__init__.py

# pylint: disable=wrong-import-position
"""
The ``mlflow`` module provides a high-level "fluent" API for starting and managing MLflow runs.
For example:

.. code:: python

    import mlflow

    mlflow.start_run()
    mlflow.log_param("my", "param")
    mlflow.log_metric("score", 100)
    mlflow.end_run()

You can also use the context manager syntax like this:

.. code:: python

    with mlflow.start_run() as run:
        mlflow.log_param("my", "param")
        mlflow.log_metric("score", 100)

which automatically terminates the run at the end of the ``with`` block.

The fluent tracking API is not currently threadsafe. Any concurrent callers to the tracking API must
implement mutual exclusion manually.

For a lower level API, see the :py:mod:`mlflow.tracking` module.
"""
import sys

from mlflow.version import VERSION as __version__  # pylint: disable=unused-import
from mlflow.utils.logging_utils import _configure_mlflow_loggers
import mlflow.tracking._model_registry.fluent
import mlflow.tracking.fluent

# Filter annoying Cython warnings that serve no good purpose, and so before
# importing other modules.
# See: https://github.com/numpy/numpy/pull/432/commits/170ed4e33d6196d7
import warnings

warnings.filterwarnings("ignore", message="numpy.dtype size changed")
warnings.filterwarnings("ignore", message="numpy.ufunc size changed")

import mlflow.projects as projects
import mlflow.tracking as tracking
import mlflow.models

# model flavors
_model_flavors_supported = []
try:
    # pylint: disable=unused-import
    import mlflow.catboost as catboost
    import mlflow.fastai as fastai
    import mlflow.gluon as gluon
    import mlflow.h2o as h2o
    import mlflow.keras as keras
    import mlflow.lightgbm as lightgbm
    import mlflow.mleap as mleap
    import mlflow.onnx as onnx
    import mlflow.pyfunc as pyfunc
    import mlflow.pytorch as pytorch
    import mlflow.sklearn as sklearn
    import mlflow.spacy as spacy
    import mlflow.spark as spark
    import mlflow.statsmodels as statsmodels
    import mlflow.tensorflow as tensorflow
    import mlflow.xgboost as xgboost
    import mlflow.shap as shap
    import mlflow.pyspark as pyspark
    import mlflow.paddle as paddle
    import mlflow.prophet as prophet

    _model_flavors_supported = [
        "catboost",
        "fastai",
        "gluon",
        "h2o",
        "keras",
        "lightgbm",
        "mleap",
        "onnx",
        "pyfunc",
        "pytorch",
        "sklearn",
        "spacy",
        "spark",
        "statsmodels",
        "tensorflow",
        "xgboost",
        "shap",
        "paddle",
        "prophet",
    ]
except ImportError as e:
    # We are conditional loading these commands since the skinny client does
    # not support them due to the pandas and numpy dependencies of MLflow Models
    pass


_configure_mlflow_loggers(root_module_name=__name__)

_major = 3
_minor = 6
_deprecated_version = (_major, _minor)
_min_supported_version = (_major, _minor + 1)

if sys.version_info[:2] == _deprecated_version:
    warnings.warn(
        "MLflow support for Python {dep_ver} is deprecated and will be dropped in "
        "an upcoming release. At that point, existing Python {dep_ver} workflows "
        "that use MLflow will continue to work without modification, but Python {dep_ver} "
        "users will no longer get access to the latest MLflow features and bugfixes. "
        "We recommend that you upgrade to Python {min_ver} or newer.".format(
            dep_ver=".".join(map(str, _deprecated_version)),
            min_ver=".".join(map(str, _min_supported_version)),
        ),
        FutureWarning,
        stacklevel=2,
    )

ActiveRun = mlflow.tracking.fluent.ActiveRun
log_param = mlflow.tracking.fluent.log_param
log_metric = mlflow.tracking.fluent.log_metric
set_tag = mlflow.tracking.fluent.set_tag
delete_tag = mlflow.tracking.fluent.delete_tag
log_artifacts = mlflow.tracking.fluent.log_artifacts
log_artifact = mlflow.tracking.fluent.log_artifact
log_text = mlflow.tracking.fluent.log_text
log_dict = mlflow.tracking.fluent.log_dict
log_image = mlflow.tracking.fluent.log_image
log_figure = mlflow.tracking.fluent.log_figure
active_run = mlflow.tracking.fluent.active_run
get_run = mlflow.tracking.fluent.get_run
start_run = mlflow.tracking.fluent.start_run
end_run = mlflow.tracking.fluent.end_run
search_runs = mlflow.tracking.fluent.search_runs
list_run_infos = mlflow.tracking.fluent.list_run_infos
get_artifact_uri = mlflow.tracking.fluent.get_artifact_uri
set_tracking_uri = tracking.set_tracking_uri
set_registry_uri = tracking.set_registry_uri
get_experiment = mlflow.tracking.fluent.get_experiment
get_experiment_by_name = mlflow.tracking.fluent.get_experiment_by_name
list_experiments = mlflow.tracking.fluent.list_experiments
get_tracking_uri = tracking.get_tracking_uri
get_registry_uri = tracking.get_registry_uri
create_experiment = mlflow.tracking.fluent.create_experiment
set_experiment = mlflow.tracking.fluent.set_experiment
log_params = mlflow.tracking.fluent.log_params
log_metrics = mlflow.tracking.fluent.log_metrics
set_tags = mlflow.tracking.fluent.set_tags
delete_experiment = mlflow.tracking.fluent.delete_experiment
delete_run = mlflow.tracking.fluent.delete_run
register_model = mlflow.tracking._model_registry.fluent.register_model
autolog = mlflow.tracking.fluent.autolog
evaluate = mlflow.models.evaluate

run = projects.run

__all__ = [
    "ActiveRun",
    "log_param",
    "log_params",
    "log_metric",
    "log_metrics",
    "set_tag",
    "set_tags",
    "delete_tag",
    "log_artifacts",
    "log_artifact",
    "log_text",
    "log_dict",
    "log_figure",
    "log_image",
    "active_run",
    "start_run",
    "end_run",
    "search_runs",
    "get_artifact_uri",
    "get_tracking_uri",
    "set_tracking_uri",
    "get_experiment",
    "get_experiment_by_name",
    "list_experiments",
    "create_experiment",
    "set_experiment",
    "delete_experiment",
    "get_run",
    "delete_run",
    "run",
    "register_model",
    "get_registry_uri",
    "set_registry_uri",
    "list_run_infos",
    "autolog",
    "evaluate",
] + _model_flavors_supported