import json
import logging
import os
import shutil
import uuid
from datetime import datetime
from pathlib import Path
from pprint import pformat
from typing import Any, Callable, Literal, NamedTuple, Optional, Union
from urllib.parse import urlparse
import yaml
from packaging.requirements import InvalidRequirement, Requirement
import mlflow
from mlflow.entities import LoggedModel, LoggedModelOutput, Metric
from mlflow.entities.model_registry.prompt_version import PromptVersion
from mlflow.environment_variables import (
MLFLOW_PRINT_MODEL_URLS_ON_CREATION,
MLFLOW_RECORD_ENV_VARS_IN_MODEL_LOGGING,
)
from mlflow.exceptions import MlflowException
from mlflow.models.auth_policy import AuthPolicy
from mlflow.models.resources import Resource, ResourceType, _ResourceBuilder
from mlflow.protos.databricks_pb2 import (
INVALID_PARAMETER_VALUE,
RESOURCE_DOES_NOT_EXIST,
)
from mlflow.store.artifact.models_artifact_repo import ModelsArtifactRepository
from mlflow.store.artifact.runs_artifact_repo import RunsArtifactRepository
from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS
from mlflow.tracking._tracking_service.utils import _resolve_tracking_uri
from mlflow.tracking.artifact_utils import _download_artifact_from_uri, _upload_artifact_to_uri
from mlflow.tracking.fluent import (
_create_logged_model,
_get_active_model_context,
_last_logged_model_id,
_set_active_model_id,
_use_logged_model,
)
from mlflow.utils.annotations import experimental
from mlflow.utils.databricks_utils import (
_construct_databricks_logged_model_url,
get_databricks_runtime_version,
get_workspace_id,
get_workspace_url,
is_in_databricks_runtime,
)
from mlflow.utils.docstring_utils import LOG_MODEL_PARAM_DOCS, format_docstring
from mlflow.utils.environment import (
_CONDA_ENV_FILE_NAME,
_PYTHON_ENV_FILE_NAME,
_REQUIREMENTS_FILE_NAME,
_add_or_overwrite_requirements,
_get_requirements_from_file,
_remove_requirements,
_write_requirements_to_file,
)
from mlflow.utils.file_utils import TempDir
from mlflow.utils.logging_utils import eprint
from mlflow.utils.mlflow_tags import MLFLOW_MODEL_IS_EXTERNAL
from mlflow.utils.uri import (
append_to_uri_path,
get_uri_scheme,
is_databricks_uri,
)
_logger = logging.getLogger(__name__)
# NOTE: The MLMODEL_FILE_NAME constant is considered @developer_stable
MLMODEL_FILE_NAME = "MLmodel"
_DATABRICKS_FS_LOADER_MODULE = "databricks.feature_store.mlflow_model"
_LOG_MODEL_METADATA_WARNING_TEMPLATE = (
"Logging model metadata to the tracking server has failed. The model artifacts "
"have been logged successfully under %s. Set logging level to DEBUG via "
'`logging.getLogger("mlflow").setLevel(logging.DEBUG)` to see the full traceback.'
)
_LOG_MODEL_MISSING_SIGNATURE_WARNING = (
"Model logged without a signature. Signatures are required for Databricks UC model registry "
"as they validate model inputs and denote the expected schema of model outputs. "
f"Please visit https://www.mlflow.org/docs/{mlflow.__version__.replace('.dev0', '')}/"
"model/signatures.html#how-to-set-signatures-on-models for instructions on setting "
"signature on models."
)
_LOG_MODEL_MISSING_INPUT_EXAMPLE_WARNING = (
"Model logged without a signature and input example. Please set `input_example` parameter "
"when logging the model to auto infer the model signature."
)
# NOTE: The _MLFLOW_VERSION_KEY constant is considered @developer_stable
_MLFLOW_VERSION_KEY = "mlflow_version"
METADATA_FILES = [
MLMODEL_FILE_NAME,
_CONDA_ENV_FILE_NAME,
_REQUIREMENTS_FILE_NAME,
_PYTHON_ENV_FILE_NAME,
]
MODEL_CONFIG = "config"
MODEL_CODE_PATH = "model_code_path"
SET_MODEL_ERROR = (
"Model should either be an instance of PyFuncModel, Langchain type, or LlamaIndex index."
)
ENV_VAR_FILE_NAME = "environment_variables.txt"
ENV_VAR_FILE_HEADER = (
"# This file records environment variable names that are used during model inference.\n"
"# They might need to be set when creating a serving endpoint from this model.\n"
"# Note: it is not guaranteed that all environment variables listed here are required\n"
)
[docs]class ModelInfo:
"""
The metadata of a logged MLflow Model.
"""
def __init__(
self,
artifact_path: str,
flavors: dict[str, Any],
model_uri: str,
model_uuid: str,
run_id: str,
saved_input_example_info: Optional[dict[str, Any]],
signature, # Optional[ModelSignature]
utc_time_created: str,
mlflow_version: str,
metadata: Optional[dict[str, Any]] = None,
registered_model_version: Optional[int] = None,
env_vars: Optional[list[str]] = None,
prompts: Optional[list[str]] = None,
logged_model: Optional[LoggedModel] = None,
):
self._artifact_path = artifact_path
self._flavors = flavors
self._model_uri = model_uri
self._model_uuid = model_uuid
self._run_id = run_id
self._saved_input_example_info = saved_input_example_info
self._signature = signature
self._utc_time_created = utc_time_created
self._mlflow_version = mlflow_version
self._metadata = metadata
self._prompts = prompts
self._registered_model_version = registered_model_version
self._env_vars = env_vars
self._logged_model = logged_model
@property
def artifact_path(self) -> str:
"""
Run relative path identifying the logged model.
:getter: Retrieves the relative path of the logged model.
:type: str
"""
return self._artifact_path
@property
def flavors(self) -> dict[str, Any]:
"""
A dictionary mapping the flavor name to how to serve
the model as that flavor.
:getter: Gets the mapping for the logged model's flavor that defines parameters used in
serving of the model
:type: Dict[str, str]
.. code-block:: python
:caption: Example flavor mapping for a scikit-learn logged model
{
"python_function": {
"model_path": "model.pkl",
"loader_module": "mlflow.sklearn",
"python_version": "3.8.10",
"env": "conda.yaml",
},
"sklearn": {
"pickled_model": "model.pkl",
"sklearn_version": "0.24.1",
"serialization_format": "cloudpickle",
},
}
"""
return self._flavors
@property
def model_uri(self) -> str:
"""
The ``model_uri`` of the logged model in the format ``'runs:/<run_id>/<artifact_path>'``.
:getter: Gets the uri path of the logged model from the uri `runs:/<run_id>` path
encapsulation
:type: str
"""
return self._model_uri
@property
def model_uuid(self) -> str:
"""
The ``model_uuid`` of the logged model,
e.g., ``'39ca11813cfc46b09ab83972740b80ca'``.
:getter: [Legacy] Gets the model_uuid (run_id) of a logged model
:type: str
"""
return self._model_uuid
@property
def run_id(self) -> str:
"""
The ``run_id`` associated with the logged model,
e.g., ``'8ede7df408dd42ed9fc39019ef7df309'``
:getter: Gets the run_id identifier for the logged model
:type: str
"""
return self._run_id
@property
def saved_input_example_info(self) -> Optional[dict[str, Any]]:
"""
A dictionary that contains the metadata of the saved input example, e.g.,
``{"artifact_path": "input_example.json", "type": "dataframe", "pandas_orient": "split"}``.
:getter: Gets the input example if specified during model logging
:type: Optional[Dict[str, str]]
"""
return self._saved_input_example_info
@property
def signature(self): # -> Optional[ModelSignature]
"""
A :py:class:`ModelSignature <mlflow.models.ModelSignature>` that describes the
model input and output.
:getter: Gets the model signature if it is defined
:type: Optional[ModelSignature]
"""
return self._signature
@property
def utc_time_created(self) -> str:
"""
The UTC time that the logged model is created, e.g., ``'2022-01-12 05:17:31.634689'``.
:getter: Gets the UTC formatted timestamp for when the model was logged
:type: str
"""
return self._utc_time_created
@property
def mlflow_version(self) -> str:
"""
Version of MLflow used to log the model
:getter: Gets the version of MLflow that was installed when a model was logged
:type: str
"""
return self._mlflow_version
@property
def env_vars(self) -> Optional[list[str]]:
"""
Environment variables used during the model logging process.
:getter: Gets the environment variables used during the model logging process.
:type: Optional[List[str]]
"""
return self._env_vars
@env_vars.setter
def env_vars(self, value: Optional[list[str]]) -> None:
if value and not (isinstance(value, list) and all(isinstance(x, str) for x in value)):
raise TypeError(f"env_vars must be a list of strings. Got: {value}")
self._env_vars = value
@property
def metadata(self) -> Optional[dict[str, Any]]:
"""
User defined metadata added to the model.
:getter: Gets the user-defined metadata about a model
:type: Optional[Dict[str, Any]]
.. code-block:: python
:caption: Example usage of Model Metadata
# Create and log a model with metadata to the Model Registry
from sklearn import datasets
from sklearn.ensemble import RandomForestClassifier
import mlflow
from mlflow.models import infer_signature
with mlflow.start_run():
iris = datasets.load_iris()
clf = RandomForestClassifier()
clf.fit(iris.data, iris.target)
signature = infer_signature(iris.data, iris.target)
mlflow.sklearn.log_model(
clf,
name="iris_rf",
signature=signature,
registered_model_name="model-with-metadata",
metadata={"metadata_key": "metadata_value"},
)
# model uri for the above model
model_uri = "models:/model-with-metadata/1"
# Load the model and access the custom metadata from its ModelInfo object
model = mlflow.pyfunc.load_model(model_uri=model_uri)
assert model.metadata.get_model_info().metadata["metadata_key"] == "metadata_value"
# Load the ModelInfo and access the custom metadata
model_info = mlflow.models.get_model_info(model_uri=model_uri)
assert model_info.metadata["metadata_key"] == "metadata_value"
"""
return self._metadata
@property
def prompts(self) -> Optional[list[str]]:
"""A list of prompt URIs associated with the model."""
return self._prompts
@property
def registered_model_version(self) -> Optional[int]:
"""
The registered model version, if the model is registered.
:getter: Gets the registered model version, if the model is registered in Model Registry.
:setter: Sets the registered model version.
:type: Optional[int]
"""
return self._registered_model_version
@registered_model_version.setter
def registered_model_version(self, value) -> None:
self._registered_model_version = value
@property
def model_id(self) -> str:
"""
The model ID of the logged model.
:getter: Gets the model ID of the logged model
"""
return self._logged_model.model_id if self._logged_model else None
@property
def metrics(self) -> Optional[list[Metric]]:
"""
Returns the metrics of the logged model.
:getter: Retrieves the metrics of the logged model
"""
return self._logged_model.metrics if self._logged_model else None
@property
def params(self) -> dict[str, str]:
"""
Returns the parameters of the logged model.
:getter: Retrieves the parameters of the logged model
"""
return self._logged_model.params if self._logged_model else None
@property
def tags(self) -> dict[str, str]:
"""
Returns the tags of the logged model.
:getter: Retrieves the tags of the logged model
"""
return self._logged_model.tags if self._logged_model else None
@property
def creation_timestamp(self) -> int:
"""
Returns the creation timestamp of the logged model.
:getter: the creation timestamp of the logged model
"""
return self._logged_model.creation_timestamp if self._logged_model else None
@property
def name(self) -> str:
"""
Returns the name of the logged model.
"""
return self._logged_model.name if self._logged_model else None
[docs]class Model:
"""
An MLflow Model that can support multiple model flavors. Provides APIs for implementing
new Model flavors.
"""
def __init__(
self,
artifact_path=None,
run_id=None,
utc_time_created=None,
flavors=None,
signature=None, # ModelSignature
saved_input_example_info: Optional[dict[str, Any]] = None,
model_uuid: Union[str, Callable[[], str], None] = lambda: uuid.uuid4().hex,
mlflow_version: Union[str, None] = mlflow.version.VERSION,
metadata: Optional[dict[str, Any]] = None,
model_size_bytes: Optional[int] = None,
resources: Optional[Union[str, list[Resource]]] = None,
env_vars: Optional[list[str]] = None,
auth_policy: Optional[AuthPolicy] = None,
model_id: Optional[str] = None,
prompts: Optional[list[str]] = None,
**kwargs,
):
# store model id instead of run_id and path to avoid confusion when model gets exported
self.run_id = run_id
self.artifact_path = artifact_path
self.utc_time_created = str(utc_time_created or datetime.utcnow())
self.flavors = flavors if flavors is not None else {}
self.signature = signature
self.saved_input_example_info = saved_input_example_info
self.model_uuid = model_uuid() if callable(model_uuid) else model_uuid
self.mlflow_version = mlflow_version
self.metadata = metadata
self.prompts = prompts
self.model_size_bytes = model_size_bytes
self.resources = resources
self.env_vars = env_vars
self.auth_policy = auth_policy
self.model_id = model_id
self.__dict__.update(kwargs)
def __eq__(self, other):
if not isinstance(other, Model):
return False
return self.__dict__ == other.__dict__
[docs] def get_output_schema(self):
"""
Retrieves the output schema of the Model iff the model was saved with a schema definition.
"""
return self.signature.outputs if self.signature is not None else None
[docs] def get_params_schema(self):
"""
Retrieves the parameters schema of the Model iff the model was saved with a schema
definition.
"""
return getattr(self.signature, "params", None)
[docs] def add_flavor(self, name, **params) -> "Model":
"""Add an entry for how to serve the model in a given format."""
self.flavors[name] = params
return self
@property
def metadata(self) -> Optional[dict[str, Any]]:
"""
Custom metadata dictionary passed to the model and stored in the MLmodel file.
:getter: Retrieves custom metadata that have been applied to a model instance.
:setter: Sets a dictionary of custom keys and values to be included with the model instance
:type: Optional[Dict[str, Any]]
Returns:
A Dictionary of user-defined metadata iff defined.
.. code-block:: python
:caption: Example
# Create and log a model with metadata to the Model Registry
from sklearn import datasets
from sklearn.ensemble import RandomForestClassifier
import mlflow
from mlflow.models import infer_signature
with mlflow.start_run():
iris = datasets.load_iris()
clf = RandomForestClassifier()
clf.fit(iris.data, iris.target)
signature = infer_signature(iris.data, iris.target)
mlflow.sklearn.log_model(
clf,
name="iris_rf",
signature=signature,
registered_model_name="model-with-metadata",
metadata={"metadata_key": "metadata_value"},
)
# model uri for the above model
model_uri = "models:/model-with-metadata/1"
# Load the model and access the custom metadata
model = mlflow.pyfunc.load_model(model_uri=model_uri)
assert model.metadata.metadata["metadata_key"] == "metadata_value"
"""
return self._metadata
@metadata.setter
def metadata(self, value: Optional[dict[str, Any]]) -> None:
self._metadata = value
@property
def signature(self): # -> Optional[ModelSignature]
"""
An optional definition of the expected inputs to and outputs from a model object, defined
with both field names and data types. Signatures support both column-based and tensor-based
inputs and outputs.
:getter: Retrieves the signature of a model instance iff the model was saved with a
signature definition.
:setter: Sets a signature to a model instance.
:type: Optional[ModelSignature]
"""
return self._signature
@signature.setter
def signature(self, value) -> None:
# signature cannot be set to `False`, which is used in `log_model` and `save_model` calls
# to disable automatic signature inference
if value is not False:
self._signature = value
@property
def saved_input_example_info(self) -> Optional[dict[str, Any]]:
"""
A dictionary that contains the metadata of the saved input example, e.g.,
``{"artifact_path": "input_example.json", "type": "dataframe", "pandas_orient": "split"}``.
"""
return self._saved_input_example_info
@saved_input_example_info.setter
def saved_input_example_info(self, value: dict[str, Any]) -> None:
self._saved_input_example_info = value
@property
def model_size_bytes(self) -> Optional[int]:
"""
An optional integer that represents the model size in bytes
:getter: Retrieves the model size if it's calculated when the model is saved
:setter: Sets the model size to a model instance
:type: Optional[int]
"""
return self._model_size_bytes
@model_size_bytes.setter
def model_size_bytes(self, value: Optional[int]) -> None:
self._model_size_bytes = value
@property
def resources(self) -> dict[str, dict[ResourceType, list[dict[str, Any]]]]:
"""
An optional dictionary that contains the resources required to serve the model.
:getter: Retrieves the resources required to serve the model
:setter: Sets the resources required to serve the model
:type: Dict[str, Dict[ResourceType, List[Dict]]]
"""
return self._resources
@resources.setter
def resources(self, value: Optional[Union[str, list[Resource]]]) -> None:
if isinstance(value, (Path, str)):
serialized_resource = _ResourceBuilder.from_yaml_file(value)
elif isinstance(value, list) and all(isinstance(resource, Resource) for resource in value):
serialized_resource = _ResourceBuilder.from_resources(value)
else:
serialized_resource = value
self._resources = serialized_resource
@experimental(version="2.21.0")
@property
def auth_policy(self) -> dict[str, dict[str, Any]]:
"""
An optional dictionary that contains the auth policy required to serve the model.
:getter: Retrieves the auth_policy required to serve the model
:setter: Sets the auth_policy required to serve the model
:type: Dict[str, dict]
"""
return self._auth_policy
@experimental(version="2.21.0")
@auth_policy.setter
def auth_policy(self, value: Optional[Union[dict[str, Any], AuthPolicy]]) -> None:
self._auth_policy = value.to_dict() if isinstance(value, AuthPolicy) else value
@property
def env_vars(self) -> Optional[list[str]]:
return self._env_vars
@env_vars.setter
def env_vars(self, value: Optional[list[str]]) -> None:
if value and not (isinstance(value, list) and all(isinstance(x, str) for x in value)):
raise TypeError(f"env_vars must be a list of strings. Got: {value}")
self._env_vars = value
def _is_signature_from_type_hint(self):
return self.signature._is_signature_from_type_hint if self.signature is not None else False
def _is_type_hint_from_example(self):
return self.signature._is_type_hint_from_example if self.signature is not None else False
[docs] def get_model_info(self, logged_model: Optional[LoggedModel] = None) -> ModelInfo:
"""
Create a :py:class:`ModelInfo <mlflow.models.model.ModelInfo>` instance that contains the
model metadata.
"""
if logged_model is None and self.model_id is not None:
logged_model = mlflow.get_logged_model(model_id=self.model_id)
return ModelInfo(
artifact_path=self.artifact_path,
flavors=self.flavors,
model_uri=(
f"models:/{self.model_id}"
if self.model_id
else f"runs:/{self.run_id}/{self.artifact_path}"
),
model_uuid=self.model_uuid,
run_id=self.run_id,
saved_input_example_info=self.saved_input_example_info,
signature=self.signature,
utc_time_created=self.utc_time_created,
mlflow_version=self.mlflow_version,
metadata=self.metadata,
prompts=self.prompts,
env_vars=self.env_vars,
logged_model=logged_model,
)
[docs] def to_dict(self) -> dict[str, Any]:
"""Serialize the model to a dictionary."""
res = {k: v for k, v in self.__dict__.items() if not k.startswith("_")}
databricks_runtime = get_databricks_runtime_version()
if databricks_runtime:
res["databricks_runtime"] = databricks_runtime
if self.signature is not None:
res["signature"] = self.signature.to_dict()
res["is_signature_from_type_hint"] = self.signature._is_signature_from_type_hint
res["type_hint_from_example"] = self.signature._is_type_hint_from_example
if self.saved_input_example_info is not None:
res["saved_input_example_info"] = self.saved_input_example_info
if self.mlflow_version is None and _MLFLOW_VERSION_KEY in res:
res.pop(_MLFLOW_VERSION_KEY)
if self.metadata is not None:
res["metadata"] = self.metadata
if self.prompts is not None:
res["prompts"] = self.prompts
if self.resources is not None:
res["resources"] = self.resources
if self.model_size_bytes is not None:
res["model_size_bytes"] = self.model_size_bytes
if self.auth_policy is not None:
res["auth_policy"] = self.auth_policy
# Exclude null fields in case MLmodel file consumers such as Model Serving may not
# handle them correctly.
if self.artifact_path is None:
res.pop("artifact_path", None)
if self.run_id is None:
res.pop("run_id", None)
if self.env_vars is not None:
res["env_vars"] = self.env_vars
return res
[docs] def to_yaml(self, stream=None) -> str:
"""Write the model as yaml string."""
return yaml.safe_dump(self.to_dict(), stream=stream, default_flow_style=False)
def __str__(self):
return self.to_yaml()
[docs] def to_json(self) -> str:
"""Write the model as json."""
return json.dumps(self.to_dict())
[docs] def save(self, path) -> None:
"""Write the model as a local YAML file."""
with open(path, "w") as out:
self.to_yaml(out)
[docs] @classmethod
def load(cls, path) -> "Model":
"""
Load a model from its YAML representation.
Args:
path: A local filesystem path or URI referring to the MLmodel YAML file
representation of the Model object or to the directory containing
the MLmodel YAML file representation.
Returns:
An instance of Model.
.. code-block:: python
:caption: example
from mlflow.models import Model
# Load the Model object from a local MLmodel file
model1 = Model.load("~/path/to/my/MLmodel")
# Load the Model object from a remote model directory
model2 = Model.load("s3://mybucket/path/to/my/model")
"""
# Check if the path is a local directory and not remote
sep = os.path.sep
path = str(path).rstrip(sep)
path_scheme = urlparse(path).scheme
if (not path_scheme or path_scheme == "file") and not os.path.exists(path):
raise MlflowException(
f'Could not find an "{MLMODEL_FILE_NAME}" configuration file at "{path}"',
RESOURCE_DOES_NOT_EXIST,
)
if ModelsArtifactRepository._is_logged_model_uri(path):
path = ModelsArtifactRepository.get_underlying_uri(path)
is_model_dir = path.rsplit(sep, maxsplit=1)[-1] != MLMODEL_FILE_NAME
mlmodel_file_path = f"{path}/{MLMODEL_FILE_NAME}" if is_model_dir else path
mlmodel_local_path = _download_artifact_from_uri(artifact_uri=mlmodel_file_path)
with open(mlmodel_local_path) as f:
model_dict = yaml.safe_load(f)
return cls.from_dict(model_dict)
[docs] @classmethod
def from_dict(cls, model_dict) -> "Model":
"""Load a model from its YAML representation."""
from mlflow.models.signature import ModelSignature
model_dict = model_dict.copy()
if "signature" in model_dict and isinstance(model_dict["signature"], dict):
signature = ModelSignature.from_dict(model_dict["signature"])
if "is_signature_from_type_hint" in model_dict:
signature._is_signature_from_type_hint = model_dict.pop(
"is_signature_from_type_hint"
)
if "type_hint_from_example" in model_dict:
signature._is_type_hint_from_example = model_dict.pop("type_hint_from_example")
model_dict["signature"] = signature
if "model_uuid" not in model_dict:
model_dict["model_uuid"] = None
if _MLFLOW_VERSION_KEY not in model_dict:
model_dict[_MLFLOW_VERSION_KEY] = None
return cls(**model_dict)
# MLflow 2.x log_model API. Only spark flavors uses this API.
# https://github.com/mlflow/mlflow/blob/fd2d9861fa52eeca178825c871d5d29fbb3b95c4/mlflow/models/model.py#L773-L982
@format_docstring(LOG_MODEL_PARAM_DOCS)
@classmethod
def _log_v2(
cls,
artifact_path,
flavor,
registered_model_name=None,
await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS,
metadata=None,
run_id=None,
resources=None,
auth_policy=None,
prompts=None,
**kwargs,
) -> ModelInfo:
"""
Log model using supplied flavor module. If no run is active, this method will create a new
active run.
Args:
artifact_path: Run relative path identifying the model.
flavor: Flavor module to save the model with. The module must have
the ``save_model`` function that will persist the model as a valid
MLflow model.
registered_model_name: If given, create a model version under
``registered_model_name``, also creating a registered model if
one with the given name does not exist.
await_registration_for: Number of seconds to wait for the model version to finish
being created and is in ``READY`` status. By default, the
function waits for five minutes. Specify 0 or None to skip
waiting.
metadata: {{ metadata }}
run_id: The run ID to associate with this model. If not provided,
a new run will be started.
resources: {{ resources }}
auth_policy: {{ auth_policy }}
prompts: {{ prompts }}
kwargs: Extra args passed to the model flavor.
Returns:
A :py:class:`ModelInfo <mlflow.models.model.ModelInfo>` instance that contains the
metadata of the logged model.
"""
# Only one of Auth policy and resources should be defined
if resources is not None and auth_policy is not None:
raise ValueError("Only one of `resources`, and `auth_policy` can be specified.")
from mlflow.utils.model_utils import _validate_and_get_model_config_from_file
registered_model = None
with TempDir() as tmp:
local_path = tmp.path("model")
if run_id is None:
run_id = mlflow.tracking.fluent._get_or_start_run().info.run_id
if prompts is not None:
# Convert to URIs for serialization
prompts = [pr.uri if isinstance(pr, PromptVersion) else pr for pr in prompts]
mlflow_model = cls(
artifact_path=artifact_path,
run_id=run_id,
metadata=metadata,
resources=resources,
auth_policy=auth_policy,
prompts=prompts,
)
flavor.save_model(path=local_path, mlflow_model=mlflow_model, **kwargs)
# `save_model` calls `load_model` to infer the model requirements, which may result in
# __pycache__ directories being created in the model directory.
for pycache in Path(local_path).rglob("__pycache__"):
shutil.rmtree(pycache, ignore_errors=True)
if is_in_databricks_runtime():
_copy_model_metadata_for_uc_sharing(local_path, flavor)
tracking_uri = _resolve_tracking_uri()
serving_input = mlflow_model.get_serving_input(local_path)
# We check signature presence here as some flavors have a default signature as a
# fallback when not provided by user, which is set during flavor's save_model() call.
if mlflow_model.signature is None:
if serving_input is None:
_logger.warning(
_LOG_MODEL_MISSING_INPUT_EXAMPLE_WARNING, extra={"color": "red"}
)
elif tracking_uri == "databricks" or get_uri_scheme(tracking_uri) == "databricks":
_logger.warning(_LOG_MODEL_MISSING_SIGNATURE_WARNING, extra={"color": "red"})
env_vars = None
# validate input example works for serving when logging the model
if serving_input and kwargs.get("validate_serving_input", True):
from mlflow.models import validate_serving_input
from mlflow.utils.model_utils import RECORD_ENV_VAR_ALLOWLIST, env_var_tracker
with env_var_tracker() as tracked_env_names:
try:
validate_serving_input(
model_uri=local_path,
serving_input=serving_input,
)
except Exception as e:
serving_input_msg = (
serving_input[:50] + "..." if len(serving_input) > 50 else serving_input
)
_logger.warning(
f"Failed to validate serving input example {serving_input_msg}. "
"Alternatively, you can avoid passing input example and pass model "
"signature instead when logging the model. To ensure the input example "
"is valid prior to serving, please try calling "
"`mlflow.models.validate_serving_input` on the model uri and serving "
"input example. A serving input example can be generated from model "
"input example using "
"`mlflow.models.convert_input_example_to_serving_input` function.\n"
f"Got error: {e}",
exc_info=_logger.isEnabledFor(logging.DEBUG),
)
env_vars = (
sorted(
x
for x in tracked_env_names
if any(env_var in x for env_var in RECORD_ENV_VAR_ALLOWLIST)
)
or None
)
if env_vars:
# Keep the environment variable file as it serves as a check
# for displaying tips in Databricks serving endpoint
env_var_path = Path(local_path, ENV_VAR_FILE_NAME)
env_var_path.write_text(ENV_VAR_FILE_HEADER + "\n".join(env_vars) + "\n")
if len(env_vars) <= 3:
env_var_info = "[" + ", ".join(env_vars) + "]"
else:
env_var_info = "[" + ", ".join(env_vars[:3]) + ", ... " + "]"
f"(check file {ENV_VAR_FILE_NAME} in the model's artifact folder for full list"
" of environment variable names)"
_logger.info(
"Found the following environment variables used during model inference: "
f"{env_var_info}. Please check if you need to set them when deploying the "
"model. To disable this message, set environment variable "
f"`{MLFLOW_RECORD_ENV_VARS_IN_MODEL_LOGGING.name}` to `false`."
)
mlflow_model.env_vars = env_vars
# mlflow_model is updated, rewrite the MLmodel file
mlflow_model.save(os.path.join(local_path, MLMODEL_FILE_NAME))
# Associate prompts to the model Run
if prompts:
client = mlflow.MlflowClient()
for prompt in prompts:
client.link_prompt_version_to_run(run_id, prompt)
mlflow.tracking.fluent.log_artifacts(local_path, mlflow_model.artifact_path, run_id)
# if the model_config kwarg is passed in, then log the model config as an params
if model_config := kwargs.get("model_config"):
if isinstance(model_config, str):
try:
file_extension = os.path.splitext(model_config)[1].lower()
if file_extension == ".json":
with open(model_config) as f:
model_config = json.load(f)
elif file_extension in [".yaml", ".yml"]:
model_config = _validate_and_get_model_config_from_file(model_config)
else:
_logger.warning(
"Unsupported file format for model config: %s. "
"Failed to load model config.",
model_config,
)
except Exception as e:
_logger.warning("Failed to load model config from %s: %s", model_config, e)
try:
from mlflow.models.utils import _flatten_nested_params
# We are using the `/` separator to flatten the nested params
# since we are using the same separator to log nested metrics.
params_to_log = _flatten_nested_params(model_config, sep="/")
except Exception as e:
_logger.warning("Failed to flatten nested params: %s", str(e))
params_to_log = model_config
try:
mlflow.tracking.fluent.log_params(params_to_log or {}, run_id=run_id)
except Exception as e:
_logger.warning("Failed to log model config as params: %s", str(e))
try:
mlflow.tracking.fluent._record_logged_model(mlflow_model, run_id)
except MlflowException:
# We need to swallow all mlflow exceptions to maintain backwards compatibility with
# older tracking servers. Only print out a warning for now.
_logger.warning(_LOG_MODEL_METADATA_WARNING_TEMPLATE, mlflow.get_artifact_uri())
_logger.debug("", exc_info=True)
if registered_model_name is not None:
registered_model = mlflow.tracking._model_registry.fluent._register_model(
f"runs:/{run_id}/{mlflow_model.artifact_path}",
registered_model_name,
await_registration_for=await_registration_for,
local_model_path=local_path,
)
model_info = mlflow_model.get_model_info()
if registered_model is not None:
model_info.registered_model_version = registered_model.version
# If the model signature is Mosaic AI Agent compatible, render a recipe for evaluation.
from mlflow.models.display_utils import maybe_render_agent_eval_recipe
maybe_render_agent_eval_recipe(model_info)
return model_info
[docs] @format_docstring(LOG_MODEL_PARAM_DOCS)
@classmethod
def log(
cls,
artifact_path,
flavor,
registered_model_name=None,
await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS,
metadata=None,
run_id=None,
resources=None,
auth_policy=None,
prompts=None,
name: Optional[str] = None,
model_type: Optional[str] = None,
params: Optional[dict[str, Any]] = None,
tags: Optional[dict[str, Any]] = None,
step: int = 0,
model_id: Optional[str] = None,
**kwargs,
) -> ModelInfo:
"""
Log model using supplied flavor module. If no run is active, this method will create a new
active run.
Args:
artifact_path: Deprecated. Use `name` instead.
flavor: Flavor module to save the model with. The module must have
the ``save_model`` function that will persist the model as a valid
MLflow model.
registered_model_name: If given, create a model version under
``registered_model_name``, also creating a registered model if
one with the given name does not exist.
await_registration_for: Number of seconds to wait for the model version to finish
being created and is in ``READY`` status. By default, the
function waits for five minutes. Specify 0 or None to skip
waiting.
metadata: {{ metadata }}
run_id: The run ID to associate with this model.
resources: {{ resources }}
auth_policy: {{ auth_policy }}
prompts: {{ prompts }}
name: The name of the model.
model_type: {{ model_type }}
params: {{ params }}
tags: {{ tags }}
step: {{ step }}
model_id: {{ model_id }}
kwargs: Extra args passed to the model flavor.
Returns:
A :py:class:`ModelInfo <mlflow.models.model.ModelInfo>` instance that contains the
metadata of the logged model.
"""
if name is not None and artifact_path is not None:
raise MlflowException.invalid_parameter_value(
"Both `artifact_path` (deprecated) and `name` parameters were specified. "
"Please only specify `name`."
)
elif artifact_path is not None:
_logger.warning("`artifact_path` is deprecated. Please use `name` instead.")
name = name or artifact_path
def log_model_metrics_for_step(client, model_id, run_id, step):
metric_names = client.get_run(run_id).data.metrics.keys()
metrics_for_step = []
for metric_name in metric_names:
history = client.get_metric_history(run_id, metric_name)
metrics_for_step.extend(
[
Metric(
key=metric.key,
value=metric.value,
timestamp=metric.timestamp,
step=metric.step,
dataset_name=metric.dataset_name,
dataset_digest=metric.dataset_digest,
run_id=metric.run_id,
model_id=model_id,
)
for metric in history
if metric.step == step and metric.model_id is None
]
)
client.log_batch(run_id=run_id, metrics=metrics_for_step)
# Only one of Auth policy and resources should be defined
if resources is not None and auth_policy is not None:
raise ValueError("Only one of `resources`, and `auth_policy` can be specified.")
registered_model = None
with TempDir() as tmp:
local_path = tmp.path("model")
tracking_uri = _resolve_tracking_uri()
client = mlflow.MlflowClient(tracking_uri)
if not run_id:
run_id = active_run.info.run_id if (active_run := mlflow.active_run()) else None
if model_id is not None:
model = client.get_logged_model(model_id)
else:
params = {
**(params or {}),
**(client.get_run(run_id).data.params if run_id else {}),
}
model = _create_logged_model(
# TODO: Update model name
name=name,
source_run_id=run_id,
model_type=model_type,
params={key: str(value) for key, value in params.items()},
tags={key: str(value) for key, value in tags.items()}
if tags is not None
else None,
flavor=flavor.__name__ if hasattr(flavor, "__name__") else "custom",
)
_last_logged_model_id.set(model.model_id)
if (
MLFLOW_PRINT_MODEL_URLS_ON_CREATION.get()
and is_databricks_uri(tracking_uri)
and (workspace_url := get_workspace_url())
):
logged_model_url = _construct_databricks_logged_model_url(
workspace_url,
model.experiment_id,
model.model_id,
get_workspace_id(),
)
eprint(f"🔗 View Logged Model at: {logged_model_url}")
with _use_logged_model(model=model):
if run_id is not None:
client.log_outputs(
run_id=run_id, models=[LoggedModelOutput(model.model_id, step=step)]
)
log_model_metrics_for_step(
client=client, model_id=model.model_id, run_id=run_id, step=step
)
if prompts is not None:
# Convert to URIs for serialization
prompts = [pr.uri if isinstance(pr, PromptVersion) else pr for pr in prompts]
mlflow_model = cls(
artifact_path=model.artifact_location,
model_uuid=model.model_id,
run_id=run_id,
metadata=metadata,
resources=resources,
auth_policy=auth_policy,
prompts=prompts,
model_id=model.model_id,
)
flavor.save_model(path=local_path, mlflow_model=mlflow_model, **kwargs)
# `save_model` calls `load_model` to infer the model requirements, which may result
# in __pycache__ directories being created in the model directory.
for pycache in Path(local_path).rglob("__pycache__"):
shutil.rmtree(pycache, ignore_errors=True)
if is_in_databricks_runtime():
_copy_model_metadata_for_uc_sharing(local_path, flavor)
serving_input = mlflow_model.get_serving_input(local_path)
# We check signature presence here as some flavors have a default signature as a
# fallback when not provided by user, which is set during flavor's save_model()
# call.
if mlflow_model.signature is None:
if serving_input is None:
_logger.warning(
_LOG_MODEL_MISSING_INPUT_EXAMPLE_WARNING, extra={"color": "red"}
)
elif (
tracking_uri == "databricks" or get_uri_scheme(tracking_uri) == "databricks"
):
_logger.warning(
_LOG_MODEL_MISSING_SIGNATURE_WARNING, extra={"color": "red"}
)
env_vars = None
# validate input example works for serving when logging the model
if serving_input and kwargs.get("validate_serving_input", True):
from mlflow.models import validate_serving_input
from mlflow.utils.model_utils import RECORD_ENV_VAR_ALLOWLIST, env_var_tracker
with env_var_tracker() as tracked_env_names:
try:
validate_serving_input(
model_uri=local_path,
serving_input=serving_input,
)
except Exception as e:
serving_input_msg = (
serving_input[:50] + "..."
if len(serving_input) > 50
else serving_input
)
_logger.warning(
f"Failed to validate serving input example {serving_input_msg}. "
"Alternatively, you can avoid passing input example and pass model "
"signature instead when logging the model. To ensure the input "
"example is valid prior to serving, please try calling "
"`mlflow.models.validate_serving_input` on the model uri and "
"serving input example. A serving input example can be generated "
"from model input example using "
"`mlflow.models.convert_input_example_to_serving_input` function.\n"
f"Got error: {e}",
exc_info=_logger.isEnabledFor(logging.DEBUG),
)
env_vars = (
sorted(
x
for x in tracked_env_names
if any(env_var in x for env_var in RECORD_ENV_VAR_ALLOWLIST)
)
or None
)
if env_vars:
# Keep the environment variable file as it serves as a check
# for displaying tips in Databricks serving endpoint
env_var_path = Path(local_path, ENV_VAR_FILE_NAME)
env_var_path.write_text(ENV_VAR_FILE_HEADER + "\n".join(env_vars) + "\n")
if len(env_vars) <= 3:
env_var_info = "[" + ", ".join(env_vars) + "]"
else:
env_var_info = "[" + ", ".join(env_vars[:3]) + ", ... " + "]"
f"(check file {ENV_VAR_FILE_NAME} in the model's artifact folder for full "
"list of environment variable names)"
_logger.info(
"Found the following environment variables used during model inference: "
f"{env_var_info}. Please check if you need to set them when deploying the "
"model. To disable this message, set environment variable "
f"`{MLFLOW_RECORD_ENV_VARS_IN_MODEL_LOGGING.name}` to `false`."
)
mlflow_model.env_vars = env_vars
# mlflow_model is updated, rewrite the MLmodel file
mlflow_model.save(os.path.join(local_path, MLMODEL_FILE_NAME))
client.log_model_artifacts(model.model_id, local_path)
# If the model was previously identified as external, delete the tag because
# the model now has artifacts in MLflow Model format
if model.tags.get(MLFLOW_MODEL_IS_EXTERNAL, "false").lower() == "true":
client.delete_logged_model_tag(model.model_id, MLFLOW_MODEL_IS_EXTERNAL)
# client.finalize_logged_model(model.model_id, status=LoggedModelStatus.READY)
# Associate prompts to the model Run
if prompts and run_id:
client = mlflow.MlflowClient()
for prompt in prompts:
client.link_prompt_version_to_run(run_id, prompt)
# if the model_config kwarg is passed in, then log the model config as an params
if model_config := kwargs.get("model_config"):
if isinstance(model_config, str):
try:
file_extension = os.path.splitext(model_config)[1].lower()
if file_extension == ".json":
with open(model_config) as f:
model_config = json.load(f)
elif file_extension in [".yaml", ".yml"]:
from mlflow.utils.model_utils import (
_validate_and_get_model_config_from_file,
)
model_config = _validate_and_get_model_config_from_file(
model_config
)
else:
_logger.warning(
"Unsupported file format for model config: %s. "
"Failed to load model config.",
model_config,
)
except Exception as e:
_logger.warning(
"Failed to load model config from %s: %s", model_config, e
)
try:
from mlflow.models.utils import _flatten_nested_params
# We are using the `/` separator to flatten the nested params
# since we are using the same separator to log nested metrics.
params_to_log = _flatten_nested_params(model_config, sep="/")
except Exception as e:
_logger.warning("Failed to flatten nested params: %s", str(e))
params_to_log = model_config
try:
# do not log params to run if run_id is None, since that could trigger
# a new run to be created
if run_id:
mlflow.tracking.fluent.log_params(params_to_log or {}, run_id=run_id)
except Exception as e:
_logger.warning("Failed to log model config as params: %s", str(e))
if registered_model_name is not None:
registered_model = mlflow.tracking._model_registry.fluent._register_model(
f"models:/{model.model_id}",
registered_model_name,
await_registration_for=await_registration_for,
local_model_path=local_path,
)
model_info = mlflow_model.get_model_info(model)
if registered_model is not None:
model_info.registered_model_version = registered_model.version
# If the model signature is Mosaic AI Agent compatible, render a recipe for evaluation.
from mlflow.models.display_utils import maybe_render_agent_eval_recipe
maybe_render_agent_eval_recipe(model_info)
return model_info
def _copy_model_metadata_for_uc_sharing(local_path: str, flavor) -> None:
"""
Copy model metadata files to a sub-directory 'metadata',
For Databricks Unity Catalog sharing use-cases.
Args:
local_path: Local path to the model directory.
flavor: Flavor module to save the model with.
"""
from mlflow.models.wheeled_model import _ORIGINAL_REQ_FILE_NAME, WheeledModel
metadata_path = os.path.join(local_path, "metadata")
if isinstance(flavor, WheeledModel):
# wheeled model updates several metadata files in original model directory
# copy these updated metadata files to the 'metadata' subdirectory
os.makedirs(metadata_path, exist_ok=True)
for file_name in METADATA_FILES + [
_ORIGINAL_REQ_FILE_NAME,
]:
src_file_path = os.path.join(local_path, file_name)
if os.path.exists(src_file_path):
dest_file_path = os.path.join(metadata_path, file_name)
shutil.copyfile(src_file_path, dest_file_path)
else:
os.makedirs(metadata_path, exist_ok=True)
for file_name in METADATA_FILES:
src_file_path = os.path.join(local_path, file_name)
if os.path.exists(src_file_path):
dest_file_path = os.path.join(metadata_path, file_name)
shutil.copyfile(src_file_path, dest_file_path)
[docs]def get_model_info(model_uri: str) -> ModelInfo:
"""
Get metadata for the specified model, such as its input/output signature.
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_run_id>/run-relative/path/to/model``
- ``models:/<model_name>/<model_version>``
- ``models:/<model_name>/<stage>``
- ``mlflow-artifacts:/path/to/model``
For more information about supported URI schemes, see
`Referencing Artifacts <https://www.mlflow.org/docs/latest/concepts.html#
artifact-locations>`_.
Returns:
A :py:class:`ModelInfo <mlflow.models.model.ModelInfo>` instance that contains the
metadata of the logged model.
.. code-block:: python
:caption: Example usage of get_model_info
import mlflow.models
import mlflow.sklearn
from sklearn.ensemble import RandomForestRegressor
with mlflow.start_run() as run:
params = {"n_estimators": 3, "random_state": 42}
X = [[0, 1]]
y = [1]
signature = mlflow.models.infer_signature(X, y)
rfr = RandomForestRegressor(**params).fit(X, y)
mlflow.log_params(params)
mlflow.sklearn.log_model(rfr, name="sklearn-model", signature=signature)
model_uri = f"runs:/{run.info.run_id}/sklearn-model"
# Get model info with model_uri
model_info = mlflow.models.get_model_info(model_uri)
# Get model signature directly
model_signature = model_info.signature
assert model_signature == signature
"""
return Model.load(model_uri).get_model_info()
class Files(NamedTuple):
requirements: Path
conda: Path
def get_model_requirements_files(resolved_uri: str) -> Files:
requirements_txt_file = _download_artifact_from_uri(
artifact_uri=append_to_uri_path(resolved_uri, _REQUIREMENTS_FILE_NAME)
)
conda_yaml_file = _download_artifact_from_uri(
artifact_uri=append_to_uri_path(resolved_uri, _CONDA_ENV_FILE_NAME)
)
return Files(
Path(requirements_txt_file),
Path(conda_yaml_file),
)
[docs]def update_model_requirements(
model_uri: str,
operation: Literal["add", "remove"],
requirement_list: list[str],
) -> None:
"""
Add or remove requirements from a model's conda.yaml and requirements.txt files.
The process involves downloading these two files from the model artifacts
(if they're non-local), updating them with the specified requirements,
and then overwriting the existing files. Should the artifact repository
associated with the model artifacts disallow overwriting, this function will
fail.
Note that model registry URIs (i.e. URIs in the form ``models:/``) are not
supported, as artifacts in the model registry are intended to be read-only.
If adding requirements, the function will overwrite any existing requirements
that overlap, or else append the new requirements to the existing list.
If removing requirements, the function will ignore any version specifiers,
and remove all the specified package names. Any requirements that are not
found in the existing files will be ignored.
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_run_id>/run-relative/path/to/model``
- ``mlflow-artifacts:/path/to/model``
For more information about supported URI schemes, see
`Referencing Artifacts <https://www.mlflow.org/docs/latest/concepts.html#
artifact-locations>`_.
operation: The operation to perform. Must be one of "add" or "remove".
requirement_list: A list of requirements to add or remove from the model.
For example: ["numpy==1.20.3", "pandas>=1.3.3"]
"""
resolved_uri = model_uri
if ModelsArtifactRepository.is_models_uri(model_uri):
if not ModelsArtifactRepository._is_logged_model_uri(model_uri):
raise MlflowException(
f'Failed to set requirements on "{model_uri}". '
+ "Model URIs with the `models:/` scheme are not supported.",
INVALID_PARAMETER_VALUE,
)
resolved_uri = ModelsArtifactRepository.get_underlying_uri(model_uri)
elif RunsArtifactRepository.is_runs_uri(model_uri):
resolved_uri = RunsArtifactRepository.get_underlying_uri(model_uri)
_logger.info(f"Retrieving model requirements files from {resolved_uri}...")
local_paths = get_model_requirements_files(resolved_uri)
conda_yaml_path = local_paths.conda
requirements_txt_path = local_paths.requirements
old_conda_reqs = _get_requirements_from_file(conda_yaml_path)
old_requirements_reqs = _get_requirements_from_file(requirements_txt_path)
requirements = []
invalid_requirements = {}
for s in requirement_list:
try:
requirements.append(Requirement(s.strip().lower()))
except InvalidRequirement as e:
invalid_requirements[s] = e
if invalid_requirements:
raise MlflowException.invalid_parameter_value(
f"Found invalid requirements: {invalid_requirements}"
)
if operation == "add":
updated_conda_reqs = _add_or_overwrite_requirements(requirements, old_conda_reqs)
updated_requirements_reqs = _add_or_overwrite_requirements(
requirements, old_requirements_reqs
)
else:
updated_conda_reqs = _remove_requirements(requirements, old_conda_reqs)
updated_requirements_reqs = _remove_requirements(requirements, old_requirements_reqs)
_write_requirements_to_file(conda_yaml_path, updated_conda_reqs)
_write_requirements_to_file(requirements_txt_path, updated_requirements_reqs)
# just print conda reqs here to avoid log spam
# it should be the same as requirements.txt anyway
_logger.info(
"Done updating requirements!\n\n"
f"Old requirements:\n{pformat([str(req) for req in old_conda_reqs])}\n\n"
f"Updated requirements:\n{pformat(updated_conda_reqs)}\n"
)
_logger.info(f"Uploading updated requirements files to {resolved_uri}...")
_upload_artifact_to_uri(conda_yaml_path, resolved_uri)
_upload_artifact_to_uri(requirements_txt_path, resolved_uri)
__mlflow_model__ = None
def _validate_langchain_model(model):
from langchain_core.runnables.base import Runnable
from mlflow.models.utils import _validate_and_get_model_code_path
if isinstance(model, str):
return _validate_and_get_model_code_path(model, None)
if not isinstance(model, Runnable):
raise MlflowException.invalid_parameter_value(
"Model must be a Langchain Runnable type or path to a Langchain model, "
f"got {type(model)}"
)
return model
def _validate_llama_index_model(model):
from mlflow.llama_index.model import _validate_and_prepare_llama_index_model_or_path
return _validate_and_prepare_llama_index_model_or_path(model, None)
[docs]def set_model(model) -> None:
"""
When logging model as code, this function can be used to set the model object
to be logged.
Args:
model: The model object to be logged. Supported model types are:
- A Python function or callable object.
- A Langchain model or path to a Langchain model.
- A Llama Index model or path to a Llama Index model.
"""
from mlflow.pyfunc import PythonModel
if isinstance(model, str):
raise mlflow.MlflowException(SET_MODEL_ERROR)
if isinstance(model, PythonModel) or callable(model):
globals()["__mlflow_model__"] = model
return
for validate_function in [_validate_langchain_model, _validate_llama_index_model]:
try:
globals()["__mlflow_model__"] = validate_function(model)
return
except Exception:
pass
raise mlflow.MlflowException(SET_MODEL_ERROR)
def _update_active_model_id_based_on_mlflow_model(mlflow_model: Model):
"""
Update the current active model ID based on the provided MLflow model.
Only set the active model ID if it is not already set by the user.
This is useful for setting the active model ID when loading a model
to ensure traces generated are associated with the loaded model.
"""
if mlflow_model.model_id is None:
return
amc = _get_active_model_context()
# only set the active model if the model is not set by the user
if amc.model_id != mlflow_model.model_id and not amc.set_by_user:
_set_active_model_id(model_id=mlflow_model.model_id)