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/default_evaluator.py | Apache-2.0 |
def log_metrics(self):
"""
Helper method to log metrics into specified run.
"""
self._add_prefix_to_metrics()
timestamp = get_current_time_millis()
self.client.log_batch(
self.run_id,
metrics=[
Metric(
key=key,
... |
Helper method to log metrics into specified run.
| log_metrics | python | mlflow/mlflow | mlflow/models/evaluation/default_evaluator.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/default_evaluator.py | Apache-2.0 |
def _log_custom_metric_artifact(self, artifact_name, raw_artifact, custom_metric_tuple):
"""
This function logs and returns a custom metric artifact. Two cases:
- The provided artifact is a path to a file, the function will make a copy of it with
a formatted name in a temporary... |
This function logs and returns a custom metric artifact. Two cases:
- The provided artifact is a path to a file, the function will make a copy of it with
a formatted name in a temporary directory and call mlflow.log_artifact.
- Otherwise: will attempt to save the artifact ... | _log_custom_metric_artifact | python | mlflow/mlflow | mlflow/models/evaluation/default_evaluator.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/default_evaluator.py | Apache-2.0 |
def _get_args_for_metrics(
self,
metric: MetricDefinition,
eval_df: pd.DataFrame,
input_df: pd.DataFrame,
other_output_df: Optional[pd.DataFrame],
) -> tuple[bool, list[Union[str, pd.DataFrame]]]:
"""
Given a metric_tuple, read the signature of the metric func... |
Given a metric_tuple, read the signature of the metric function and get the appropriate
arguments from the input/output columns, other calculated metrics, and evaluator_config.
Args:
metric: The metric definition containing a user provided function and its index
in ... | _get_args_for_metrics | python | mlflow/mlflow | mlflow/models/evaluation/default_evaluator.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/default_evaluator.py | Apache-2.0 |
def evaluate_and_log_custom_artifacts(
self,
custom_artifacts: list[_CustomArtifact],
prediction: pd.Series,
target: Optional[np.array] = None,
):
"""Evaluate custom artifacts provided by users."""
if not custom_artifacts:
return
eval_df = self._g... | Evaluate custom artifacts provided by users. | evaluate_and_log_custom_artifacts | python | mlflow/mlflow | mlflow/models/evaluation/default_evaluator.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/default_evaluator.py | Apache-2.0 |
def _get_eval_df(self, prediction: pd.Series, target: Optional[np.array] = None):
"""
Create a DataFrame with "prediction" and "target" columns.
This is a standard format that can be passed to the metric functions.
"""
eval_df = pd.DataFrame({"prediction": copy.deepcopy(predicti... |
Create a DataFrame with "prediction" and "target" columns.
This is a standard format that can be passed to the metric functions.
| _get_eval_df | python | mlflow/mlflow | mlflow/models/evaluation/default_evaluator.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/default_evaluator.py | Apache-2.0 |
def _order_metrics(
self,
metrics: list[EvaluationMetric],
eval_df: pd.DataFrame,
other_output_df: Optional[pd.DataFrame],
):
"""
Order the list metrics so they can be computed in sequence.
Some metrics might use the results of other metrics to compute their ... |
Order the list metrics so they can be computed in sequence.
Some metrics might use the results of other metrics to compute their own results. This
function iteratively resolve this dependency, by checking if each metric can be computed
with the current available columns and metrics val... | _order_metrics | python | mlflow/mlflow | mlflow/models/evaluation/default_evaluator.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/default_evaluator.py | Apache-2.0 |
def evaluate_metrics(
self,
metrics: list[EvaluationMetric],
prediction: pd.Series,
target: Optional[np.array] = None,
other_output_df: Optional[pd.DataFrame] = None,
):
"""
Evaluate the metrics on the given prediction and target data.
Args:
... |
Evaluate the metrics on the given prediction and target data.
Args:
metrics: A list of metrics to evaluate.
prediction: A Pandas Series containing the predictions.
target: A numpy array containing the target values.
other_output_df: A Pandas DataFrame co... | evaluate_metrics | python | mlflow/mlflow | mlflow/models/evaluation/default_evaluator.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/default_evaluator.py | Apache-2.0 |
def X(self) -> pd.DataFrame:
"""
The features (`X`) portion of the dataset, guarded against accidental mutations.
"""
return BuiltInEvaluator._MutationGuardedData(
_get_dataframe_with_renamed_columns(
self.dataset.features_data, self.dataset.feature_names
... |
The features (`X`) portion of the dataset, guarded against accidental mutations.
| X | python | mlflow/mlflow | mlflow/models/evaluation/default_evaluator.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/default_evaluator.py | Apache-2.0 |
def copy_to_avoid_mutation(self):
"""
Obtain a copy of the data. This method should be called every time the data needs
to be used in a context where it may be subsequently mutated, guarding against
accidental reuse after mutation.
Returns:
A ... |
Obtain a copy of the data. This method should be called every time the data needs
to be used in a context where it may be subsequently mutated, guarding against
accidental reuse after mutation.
Returns:
A copy of the data object.
| copy_to_avoid_mutation | python | mlflow/mlflow | mlflow/models/evaluation/default_evaluator.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/default_evaluator.py | Apache-2.0 |
def get_evaluator(self, evaluator_name):
"""
Get an evaluator instance from the registry based on the name of evaluator
"""
evaluator_cls = self._registry.get(evaluator_name)
if evaluator_cls is None:
raise MlflowException(
f"Could not find a registere... |
Get an evaluator instance from the registry based on the name of evaluator
| get_evaluator | python | mlflow/mlflow | mlflow/models/evaluation/evaluator_registry.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/evaluator_registry.py | Apache-2.0 |
def _cumulative_gain_curve(y_true, y_score, pos_label=None):
"""
This method is copied from scikit-plot package.
See https://github.com/reiinakano/scikit-plot/blob/2dd3e6a76df77edcbd724c4db25575f70abb57cb/scikitplot/helpers.py#L157
This function generates the points necessary to plot the Cumulative Gai... |
This method is copied from scikit-plot package.
See https://github.com/reiinakano/scikit-plot/blob/2dd3e6a76df77edcbd724c4db25575f70abb57cb/scikitplot/helpers.py#L157
This function generates the points necessary to plot the Cumulative Gain
Note: This implementation is restricted to the binary classif... | _cumulative_gain_curve | python | mlflow/mlflow | mlflow/models/evaluation/lift_curve.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/lift_curve.py | Apache-2.0 |
def plot_lift_curve(
y_true,
y_probas,
title="Lift Curve",
ax=None,
figsize=None,
title_fontsize="large",
text_fontsize="medium",
pos_label=None,
):
"""
This method is copied from scikit-plot package.
See https://github.com/reiinakano/scikit-plot/blob/2dd3e6a76df77edcbd724c4d... |
This method is copied from scikit-plot package.
See https://github.com/reiinakano/scikit-plot/blob/2dd3e6a76df77edcbd724c4db25575f70abb57cb/scikitplot/metrics.py#L1133
Generates the Lift Curve from labels and scores/probabilities
The lift curve is used to determine the effectiveness of a
binary c... | plot_lift_curve | python | mlflow/mlflow | mlflow/models/evaluation/lift_curve.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/lift_curve.py | Apache-2.0 |
def __str__(self):
"""
Returns a human-readable string consisting of all specified thresholds.
"""
threshold_strs = []
if self._threshold is not None:
threshold_strs.append(f"Threshold: {self._threshold}.")
if self._min_absolute_change is not None:
... |
Returns a human-readable string consisting of all specified thresholds.
| __str__ | python | mlflow/mlflow | mlflow/models/evaluation/validation.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/validation.py | Apache-2.0 |
def __str__(self):
"""
Returns a human-readable string representing the validation result for the metric.
"""
if self.is_success():
return f"Metric {self.metric_name} passed the validation."
if self.missing_candidate:
return (
f"Metric val... |
Returns a human-readable string representing the validation result for the metric.
| __str__ | python | mlflow/mlflow | mlflow/models/evaluation/validation.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/validation.py | Apache-2.0 |
def validate_evaluation_results(
validation_thresholds: dict[str, MetricThreshold],
candidate_result: EvaluationResult,
baseline_result: Optional[EvaluationResult] = None,
):
"""
Validate the evaluation result from one model (candidate) against another
model (baseline). If the candidate results ... |
Validate the evaluation result from one model (candidate) against another
model (baseline). If the candidate results do not meet the validation
thresholds, an ModelValidationFailedException will be raised.
.. note::
This API is a replacement for the deprecated model validation
functio... | validate_evaluation_results | python | mlflow/mlflow | mlflow/models/evaluation/validation.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/validation.py | Apache-2.0 |
def _validate(
validation_thresholds: dict[str, MetricThreshold],
candidate_metrics: dict[str, float],
baseline_metrics: dict[str, float],
):
"""
Validate the model based on validation_thresholds by metrics value and
metrics comparison between candidate model's metrics (candidate_metrics) and
... |
Validate the model based on validation_thresholds by metrics value and
metrics comparison between candidate model's metrics (candidate_metrics) and
baseline model's metrics (baseline_metrics).
Args:
validation_thresholds: A dictionary from metric_name to MetricThreshold.
candidate_metr... | _validate | python | mlflow/mlflow | mlflow/models/evaluation/validation.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/validation.py | Apache-2.0 |
def save(self, out_file, model_saver=None, masker_saver=None):
"""
This patched `save` method fix `KernelExplainer.save`.
Issues in original `KernelExplainer.save`:
- It saves model by calling model.save, but shap.utils._legacy.Model has no save method
- It tries to save "maske... |
This patched `save` method fix `KernelExplainer.save`.
Issues in original `KernelExplainer.save`:
- It saves model by calling model.save, but shap.utils._legacy.Model has no save method
- It tries to save "masker", but there's no "masker" in KernelExplainer
- It does not save... | save | python | mlflow/mlflow | mlflow/models/evaluation/_shap_patch.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/_shap_patch.py | Apache-2.0 |
def load(cls, in_file, model_loader=None, masker_loader=None, instantiate=True):
"""
This patched `load` method fix `KernelExplainer.load`.
Issues in original KernelExplainer.load:
- Use mismatched model loader to load model
- Try to load non-existent "masker" attribute
... |
This patched `load` method fix `KernelExplainer.load`.
Issues in original KernelExplainer.load:
- Use mismatched model loader to load model
- Try to load non-existent "masker" attribute
- Does not load "data" attribute and then cause calling " KernelExplainer"
cons... | load | python | mlflow/mlflow | mlflow/models/evaluation/_shap_patch.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/_shap_patch.py | Apache-2.0 |
def _log_confusion_matrix(self):
"""
Helper method for logging confusion matrix
"""
# normalize the confusion matrix, keep consistent with sklearn autologging.
confusion_matrix = sk_metrics.confusion_matrix(
self.y_true,
self.y_pred,
labels=sel... |
Helper method for logging confusion matrix
| _log_confusion_matrix | python | mlflow/mlflow | mlflow/models/evaluation/evaluators/classifier.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/evaluators/classifier.py | Apache-2.0 |
def _is_categorical(values):
"""
Infer whether input values are categorical on best effort.
Return True represent they are categorical, return False represent we cannot determine result.
"""
dtype_name = pd.Series(values).convert_dtypes().dtype.name.lower()
return dtype_name in ["category", "str... |
Infer whether input values are categorical on best effort.
Return True represent they are categorical, return False represent we cannot determine result.
| _is_categorical | python | mlflow/mlflow | mlflow/models/evaluation/evaluators/classifier.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/evaluators/classifier.py | Apache-2.0 |
def _infer_model_type_by_labels(labels):
"""
Infer model type by target values.
"""
if _is_categorical(labels):
return _ModelType.CLASSIFIER
elif _is_continuous(labels):
return _ModelType.REGRESSOR
else:
return None # Unknown |
Infer model type by target values.
| _infer_model_type_by_labels | python | mlflow/mlflow | mlflow/models/evaluation/evaluators/classifier.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/evaluators/classifier.py | Apache-2.0 |
def _suppress_class_imbalance_errors(exception_type=Exception, log_warning=True):
"""
Exception handler context manager to suppress Exceptions if the private environment
variable `_MLFLOW_EVALUATE_SUPPRESS_CLASSIFICATION_ERRORS` is set to `True`.
The purpose of this handler is to prevent an evaluation c... |
Exception handler context manager to suppress Exceptions if the private environment
variable `_MLFLOW_EVALUATE_SUPPRESS_CLASSIFICATION_ERRORS` is set to `True`.
The purpose of this handler is to prevent an evaluation call for a binary or multiclass
classification automl run from aborting due to an extr... | _suppress_class_imbalance_errors | python | mlflow/mlflow | mlflow/models/evaluation/evaluators/classifier.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/evaluators/classifier.py | Apache-2.0 |
def _gen_classifier_curve(
is_binomial,
y,
y_probs,
labels,
pos_label,
curve_type,
sample_weights,
):
"""
Generate precision-recall curve or ROC curve for classifier.
Args:
is_binomial: True if it is binary classifier otherwise False
y: True label values
... |
Generate precision-recall curve or ROC curve for classifier.
Args:
is_binomial: True if it is binary classifier otherwise False
y: True label values
y_probs: if binary classifier, the predicted probability for positive class.
if multiclass classifier, the predicted pr... | _gen_classifier_curve | python | mlflow/mlflow | mlflow/models/evaluation/evaluators/classifier.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/evaluators/classifier.py | Apache-2.0 |
def _builtin_metrics(self) -> list[Metric]:
"""
Get a list of builtin metrics for the model type.
"""
text_metrics = [
token_count(),
toxicity(),
flesch_kincaid_grade_level(),
ari_grade_level(),
]
builtin_metrics = []
... |
Get a list of builtin metrics for the model type.
| _builtin_metrics | python | mlflow/mlflow | mlflow/models/evaluation/evaluators/default.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/evaluators/default.py | Apache-2.0 |
def _generate_model_predictions(
self,
model: Optional["mlflow.pyfunc.PyFuncModel"],
input_df: pd.DataFrame,
compute_latency=False,
):
"""
Helper method for generating model predictions
"""
predict_fn = _extract_predict_fn(model)
def predict_w... |
Helper method for generating model predictions
| _generate_model_predictions | python | mlflow/mlflow | mlflow/models/evaluation/evaluators/default.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/evaluators/default.py | Apache-2.0 |
def _compute_df_mode_or_mean(df):
"""
Compute mean (for continuous columns) and compute mode (for other columns) for the
input dataframe, return a dict, key is column name, value is the corresponding mode or
mean value, this function calls `_is_continuous` to determine whether the
column is continuo... |
Compute mean (for continuous columns) and compute mode (for other columns) for the
input dataframe, return a dict, key is column name, value is the corresponding mode or
mean value, this function calls `_is_continuous` to determine whether the
column is continuous column.
| _compute_df_mode_or_mean | python | mlflow/mlflow | mlflow/models/evaluation/evaluators/shap.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/evaluators/shap.py | Apache-2.0 |
def evaluate(self, eval_fn_args) -> Optional[MetricValue]:
"""
This function calls the metric function and performs validations on the returned
result to ensure that they are in the expected format. It will warn and will not log metrics
that are in the wrong format.
Args:
... |
This function calls the metric function and performs validations on the returned
result to ensure that they are in the expected format. It will warn and will not log metrics
that are in the wrong format.
Args:
eval_fn_args: A dictionary of args needed to compute the eval me... | evaluate | python | mlflow/mlflow | mlflow/models/evaluation/utils/metric.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/utils/metric.py | Apache-2.0 |
def configure_autologging_for_evaluation(enable_tracing: bool = True):
"""
Temporarily override the autologging configuration for all flavors during the model evaluation.
For example, model auto-logging must be disabled during the evaluation. After the evaluation
is done, the original autologging config... |
Temporarily override the autologging configuration for all flavors during the model evaluation.
For example, model auto-logging must be disabled during the evaluation. After the evaluation
is done, the original autologging configurations are restored.
Args:
enable_tracing (bool): Whether to en... | configure_autologging_for_evaluation | python | mlflow/mlflow | mlflow/models/evaluation/utils/trace.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/utils/trace.py | Apache-2.0 |
def _should_enable_tracing(flavor: str, autologging_config: dict[str, Any]) -> bool:
"""
Check if tracing should be enabled for the given flavor during the model evaluation.
"""
# 1. Check if the autologging or tracing is globally disabled
# TODO: This check should not take precedence over the flavo... |
Check if tracing should be enabled for the given flavor during the model evaluation.
| _should_enable_tracing | python | mlflow/mlflow | mlflow/models/evaluation/utils/trace.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/utils/trace.py | Apache-2.0 |
def _kwargs_safe_invoke(func: Callable[..., Any], kwargs: dict[str, Any]):
"""
Invoke the function with the given dictionary as keyword arguments, but only include the
arguments that are present in the function's signature.
This is particularly used for calling autolog() function with the configuration... |
Invoke the function with the given dictionary as keyword arguments, but only include the
arguments that are present in the function's signature.
This is particularly used for calling autolog() function with the configuration dictionary
stored in AUTOLOGGING_INTEGRATIONS. While the config keys mostly a... | _kwargs_safe_invoke | python | mlflow/mlflow | mlflow/models/evaluation/utils/trace.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/utils/trace.py | Apache-2.0 |
def _is_trace_autologging_supported(flavor_name: str) -> bool:
"""Check if the given flavor supports trace autologging."""
if autolog_func := get_autolog_function(flavor_name):
return "log_traces" in inspect.signature(autolog_func).parameters
return False | Check if the given flavor supports trace autologging. | _is_trace_autologging_supported | python | mlflow/mlflow | mlflow/models/evaluation/utils/trace.py | https://github.com/mlflow/mlflow/blob/master/mlflow/models/evaluation/utils/trace.py | Apache-2.0 |
def predict(self, data, params: Optional[dict[str, Any]] = None):
"""
Args:
data: Either a pandas DataFrame, numpy.ndarray or a dictionary.
Dictionary input is expected to be a valid ONNX model feed dictionary.
Numpy array input is supported iff the model has... |
Args:
data: Either a pandas DataFrame, numpy.ndarray or a dictionary.
Dictionary input is expected to be a valid ONNX model feed dictionary.
Numpy array input is supported iff the model has a single tensor input and is
converted into an ONNX feed dic... | predict | python | mlflow/mlflow | mlflow/onnx/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/onnx/__init__.py | Apache-2.0 |
def load_model(model_uri, dst_path=None):
"""
Load an ONNX 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... |
Load an ONNX 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:/<mlflow_ru... | load_model | python | mlflow/mlflow | mlflow/onnx/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/onnx/__init__.py | Apache-2.0 |
def autolog(
disable=False,
exclusive=False,
disable_for_unsupported_versions=False,
silent=False,
log_traces=True,
):
"""
Enables (or disables) and configures autologging from OpenAI to MLflow.
Raises :py:class:`MlflowException <mlflow.exceptions.MlflowException>`
if the OpenAI vers... |
Enables (or disables) and configures autologging from OpenAI to MLflow.
Raises :py:class:`MlflowException <mlflow.exceptions.MlflowException>`
if the OpenAI version < 1.0.
Args:
disable: If ``True``, disables the OpenAI autologging integration. If ``False``,
enables the OpenAI auto... | autolog | python | mlflow/mlflow | mlflow/openai/autolog.py | https://github.com/mlflow/mlflow/blob/master/mlflow/openai/autolog.py | Apache-2.0 |
def _try_parse_raw_response(response: Any) -> Any:
"""
As documented at https://github.com/openai/openai-python/tree/52357cff50bee57ef442e94d78a0de38b4173fc2?tab=readme-ov-file#accessing-raw-response-data-eg-headers,
a `LegacyAPIResponse` (https://github.com/openai/openai-python/blob/52357cff50bee57ef442e94... |
As documented at https://github.com/openai/openai-python/tree/52357cff50bee57ef442e94d78a0de38b4173fc2?tab=readme-ov-file#accessing-raw-response-data-eg-headers,
a `LegacyAPIResponse` (https://github.com/openai/openai-python/blob/52357cff50bee57ef442e94d78a0de38b4173fc2/src/openai/_legacy_response.py#L45)
... | _try_parse_raw_response | python | mlflow/mlflow | mlflow/openai/autolog.py | https://github.com/mlflow/mlflow/blob/master/mlflow/openai/autolog.py | Apache-2.0 |
def _process_chunk(span: LiveSpan, index: int, chunk: Any) -> str:
"""Parse the chunk and log it as a span event in the trace."""
from openai.types.chat.chat_completion_chunk import ChatCompletionChunk
from openai.types.completion import Completion
# `chunk.choices` can be empty: https://github.com/mlf... | Parse the chunk and log it as a span event in the trace. | _process_chunk | python | mlflow/mlflow | mlflow/openai/autolog.py | https://github.com/mlflow/mlflow/blob/master/mlflow/openai/autolog.py | Apache-2.0 |
def patched_agent_get_chat_completion(original, self, *args, **kwargs):
"""
Patch the `get_chat_completion` method of the ChatCompletion object.
OpenAI autolog already handles the raw completion request, but tracing
the swarm's method is useful to track other parameters like agent name.
"""
agen... |
Patch the `get_chat_completion` method of the ChatCompletion object.
OpenAI autolog already handles the raw completion request, but tracing
the swarm's method is useful to track other parameters like agent name.
| patched_agent_get_chat_completion | python | mlflow/mlflow | mlflow/openai/autolog.py | https://github.com/mlflow/mlflow/blob/master/mlflow/openai/autolog.py | Apache-2.0 |
def patched_swarm_run(original, self, *args, **kwargs):
"""
Patched version of `run` method of the Swarm object.
"""
traced_fn = mlflow.trace(original, span_type=SpanType.AGENT)
return traced_fn(self, *args, **kwargs) |
Patched version of `run` method of the Swarm object.
| patched_swarm_run | python | mlflow/mlflow | mlflow/openai/autolog.py | https://github.com/mlflow/mlflow/blob/master/mlflow/openai/autolog.py | Apache-2.0 |
def _get_api_config() -> _OpenAIApiConfig:
"""Gets the parameters and configuration of the OpenAI API connected to."""
import openai
api_type = os.getenv(_OpenAIEnvVar.OPENAI_API_TYPE.value, openai.api_type)
api_version = os.getenv(_OpenAIEnvVar.OPENAI_API_VERSION.value, openai.api_version)
api_bas... | Gets the parameters and configuration of the OpenAI API connected to. | _get_api_config | python | mlflow/mlflow | mlflow/openai/model.py | https://github.com/mlflow/mlflow/blob/master/mlflow/openai/model.py | Apache-2.0 |
def load_model(model_uri, dst_path=None):
"""
Load an OpenAI 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_buck... |
Load an OpenAI 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:/<mlflow_... | load_model | python | mlflow/mlflow | mlflow/openai/model.py | https://github.com/mlflow/mlflow/blob/master/mlflow/openai/model.py | Apache-2.0 |
def _is_responses_output(output: Any) -> bool:
"""
Check whether the output is OpenAI Responses API output, or
a response from the MLflow ResponsesAgent instance.
"""
try:
from openai.types.responses import Response
if isinstance(output, Response):
return True
except... |
Check whether the output is OpenAI Responses API output, or
a response from the MLflow ResponsesAgent instance.
| _is_responses_output | python | mlflow/mlflow | mlflow/openai/utils/chat_schema.py | https://github.com/mlflow/mlflow/blob/master/mlflow/openai/utils/chat_schema.py | Apache-2.0 |
def _parse_response_item(
item: Union[dict[str, Any], BaseModel],
past_messages: list[ChatMessage],
) -> list[ChatMessage]:
"""Parse Response API output into MLflow standard chat messages"""
if isinstance(item, BaseModel):
item = item.model_dump()
item_type = item.get("type", "message")
... | Parse Response API output into MLflow standard chat messages | _parse_response_item | python | mlflow/mlflow | mlflow/openai/utils/chat_schema.py | https://github.com/mlflow/mlflow/blob/master/mlflow/openai/utils/chat_schema.py | Apache-2.0 |
def _populate_tool_result_message(
annotations: dict[str, Any], messages: list[ChatMessage]
) -> ChatMessage:
"""
Parses annotations from the Response API output into MLflow standard chat spec.
In OpenAI spec, annotations are used for populating information from file/web
search tools in the Respons... |
Parses annotations from the Response API output into MLflow standard chat spec.
In OpenAI spec, annotations are used for populating information from file/web
search tools in the Responses API response. When converting to MLflow standard
chat spec, it should be presented as a tool result message.
| _populate_tool_result_message | python | mlflow/mlflow | mlflow/openai/utils/chat_schema.py | https://github.com/mlflow/mlflow/blob/master/mlflow/openai/utils/chat_schema.py | Apache-2.0 |
def _parse_usage(output: Any) -> Optional[dict]:
"""
Parse token usage information from OpenAI response objects.
Args:
output: The response object from OpenAI API calls
Returns:
A dictionary containing token usage information.
"""
if output is None:
return None
# H... |
Parse token usage information from OpenAI response objects.
Args:
output: The response object from OpenAI API calls
Returns:
A dictionary containing token usage information.
| _parse_usage | python | mlflow/mlflow | mlflow/openai/utils/chat_schema.py | https://github.com/mlflow/mlflow/blob/master/mlflow/openai/utils/chat_schema.py | Apache-2.0 |
def __init__(
self,
experiment_id: str,
name: Optional[str] = None,
batch_flush_interval: float = 1.0,
batch_size_threshold: int = 100,
):
"""
Initialize MLFlowStorage with batching capabilities.
Parameters
----------
experiment_id : s... |
Initialize MLFlowStorage with batching capabilities.
Parameters
----------
experiment_id : str
MLflow experiment ID
name : Optional[str]
Optional name for the storage
batch_flush_interval : float
Time in seconds between automatic batc... | __init__ | python | mlflow/mlflow | mlflow/optuna/storage.py | https://github.com/mlflow/mlflow/blob/master/mlflow/optuna/storage.py | Apache-2.0 |
def __getstate__(self):
"""
Prepare the object for serialization by removing non-picklable components.
This is called when the object is being pickled.
"""
state = self.__dict__.copy()
# Remove thread-related attributes that can't be pickled
state.pop("_batch_loc... |
Prepare the object for serialization by removing non-picklable components.
This is called when the object is being pickled.
| __getstate__ | python | mlflow/mlflow | mlflow/optuna/storage.py | https://github.com/mlflow/mlflow/blob/master/mlflow/optuna/storage.py | Apache-2.0 |
def __setstate__(self, state):
"""
Restore the object after deserialization by recreating non-picklable components.
This is called when the object is being unpickled.
"""
# First, update the instance with the pickled state
self.__dict__.update(state)
# Recreate t... |
Restore the object after deserialization by recreating non-picklable components.
This is called when the object is being unpickled.
| __setstate__ | python | mlflow/mlflow | mlflow/optuna/storage.py | https://github.com/mlflow/mlflow/blob/master/mlflow/optuna/storage.py | Apache-2.0 |
def __del__(self):
"""Ensure all queued data is flushed before destroying the object."""
# Set the stop flag
if hasattr(self, "_stop_worker"):
self._stop_worker = True
# Join the thread if it exists and is alive
if hasattr(self, "_flush_thread") and self._flush_threa... | Ensure all queued data is flushed before destroying the object. | __del__ | python | mlflow/mlflow | mlflow/optuna/storage.py | https://github.com/mlflow/mlflow/blob/master/mlflow/optuna/storage.py | Apache-2.0 |
def _periodic_flush_worker(self):
"""Background worker that periodically flushes batched data."""
while not self._stop_worker:
try:
time.sleep(min(0.1, self._batch_flush_interval / 10)) # Sleep in small increments
# Check if it's time to flush
... | Background worker that periodically flushes batched data. | _periodic_flush_worker | python | mlflow/mlflow | mlflow/optuna/storage.py | https://github.com/mlflow/mlflow/blob/master/mlflow/optuna/storage.py | Apache-2.0 |
def _queue_batch_operation(
self,
run_id: str,
metrics: Optional[list[Metric]] = None,
params: Optional[list[Param]] = None,
tags: Optional[list[RunTag]] = None,
):
"""Queue metrics, parameters, or tags for batched processing."""
with self._batch_lock:
... | Queue metrics, parameters, or tags for batched processing. | _queue_batch_operation | python | mlflow/mlflow | mlflow/optuna/storage.py | https://github.com/mlflow/mlflow/blob/master/mlflow/optuna/storage.py | Apache-2.0 |
def _flush_batch(self, run_id: str):
"""Flush the batch for a specific run_id to MLflow."""
with self._batch_lock:
if run_id not in self._batch_queue:
return
batch = self._batch_queue[run_id]
# Only make the API call if there's something to flush
... | Flush the batch for a specific run_id to MLflow. | _flush_batch | python | mlflow/mlflow | mlflow/optuna/storage.py | https://github.com/mlflow/mlflow/blob/master/mlflow/optuna/storage.py | Apache-2.0 |
def flush_all_batches(self):
"""Flush all pending batches to MLflow."""
with self._batch_lock:
run_ids = list(self._batch_queue.keys())
# Flush each run's batch
for run_id in run_ids:
self._flush_batch(run_id) | Flush all pending batches to MLflow. | flush_all_batches | python | mlflow/mlflow | mlflow/optuna/storage.py | https://github.com/mlflow/mlflow/blob/master/mlflow/optuna/storage.py | Apache-2.0 |
def create_new_study(
self, directions: Sequence[StudyDirection], study_name: Optional[str] = None
) -> int:
"""Create a new study as a mlflow run."""
study_name = study_name or DEFAULT_STUDY_NAME_PREFIX + str(uuid.uuid4())
tags = {
"mlflow.runName": study_name,
... | Create a new study as a mlflow run. | create_new_study | python | mlflow/mlflow | mlflow/optuna/storage.py | https://github.com/mlflow/mlflow/blob/master/mlflow/optuna/storage.py | Apache-2.0 |
def set_study_user_attr(self, study_id, key: str, value: JSONSerializable) -> None:
"""Register a user-defined attribute as mlflow run tags to a study run."""
# Verify the run exists first to fail fast if it doesn't
self._mlflow_client.get_run(study_id)
# Queue the tag if the run exists... | Register a user-defined attribute as mlflow run tags to a study run. | set_study_user_attr | python | mlflow/mlflow | mlflow/optuna/storage.py | https://github.com/mlflow/mlflow/blob/master/mlflow/optuna/storage.py | Apache-2.0 |
def set_study_system_attr(self, study_id, key: str, value: JSONSerializable) -> None:
"""Register a optuna-internal attribute as mlflow run tags to a study run."""
# Verify the run exists first to fail fast if it doesn't
self._mlflow_client.get_run(study_id)
# Queue the tag if the run e... | Register a optuna-internal attribute as mlflow run tags to a study run. | set_study_system_attr | python | mlflow/mlflow | mlflow/optuna/storage.py | https://github.com/mlflow/mlflow/blob/master/mlflow/optuna/storage.py | Apache-2.0 |
def _log_early_stop_params(early_stop_callback, client, run_id):
"""
Logs early stopping configuration parameters to MLflow.
Args:
early_stop_callback: The early stopping callback instance used during training.
client: An `MlflowAutologgingQueueingClient` instance used for MLflow logging.
... |
Logs early stopping configuration parameters to MLflow.
Args:
early_stop_callback: The early stopping callback instance used during training.
client: An `MlflowAutologgingQueueingClient` instance used for MLflow logging.
run_id: The ID of the MLflow Run to which to log configuration pa... | _log_early_stop_params | python | mlflow/mlflow | mlflow/paddle/_paddle_autolog.py | https://github.com/mlflow/mlflow/blob/master/mlflow/paddle/_paddle_autolog.py | Apache-2.0 |
def _log_early_stop_metrics(early_stop_callback, client, run_id, model_id=None):
"""
Logs early stopping behavior results (e.g. stopped epoch) as metrics to MLflow.
Args:
early_stop_callback: The early stopping callback instance used during training.
client: An `MlflowAutologgingQueueingCli... |
Logs early stopping behavior results (e.g. stopped epoch) as metrics to MLflow.
Args:
early_stop_callback: The early stopping callback instance used during training.
client: An `MlflowAutologgingQueueingClient` instance used for MLflow logging.
run_id: The ID of the MLflow Run to which... | _log_early_stop_metrics | python | mlflow/mlflow | mlflow/paddle/_paddle_autolog.py | https://github.com/mlflow/mlflow/blob/master/mlflow/paddle/_paddle_autolog.py | Apache-2.0 |
def save_model(
pd_model,
path,
training=False,
conda_env=None,
code_paths=None,
mlflow_model=None,
signature: ModelSignature = None,
input_example: ModelInputExample = None,
pip_requirements=None,
extra_pip_requirements=None,
metadata=None,
):
"""
Save a paddle model... |
Save a paddle model to a path on the local file system. Produces an MLflow Model
containing the following flavors:
- :py:mod:`mlflow.paddle`
- :py:mod:`mlflow.pyfunc`. NOTE: This flavor is only included for paddle models
that define `predict()`, since `predict()` is required for pyfu... | save_model | python | mlflow/mlflow | mlflow/paddle/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/paddle/__init__.py | Apache-2.0 |
def load_model(model_uri, model=None, dst_path=None, **kwargs):
"""
Load a paddle 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``
... |
Load a paddle 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:/<mlflow_ru... | load_model | python | mlflow/mlflow | mlflow/paddle/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/paddle/__init__.py | Apache-2.0 |
def log_model(
pd_model,
artifact_path: Optional[str] = None,
training=False,
conda_env=None,
code_paths=None,
registered_model_name=None,
signature: ModelSignature = None,
input_example: ModelInputExample = None,
await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS,
pip_requir... |
Log a paddle model as an MLflow artifact for the current run. Produces an MLflow Model
containing the following flavors:
- :py:mod:`mlflow.paddle`
- :py:mod:`mlflow.pyfunc`. NOTE: This flavor is only included for paddle models
that define `predict()`, since `predict()` is required fo... | log_model | python | mlflow/mlflow | mlflow/paddle/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/paddle/__init__.py | Apache-2.0 |
def autolog(
log_every_n_epoch=1,
log_models=True,
disable=False,
exclusive=False,
silent=False,
registered_model_name=None,
extra_tags=None,
):
"""
Enables (or disables) and configures autologging from PaddlePaddle to MLflow.
Autologging is performed when the `fit` method of `p... |
Enables (or disables) and configures autologging from PaddlePaddle to MLflow.
Autologging is performed when the `fit` method of `paddle.Model`_ is called.
.. _paddle.Model:
https://www.paddlepaddle.org.cn/documentation/docs/en/api/paddle/Model_en.html
Args:
log_every_n_epoch: If spec... | autolog | python | mlflow/mlflow | mlflow/paddle/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/paddle/__init__.py | Apache-2.0 |
def load_model(model_uri, dst_path=None):
"""
Load a ``pmdarima`` ``ARIMA`` model or ``Pipeline`` object 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/loca... |
Load a ``pmdarima`` ``ARIMA`` model or ``Pipeline`` object 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_model | python | mlflow/mlflow | mlflow/pmdarima/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pmdarima/__init__.py | Apache-2.0 |
def before_run_validations(tracking_uri, backend_config):
"""Validations to perform before running a project on Databricks."""
if backend_config is None:
raise ExecutionException(
"Backend spec must be provided when launching MLflow project runs on Databricks."
)
elif "existing_c... | Validations to perform before running a project on Databricks. | before_run_validations | python | mlflow/mlflow | mlflow/projects/databricks.py | https://github.com/mlflow/mlflow/blob/master/mlflow/projects/databricks.py | Apache-2.0 |
def _upload_to_dbfs(self, src_path, dbfs_fuse_uri):
"""
Upload the file at `src_path` to the specified DBFS URI within the Databricks workspace
corresponding to the default Databricks CLI profile.
"""
_logger.info("=== Uploading project to DBFS path %s ===", dbfs_fuse_uri)
... |
Upload the file at `src_path` to the specified DBFS URI within the Databricks workspace
corresponding to the default Databricks CLI profile.
| _upload_to_dbfs | python | mlflow/mlflow | mlflow/projects/databricks.py | https://github.com/mlflow/mlflow/blob/master/mlflow/projects/databricks.py | Apache-2.0 |
def _dbfs_path_exists(self, dbfs_path):
"""
Return True if the passed-in path exists in DBFS for the workspace corresponding to the
default Databricks CLI profile. The path is expected to be a relative path to the DBFS root
directory, e.g. 'path/to/file'.
"""
host_creds =... |
Return True if the passed-in path exists in DBFS for the workspace corresponding to the
default Databricks CLI profile. The path is expected to be a relative path to the DBFS root
directory, e.g. 'path/to/file'.
| _dbfs_path_exists | python | mlflow/mlflow | mlflow/projects/databricks.py | https://github.com/mlflow/mlflow/blob/master/mlflow/projects/databricks.py | Apache-2.0 |
def _upload_project_to_dbfs(self, project_dir, experiment_id): # noqa: D417
"""
Tars a project directory into an archive in a temp dir and uploads it to DBFS, returning
the HDFS-style URI of the tarball in DBFS (e.g. dbfs:/path/to/tar).
Args:
project_dir: Path to a director... |
Tars a project directory into an archive in a temp dir and uploads it to DBFS, returning
the HDFS-style URI of the tarball in DBFS (e.g. dbfs:/path/to/tar).
Args:
project_dir: Path to a directory containing an MLflow project to upload to DBFS (e.g.
a directory conta... | _upload_project_to_dbfs | python | mlflow/mlflow | mlflow/projects/databricks.py | https://github.com/mlflow/mlflow/blob/master/mlflow/projects/databricks.py | Apache-2.0 |
def _run_shell_command_job(self, project_uri, command, env_vars, cluster_spec):
"""
Run the specified shell command on a Databricks cluster.
Args:
project_uri: URI of the project from which the shell command originates.
command: Shell command to run.
env_vars... |
Run the specified shell command on a Databricks cluster.
Args:
project_uri: URI of the project from which the shell command originates.
command: Shell command to run.
env_vars: Environment variables to set in the process running ``command``.
cluster_spec... | _run_shell_command_job | python | mlflow/mlflow | mlflow/projects/databricks.py | https://github.com/mlflow/mlflow/blob/master/mlflow/projects/databricks.py | Apache-2.0 |
def get_run_result_state(self, databricks_run_id):
"""
Get the run result state (string) of a Databricks job run.
Args:
databricks_run_id: Integer Databricks job run ID.
Returns:
`RunResultState <https://docs.databricks.com/api/latest/jobs.html#runresultstate>`_... |
Get the run result state (string) of a Databricks job run.
Args:
databricks_run_id: Integer Databricks job run ID.
Returns:
`RunResultState <https://docs.databricks.com/api/latest/jobs.html#runresultstate>`_ or
None if the run is still active.
| get_run_result_state | python | mlflow/mlflow | mlflow/projects/databricks.py | https://github.com/mlflow/mlflow/blob/master/mlflow/projects/databricks.py | Apache-2.0 |
def _get_databricks_run_cmd(dbfs_fuse_tar_uri, run_id, entry_point, parameters, env_manager):
"""
Generate MLflow CLI command to run on Databricks cluster in order to launch a run on Databricks.
"""
project_dir, extracting_tar_command = _get_project_dir_and_extracting_tar_command(
dbfs_fuse_tar_... |
Generate MLflow CLI command to run on Databricks cluster in order to launch a run on Databricks.
| _get_databricks_run_cmd | python | mlflow/mlflow | mlflow/projects/databricks.py | https://github.com/mlflow/mlflow/blob/master/mlflow/projects/databricks.py | Apache-2.0 |
def validate_docker_installation():
"""
Verify if Docker is installed and running on host machine.
"""
if shutil.which("docker") is None:
raise ExecutionException(
"Could not find Docker executable. "
"Ensure Docker is installed as per the instructions "
"at h... |
Verify if Docker is installed and running on host machine.
| validate_docker_installation | python | mlflow/mlflow | mlflow/projects/docker.py | https://github.com/mlflow/mlflow/blob/master/mlflow/projects/docker.py | Apache-2.0 |
def build_docker_image(work_dir, repository_uri, base_image, run_id, build_image, docker_auth):
"""
Build a docker image containing the project in `work_dir`, using the base image.
"""
image_uri = _get_docker_image_uri(repository_uri=repository_uri, work_dir=work_dir)
client = docker.from_env()
... |
Build a docker image containing the project in `work_dir`, using the base image.
| build_docker_image | python | mlflow/mlflow | mlflow/projects/docker.py | https://github.com/mlflow/mlflow/blob/master/mlflow/projects/docker.py | Apache-2.0 |
def _create_docker_build_ctx(work_dir, dockerfile_contents):
"""
Creates build context tarfile containing Dockerfile and project code, returning path to tarfile
"""
directory = tempfile.mkdtemp()
try:
dst_path = os.path.join(directory, "mlflow-project-contents")
shutil.copytree(src=w... |
Creates build context tarfile containing Dockerfile and project code, returning path to tarfile
| _create_docker_build_ctx | python | mlflow/mlflow | mlflow/projects/docker.py | https://github.com/mlflow/mlflow/blob/master/mlflow/projects/docker.py | Apache-2.0 |
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