"""
The ``mlflow.pyfunc.model`` module defines logic for saving and loading custom "python_function"
models with a user-defined ``PythonModel`` subclass.
"""
import inspect
import logging
import os
import shutil
from abc import ABCMeta, abstractmethod
from pathlib import Path
from typing import Any, Dict, Iterator, List, Optional, Union
import cloudpickle
import pandas as pd
import yaml
import mlflow.pyfunc
import mlflow.utils
from mlflow.exceptions import MlflowException
from mlflow.models import Model
from mlflow.models.model import MLMODEL_FILE_NAME, MODEL_CODE_PATH
from mlflow.models.rag_signatures import ChatCompletionRequest, SplitChatMessagesRequest
from mlflow.models.signature import _extract_type_hints
from mlflow.models.utils import _load_model_code_path
from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE
from mlflow.pyfunc.utils.input_converter import _hydrate_dataclass
from mlflow.tracking.artifact_utils import _download_artifact_from_uri
from mlflow.types.llm import ChatMessage, ChatParams, ChatResponse
from mlflow.utils.annotations import experimental
from mlflow.utils.environment import (
_CONDA_ENV_FILE_NAME,
_CONSTRAINTS_FILE_NAME,
_PYTHON_ENV_FILE_NAME,
_REQUIREMENTS_FILE_NAME,
_mlflow_conda_env,
_process_conda_env,
_process_pip_requirements,
_PythonEnv,
)
from mlflow.utils.file_utils import TempDir, get_total_file_size, write_to
from mlflow.utils.model_utils import _get_flavor_configuration, _validate_infer_and_copy_code_paths
from mlflow.utils.requirements_utils import _get_pinned_requirement
CONFIG_KEY_ARTIFACTS = "artifacts"
CONFIG_KEY_ARTIFACT_RELATIVE_PATH = "path"
CONFIG_KEY_ARTIFACT_URI = "uri"
CONFIG_KEY_PYTHON_MODEL = "python_model"
CONFIG_KEY_CLOUDPICKLE_VERSION = "cloudpickle_version"
_SAVED_PYTHON_MODEL_SUBPATH = "python_model.pkl"
_logger = logging.getLogger(__name__)
[docs]def get_default_pip_requirements():
"""
Returns:
A list of default pip requirements for MLflow Models produced by this flavor. Calls to
:func:`save_model()` and :func:`log_model()` produce a pip environment that, at minimum,
contains these requirements.
"""
return [_get_pinned_requirement("cloudpickle")]
[docs]def get_default_conda_env():
"""
Returns:
The default Conda environment for MLflow Models produced by calls to
:func:`save_model() <mlflow.pyfunc.save_model>`
and :func:`log_model() <mlflow.pyfunc.log_model>` when a user-defined subclass of
:class:`PythonModel` is provided.
"""
return _mlflow_conda_env(additional_pip_deps=get_default_pip_requirements())
def _log_warning_if_params_not_in_predict_signature(logger, params):
if params:
logger.warning(
"The underlying model does not support passing additional parameters to the predict"
f" function. `params` {params} will be ignored."
)
[docs]class PythonModel:
"""
Represents a generic Python model that evaluates inputs and produces API-compatible outputs.
By subclassing :class:`~PythonModel`, users can create customized MLflow models with the
"python_function" ("pyfunc") flavor, leveraging custom inference logic and artifact
dependencies.
"""
__metaclass__ = ABCMeta
[docs] def load_context(self, context):
"""
Loads artifacts from the specified :class:`~PythonModelContext` that can be used by
:func:`~PythonModel.predict` when evaluating inputs. When loading an MLflow model with
:func:`~load_model`, this method is called as soon as the :class:`~PythonModel` is
constructed.
The same :class:`~PythonModelContext` will also be available during calls to
:func:`~PythonModel.predict`, but it may be more efficient to override this method
and load artifacts from the context at model load time.
Args:
context: A :class:`~PythonModelContext` instance containing artifacts that the model
can use to perform inference.
"""
def _get_type_hints(self):
return _extract_type_hints(self.predict, input_arg_index=1)
[docs] @abstractmethod
def predict(self, context, model_input, params: Optional[Dict[str, Any]] = None):
"""
Evaluates a pyfunc-compatible input and produces a pyfunc-compatible output.
For more information about the pyfunc input/output API, see the :ref:`pyfunc-inference-api`.
Args:
context: A :class:`~PythonModelContext` instance containing artifacts that the model
can use to perform inference.
model_input: A pyfunc-compatible input for the model to evaluate.
params: Additional parameters to pass to the model for inference.
"""
[docs] def predict_stream(self, context, model_input, params: Optional[Dict[str, Any]] = None):
"""
Evaluates a pyfunc-compatible input and produces an iterator of output.
For more information about the pyfunc input API, see the :ref:`pyfunc-inference-api`.
Args:
context: A :class:`~PythonModelContext` instance containing artifacts that the model
can use to perform inference.
model_input: A pyfunc-compatible input for the model to evaluate.
params: Additional parameters to pass to the model for inference.
"""
raise NotImplementedError()
class _FunctionPythonModel(PythonModel):
"""
When a user specifies a ``python_model`` argument that is a function, we wrap the function
in an instance of this class.
"""
def __init__(self, func, hints=None, signature=None):
self.func = func
self.hints = hints
self.signature = signature
def _get_type_hints(self):
return _extract_type_hints(self.func, input_arg_index=0)
def predict(
self,
context,
model_input,
params: Optional[Dict[str, Any]] = None,
):
"""
Args:
context: A instance containing artifacts that the model
can use to perform inference.
model_input: A pyfunc-compatible input for the model to evaluate.
params: Additional parameters to pass to the model for inference.
Returns:
Model predictions.
"""
if inspect.signature(self.func).parameters.get("params"):
return self.func(model_input, params=params)
_log_warning_if_params_not_in_predict_signature(_logger, params)
return self.func(model_input)
[docs]class PythonModelContext:
"""
A collection of artifacts that a :class:`~PythonModel` can use when performing inference.
:class:`~PythonModelContext` objects are created *implicitly* by the
:func:`save_model() <mlflow.pyfunc.save_model>` and
:func:`log_model() <mlflow.pyfunc.log_model>` persistence methods, using the contents specified
by the ``artifacts`` parameter of these methods.
"""
def __init__(self, artifacts, model_config):
"""
Args:
artifacts: A dictionary of ``<name, artifact_path>`` entries, where ``artifact_path``
is an absolute filesystem path to a given artifact.
model_config: The model configuration to make available to the model at
loading time.
"""
self._artifacts = artifacts
self._model_config = model_config
@property
def artifacts(self):
"""
A dictionary containing ``<name, artifact_path>`` entries, where ``artifact_path`` is an
absolute filesystem path to the artifact.
"""
return self._artifacts
@experimental
@property
def model_config(self):
"""
A dictionary containing ``<config, value>`` entries, where ``config`` is the name
of the model configuration keys and ``value`` is the value of the given configuration.
"""
return self._model_config
[docs]@experimental
class ChatModel(PythonModel, metaclass=ABCMeta):
"""
A subclass of :class:`~PythonModel` that makes it more convenient to implement models
that are compatible with popular LLM chat APIs. By subclassing :class:`~ChatModel`,
users can create MLflow models with a ``predict()`` method that is more convenient
for chat tasks than the generic :class:`~PythonModel` API. ChatModels automatically
define input/output signatures and an input example, so manually specifying these values
when calling :func:`mlflow.pyfunc.save_model() <mlflow.pyfunc.save_model>` is not necessary.
See the documentation of the ``predict()`` method below for details on that parameters and
outputs that are expected by the ``ChatModel`` API.
"""
[docs] @abstractmethod
def predict(self, context, messages: List[ChatMessage], params: ChatParams) -> ChatResponse:
"""
Evaluates a chat input and produces a chat output.
Args:
context: A :class:`~PythonModelContext` instance containing artifacts that the model
can use to perform inference.
messages (List[:py:class:`ChatMessage <mlflow.types.llm.ChatMessage>`]):
A list of :py:class:`ChatMessage <mlflow.types.llm.ChatMessage>`
objects representing chat history.
params (:py:class:`ChatParams <mlflow.types.llm.ChatParams>`):
A :py:class:`ChatParams <mlflow.types.llm.ChatParams>` object
containing various parameters used to modify model behavior during
inference.
Returns:
A :py:class:`ChatResponse <mlflow.types.llm.ChatResponse>` object containing
the model's response(s), as well as other metadata.
"""
[docs] def predict_stream(
self, context, messages: List[ChatMessage], params: ChatParams
) -> Iterator[ChatResponse]:
"""
Evaluates a chat input and produces a chat output.
Overrides this function to implement a real stream prediction.
By default, this function just yields result of `predict` function.
Args:
context: A :class:`~PythonModelContext` instance containing artifacts that the model
can use to perform inference.
messages (List[:py:class:`ChatMessage <mlflow.types.llm.ChatMessage>`]):
A list of :py:class:`ChatMessage <mlflow.types.llm.ChatMessage>`
objects representing chat history.
params (:py:class:`ChatParams <mlflow.types.llm.ChatParams>`):
A :py:class:`ChatParams <mlflow.types.llm.ChatParams>` object
containing various parameters used to modify model behavior during
inference.
Returns:
An iterator over :py:class:`ChatResponse <mlflow.types.llm.ChatResponse>` object
containing the model's response(s), as well as other metadata.
"""
yield self.predict(context, messages, params)
def _save_model_with_class_artifacts_params( # noqa: D417
path,
python_model,
signature=None,
hints=None,
artifacts=None,
conda_env=None,
code_paths=None,
mlflow_model=None,
pip_requirements=None,
extra_pip_requirements=None,
model_config=None,
streamable=None,
model_code_path=None,
infer_code_paths=False,
):
"""
Args:
path: The path to which to save the Python model.
python_model: An instance of a subclass of :class:`~PythonModel`. ``python_model``
defines how the model loads artifacts and how it performs inference.
artifacts: A dictionary containing ``<name, artifact_uri>`` entries. Remote artifact URIs
are resolved to absolute filesystem paths, producing a dictionary of
``<name, absolute_path>`` entries, (e.g. {"file": "aboslute_path"}).
``python_model`` can reference these resolved entries as the ``artifacts`` property
of the ``context`` attribute. If ``<artifact_name, 'hf:/repo_id'>``(e.g.
{"bert-tiny-model": "hf:/prajjwal1/bert-tiny"}) is provided, then the model can be
fetched from huggingface hub using repo_id `prajjwal1/bert-tiny` directly. If ``None``,
no artifacts are added to the model.
conda_env: Either a dictionary representation of a Conda environment or the path to a Conda
environment yaml file. If provided, this decsribes the environment this model should be
run in. At minimum, it should specify the dependencies contained in
:func:`get_default_conda_env()`. If ``None``, the default
:func:`get_default_conda_env()` environment is added to the model.
code_paths: A list of local filesystem paths to Python file dependencies (or directories
containing file dependencies). These files are *prepended* to the system path before the
model is loaded.
mlflow_model: The model to which to add the ``mlflow.pyfunc`` flavor.
model_config: The model configuration for the flavor. Model configuration is available
during model loading time.
.. Note:: Experimental: This parameter may change or be removed in a future release
without warning.
model_code_path: The path to the code that is being logged as a PyFunc model. Can be used
to load python_model when python_model is None.
.. Note:: Experimental: This parameter may change or be removed in a future release
without warning.
streamable: A boolean value indicating if the model supports streaming prediction,
If None, MLflow will try to inspect if the model supports streaming
by checking if `predict_stream` method exists. Default None.
"""
if mlflow_model is None:
mlflow_model = Model()
custom_model_config_kwargs = {
CONFIG_KEY_CLOUDPICKLE_VERSION: cloudpickle.__version__,
}
if callable(python_model):
python_model = _FunctionPythonModel(python_model, hints, signature)
saved_python_model_subpath = _SAVED_PYTHON_MODEL_SUBPATH
# If model_code_path is defined, we load the model into python_model, but we don't want to
# pickle/save the python_model since the module won't be able to be imported.
if not model_code_path:
try:
with open(os.path.join(path, saved_python_model_subpath), "wb") as out:
cloudpickle.dump(python_model, out)
except Exception as e:
# cloudpickle sometimes raises TypeError instead of PicklingError.
# catching generic Exception and checking message to handle both cases.
if "cannot pickle" in str(e).lower():
raise MlflowException(
"Failed to serialize Python model. Please audit your "
"class variables (e.g. in `__init__()`) for any "
"unpicklable objects. If you're trying to save an external model "
"in your custom pyfunc, Please use the `artifacts` parameter "
"in `mlflow.pyfunc.save_model()`, and load your external model "
"in the `load_context()` method instead. For example:\n\n"
"class MyModel(mlflow.pyfunc.PythonModel):\n"
" def load_context(self, context):\n"
" model_path = context.artifacts['my_model_path']\n"
" // custom load logic here\n"
" self.model = load_model(model_path)\n\n"
"For more information, see our full tutorial at: "
"https://mlflow.org/docs/latest/traditional-ml/creating-custom-pyfunc/index.html"
f"\n\nFull serialization error: {e}"
) from None
else:
raise e
custom_model_config_kwargs[CONFIG_KEY_PYTHON_MODEL] = saved_python_model_subpath
if artifacts:
saved_artifacts_config = {}
with TempDir() as tmp_artifacts_dir:
saved_artifacts_dir_subpath = "artifacts"
hf_prefix = "hf:/"
for artifact_name, artifact_uri in artifacts.items():
if artifact_uri.startswith(hf_prefix):
try:
from huggingface_hub import snapshot_download
except ImportError as e:
raise MlflowException(
"Failed to import huggingface_hub. Please install huggingface_hub "
f"to log the model with artifact_uri {artifact_uri}. Error: {e}"
)
repo_id = artifact_uri[len(hf_prefix) :]
try:
snapshot_location = snapshot_download(
repo_id=repo_id,
local_dir=os.path.join(
path, saved_artifacts_dir_subpath, artifact_name
),
local_dir_use_symlinks=False,
)
except Exception as e:
raise MlflowException.invalid_parameter_value(
"Failed to download snapshot from Hugging Face Hub with artifact_uri: "
f"{artifact_uri}. Error: {e}"
)
saved_artifact_subpath = (
Path(snapshot_location).relative_to(Path(os.path.realpath(path))).as_posix()
)
else:
tmp_artifact_path = _download_artifact_from_uri(
artifact_uri=artifact_uri, output_path=tmp_artifacts_dir.path()
)
relative_path = (
Path(tmp_artifact_path)
.relative_to(Path(tmp_artifacts_dir.path()))
.as_posix()
)
saved_artifact_subpath = os.path.join(
saved_artifacts_dir_subpath, relative_path
)
saved_artifacts_config[artifact_name] = {
CONFIG_KEY_ARTIFACT_RELATIVE_PATH: saved_artifact_subpath,
CONFIG_KEY_ARTIFACT_URI: artifact_uri,
}
shutil.move(tmp_artifacts_dir.path(), os.path.join(path, saved_artifacts_dir_subpath))
custom_model_config_kwargs[CONFIG_KEY_ARTIFACTS] = saved_artifacts_config
if streamable is None:
streamable = python_model.__class__.predict_stream != PythonModel.predict_stream
if model_code_path:
loader_module = mlflow.pyfunc.loaders.code_model.__name__
elif python_model:
loader_module = _get_pyfunc_loader_module(python_model)
else:
raise MlflowException(
"Either `python_model` or `model_code_path` must be provided to save the model.",
error_code=INVALID_PARAMETER_VALUE,
)
mlflow.pyfunc.add_to_model(
model=mlflow_model,
loader_module=loader_module,
code=None,
conda_env=_CONDA_ENV_FILE_NAME,
python_env=_PYTHON_ENV_FILE_NAME,
model_config=model_config,
streamable=streamable,
model_code_path=model_code_path,
**custom_model_config_kwargs,
)
if size := get_total_file_size(path):
mlflow_model.model_size_bytes = size
mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME))
saved_code_subpath = _validate_infer_and_copy_code_paths(
code_paths,
path,
infer_code_paths,
mlflow.pyfunc.FLAVOR_NAME,
)
mlflow_model.flavors[mlflow.pyfunc.FLAVOR_NAME][mlflow.pyfunc.CODE] = saved_code_subpath
# `mlflow_model.code` is updated, re-generate `MLmodel` file.
mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME))
if conda_env is None:
if pip_requirements is None:
default_reqs = get_default_pip_requirements()
# To ensure `_load_pyfunc` can successfully load the model during the dependency
# inference, `mlflow_model.save` must be called beforehand to save an MLmodel file.
inferred_reqs = mlflow.models.infer_pip_requirements(
path,
mlflow.pyfunc.FLAVOR_NAME,
fallback=default_reqs,
)
default_reqs = sorted(set(inferred_reqs).union(default_reqs))
else:
default_reqs = None
conda_env, pip_requirements, pip_constraints = _process_pip_requirements(
default_reqs,
pip_requirements,
extra_pip_requirements,
)
else:
conda_env, pip_requirements, pip_constraints = _process_conda_env(conda_env)
with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f:
yaml.safe_dump(conda_env, stream=f, default_flow_style=False)
# Save `constraints.txt` if necessary
if pip_constraints:
write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME), "\n".join(pip_constraints))
# Save `requirements.txt`
write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME), "\n".join(pip_requirements))
_PythonEnv.current().to_yaml(os.path.join(path, _PYTHON_ENV_FILE_NAME))
def _load_context_model_and_signature(
model_path: str, model_config: Optional[Dict[str, Any]] = None
):
pyfunc_config = _get_flavor_configuration(
model_path=model_path, flavor_name=mlflow.pyfunc.FLAVOR_NAME
)
signature = mlflow.models.Model.load(model_path).signature
if MODEL_CODE_PATH in pyfunc_config:
conf_model_code_path = pyfunc_config.get(MODEL_CODE_PATH)
model_code_path = os.path.join(model_path, os.path.basename(conf_model_code_path))
python_model = _load_model_code_path(model_code_path, model_config)
if callable(python_model):
python_model = _FunctionPythonModel(python_model, signature=signature)
else:
python_model_cloudpickle_version = pyfunc_config.get(CONFIG_KEY_CLOUDPICKLE_VERSION, None)
if python_model_cloudpickle_version is None:
mlflow.pyfunc._logger.warning(
"The version of CloudPickle used to save the model could not be found in the "
"MLmodel configuration"
)
elif python_model_cloudpickle_version != cloudpickle.__version__:
# CloudPickle does not have a well-defined cross-version compatibility policy. Micro
# version releases have been known to cause incompatibilities. Therefore, we match on
# the full library version
mlflow.pyfunc._logger.warning(
"The version of CloudPickle that was used to save the model, `CloudPickle %s`, "
"differs from the version of CloudPickle that is currently running, `CloudPickle "
"%s`, and may be incompatible",
python_model_cloudpickle_version,
cloudpickle.__version__,
)
python_model_subpath = pyfunc_config.get(CONFIG_KEY_PYTHON_MODEL, None)
if python_model_subpath is None:
raise MlflowException("Python model path was not specified in the model configuration")
with open(os.path.join(model_path, python_model_subpath), "rb") as f:
python_model = cloudpickle.load(f)
artifacts = {}
for saved_artifact_name, saved_artifact_info in pyfunc_config.get(
CONFIG_KEY_ARTIFACTS, {}
).items():
artifacts[saved_artifact_name] = os.path.join(
model_path, saved_artifact_info[CONFIG_KEY_ARTIFACT_RELATIVE_PATH]
)
context = PythonModelContext(artifacts=artifacts, model_config=model_config)
python_model.load_context(context=context)
return context, python_model, signature
def _load_pyfunc(model_path: str, model_config: Optional[Dict[str, Any]] = None):
context, python_model, signature = _load_context_model_and_signature(model_path, model_config)
return _PythonModelPyfuncWrapper(
python_model=python_model,
context=context,
signature=signature,
)
def _get_first_string_column(pdf):
iter_string_columns = (col for col, val in pdf.iloc[0].items() if isinstance(val, str))
return next(iter_string_columns, None)
class _PythonModelPyfuncWrapper:
"""
Wrapper class that creates a predict function such that
predict(model_input: pd.DataFrame) -> model's output as pd.DataFrame (pandas DataFrame)
"""
def __init__(self, python_model, context, signature):
"""
Args:
python_model: An instance of a subclass of :class:`~PythonModel`.
context: A :class:`~PythonModelContext` instance containing artifacts that
``python_model`` may use when performing inference.
signature: :class:`~ModelSignature` instance describing model input and output.
"""
self.python_model = python_model
self.context = context
self.signature = signature
def _convert_input(self, model_input):
import pandas as pd
hints = self.python_model._get_type_hints()
if hints.input == List[str]:
if isinstance(model_input, pd.DataFrame):
first_string_column = _get_first_string_column(model_input)
if first_string_column is None:
raise MlflowException.invalid_parameter_value(
"Expected model input to contain at least one string column"
)
return model_input[first_string_column].tolist()
elif isinstance(model_input, list):
if all(isinstance(x, dict) for x in model_input):
return [next(iter(d.values())) for d in model_input]
elif all(isinstance(x, str) for x in model_input):
return model_input
elif hints.input == List[Dict[str, str]]:
if isinstance(model_input, pd.DataFrame):
if (
len(self.signature.inputs) == 1
and next(iter(self.signature.inputs)).name is None
):
first_string_column = _get_first_string_column(model_input)
return model_input[[first_string_column]].to_dict(orient="records")
columns = [x.name for x in self.signature.inputs]
return model_input[columns].to_dict(orient="records")
elif isinstance(model_input, list) and all(isinstance(x, dict) for x in model_input):
keys = [x.name for x in self.signature.inputs]
return [{k: d[k] for k in keys} for d in model_input]
elif isinstance(hints.input, type) and (
issubclass(hints.input, ChatCompletionRequest)
or issubclass(hints.input, SplitChatMessagesRequest)
):
# If the type hint is a RAG dataclass, we hydrate it
if isinstance(model_input, pd.DataFrame):
# If there are multiple rows, we should throw
if len(model_input) > 1:
raise MlflowException(
"Expected a single input for dataclass type hint, but got multiple rows"
)
# Since single input is expected, we take the first row
return _hydrate_dataclass(hints.input, model_input.iloc[0])
return model_input
def predict(self, model_input, params: Optional[Dict[str, Any]] = None):
"""
Args:
model_input: Model input data as one of dict, str, bool, bytes, float, int, str type.
params: Additional parameters to pass to the model for inference.
Returns:
Model predictions as an iterator of chunks. The chunks in the iterator must be type of
dict or string. Chunk dict fields are determined by the model implementation.
"""
if inspect.signature(self.python_model.predict).parameters.get("params"):
return self.python_model.predict(
self.context, self._convert_input(model_input), params=params
)
_log_warning_if_params_not_in_predict_signature(_logger, params)
return self.python_model.predict(self.context, self._convert_input(model_input))
def predict_stream(self, model_input, params: Optional[Dict[str, Any]] = None):
"""
Args:
model_input: LLM Model single input.
params: Additional parameters to pass to the model for inference.
Returns:
Streaming predictions.
"""
if inspect.signature(self.python_model.predict_stream).parameters.get("params"):
return self.python_model.predict_stream(
self.context, self._convert_input(model_input), params=params
)
_log_warning_if_params_not_in_predict_signature(_logger, params)
return self.python_model.predict_stream(self.context, self._convert_input(model_input))
def _get_pyfunc_loader_module(python_model):
if isinstance(python_model, ChatModel):
return mlflow.pyfunc.loaders.chat_model.__name__
return __name__
class ModelFromDeploymentEndpoint(PythonModel):
"""
A PythonModel wrapper for invoking an MLflow Deployments endpoint.
This class is particularly used for running evaluation against an MLflow Deployments endpoint.
"""
def __init__(self, endpoint, params):
self.endpoint = endpoint
self.params = params
def predict(
self, context, model_input: Union[pd.DataFrame, Dict[str, Any], List[Dict[str, Any]]]
):
"""
Run prediction on the input data.
Args:
context: A :class:`~PythonModelContext` instance containing artifacts that the model
can use to perform inference.
model_input: The input data for prediction, either of the following:
- Pandas DataFrame: If the default evaluator is used, input is a DF
that contains the multiple request payloads in a single column.
- A dictionary: If the model_type is "databricks-agents" and the
Databricks RAG evaluator is used, this PythonModel can be invoked
with a single dict corresponding to the ChatCompletionsRequest schema.
- A list of dictionaries: Currently we don't have any evaluator that
gives this input format, but we keep this for future use cases and
compatibility with normal pyfunc models.
Return:
The prediction result. The return type will be consistent with the model input type,
e.g., if the input is a Pandas DataFrame, the return will be a Pandas Series.
"""
if isinstance(model_input, dict):
return self._predict_single(model_input)
elif isinstance(model_input, list) and all(isinstance(data, dict) for data in model_input):
return [self._predict_single(data) for data in model_input]
elif isinstance(model_input, pd.DataFrame):
if len(model_input.columns) != 1:
raise MlflowException(
f"The number of input columns must be 1, but got {model_input.columns}. "
"Multi-column input is not supported for evaluating an MLflow Deployments "
"endpoint. Please include the input text or payload in a single column.",
error_code=INVALID_PARAMETER_VALUE,
)
input_column = model_input.columns[0]
predictions = [self._predict_single(data) for data in model_input[input_column]]
return pd.Series(predictions)
else:
raise MlflowException(
f"Invalid input data type: {type(model_input)}. The input data must be either "
"a Pandas DataFrame, a dictionary, or a list of dictionaries containing the "
"request payloads for evaluating an MLflow Deployments endpoint.",
error_code=INVALID_PARAMETER_VALUE,
)
def _predict_single(self, data: Union[str, Dict[str, Any]]) -> Dict[str, Any]:
"""
Send a single prediction request to the MLflow Deployments endpoint.
Args:
data: The single input data for prediction. If the input data is a string, we will
construct the request payload from it. If the input data is a dictionary, we
will directly use it as the request payload.
Returns:
The prediction result from the MLflow Deployments endpoint as a dictionary.
"""
from mlflow.metrics.genai.model_utils import call_deployments_api, get_endpoint_type
endpoint_type = get_endpoint_type(f"endpoints:/{self.endpoint}")
if isinstance(data, str):
# If the input payload is string, MLflow needs to construct the JSON
# payload based on the endpoint type. If the endpoint type is not
# set on the endpoint, we will default to chat format.
endpoint_type = endpoint_type or "llm/v1/chat"
prediction = call_deployments_api(self.endpoint, data, self.params, endpoint_type)
elif isinstance(data, dict):
# If the input is dictionary, we assume the input is already in the
# compatible format for the endpoint.
prediction = call_deployments_api(self.endpoint, data, self.params, endpoint_type)
else:
raise MlflowException(
f"Invalid input data type: {type(data)}. The feature column of the evaluation "
"dataset must contain only strings or dictionaries containing the request "
"payload for evaluating an MLflow Deployments endpoint.",
error_code=INVALID_PARAMETER_VALUE,
)
return prediction