MLflow Models

An MLflow Model is a standard format for packaging machine learning models that can be used in a variety of downstream tools—for example, real-time serving through a REST API or batch inference on Apache Spark. The format defines a convention that lets you save a model in different “flavors” that can be understood by different downstream tools.

Storage Format

Each MLflow Model is a directory containing arbitrary files, together with an MLmodel file in the root of the directory that can define multiple flavors that the model can be viewed in.

The model aspect of the MLflow Model can either be a serialized object (e.g., a pickled scikit-learn model) or a Python script (or notebook, if running in Databricks) that contains the model instance that has been defined with the mlflow.models.set_model() API.

Flavors are the key concept that makes MLflow Models powerful: they are a convention that deployment tools can use to understand the model, which makes it possible to write tools that work with models from any ML library without having to integrate each tool with each library. MLflow defines several “standard” flavors that all of its built-in deployment tools support, such as a “Python function” flavor that describes how to run the model as a Python function. However, libraries can also define and use other flavors. For example, MLflow’s mlflow.sklearn library allows loading models back as a scikit-learn Pipeline object for use in code that is aware of scikit-learn, or as a generic Python function for use in tools that just need to apply the model (for example, the mlflow deployments tool with the option -t sagemaker for deploying models to Amazon SageMaker).

MLmodel file

All of the flavors that a particular model supports are defined in its MLmodel file in YAML format. For example, mlflow.sklearn outputs models as follows:

# Directory written by mlflow.sklearn.save_model(model, "my_model")
my_model/
├── MLmodel
├── model.pkl
├── conda.yaml
├── python_env.yaml
└── requirements.txt

And its MLmodel file describes two flavors:

time_created: 2018-05-25T17:28:53.35

flavors:
  sklearn:
    sklearn_version: 0.19.1
    pickled_model: model.pkl
  python_function:
    loader_module: mlflow.sklearn

Apart from a flavors field listing the model flavors, the MLmodel YAML format can contain the following fields:

  • time_created: Date and time when the model was created, in UTC ISO 8601 format.

  • run_id: ID of the run that created the model, if the model was saved using MLflow Tracking.

  • signature: model signature in JSON format.

  • input_example: reference to an artifact with input example.

  • databricks_runtime: Databricks runtime version and type, if the model was trained in a Databricks notebook or job.

  • mlflow_version: The version of MLflow that was used to log the model.

Additional Logged Files

For environment recreation, we automatically log conda.yaml, python_env.yaml, and requirements.txt files whenever a model is logged. These files can then be used to reinstall dependencies using conda or virtualenv with pip. Please see How MLflow Model Records Dependencies for more details about these files.

When logging a model, model metadata files (MLmodel, conda.yaml, python_env.yaml, requirements.txt) are copied to a subdirectory named metadata. For wheeled models, original_requirements.txt file is also copied.

Note

When a model registered in the MLflow Model Registry is downloaded, a YAML file named registered_model_meta is added to the model directory on the downloader’s side. This file contains the name and version of the model referenced in the MLflow Model Registry, and will be used for deployment and other purposes.

Attention

If you log a model within Databricks, MLflow also creates a metadata subdirectory within the model directory. This subdirectory contains the lightweight copy of aforementioned metadata files for internal use.

Managing Model Dependencies

An MLflow Model infers dependencies required for the model flavor and automatically logs them. However, it also allows you to define extra dependencies or custom Python code, and offer a tool to validate them in a sandbox environment. Please refer to Managing Dependencies in MLflow Models for more details.

Model Signatures And Input Examples

In MLflow, understanding the intricacies of model signatures and input examples is crucial for effective model management and deployment.

  • Model Signature: Defines the schema for model inputs, outputs, and additional inference parameters, promoting a standardized interface for model interaction.

  • Model Input Example: Provides a concrete instance of valid model input, aiding in understanding and testing model requirements. Additionally, if an input example is provided when logging a model, a model signature will be automatically inferred and stored if not explicitly provided.

  • Model Serving Payload Example: Provides a json payload example for querying a deployed model endpoint. If an input example is provided when logging a model, a serving paylod example is automatically generated from the input example and saved as serving_input_example.json.

Our documentation delves into several key areas:

  • Supported Signature Types: We cover the different data types that are supported, such as tabular data for traditional machine learning models and tensors for deep learning models.

  • Signature Enforcement: Discusses how MLflow enforces schema compliance, ensuring that the provided inputs match the model’s expectations.

  • Logging Models with Signatures: Guides on how to incorporate signatures when logging models, enhancing clarity and reliability in model operations.

For a detailed exploration of these concepts, including examples and best practices, visit the Model Signatures and Examples Guide. If you would like to see signature enforcement in action, see the notebook tutorial on Model Signatures to learn more.

Model API

You can save and load MLflow Models in multiple ways. First, MLflow includes integrations with several common libraries. For example, mlflow.sklearn contains save_model, log_model, and load_model functions for scikit-learn models. Second, you can use the mlflow.models.Model class to create and write models. This class has four key functions:

  • add_flavor to add a flavor to the model. Each flavor has a string name and a dictionary of key-value attributes, where the values can be any object that can be serialized to YAML.

  • save to save the model to a local directory.

  • log to log the model as an artifact in the current run using MLflow Tracking.

  • load to load a model from a local directory or from an artifact in a previous run.

Models From Code

To learn more about the Models From Code feature, please visit the deep dive guide for more in-depth explanation and to see additional examples.

Note

The Models from Code feature is available in MLflow versions 2.12.2 and later. This feature is experimental and may change in future releases.

The Models from Code feature allows you to define and log models directly from a stand-alone python script. This feature is particularly useful when you want to log models that can be effectively stored as a code representation (models that do not need optimized weights through training) or applications that rely on external services (e.g., LangChain chains). Another benefit is that this approach entirely bypasses the use of the pickle or cloudpickle modules within Python, which can carry security risks when loading untrusted models.

Note

This feature is only supported for LangChain, LlamaIndex, and PythonModel models.

In order to log a model from code, you can leverage the mlflow.models.set_model() API. This API allows you to define a model by specifying an instance of the model class directly within the file where the model is defined. When logging such a model, a file path is specified (instead of an object) that points to the Python file containing both the model class definition and the usage of the set_model API applied on an instance of your custom model.

The figure below provides a comparison of the standard model logging process and the Models from Code feature for models that are eligible to be saved using the Models from Code feature:

Models from Code

For example, defining a model in a separate file named my_model.py:

import mlflow
from mlflow.models import set_model


class MyModel(mlflow.pyfunc.PythonModel):
    def predict(self, context, model_input):
        return model_input


# Define the custom PythonModel instance that will be used for inference
set_model(MyModel())

Note

The Models from code feature does not support capturing import statements that are from external file references. If you have dependencies that are not captured via a pip install, dependencies will need to be included and resolved via appropriate absolute path import references from using the code_paths feature. For simplicity’s sake, it is recommended to encapsulate all of your required local dependencies for a model defined from code within the same python script file due to limitations around code_paths dependency pathing resolution.

Tip

When defining a model from code and using the mlflow.models.set_model() API, the code that is defined in the script that is being logged will be executed internally to ensure that it is valid code. If you have connections to external services within your script (e.g. you are connecting to a GenAI service within LangChain), be aware that you will incur a connection request to that service when the model is being logged.

Then, logging the model from the file path in a different python script:

import mlflow

model_path = "my_model.py"

with mlflow.start_run():
    model_info = mlflow.pyfunc.log_model(
        python_model=model_path,  # Define the model as the path to the Python file
        artifact_path="my_model",
    )

# Loading the model behaves exactly as if an instance of MyModel had been logged
my_model = mlflow.pyfunc.load_model(model_info.model_uri)

Warning

The mlflow.models.set_model() API is not threadsafe. Do not attempt to use this feature if you are logging models concurrently from multiple threads. This fluent API utilizes a global active model state that has no consistency guarantees. If you are interested in threadsafe logging APIs, please use the mlflow.client.MlflowClient APIs for logging models.

Built-In Model Flavors

MLflow provides several standard flavors that might be useful in your applications. Specifically, many of its deployment tools support these flavors, so you can export your own model in one of these flavors to benefit from all these tools:

Python Function (python_function)

The python_function model flavor serves as a default model interface for MLflow Python models. Any MLflow Python model is expected to be loadable as a python_function model. This enables other MLflow tools to work with any python model regardless of which persistence module or framework was used to produce the model. This interoperability is very powerful because it allows any Python model to be productionized in a variety of environments.

In addition, the python_function model flavor defines a generic filesystem model format for Python models and provides utilities for saving and loading models to and from this format. The format is self-contained in the sense that it includes all the information necessary to load and use a model. Dependencies are stored either directly with the model or referenced via conda environment. This model format allows other tools to integrate their models with MLflow.

How To Save Model As Python Function

Most python_function models are saved as part of other model flavors - for example, all mlflow built-in flavors include the python_function flavor in the exported models. In addition, the mlflow.pyfunc module defines functions for creating python_function models explicitly. This module also includes utilities for creating custom Python models, which is a convenient way of adding custom python code to ML models. For more information, see the custom Python models documentation.

For information on how to store a custom model from a python script (models from code functionality), see the guide to models from code for the recommended approaches.

How To Load And Score Python Function Models

Loading Models

You can load python_function models in Python by using the mlflow.pyfunc.load_model() function. It is important to note that load_model assumes all dependencies are already available and will not perform any checks or installations of dependencies. For deployment options that handle dependencies, refer to the model deployment section.

Scoring Models

Once a model is loaded, it can be scored in two primary ways:

  1. Synchronous Scoring The standard method for scoring is using the predict method, which supports various input types and returns a scalar or collection based on the input data. The method signature is:

    predict(data: Union[pandas.Series, pandas.DataFrame, numpy.ndarray, csc_matrix, csr_matrix, List[Any], Dict[str, Any], str],
        params: Optional[Dict[str, Any]] = None) → Union[pandas.Series, pandas.DataFrame, numpy.ndarray, list, str]

  2. Synchronous Streaming Scoring

    Note

    predict_stream is a new interface that was added to MLflow in the 2.12.2 release. Previous versions of MLflow will not support this interface. In order to utilize predict_stream in a custom Python Function Model, you must implement the predict_stream method in your model class and return a generator type.

    For models that support streaming data processing, predict_stream method is available. This method returns a generator, which yields a stream of responses, allowing for efficient processing of large datasets or continuous data streams. Note that the predict_stream method is not available for all model types. The usage involves iterating over the generator to consume the responses:

    predict_stream(data: Any, params: Optional[Dict[str, Any]] = None) → GeneratorType
Demonstrating predict_stream()

Below is an example demonstrating how to define, save, load, and use a streamable model with the predict_stream() method:

import mlflow
import os


# Define a custom model that supports streaming
class StreamableModel(mlflow.pyfunc.PythonModel):
    def predict(self, context, model_input, params=None):
        # Regular predict method implementation (optional for this demo)
        return "regular-predict-output"

    def predict_stream(self, context, model_input, params=None):
        # Yielding elements one at a time
        for element in ["a", "b", "c", "d", "e"]:
            yield element


# Save the model to a directory
tmp_path = "/tmp/test_model"
pyfunc_model_path = os.path.join(tmp_path, "pyfunc_model")
python_model = StreamableModel()
mlflow.pyfunc.save_model(path=pyfunc_model_path, python_model=python_model)

# Load the model
loaded_pyfunc_model = mlflow.pyfunc.load_model(model_uri=pyfunc_model_path)

# Use predict_stream to get a generator
stream_output = loaded_pyfunc_model.predict_stream("single-input")

# Consuming the generator using next
print(next(stream_output))  # Output: 'a'
print(next(stream_output))  # Output: 'b'

# Alternatively, consuming the generator using a for-loop
for response in stream_output:
    print(response)  # This will print 'c', 'd', 'e'

Python Function Model Interfaces

All PyFunc models will support pandas.DataFrame as an input. In addition to pandas.DataFrame, DL PyFunc models will also support tensor inputs in the form of numpy.ndarrays. To verify whether a model flavor supports tensor inputs, please check the flavor’s documentation.

For models with a column-based schema, inputs are typically provided in the form of a pandas.DataFrame. If a dictionary mapping column name to values is provided as input for schemas with named columns or if a python List or a numpy.ndarray is provided as input for schemas with unnamed columns, MLflow will cast the input to a DataFrame. Schema enforcement and casting with respect to the expected data types is performed against the DataFrame.

For models with a tensor-based schema, inputs are typically provided in the form of a numpy.ndarray or a dictionary mapping the tensor name to its np.ndarray value. Schema enforcement will check the provided input’s shape and type against the shape and type specified in the model’s schema and throw an error if they do not match.

For models where no schema is defined, no changes to the model inputs and outputs are made. MLflow will propagate any errors raised by the model if the model does not accept the provided input type.

The python environment that a PyFunc model is loaded into for prediction or inference may differ from the environment in which it was trained. In the case of an environment mismatch, a warning message will be printed when calling mlflow.pyfunc.load_model(). This warning statement will identify the packages that have a version mismatch between those used during training and the current environment. In order to get the full dependencies of the environment in which the model was trained, you can call mlflow.pyfunc.get_model_dependencies(). Furthermore, if you want to run model inference in the same environment used in model training, you can call mlflow.pyfunc.spark_udf() with the env_manager argument set as “conda”. This will generate the environment from the conda.yaml file, ensuring that the python UDF will execute with the exact package versions that were used during training.

Some PyFunc models may accept model load configuration, which controls how the model is loaded and predictions computed. You can learn which configuration the model supports by inspecting the model’s flavor metadata:

model_info = mlflow.models.get_model_info(model_uri)
model_info.flavors[mlflow.pyfunc.FLAVOR_NAME][mlflow.pyfunc.MODEL_CONFIG]

Alternatively, you can load the PyFunc model and inspect the model_config property:

pyfunc_model = mlflow.pyfunc.load_model(model_uri)
pyfunc_model.model_config

Model configuration can be changed at loading time by indicating model_config parameter in the mlflow.pyfunc.load_model() method:

pyfunc_model = mlflow.pyfunc.load_model(model_uri, model_config=dict(temperature=0.93))

When a model configuration value is changed, those values the configuration the model was saved with. Indicating an invalid model configuration key for a model results in that configuration being ignored. A warning is displayed mentioning the ignored entries.

Note

Model configuration vs parameters with default values in signatures: Use model configuration when you need to provide model publishers for a way to change how the model is loaded into memory and how predictions are computed for all the samples. For instance, a key like user_gpu. Model consumers are not able to change those values at predict time. Use parameters with default values in the signature to provide a users the ability to change how predictions are computed on each data sample.

R Function (crate)

The crate model flavor defines a generic model format for representing an arbitrary R prediction function as an MLflow model using the crate function from the carrier package. The prediction function is expected to take a dataframe as input and produce a dataframe, a vector or a list with the predictions as output.

This flavor requires R to be installed in order to be used.

crate usage

For a minimal crate model, an example configuration for the predict function is:

library(mlflow)
library(carrier)
# Load iris dataset
data("iris")

# Learn simple linear regression model
model <- lm(Sepal.Width~Sepal.Length, data = iris)

# Define a crate model
# call package functions with an explicit :: namespace.
crate_model <- crate(
  function(new_obs)  stats::predict(model, data.frame("Sepal.Length" = new_obs)),
  model = model
)

# log the model
model_path <- mlflow_log_model(model = crate_model, artifact_path = "iris_prediction")

# load the logged model and make a prediction
model_uri <- paste0(mlflow_get_run()$artifact_uri, "/iris_prediction")
mlflow_model <- mlflow_load_model(model_uri = model_uri,
                                  flavor = NULL,
                                  client = mlflow_client())

prediction <- mlflow_predict(model = mlflow_model, data = 5)
print(prediction)

H2O (h2o)

The h2o model flavor enables logging and loading H2O models.

The mlflow.h2o module defines save_model() and log_model() methods in python, and mlflow_save_model and mlflow_log_model in R for saving H2O models in MLflow Model format. These methods produce MLflow Models with the python_function flavor, allowing you to load them as generic Python functions for inference via mlflow.pyfunc.load_model(). This loaded PyFunc model can be scored with only DataFrame input. When you load MLflow Models with the h2o flavor using mlflow.pyfunc.load_model(), the h2o.init() method is called. Therefore, the correct version of h2o(-py) must be installed in the loader’s environment. You can customize the arguments given to h2o.init() by modifying the init entry of the persisted H2O model’s YAML configuration file: model.h2o/h2o.yaml.

Finally, you can use the mlflow.h2o.load_model() method to load MLflow Models with the h2o flavor as H2O model objects.

For more information, see mlflow.h2o.

h2o pyfunc usage

For a minimal h2o model, here is an example of the pyfunc predict() method in a classification scenario :

import mlflow
import h2o

h2o.init()
from h2o.estimators.glm import H2OGeneralizedLinearEstimator

# import the prostate data
df = h2o.import_file(
    "http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip"
)

# convert the columns to factors
df["CAPSULE"] = df["CAPSULE"].asfactor()
df["RACE"] = df["RACE"].asfactor()
df["DCAPS"] = df["DCAPS"].asfactor()
df["DPROS"] = df["DPROS"].asfactor()

# split the data
train, test, valid = df.split_frame(ratios=[0.7, 0.15])

# generate a GLM model
glm_classifier = H2OGeneralizedLinearEstimator(
    family="binomial", lambda_=0, alpha=0.5, nfolds=5, compute_p_values=True
)

with mlflow.start_run():
    glm_classifier.train(
        y="CAPSULE", x=["AGE", "RACE", "VOL", "GLEASON"], training_frame=train
    )
    metrics = glm_classifier.model_performance()
    metrics_to_track = ["MSE", "RMSE", "r2", "logloss"]
    metrics_to_log = {
        key: value
        for key, value in metrics._metric_json.items()
        if key in metrics_to_track
    }
    params = glm_classifier.params
    mlflow.log_params(params)
    mlflow.log_metrics(metrics_to_log)
    model_info = mlflow.h2o.log_model(glm_classifier, artifact_path="h2o_model_info")

# load h2o model and make a prediction
h2o_pyfunc = mlflow.pyfunc.load_model(model_uri=model_info.model_uri)
test_df = test.as_data_frame()
predictions = h2o_pyfunc.predict(test_df)
print(predictions)

# it is also possible to load the model and predict using h2o methods on the h2o frame

# h2o_model = mlflow.h2o.load_model(model_info.model_uri)
# predictions = h2o_model.predict(test)

Keras (keras)

The keras model flavor enables logging and loading Keras models. It is available in both Python and R clients. In R, you can save or log the model using mlflow_save_model and mlflow_log_model. These functions serialize Keras models as HDF5 files using the Keras library’s built-in model persistence functions. You can use mlflow_load_model function in R to load MLflow Models with the keras flavor as Keras Model objects.

Keras pyfunc usage

For a minimal Sequential model, an example configuration for the pyfunc predict() method is:

import mlflow
import numpy as np
import pathlib
import shutil
from tensorflow import keras

mlflow.tensorflow.autolog()

X = np.array([-2, -1, 0, 1, 2, 1]).reshape(-1, 1)
y = np.array([0, 0, 1, 1, 1, 0])
model = keras.Sequential(
    [
        keras.Input(shape=(1,)),
        keras.layers.Dense(1, activation="sigmoid"),
    ]
)
model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"])
model.fit(X, y, batch_size=3, epochs=5, validation_split=0.2)

local_artifact_dir = "/tmp/mlflow/keras_model"
pathlib.Path(local_artifact_dir).mkdir(parents=True, exist_ok=True)

model_uri = f"runs:/{mlflow.last_active_run().info.run_id}/model"
keras_pyfunc = mlflow.pyfunc.load_model(
    model_uri=model_uri, dst_path=local_artifact_dir
)

data = np.array([-4, 1, 0, 10, -2, 1]).reshape(-1, 1)
predictions = keras_pyfunc.predict(data)

shutil.rmtree(local_artifact_dir)

MLeap (mleap)

Warning

The mleap model flavor is deprecated as of MLflow 2.6.0 and will be removed in a future release.

The mleap model flavor supports saving Spark models in MLflow format using the MLeap persistence mechanism. MLeap is an inference-optimized format and execution engine for Spark models that does not depend on SparkContext to evaluate inputs.

Note

You can save Spark models in MLflow format with the mleap flavor by specifying the sample_input argument of the mlflow.spark.save_model() or mlflow.spark.log_model() method (recommended). For more details see Spark MLlib.

The mlflow.mleap module also defines save_model() and log_model() methods for saving MLeap models in MLflow format, but these methods do not include the python_function flavor in the models they produce. Similarly, mleap models can be saved in R with mlflow_save_model and loaded with mlflow_load_model, with mlflow_save_model requiring sample_input to be specified as a sample Spark dataframe containing input data to the model is required by MLeap for data schema inference.

A companion module for loading MLflow Models with the MLeap flavor is available in the mlflow/java package.

For more information, see mlflow.spark, mlflow.mleap, and the MLeap documentation.

PyTorch (pytorch)

The pytorch model flavor enables logging and loading PyTorch models.

The mlflow.pytorch module defines utilities for saving and loading MLflow Models with the pytorch flavor. You can use the mlflow.pytorch.save_model() and mlflow.pytorch.log_model() methods to save PyTorch models in MLflow format; both of these functions use the torch.save() method to serialize PyTorch models. Additionally, you can use the mlflow.pytorch.load_model() method to load MLflow Models with the pytorch flavor as PyTorch model objects. This loaded PyFunc model can be scored with both DataFrame input and numpy array input. Finally, models produced by mlflow.pytorch.save_model() and mlflow.pytorch.log_model() contain the python_function flavor, allowing you to load them as generic Python functions for inference via mlflow.pyfunc.load_model().

Note

When using the PyTorch flavor, if a GPU is available at prediction time, the default GPU will be used to run inference. To disable this behavior, users can use the MLFLOW_DEFAULT_PREDICTION_DEVICE or pass in a device with the device parameter for the predict function.

Note

In case of multi gpu training, ensure to save the model only with global rank 0 gpu. This avoids logging multiple copies of the same model.

PyTorch pyfunc usage

For a minimal PyTorch model, an example configuration for the pyfunc predict() method is:

import numpy as np
import mlflow
from mlflow.models import infer_signature
import torch
from torch import nn


net = nn.Linear(6, 1)
loss_function = nn.L1Loss()
optimizer = torch.optim.Adam(net.parameters(), lr=1e-4)

X = torch.randn(6)
y = torch.randn(1)

epochs = 5
for epoch in range(epochs):
    optimizer.zero_grad()
    outputs = net(X)

    loss = loss_function(outputs, y)
    loss.backward()

    optimizer.step()

with mlflow.start_run() as run:
    signature = infer_signature(X.numpy(), net(X).detach().numpy())
    model_info = mlflow.pytorch.log_model(net, "model", signature=signature)

pytorch_pyfunc = mlflow.pyfunc.load_model(model_uri=model_info.model_uri)

predictions = pytorch_pyfunc.predict(torch.randn(6).numpy())
print(predictions)

For more information, see mlflow.pytorch.

Scikit-learn (sklearn)

The sklearn model flavor provides an easy-to-use interface for saving and loading scikit-learn models. The mlflow.sklearn module defines save_model() and log_model() functions that save scikit-learn models in MLflow format, using either Python’s pickle module (Pickle) or CloudPickle for model serialization. These functions produce MLflow Models with the python_function flavor, allowing them to be loaded as generic Python functions for inference via mlflow.pyfunc.load_model(). This loaded PyFunc model can only be scored with DataFrame input. Finally, you can use the mlflow.sklearn.load_model() method to load MLflow Models with the sklearn flavor as scikit-learn model objects.

Scikit-learn pyfunc usage

For a Scikit-learn LogisticRegression model, an example configuration for the pyfunc predict() method is:

import mlflow
from mlflow.models import infer_signature
import numpy as np
from sklearn.linear_model import LogisticRegression

with mlflow.start_run():
    X = np.array([-2, -1, 0, 1, 2, 1]).reshape(-1, 1)
    y = np.array([0, 0, 1, 1, 1, 0])
    lr = LogisticRegression()
    lr.fit(X, y)
    signature = infer_signature(X, lr.predict(X))

    model_info = mlflow.sklearn.log_model(
        sk_model=lr, artifact_path="model", signature=signature
    )

sklearn_pyfunc = mlflow.pyfunc.load_model(model_uri=model_info.model_uri)

data = np.array([-4, 1, 0, 10, -2, 1]).reshape(-1, 1)

predictions = sklearn_pyfunc.predict(data)

For more information, see mlflow.sklearn.

Spark MLlib (spark)

The spark model flavor enables exporting Spark MLlib models as MLflow Models.

The mlflow.spark module defines

MLflow Models produced by these functions contain the python_function flavor, allowing you to load them as generic Python functions via mlflow.pyfunc.load_model(). This loaded PyFunc model can only be scored with DataFrame input. When a model with the spark flavor is loaded as a Python function via mlflow.pyfunc.load_model(), a new SparkContext is created for model inference; additionally, the function converts all Pandas DataFrame inputs to Spark DataFrames before scoring. While this initialization overhead and format translation latency is not ideal for high-performance use cases, it enables you to easily deploy any MLlib PipelineModel to any production environment supported by MLflow (SageMaker, AzureML, etc).

Spark MLlib pyfunc usage 

from pyspark.ml.classification import LogisticRegression
from pyspark.ml.linalg import Vectors
from pyspark.sql import SparkSession
import mlflow

# Prepare training data from a list of (label, features) tuples.
spark = SparkSession.builder.appName("LogisticRegressionExample").getOrCreate()
training = spark.createDataFrame(
    [
        (1.0, Vectors.dense([0.0, 1.1, 0.1])),
        (0.0, Vectors.dense([2.0, 1.0, -1.0])),
        (0.0, Vectors.dense([2.0, 1.3, 1.0])),
        (1.0, Vectors.dense([0.0, 1.2, -0.5])),
    ],
    ["label", "features"],
)

# Create and fit a LogisticRegression instance
lr = LogisticRegression(maxIter=10, regParam=0.01)
lr_model = lr.fit(training)

# Serialize the Model
with mlflow.start_run():
    model_info = mlflow.spark.log_model(lr_model, "spark-model")

# Load saved model
lr_model_saved = mlflow.pyfunc.load_model(model_info.model_uri)

# Make predictions on test data.
# The DataFrame used in the predict method must be a Pandas DataFrame
test = spark.createDataFrame(
    [
        (1.0, Vectors.dense([-1.0, 1.5, 1.3])),
        (0.0, Vectors.dense([3.0, 2.0, -0.1])),
        (1.0, Vectors.dense([0.0, 2.2, -1.5])),
    ],
    ["label", "features"],
).toPandas()

prediction = lr_model_saved.predict(test)

Note

Note that when the sample_input parameter is provided to log_model() or save_model(), the Spark model is automatically saved as an mleap flavor by invoking mlflow.mleap.add_to_model().

For example, the follow code block:

training_df = spark.createDataFrame([
    (0, "a b c d e spark", 1.0),
    (1, "b d", 0.0),
    (2, "spark f g h", 1.0),
    (3, "hadoop mapreduce", 0.0) ], ["id", "text", "label"])

tokenizer = Tokenizer(inputCol="text", outputCol="words")
hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features")
lr = LogisticRegression(maxIter=10, regParam=0.001)
pipeline = Pipeline(stages=[tokenizer, hashingTF, lr])
model = pipeline.fit(training_df)

mlflow.spark.log_model(model, "spark-model", sample_input=training_df)

results in the following directory structure logged to the MLflow Experiment:

# Directory written by with the addition of mlflow.mleap.add_to_model(model, "spark-model", training_df)
# Note the addition of the mleap directory
spark-model/
├── mleap
├── sparkml
├── MLmodel
├── conda.yaml
├── python_env.yaml
└── requirements.txt

For more information, see mlflow.mleap.

For more information, see mlflow.spark.

TensorFlow (tensorflow)

The simple example below shows how to log params and metrics in mlflow for a custom training loop using low-level TensorFlow API. See tf-keras-example. for an example of mlflow and tf.keras models.

import numpy as np
import tensorflow as tf

import mlflow

x = np.linspace(-4, 4, num=512)
y = 3 * x + 10

# estimate w and b where y = w * x + b
learning_rate = 0.1
x_train = tf.Variable(x, trainable=False, dtype=tf.float32)
y_train = tf.Variable(y, trainable=False, dtype=tf.float32)

# initial values
w = tf.Variable(1.0)
b = tf.Variable(1.0)

with mlflow.start_run():
    mlflow.log_param("learning_rate", learning_rate)

    for i in range(1000):
        with tf.GradientTape(persistent=True) as tape:
            # calculate MSE = 0.5 * (y_predict - y_train)^2
            y_predict = w * x_train + b
            loss = 0.5 * tf.reduce_mean(tf.square(y_predict - y_train))
            mlflow.log_metric("loss", value=loss.numpy(), step=i)

        # Update the trainable variables
        # w = w - learning_rate * gradient of loss function w.r.t. w
        # b = b - learning_rate * gradient of loss function w.r.t. b
        w.assign_sub(learning_rate * tape.gradient(loss, w))
        b.assign_sub(learning_rate * tape.gradient(loss, b))

print(f"W = {w.numpy():.2f}, b = {b.numpy():.2f}")

ONNX (onnx)

The onnx model flavor enables logging of ONNX models in MLflow format via the mlflow.onnx.save_model() and mlflow.onnx.log_model() methods. These methods also add the python_function flavor to the MLflow Models that they produce, allowing the models to be interpreted as generic Python functions for inference via mlflow.pyfunc.load_model(). This loaded PyFunc model can be scored with both DataFrame input and numpy array input. The python_function representation of an MLflow ONNX model uses the ONNX Runtime execution engine for evaluation. Finally, you can use the mlflow.onnx.load_model() method to load MLflow Models with the onnx flavor in native ONNX format.

For more information, see mlflow.onnx and http://onnx.ai/.

Warning

The default behavior for saving ONNX files is to use the ONNX save option save_as_external_data=True in order to support model files that are in excess of 2GB. For edge deployments of small model files, this may create issues. If you need to save a small model as a single file for such deployment considerations, you can set the parameter save_as_external_data=False in either mlflow.onnx.save_model() or mlflow.onnx.log_model() to force the serialization of the model as a small file. Note that if the model is in excess of 2GB, saving as a single file will not work.

ONNX pyfunc usage example

For an ONNX model, an example configuration that uses pytorch to train a dummy model, converts it to ONNX, logs to mlflow and makes a prediction using pyfunc predict() method is:

import numpy as np
import mlflow
from mlflow.models import infer_signature
import onnx
import torch
from torch import nn

# define a torch model
net = nn.Linear(6, 1)
loss_function = nn.L1Loss()
optimizer = torch.optim.Adam(net.parameters(), lr=1e-4)

X = torch.randn(6)
y = torch.randn(1)

# run model training
epochs = 5
for epoch in range(epochs):
    optimizer.zero_grad()
    outputs = net(X)

    loss = loss_function(outputs, y)
    loss.backward()

    optimizer.step()

# convert model to ONNX and load it
torch.onnx.export(net, X, "model.onnx")
onnx_model = onnx.load_model("model.onnx")

# log the model into a mlflow run
with mlflow.start_run():
    signature = infer_signature(X.numpy(), net(X).detach().numpy())
    model_info = mlflow.onnx.log_model(onnx_model, "model", signature=signature)

# load the logged model and make a prediction
onnx_pyfunc = mlflow.pyfunc.load_model(model_info.model_uri)

predictions = onnx_pyfunc.predict(X.numpy())
print(predictions)

MXNet Gluon (gluon)

Warning

The gluon model flavor is deprecated and will be removed in a future release.

The gluon model flavor enables logging of Gluon models in MLflow format via the mlflow.gluon.save_model() and mlflow.gluon.log_model() methods. These methods also add the python_function flavor to the MLflow Models that they produce, allowing the models to be interpreted as generic Python functions for inference via mlflow.pyfunc.load_model(). This loaded PyFunc model can be scored with both DataFrame input and numpy array input. You can also use the mlflow.gluon.load_model() method to load MLflow Models with the gluon flavor in native Gluon format.

Gluon pyfunc usage

For a minimal gluon model, here is an example of the pyfunc predict() method with a logistic regression model :

import mlflow
import mxnet as mx
from mxnet import nd, autograd, gluon
from mxnet.gluon import nn, Trainer
from mxnet.gluon.data import DataLoader, ArrayDataset
import numpy as np

# this example requires a compatible version of numpy : numpy == 1.23.1
# `pip uninstall numpy`  `python -m pip install numpy==1.23.1`


def get_random_data(size, ctx):
    x = nd.normal(0, 1, shape=(size, 10), ctx=ctx)
    y = x.sum(axis=1) > 3
    return x, y


# use cpu for this example, gpu could be used with ctx=gpu()
ctx = mx.cpu()
train_data_size = 1000
val_data_size = 100
batch_size = 10

train_x, train_ground_truth_class = get_random_data(train_data_size, ctx)
train_dataset = ArrayDataset(train_x, train_ground_truth_class)
train_dataloader = DataLoader(
    train_dataset,
    batch_size=batch_size,
    shuffle=True,
)

val_x, val_ground_truth_class = get_random_data(val_data_size, ctx)
val_dataset = ArrayDataset(val_x, val_ground_truth_class)
val_dataloader = DataLoader(val_dataset, batch_size=batch_size, shuffle=True)

net = nn.HybridSequential()

with net.name_scope():
    net.add(nn.Dense(units=10, activation="relu"))  # input layer
    net.add(nn.Dense(units=10, activation="relu"))  # inner layer 1
    net.add(nn.Dense(units=10, activation="relu"))  # inner layer 2
    net.add(nn.Dense(units=1))  # output layer: must have only 1 neuron

net.initialize(mx.init.Xavier())

loss = gluon.loss.SigmoidBinaryCrossEntropyLoss()
trainer = Trainer(
    params=net.collect_params(),
    optimizer="sgd",
    optimizer_params={"learning_rate": 0.1},
)

accuracy = mx.metric.Accuracy()
f1 = mx.metric.F1()
threshold = 0.5


def train_model():
    cumulative_train_loss = 0

    for i, (data, label) in enumerate(train_dataloader):
        with autograd.record():
            # do forward pass on a batch of training data
            output = net(data)
            # calculate loss for the training data batch
            loss_result = loss(output, label)
        # calculate gradients
        loss_result.backward()
        # update parameters of the network
        trainer.step(batch_size)
        # sum losses of every batch
        cumulative_train_loss += nd.sum(loss_result).asscalar()

    return cumulative_train_loss


def validate_model(threshold):
    cumulative_val_loss = 0

    for i, (val_data, val_ground_truth_class) in enumerate(val_dataloader):
        # do forward pass on a batch of validation data
        output = net(val_data)
        # calculate cumulative validation loss
        cumulative_val_loss += nd.sum(loss(output, val_ground_truth_class)).asscalar()
        # prediction as a sigmoid
        prediction = net(val_data).sigmoid()
        # converting neuron outputs to classes
        predicted_classes = mx.nd.ceil(prediction - threshold)
        # update validation accuracy
        accuracy.update(val_ground_truth_class, predicted_classes.reshape(-1))
        # calculate probabilities of belonging to different classes
        prediction = prediction.reshape(-1)
        probabilities = mx.nd.stack(1 - prediction, prediction, axis=1)

        f1.update(val_ground_truth_class, probabilities)

    return cumulative_val_loss


# train model and get metrics
cumulative_train_loss = train_model()
cumulative_val_loss = validate_model(threshold)
net.collect_params().initialize()
metrics_to_log = {
    "training_loss": cumulative_train_loss,
    "val_loss": cumulative_val_loss,
    "f1": f1.get()[1],
    "accuracy": accuracy.get()[1],
}
params_to_log = {"learning_rate": trainer.learning_rate, "threshold": threshold}

# the model needs to be hybridized and run forward at least once before export is called
net.hybridize()
net.forward(train_x)

with mlflow.start_run():
    mlflow.log_params(params_to_log)
    mlflow.log_metrics(metrics_to_log)
    model_info = mlflow.gluon.log_model(net, "model")

# load the model
pytorch_pyfunc = mlflow.pyfunc.load_model(model_uri=model_info.model_uri)

# make a prediction
X = np.random.randn(10, 10)
predictions = pytorch_pyfunc.predict(X)
print(predictions)

For more information, see mlflow.gluon.

XGBoost (xgboost)

The xgboost model flavor enables logging of XGBoost models in MLflow format via the mlflow.xgboost.save_model() and mlflow.xgboost.log_model() methods in python and mlflow_save_model and mlflow_log_model in R respectively. These methods also add the python_function flavor to the MLflow Models that they produce, allowing the models to be interpreted as generic Python functions for inference via mlflow.pyfunc.load_model(). This loaded PyFunc model can only be scored with DataFrame input. You can also use the mlflow.xgboost.load_model() method to load MLflow Models with the xgboost model flavor in native XGBoost format.

Note that the xgboost model flavor only supports an instance of xgboost.Booster, not models that implement the scikit-learn API.

XGBoost pyfunc usage

The example below

  • Loads the IRIS dataset from scikit-learn

  • Trains an XGBoost Classifier

  • Logs the model and params using mlflow

  • Loads the logged model and makes predictions

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
import mlflow
from mlflow.models import infer_signature

data = load_iris()
X_train, X_test, y_train, y_test = train_test_split(
    data["data"], data["target"], test_size=0.2
)

xgb_classifier = XGBClassifier(
    n_estimators=10,
    max_depth=3,
    learning_rate=1,
    objective="binary:logistic",
    random_state=123,
)

# log fitted model and XGBClassifier parameters
with mlflow.start_run():
    xgb_classifier.fit(X_train, y_train)
    clf_params = xgb_classifier.get_xgb_params()
    mlflow.log_params(clf_params)
    signature = infer_signature(X_train, xgb_classifier.predict(X_train))
    model_info = mlflow.xgboost.log_model(
        xgb_classifier, "iris-classifier", signature=signature
    )

# Load saved model and make predictions
xgb_classifier_saved = mlflow.pyfunc.load_model(model_info.model_uri)
y_pred = xgb_classifier_saved.predict(X_test)

For more information, see mlflow.xgboost.

LightGBM (lightgbm)

The lightgbm model flavor enables logging of LightGBM models in MLflow format via the mlflow.lightgbm.save_model() and mlflow.lightgbm.log_model() methods. These methods also add the python_function flavor to the MLflow Models that they produce, allowing the models to be interpreted as generic Python functions for inference via mlflow.pyfunc.load_model(). You can also use the mlflow.lightgbm.load_model() method to load MLflow Models with the lightgbm model flavor in native LightGBM format.

Note that the scikit-learn API for LightGBM is now supported. For more information, see mlflow.lightgbm.

LightGBM pyfunc usage

The example below

  • Loads the IRIS dataset from scikit-learn

  • Trains a LightGBM LGBMClassifier

  • Logs the model and feature importance’s using mlflow

  • Loads the logged model and makes predictions

from lightgbm import LGBMClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
import mlflow
from mlflow.models import infer_signature

data = load_iris()

# Remove special characters from feature names to be able to use them as keys for mlflow metrics
feature_names = [
    name.replace(" ", "_").replace("(", "").replace(")", "")
    for name in data["feature_names"]
]
X_train, X_test, y_train, y_test = train_test_split(
    data["data"], data["target"], test_size=0.2
)
# create model instance
lgb_classifier = LGBMClassifier(
    n_estimators=10,
    max_depth=3,
    learning_rate=1,
    objective="binary:logistic",
    random_state=123,
)

# Fit and save model and LGBMClassifier feature importances as mlflow metrics
with mlflow.start_run():
    lgb_classifier.fit(X_train, y_train)
    feature_importances = dict(zip(feature_names, lgb_classifier.feature_importances_))
    feature_importance_metrics = {
        f"feature_importance_{feature_name}": imp_value
        for feature_name, imp_value in feature_importances.items()
    }
    mlflow.log_metrics(feature_importance_metrics)
    signature = infer_signature(X_train, lgb_classifier.predict(X_train))
    model_info = mlflow.lightgbm.log_model(
        lgb_classifier, "iris-classifier", signature=signature
    )

# Load saved model and make predictions
lgb_classifier_saved = mlflow.pyfunc.load_model(model_info.model_uri)
y_pred = lgb_classifier_saved.predict(X_test)
print(y_pred)

CatBoost (catboost)

The catboost model flavor enables logging of CatBoost models in MLflow format via the mlflow.catboost.save_model() and mlflow.catboost.log_model() methods. These methods also add the python_function flavor to the MLflow Models that they produce, allowing the models to be interpreted as generic Python functions for inference via mlflow.pyfunc.load_model(). You can also use the mlflow.catboost.load_model() method to load MLflow Models with the catboost model flavor in native CatBoost format.

For more information, see mlflow.catboost.

CatBoost pyfunc usage

For a CatBoost Classifier model, an example configuration for the pyfunc predict() method is:

import mlflow
from mlflow.models import infer_signature
from catboost import CatBoostClassifier
from sklearn import datasets

# prepare data
X, y = datasets.load_wine(as_frame=False, return_X_y=True)

# train the model
model = CatBoostClassifier(
    iterations=5,
    loss_function="MultiClass",
    allow_writing_files=False,
)
model.fit(X, y)

# create model signature
predictions = model.predict(X)
signature = infer_signature(X, predictions)

# log the model into a mlflow run
with mlflow.start_run():
    model_info = mlflow.catboost.log_model(model, "model", signature=signature)

# load the logged model and make a prediction
catboost_pyfunc = mlflow.pyfunc.load_model(model_uri=model_info.model_uri)
print(catboost_pyfunc.predict(X[:5]))

Spacy(spaCy)

The spaCy model flavor enables logging of spaCy models in MLflow format via the mlflow.spacy.save_model() and mlflow.spacy.log_model() methods. Additionally, these methods add the python_function flavor to the MLflow Models that they produce, allowing the models to be interpreted as generic Python functions for inference via mlflow.pyfunc.load_model(). This loaded PyFunc model can only be scored with DataFrame input. You can also use the mlflow.spacy.load_model() method to load MLflow Models with the spacy model flavor in native spaCy format.

For more information, see mlflow.spacy.

Spacy pyfunc usage

The example below shows how to train a Spacy TextCategorizer model, log the model artifact and metrics to the mlflow tracking server and then load the saved model to make predictions. For this example, we will be using the Polarity 2.0 dataset available in the nltk package. This dataset consists of 10000 positive and 10000 negative short movie reviews.

First we convert the texts and sentiment labels (“pos” or “neg”) from NLTK native format to Spacy’s DocBin format:

import pandas as pd
import spacy
from nltk.corpus import movie_reviews
from spacy import Language
from spacy.tokens import DocBin

nltk.download("movie_reviews")


def get_sentences(sentiment_type: str) -> pd.DataFrame:
    """Reconstruct the sentences from the word lists for each review record for a specific ``sentiment_type``
    as a pandas DataFrame with two columns: 'sentence' and 'sentiment'.
    """
    file_ids = movie_reviews.fileids(sentiment_type)
    sent_df = []
    for file_id in file_ids:
        sentence = " ".join(movie_reviews.words(file_id))
        sent_df.append({"sentence": sentence, "sentiment": sentiment_type})
    return pd.DataFrame(sent_df)


def convert(data_df: pd.DataFrame, target_file: str):
    """Convert a DataFrame with 'sentence' and 'sentiment' columns to a
    spacy DocBin object and save it to 'target_file'.
    """
    nlp = spacy.blank("en")
    sentiment_labels = data_df.sentiment.unique()
    spacy_doc = DocBin()

    for _, row in data_df.iterrows():
        sent_tokens = nlp.make_doc(row["sentence"])
        # To train a Spacy TextCategorizer model, the label must be attached to the "cats" dictionary of the "Doc"
        # object, e.g. {"pos": 1.0, "neg": 0.0} for a "pos" label.
        for label in sentiment_labels:
            sent_tokens.cats[label] = 1.0 if label == row["sentiment"] else 0.0
        spacy_doc.add(sent_tokens)

    spacy_doc.to_disk(target_file)


# Build a single DataFrame with both positive and negative reviews, one row per review
review_data = [get_sentences(sentiment_type) for sentiment_type in ("pos", "neg")]
review_data = pd.concat(review_data, axis=0)

# Split the DataFrame into a train and a dev set
train_df = review_data.groupby("sentiment", group_keys=False).apply(
    lambda x: x.sample(frac=0.7, random_state=100)
)
dev_df = review_data.loc[review_data.index.difference(train_df.index), :]

# Save the train and dev data files to the current directory as "corpora.train" and "corpora.dev", respectively
convert(train_df, "corpora.train")
convert(dev_df, "corpora.dev")

To set up the training job, we first need to generate a configuration file as described in the Spacy Documentation For simplicity, we will only use a TextCategorizer in the pipeline.

python -m spacy init config --pipeline textcat --lang en mlflow-textcat.cfg

Change the default train and dev paths in the config file to the current directory:

  [paths]
- train = null
- dev = null
+ train = "."
+ dev = "."

In Spacy, the training loop is defined internally in Spacy’s code. Spacy provides a “logging” extension point where we can use mlflow. To do this,

  • We have to define a function to write metrics / model input to mlfow

  • Register it as a logger in Spacy’s component registry

  • Change the default console logger in the Spacy’s configuration file (mlflow-textcat.cfg)

from typing import IO, Callable, Tuple, Dict, Any, Optional
import spacy
from spacy import Language
import mlflow


@spacy.registry.loggers("mlflow_logger.v1")
def mlflow_logger():
    """Returns a function, ``setup_logger`` that returns two functions:

    * ``log_step`` is called internally by Spacy for every evaluation step. We can log the intermediate train and
    validation scores to the mlflow tracking server here.
    * ``finalize``: is called internally by Spacy after training is complete. We can log the model artifact to the
    mlflow tracking server here.
    """

    def setup_logger(
        nlp: Language,
        stdout: IO = sys.stdout,
        stderr: IO = sys.stderr,
    ) -> Tuple[Callable, Callable]:
        def log_step(info: Optional[Dict[str, Any]]):
            if info:
                step = info["step"]
                score = info["score"]
                metrics = {}

                for pipe_name in nlp.pipe_names:
                    loss = info["losses"][pipe_name]
                    metrics[f"{pipe_name}_loss"] = loss
                    metrics[f"{pipe_name}_score"] = score
                mlflow.log_metrics(metrics, step=step)

        def finalize():
            uri = mlflow.spacy.log_model(nlp, "mlflow_textcat_example")
            mlflow.end_run()

        return log_step, finalize

    return setup_logger

Check the spacy-loggers library <https://pypi.org/project/spacy-loggers/> _ for a more complete implementation.

Point to our mlflow logger in Spacy configuration file. For this example, we will lower the number of training steps and eval frequency:

  [training.logger]
- @loggers = "spacy.ConsoleLogger.v1"
- dev = null
+ @loggers = "mlflow_logger.v1"

  [training]
- max_steps = 20000
- eval_frequency = 100
+ max_steps = 100
+ eval_frequency = 10

Train our model:

from spacy.cli.train import train as spacy_train

spacy_train("mlflow-textcat.cfg")

To make predictions, we load the saved model from the last run:

from mlflow import MlflowClient

# look up the last run info from mlflow
client = MlflowClient()
last_run = client.search_runs(experiment_ids=["0"], max_results=1)[0]

# We need to append the spacy model directory name to the artifact uri
spacy_model = mlflow.pyfunc.load_model(
    f"{last_run.info.artifact_uri}/mlflow_textcat_example"
)
predictions_in = dev_df.loc[:, ["sentence"]]
predictions_out = spacy_model.predict(predictions_in).squeeze().tolist()
predicted_labels = [
    "pos" if row["pos"] > row["neg"] else "neg" for row in predictions_out
]
print(dev_df.assign(predicted_sentiment=predicted_labels))

Fastai(fastai)

The fastai model flavor enables logging of fastai Learner models in MLflow format via the mlflow.fastai.save_model() and mlflow.fastai.log_model() methods. Additionally, these methods add the python_function flavor to the MLflow Models that they produce, allowing the models to be interpreted as generic Python functions for inference via mlflow.pyfunc.load_model(). This loaded PyFunc model can only be scored with DataFrame input. You can also use the mlflow.fastai.load_model() method to load MLflow Models with the fastai model flavor in native fastai format.

The interface for utilizing a fastai model loaded as a pyfunc type for generating predictions uses a Pandas DataFrame argument.

This example runs the fastai tabular tutorial, logs the experiments, saves the model in fastai format and loads the model to get predictions using a fastai data loader:

from fastai.data.external import URLs, untar_data
from fastai.tabular.core import Categorify, FillMissing, Normalize, TabularPandas
from fastai.tabular.data import TabularDataLoaders
from fastai.tabular.learner import tabular_learner
from fastai.data.transforms import RandomSplitter
from fastai.metrics import accuracy
from fastcore.basics import range_of
import pandas as pd
import mlflow
import mlflow.fastai


def print_auto_logged_info(r):
    tags = {k: v for k, v in r.data.tags.items() if not k.startswith("mlflow.")}
    artifacts = [
        f.path for f in mlflow.MlflowClient().list_artifacts(r.info.run_id, "model")
    ]
    print(f"run_id: {r.info.run_id}")
    print(f"artifacts: {artifacts}")
    print(f"params: {r.data.params}")
    print(f"metrics: {r.data.metrics}")
    print(f"tags: {tags}")


def main(epochs=5, learning_rate=0.01):
    path = untar_data(URLs.ADULT_SAMPLE)
    path.ls()

    df = pd.read_csv(path / "adult.csv")

    dls = TabularDataLoaders.from_csv(
        path / "adult.csv",
        path=path,
        y_names="salary",
        cat_names=[
            "workclass",
            "education",
            "marital-status",
            "occupation",
            "relationship",
            "race",
        ],
        cont_names=["age", "fnlwgt", "education-num"],
        procs=[Categorify, FillMissing, Normalize],
    )

    splits = RandomSplitter(valid_pct=0.2)(range_of(df))

    to = TabularPandas(
        df,
        procs=[Categorify, FillMissing, Normalize],
        cat_names=[
            "workclass",
            "education",
            "marital-status",
            "occupation",
            "relationship",
            "race",
        ],
        cont_names=["age", "fnlwgt", "education-num"],
        y_names="salary",
        splits=splits,
    )

    dls = to.dataloaders(bs=64)

    model = tabular_learner(dls, metrics=accuracy)

    mlflow.fastai.autolog()

    with mlflow.start_run() as run:
        model.fit(5, 0.01)
        mlflow.fastai.log_model(model, "model")

    print_auto_logged_info(mlflow.get_run(run_id=run.info.run_id))

    model_uri = f"runs:/{run.info.run_id}/model"
    loaded_model = mlflow.fastai.load_model(model_uri)

    test_df = df.copy()
    test_df.drop(["salary"], axis=1, inplace=True)
    dl = learn.dls.test_dl(test_df)

    predictions, _ = loaded_model.get_preds(dl=dl)
    px = pd.DataFrame(predictions).astype("float")
    px.head(5)


main()

Output (Pandas DataFrame):

Index

Probability of first class

Probability of second class

0

0.545088

0.454912

1

0.503172

0.496828

2

0.962663

0.037337

3

0.206107

0.793893

4

0.807599

0.192401

Alternatively, when using the python_function flavor, get predictions from a DataFrame.

from fastai.data.external import URLs, untar_data
from fastai.tabular.core import Categorify, FillMissing, Normalize, TabularPandas
from fastai.tabular.data import TabularDataLoaders
from fastai.tabular.learner import tabular_learner
from fastai.data.transforms import RandomSplitter
from fastai.metrics import accuracy
from fastcore.basics import range_of
import pandas as pd
import mlflow
import mlflow.fastai

model_uri = ...

path = untar_data(URLs.ADULT_SAMPLE)
df = pd.read_csv(path / "adult.csv")
test_df = df.copy()
test_df.drop(["salary"], axis=1, inplace=True)

loaded_model = mlflow.pyfunc.load_model(model_uri)
loaded_model.predict(test_df)

Output (Pandas DataFrame):

Index

Probability of first class, Probability of second class

0

[0.5450878, 0.45491222]

1

[0.50317234, 0.49682766]

2

[0.9626626, 0.037337445]

3

[0.20610662, 0.7938934]

4

[0.8075987, 0.19240129]

For more information, see mlflow.fastai.

Statsmodels (statsmodels)

The statsmodels model flavor enables logging of Statsmodels models in MLflow format via the mlflow.statsmodels.save_model() and mlflow.statsmodels.log_model() methods. These methods also add the python_function flavor to the MLflow Models that they produce, allowing the models to be interpreted as generic Python functions for inference via mlflow.pyfunc.load_model(). This loaded PyFunc model can only be scored with DataFrame input. You can also use the mlflow.statsmodels.load_model() method to load MLflow Models with the statsmodels model flavor in native statsmodels format.

As for now, automatic logging is restricted to parameters, metrics and models generated by a call to fit on a statsmodels model.

Statsmodels pyfunc usage

The following 2 examples illustrate usage of a basic regression model (OLS) and an ARIMA time series model from the following statsmodels apis : statsmodels.formula.api and statsmodels.tsa.api

For a minimal statsmodels regression model, here is an example of the pyfunc predict() method :

import mlflow
import pandas as pd
from sklearn.datasets import load_diabetes
import statsmodels.formula.api as smf

# load the diabetes dataset from sklearn
diabetes = load_diabetes()

# create X and y dataframes for the features and target
X = pd.DataFrame(data=diabetes.data, columns=diabetes.feature_names)
y = pd.DataFrame(data=diabetes.target, columns=["target"])

# concatenate X and y dataframes
df = pd.concat([X, y], axis=1)

# create the linear regression model (ordinary least squares)
model = smf.ols(
    formula="target ~ age + sex + bmi + bp + s1 + s2 + s3 + s4 + s5 + s6", data=df
)

mlflow.statsmodels.autolog(
    log_models=True,
    disable=False,
    exclusive=False,
    disable_for_unsupported_versions=False,
    silent=False,
    registered_model_name=None,
)

with mlflow.start_run():
    res = model.fit(method="pinv", use_t=True)
    model_info = mlflow.statsmodels.log_model(res, artifact_path="OLS_model")

# load the pyfunc model
statsmodels_pyfunc = mlflow.pyfunc.load_model(model_uri=model_info.model_uri)

# generate predictions
predictions = statsmodels_pyfunc.predict(X)
print(predictions)

For a minimal time series ARIMA model, here is an example of the pyfunc predict() method :

import mlflow
import numpy as np
import pandas as pd
from statsmodels.tsa.arima.model import ARIMA

# create a time series dataset with seasonality
np.random.seed(0)

# generate a time index with a daily frequency
dates = pd.date_range(start="2022-12-01", end="2023-12-01", freq="D")

# generate the seasonal component (weekly)
seasonality = np.sin(np.arange(len(dates)) * (2 * np.pi / 365.25) * 7)

# generate the trend component
trend = np.linspace(-5, 5, len(dates)) + 2 * np.sin(
    np.arange(len(dates)) * (2 * np.pi / 365.25) * 0.1
)

# generate the residual component
residuals = np.random.normal(0, 1, len(dates))

# generate the final time series by adding the components
time_series = seasonality + trend + residuals

# create a dataframe from the time series
data = pd.DataFrame({"date": dates, "value": time_series})
data.set_index("date", inplace=True)

order = (1, 0, 0)
# create the ARIMA model
model = ARIMA(data, order=order)

mlflow.statsmodels.autolog(
    log_models=True,
    disable=False,
    exclusive=False,
    disable_for_unsupported_versions=False,
    silent=False,
    registered_model_name=None,
)

with mlflow.start_run():
    res = model.fit()
    mlflow.log_params(
        {
            "order": order,
            "trend": model.trend,
            "seasonal_order": model.seasonal_order,
        }
    )
    mlflow.log_params(res.params)
    mlflow.log_metric("aic", res.aic)
    mlflow.log_metric("bic", res.bic)
    model_info = mlflow.statsmodels.log_model(res, artifact_path="ARIMA_model")

# load the pyfunc model
statsmodels_pyfunc = mlflow.pyfunc.load_model(model_uri=model_info.model_uri)

# prediction dataframes for a TimeSeriesModel must have exactly one row and include columns called start and end
start = pd.to_datetime("2024-01-01")
end = pd.to_datetime("2024-01-07")

# generate predictions
prediction_data = pd.DataFrame({"start": start, "end": end}, index=[0])
predictions = statsmodels_pyfunc.predict(prediction_data)
print(predictions)

For more information, see mlflow.statsmodels.

Prophet (prophet)

The prophet model flavor enables logging of Prophet models in MLflow format via the mlflow.prophet.save_model() and mlflow.prophet.log_model() methods. These methods also add the python_function flavor to the MLflow Models that they produce, allowing the models to be interpreted as generic Python functions for inference via mlflow.pyfunc.load_model(). This loaded PyFunc model can only be scored with DataFrame input. You can also use the mlflow.prophet.load_model() method to load MLflow Models with the prophet model flavor in native prophet format.

Prophet pyfunc usage

This example uses a time series dataset from Prophet’s GitHub repository, containing log number of daily views to Peyton Manning’s Wikipedia page for several years. A sample of the dataset is as follows:

ds

y

2007-12-10

9.59076113897809

2007-12-11

8.51959031601596

2007-12-12

8.18367658262066

2007-12-13

8.07246736935477

 
import numpy as np
import pandas as pd
from prophet import Prophet, serialize
from prophet.diagnostics import cross_validation, performance_metrics

import mlflow
from mlflow.models import infer_signature

# URL to the dataset
SOURCE_DATA = "https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_peyton_manning.csv"

np.random.seed(12345)


def extract_params(pr_model):
    params = {attr: getattr(pr_model, attr) for attr in serialize.SIMPLE_ATTRIBUTES}
    return {k: v for k, v in params.items() if isinstance(v, (int, float, str, bool))}


# Load the training data
train_df = pd.read_csv(SOURCE_DATA)

# Create a "test" DataFrame with the "ds" column containing 10 days after the end date in train_df
test_dates = pd.date_range(start="2016-01-21", end="2016-01-31", freq="D")
test_df = pd.DataFrame({"ds": test_dates})

# Initialize Prophet model with specific parameters
prophet_model = Prophet(changepoint_prior_scale=0.5, uncertainty_samples=7)

with mlflow.start_run():
    # Fit the model on the training data
    prophet_model.fit(train_df)

    # Extract and log model parameters
    params = extract_params(prophet_model)
    mlflow.log_params(params)

    # Perform cross-validation
    cv_results = cross_validation(
        prophet_model,
        initial="900 days",
        period="30 days",
        horizon="30 days",
        parallel="threads",
        disable_tqdm=True,
    )

    # Calculate and log performance metrics
    cv_metrics = performance_metrics(cv_results, metrics=["mse", "rmse", "mape"])
    average_metrics = cv_metrics.drop(columns=["horizon"]).mean(axis=0).to_dict()
    mlflow.log_metrics(average_metrics)

    # Generate predictions and infer model signature
    train = prophet_model.history

    # Log the Prophet model with MLflow
    model_info = mlflow.prophet.log_model(
        prophet_model,
        artifact_path="prophet_model",
        input_example=train[["ds"]].head(10),
    )

# Load the saved model as a pyfunc
prophet_model_saved = mlflow.pyfunc.load_model(model_info.model_uri)

# Generate predictions for the test set
predictions = prophet_model_saved.predict(test_df)

# Truncate and display the forecast if needed
forecast = predictions[["ds", "yhat"]]

print(f"forecast:\n{forecast.head(5)}")

Output (Pandas DataFrame):

Index

ds

yhat

yhat_upper

yhat_lower

0

2016-01-21

8.526513

8.827397

8.328563

1

2016-01-22

8.541355

9.434994

8.112758

2

2016-01-23

8.308332

8.633746

8.201323

3

2016-01-24

8.676326

9.534593

8.020874

4

2016-01-25

8.983457

9.430136

8.121798

For more information, see mlflow.prophet.

Pmdarima (pmdarima)

The pmdarima model flavor enables logging of pmdarima models in MLflow format via the mlflow.pmdarima.save_model() and mlflow.pmdarima.log_model() methods. These methods also add the python_function flavor to the MLflow Models that they produce, allowing the model to be interpreted as generic Python functions for inference via mlflow.pyfunc.load_model(). This loaded PyFunc model can only be scored with a DataFrame input. You can also use the mlflow.pmdarima.load_model() method to load MLflow Models with the pmdarima model flavor in native pmdarima formats.

The interface for utilizing a pmdarima model loaded as a pyfunc type for generating forecast predictions uses a single-row Pandas DataFrame configuration argument. The following columns in this configuration Pandas DataFrame are supported:

  • n_periods (required) - specifies the number of future periods to generate starting from the last datetime value

    of the training dataset, utilizing the frequency of the input training series when the model was trained. (for example, if the training data series elements represent one value per hour, in order to forecast 3 days of future data, set the column n_periods to 72.

  • X (optional) - exogenous regressor values (only supported in pmdarima version >= 1.8.0) a 2D array of values for

    future time period events. For more information, read the underlying library explanation.

  • return_conf_int (optional) - a boolean (Default: False) for whether to return confidence interval values.

    See above note.

  • alpha (optional) - the significance value for calculating confidence intervals. (Default: 0.05)

An example configuration for the pyfunc predict of a pmdarima model is shown below, with a future period prediction count of 100, a confidence interval calculation generation, no exogenous regressor elements, and a default alpha of 0.05:

Index

n_periods

return_conf_int

0

100

True

Warning

The Pandas DataFrame passed to a pmdarima pyfunc flavor must only contain 1 row.

Note

When predicting a pmdarima flavor, the predict method’s DataFrame configuration column return_conf_int’s value controls the output format. When the column’s value is set to False or None (which is the default if this column is not supplied in the configuration DataFrame), the schema of the returned Pandas DataFrame is a single column: ["yhat"]. When set to True, the schema of the returned DataFrame is: ["yhat", "yhat_lower", "yhat_upper"] with the respective lower (yhat_lower) and upper (yhat_upper) confidence intervals added to the forecast predictions (yhat).

Example usage of pmdarima artifact loaded as a pyfunc with confidence intervals calculated:

import pmdarima
import mlflow
import pandas as pd

data = pmdarima.datasets.load_airpassengers()

with mlflow.start_run():
    model = pmdarima.auto_arima(data, seasonal=True)
    mlflow.pmdarima.save_model(model, "/tmp/model.pmd")

loaded_pyfunc = mlflow.pyfunc.load_model("/tmp/model.pmd")

prediction_conf = pd.DataFrame(
    [{"n_periods": 4, "return_conf_int": True, "alpha": 0.1}]
)

predictions = loaded_pyfunc.predict(prediction_conf)

Output (Pandas DataFrame):

Index

yhat

yhat_lower

yhat_upper

0

467.573731

423.30995

511.83751

1

490.494467

416.17449

564.81444

2

509.138684

420.56255

597.71117

3

492.554714

397.30634

587.80309

Warning

Signature logging for pmdarima will not function correctly if return_conf_int is set to True from a non-pyfunc artifact. The output of the native ARIMA.predict() when returning confidence intervals is not a recognized signature type.

OpenAI (openai) (Experimental)

The full guide, including tutorials and detailed documentation for using the openai flavor can be viewed here.

LangChain (langchain) (Experimental)

The full guide, including tutorials and detailed documentation for using the langchain flavor can be viewed here.

John Snow Labs (johnsnowlabs) (Experimental)

Attention

The johnsnowlabs flavor is in active development and is marked as Experimental. Public APIs may change and new features are subject to be added as additional functionality is brought to the flavor.

The johnsnowlabs model flavor gives you access to 20.000+ state-of-the-art enterprise NLP models in 200+ languages for medical, finance, legal and many more domains.

You can use mlflow.johnsnowlabs.log_model() to log and export your model as mlflow.pyfunc.PyFuncModel.

This enables you to integrate any John Snow Labs model into the MLflow framework. You can easily deploy your models for inference with MLflows serve functionalities. Models are interpreted as a generic Python function for inference via mlflow.pyfunc.load_model(). You can also use the mlflow.johnsnowlabs.load_model() function to load a saved or logged MLflow Model with the johnsnowlabs flavor from an stored artifact.

Features include: LLM’s, Text Summarization, Question Answering, Named Entity Recognition, Relation Extraction, Sentiment Analysis, Spell Checking, Image Classification, Automatic Speech Recognition and much more, powered by the latest Transformer Architectures. The models are provided by John Snow Labs and requires a John Snow Labs Enterprise NLP License. You can reach out to us for a research or industry license.

Example: Export a John Snow Labs to MLflow format

import json
import os

import pandas as pd
from johnsnowlabs import nlp

import mlflow
from mlflow.pyfunc import spark_udf

# 1) Write your raw license.json string into the 'JOHNSNOWLABS_LICENSE_JSON' env variable for MLflow
creds = {
    "AWS_ACCESS_KEY_ID": "...",
    "AWS_SECRET_ACCESS_KEY": "...",
    "SPARK_NLP_LICENSE": "...",
    "SECRET": "...",
}
os.environ["JOHNSNOWLABS_LICENSE_JSON"] = json.dumps(creds)

# 2) Install enterprise libraries
nlp.install()
# 3) Start a Spark session with enterprise libraries
spark = nlp.start()

# 4) Load a model and test it
nlu_model = "en.classify.bert_sequence.covid_sentiment"
model_save_path = "my_model"
johnsnowlabs_model = nlp.load(nlu_model)
johnsnowlabs_model.predict(["I hate COVID,", "I love COVID"])

# 5) Export model with pyfunc and johnsnowlabs flavors
with mlflow.start_run():
    model_info = mlflow.johnsnowlabs.log_model(johnsnowlabs_model, model_save_path)

# 6) Load model with johnsnowlabs flavor
mlflow.johnsnowlabs.load_model(model_info.model_uri)

# 7) Load model with pyfunc flavor
mlflow.pyfunc.load_model(model_save_path)

pandas_df = pd.DataFrame({"text": ["Hello World"]})
spark_df = spark.createDataFrame(pandas_df).coalesce(1)
pyfunc_udf = spark_udf(
    spark=spark,
    model_uri=model_save_path,
    env_manager="virtualenv",
    result_type="string",
)
new_df = spark_df.withColumn("prediction", pyfunc_udf(*pandas_df.columns))

# 9) You can now use the mlflow models serve command to serve the model see next section

# 10)  You can also use x command to deploy model inside of a container see next section

To deploy the John Snow Labs model as a container

  1. Start the Docker Container

docker run -p 5001:8080 -e JOHNSNOWLABS_LICENSE_JSON=your_json_string "mlflow-pyfunc"

  1. Query server

curl http://127.0.0.1:5001/invocations -H 'Content-Type: application/json' -d '{
  "dataframe_split": {
      "columns": ["text"],
      "data": [["I hate covid"], ["I love covid"]]
  }
}'

To deploy the John Snow Labs model without a container

  1. Export env variable and start server

export JOHNSNOWLABS_LICENSE_JSON=your_json_string
mlflow models serve -m <model_uri>

  1. Query server

curl http://127.0.0.1:5000/invocations -H 'Content-Type: application/json' -d '{
  "dataframe_split": {
      "columns": ["text"],
      "data": [["I hate covid"], ["I love covid"]]
  }
}'

Diviner (diviner)

The diviner model flavor enables logging of diviner models in MLflow format via the mlflow.diviner.save_model() and mlflow.diviner.log_model() methods. These methods also add the python_function flavor to the MLflow Models that they produce, allowing the model to be interpreted as generic Python functions for inference via mlflow.pyfunc.load_model(). This loaded PyFunc model can only be scored with a DataFrame input. You can also use the mlflow.diviner.load_model() method to load MLflow Models with the diviner model flavor in native diviner formats.

Diviner Types

Diviner is a library that provides an orchestration framework for performing time series forecasting on groups of related series. Forecasting in diviner is accomplished through wrapping popular open source libraries such as prophet and pmdarima. The diviner library offers a simplified set of APIs to simultaneously generate distinct time series forecasts for multiple data groupings using a single input DataFrame and a unified high-level API.

Metrics and Parameters logging for Diviner

Unlike other flavors that are supported in MLflow, Diviner has the concept of grouped models. As a collection of many (perhaps thousands) of individual forecasting models, the burden to the tracking server to log individual metrics and parameters for each of these models is significant. For this reason, metrics and parameters are exposed for retrieval from Diviner’s APIs as Pandas DataFrames, rather than discrete primitive values.

To illustrate, let us assume we are forecasting hourly electricity consumption from major cities around the world. A sample of our input data looks like this:

country

city

datetime

watts

US

NewYork

2022-03-01 00:01:00

23568.9

US

NewYork

2022-03-01 00:02:00

22331.7

US

Boston

2022-03-01 00:01:00

14220.1

US

Boston

2022-03-01 00:02:00

14183.4

CA

Toronto

2022-03-01 00:01:00

18562.2

CA

Toronto

2022-03-01 00:02:00

17681.6

MX

MexicoCity

2022-03-01 00:01:00

19946.8

MX

MexicoCity

2022-03-01 00:02:00

19444.0

If we were to fit a model on this data, supplying the grouping keys as:

grouping_keys = ["country", "city"]

We will have a model generated for each of the grouping keys that have been supplied:

[("US", "NewYork"), ("US", "Boston"), ("CA", "Toronto"), ("MX", "MexicoCity")]

With a model constructed for each of these, entering each of their metrics and parameters wouldn’t be an issue for the MLflow tracking server. What would become a problem, however, is if we modeled each major city on the planet and ran this forecasting scenario every day. If we were to adhere to the conditions of the World Bank, that would mean just over 10,000 models as of 2022. After a mere few weeks of running this forecasting every day we would have a very large metrics table.

To eliminate this issue for large-scale forecasting, the metrics and parameters for diviner are extracted as a grouping key indexed Pandas DataFrame, as shown below for example (float values truncated for visibility):

grouping_key_columns

country

city

mse

rmse

mae

mape

mdape

smape

“(‘country’, ‘city’)”

CA

Toronto

8276851.6

2801.7

2417.7

0.16

0.16

0.159

“(‘country’, ‘city’)”

MX

MexicoCity

3548872.4

1833.8

1584.5

0.15

0.16

0.159

“(‘country’, ‘city’)”

US

NewYork

3167846.4

1732.4

1498.2

0.15

0.16

0.158

“(‘country’, ‘city’)”

US

Boston

14082666.4

3653.2

3156.2

0.15

0.16

0.159

There are two recommended means of logging the metrics and parameters from a diviner model :

import os
import mlflow
import tempfile

with tempfile.TemporaryDirectory() as tmpdir:
    params = model.extract_model_params()
    metrics = model.cross_validate_and_score(
        horizon="72 hours",
        period="240 hours",
        initial="480 hours",
        parallel="threads",
        rolling_window=0.1,
        monthly=False,
    )
    params.to_csv(f"{tmpdir}/params.csv", index=False, header=True)
    metrics.to_csv(f"{tmpdir}/metrics.csv", index=False, header=True)

    mlflow.log_artifacts(tmpdir, artifact_path="data")

Note

The parameters extract from diviner models may require casting (or dropping of columns) if using the pd.DataFrame.to_dict() approach due to the inability of this method to serialize objects.

import mlflow

params = model.extract_model_params()
metrics = model.cross_validate_and_score(
    horizon="72 hours",
    period="240 hours",
    initial="480 hours",
    parallel="threads",
    rolling_window=0.1,
    monthly=False,
)
params["t_scale"] = params["t_scale"].astype(str)
params["start"] = params["start"].astype(str)
params = params.drop("stan_backend", axis=1)

mlflow.log_dict(params.to_dict(), "params.json")
mlflow.log_dict(metrics.to_dict(), "metrics.json")

Logging of the model artifact is shown in the pyfunc example below.

Diviner pyfunc usage

The MLflow Diviner flavor includes an implementation of the pyfunc interface for Diviner models. To control prediction behavior, you can specify configuration arguments in the first row of a Pandas DataFrame input.

As this configuration is dependent upon the underlying model type (i.e., the diviner.GroupedProphet.forecast() method has a different signature than does diviner.GroupedPmdarima.predict()), the Diviner pyfunc implementation attempts to coerce arguments to the types expected by the underlying model.

Note

Diviner models support both “full group” and “partial group” forecasting. If a column named "groups" is present in the configuration DataFrame submitted to the pyfunc flavor, the grouping key values in the first row will be used to generate a subset of forecast predictions. This functionality removes the need to filter a subset from the full output of all groups forecasts if the results of only a few (or one) groups are needed.

For a GroupedPmdarima model, an example configuration for the pyfunc predict() method is:

import mlflow
import pandas as pd
from pmdarima.arima.auto import AutoARIMA
from diviner import GroupedPmdarima

with mlflow.start_run():
    base_model = AutoARIMA(out_of_sample_size=96, maxiter=200)
    model = GroupedPmdarima(model_template=base_model).fit(
        df=df,
        group_key_columns=["country", "city"],
        y_col="watts",
        datetime_col="datetime",
        silence_warnings=True,
    )

    mlflow.diviner.save_model(diviner_model=model, path="/tmp/diviner_model")

diviner_pyfunc = mlflow.pyfunc.load_model(model_uri="/tmp/diviner_model")

predict_conf = pd.DataFrame(
    {
        "n_periods": 120,
        "groups": [
            ("US", "NewYork"),
            ("CA", "Toronto"),
            ("MX", "MexicoCity"),
        ],  # NB: List of tuples required.
        "predict_col": "wattage_forecast",
        "alpha": 0.1,
        "return_conf_int": True,
        "on_error": "warn",
    },
    index=[0],
)

subset_forecasts = diviner_pyfunc.predict(predict_conf)

Note

There are several instances in which a configuration DataFrame submitted to the pyfunc predict() method will cause an MlflowException to be raised:

  • If neither horizon or n_periods are provided.

  • The value of n_periods or horizon is not an integer.

  • If the model is of type GroupedProphet, frequency as a string type must be provided.

  • If both horizon and n_periods are provided with different values.

Transformers (transformers) (Experimental)

The full guide, including tutorials and detailed documentation for using the transformers flavor is available at this location.

SentenceTransformers (sentence_transformers) (Experimental)

Attention

The sentence_transformers flavor is in active development and is marked as Experimental. Public APIs may change and new features are subject to be added as additional functionality is brought to the flavor.

The sentence_transformers model flavor enables logging of sentence-transformers models in MLflow format via the mlflow.sentence_transformers.save_model() and mlflow.sentence_transformers.log_model() functions. Use of these functions also adds the python_function flavor to the MLflow Models that they produce, allowing the model to be interpreted as a generic Python function for inference via mlflow.pyfunc.load_model(). You can also use the mlflow.sentence_transformers.load_model() function to load a saved or logged MLflow Model with the sentence_transformers flavor as a native sentence-transformers model.

Example:

from sentence_transformers import SentenceTransformer

import mlflow
import mlflow.sentence_transformers

model = SentenceTransformer("all-MiniLM-L6-v2")

example_sentences = ["This is a sentence.", "This is another sentence."]

# Define the signature
signature = mlflow.models.infer_signature(
    model_input=example_sentences,
    model_output=model.encode(example_sentences),
)

# Log the model using mlflow
with mlflow.start_run():
    logged_model = mlflow.sentence_transformers.log_model(
        model=model,
        artifact_path="sbert_model",
        signature=signature,
        input_example=example_sentences,
    )

# Load option 1: mlflow.pyfunc.load_model returns a PyFuncModel
loaded_model = mlflow.pyfunc.load_model(logged_model.model_uri)
embeddings1 = loaded_model.predict(["hello world", "i am mlflow"])

# Load option 2: mlflow.sentence_transformers.load_model returns a SentenceTransformer
loaded_model = mlflow.sentence_transformers.load_model(logged_model.model_uri)
embeddings2 = loaded_model.encode(["hello world", "i am mlflow"])

print(embeddings1)

"""
>> [[-3.44772562e-02  3.10232025e-02  6.73496164e-03  2.61089969e-02
  ...
  2.37922110e-02 -2.28897743e-02  3.89375277e-02  3.02067865e-02]
 [ 4.81191138e-03 -9.33756605e-02  6.95968643e-02  8.09735525e-03
  ...
   6.57437667e-02 -2.72239652e-02  4.02687863e-02 -1.05599344e-01]]
"""

Promptflow (promptflow) (Experimental)

Attention

The promptflow flavor is in active development and is marked as Experimental. Public APIs may change and new features are subject to be added as additional functionality is brought to the flavor.

The promptflow model flavor is capable of packaging your flow in MLflow format via the mlflow.promptflow.save_model() and mlflow.promptflow.log_model() functions. Currently, a flow.dag.yaml file is required to be present in the flow’s directory. These functions also add the python_function flavor to the MLflow Models, allowing the models to be interpreted as generic Python functions for inference via mlflow.pyfunc.load_model(). You can also use the mlflow.promptflow.load_model() method to load MLflow Models with the promptflow model flavor in native promptflow format.

Please note that the signature in MLmodel file will NOT BE automatically inferred from the flow itself. To save model with the signature, you can either pass the input_example or specify the input signature manually.

Example:

Reach the flow source at example from the MLflow GitHub Repository.

import os
from pathlib import Path

from promptflow import load_flow

import mlflow

assert "OPENAI_API_KEY" in os.environ, "Please set the OPENAI_API_KEY environment variable."


# The example flow will write a simple code snippet that displays the greeting message with specific language.
flow_folder = Path(__file__).parent / "basic"
flow = load_flow(flow_folder)

with mlflow.start_run():
    logged_model = mlflow.promptflow.log_model(flow, artifact_path="promptflow_model")

loaded_model = mlflow.pyfunc.load_model(logged_model.model_uri)
print(loaded_model.predict({"text": "Python Hello World!"}))

Model Evaluation

After building and training your MLflow Model, you can use the mlflow.evaluate() API to evaluate its performance on one or more datasets of your choosing. mlflow.evaluate() currently supports evaluation of MLflow Models with the python_function (pyfunc) model flavor for classification, regression, and numerous language modeling tasks (see Evaluating with LLMs), computing a variety of task-specific performance metrics, model performance plots, and model explanations. Evaluation results are logged to MLflow Tracking.

The following example from the MLflow GitHub Repository uses mlflow.evaluate() to evaluate the performance of a classifier on the UCI Adult Data Set, logging a comprehensive collection of MLflow Metrics and Artifacts that provide insight into model performance and behavior:

import xgboost
import shap
import mlflow
from mlflow.models import infer_signature
from sklearn.model_selection import train_test_split

# Load the UCI Adult Dataset
X, y = shap.datasets.adult()

# Split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.33, random_state=42
)

# Fit an XGBoost binary classifier on the training data split
model = xgboost.XGBClassifier().fit(X_train, y_train)

# Create a model signature
signature = infer_signature(X_test, model.predict(X_test))

# Build the Evaluation Dataset from the test set
eval_data = X_test
eval_data["label"] = y_test

with mlflow.start_run() as run:
    # Log the baseline model to MLflow
    mlflow.sklearn.log_model(model, "model", signature=signature)
    model_uri = mlflow.get_artifact_uri("model")

    # Evaluate the logged model
    result = mlflow.evaluate(
        model_uri,
        eval_data,
        targets="label",
        model_type="classifier",
        evaluators=["default"],
    )

eval_metrics_img eval_importance_img

Evaluating with LLMs

As of MLflow 2.4.0, mlflow.evaluate() has built-in support for a variety of tasks with LLMs, including text summarization, text classification, question answering, and text generation. The following example uses mlflow.evaluate() to evaluate a model that answers questions about MLflow (note that you must have the OPENAI_API_TOKEN environment variable set in your current system environment in order to run the example):

import os
import pandas as pd

import mlflow
import openai

# Create a question answering model using prompt engineering with OpenAI. Log the
# prompt and the model to MLflow Tracking
mlflow.start_run()
system_prompt = (
    "Your job is to answer questions about MLflow. When you are asked a question about MLflow,"
    " respond to it. Make sure to include code examples. If the question is not related to"
    " MLflow, refuse to answer and say that the question is unrelated."
)
mlflow.log_param("system_prompt", system_prompt)
logged_model = mlflow.openai.log_model(
    model="gpt-4o-mini",
    task=openai.chat.completions,
    artifact_path="model",
    messages=[
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": "{question}"},
    ],
)

# Evaluate the model on some example questions
questions = pd.DataFrame(
    {
        "question": [
            "How do you create a run with MLflow?",
            "How do you log a model with MLflow?",
            "What is the capital of France?",
        ]
    }
)
mlflow.evaluate(
    model=logged_model.model_uri,
    model_type="question-answering",
    data=questions,
)

# Load and inspect the evaluation results
results: pd.DataFrame = mlflow.load_table(
    "eval_results_table.json", extra_columns=["run_id", "params.system_prompt"]
)
print("Evaluation results:")
print(results)

MLflow also provides an Artifact View UI for comparing inputs and outputs across multiple models built with LLMs. For example, after evaluating multiple prompts for question answering (see the MLflow OpenAI question answering full example), you can navigate to the Artifact View to view the questions and compare the answers for each model:

_images/artifact-view-ui.png


For additional examples demonstrating the use of mlflow.evaluate() with LLMs, check out the MLflow LLMs example repository.

Evaluating with Extra Metrics

If the default set of metrics is insufficient, you can supply extra_metrics and custom_artifacts to mlflow.evaluate() to produce extra metrics and artifacts for the model(s) that you’re evaluating.

To define an extra metric, you should define an eval_fn function that takes in predictions and targets as arguments and outputs a MetricValue object. predictions and targets are pandas.Series objects. If predictions or targets specified in mlflow.evaluate() is either numpy.array or List, they will be converted to pandas.Series.

To use values from other metrics to compute your custom metric, include the name of the metric as an argument to eval_fn. This argument will contain a MetricValue object which contains the values calculated from the specified metric and can be used to compute your custom metric.

{
    "accuracy_score": MetricValue(
        scores=None, justifications=None, aggregate_results={"accuracy_score": 1.0}
    )
}

The MetricValue class has three attributes:

  • scores: a list that contains per-row metrics.

  • aggregate_results: a dictionary that maps the aggregation method names to the corresponding aggregated values. This is intended to be used to aggregate scores.

  • justifications: a list that contains per-row justifications of the values in scores. This is optional, and is usually used with genai metrics.

The code block below demonstrates how to define a custom metric evaluation function:

from mlflow.metrics import MetricValue


def my_metric_eval_fn(predictions, targets):
    scores = np.abs(predictions - targets)
    return MetricValue(
        scores=list(scores),
        aggregate_results={
            "mean": np.mean(scores),
            "variance": np.var(scores),
            "median": np.median(scores),
        },
    )

Once you have defined an eval_fn, you then use make_metric() to wrap this eval_fn function into a metric. In addition to eval_fn, make_metric() requires an additional parameter , greater_is_better, for optimization purposes. This parameter indicates whether this is a metric we want to maximize or minimize.

from mlflow.metrics import make_metric

mymetric = make_metric(eval_fn=my_metric_eval_fn, greater_is_better=False)

The extra metric allows you to either evaluate a model directly, or to evaluate an output dataframe.

To evaluate the model directly, you will have to provide mlflow.evaluate() either a pyfunc model instance, a URI referring to a pyfunc model, or a callable function that takes in the data as input and outputs the predictions.

def model(x):
    return x["inputs"]


eval_dataset = pd.DataFrame(
    {
        "targets": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0],
        "inputs": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0],
    }
)

mlflow.evaluate(model, eval_dataset, targets="targets", extra_metrics=[mymetric])
To directly evaluate an output dataframe, you can omit the model parameter. However, you will need

to set the predictions parameter in mlflow.evaluate() in order to evaluate an inference output dataframe.

eval_dataset = pd.DataFrame(
    {
        "targets": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0],
        "predictions": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0],
    }
)

result = mlflow.evaluate(
    data=eval_dataset,
    predictions="predictions",
    targets="targets",
    extra_metrics=[mymetric],
)

When your model has multiple outputs, the model must return a pandas DataFrame with multiple columns. You must specify one column among the model output columns as the predictions column using the predictions parameter, and other output columns of the model will be accessible from the eval_fn based on their column names. For example, if your model has two outputs retrieved_context and answer, you can specify answer as the predictions column, and retrieved_context column will be accessible as the context parameter from eval_fn via col_mapping:

def eval_fn(predictions, targets, context):
    scores = (predictions == targets) + context
    return MetricValue(
        scores=list(scores),
        aggregate_results={"mean": np.mean(scores), "sum": np.sum(scores)},
    )


mymetric = make_metric(eval_fn=eval_fn, greater_is_better=False, name="mymetric")


def model(x):
    return pd.DataFrame({"retrieved_context": x["inputs"] + 1, "answer": x["inputs"]})


eval_dataset = pd.DataFrame(
    {
        "targets": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0],
        "inputs": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0],
    }
)

config = {"col_mapping": {"context": "retrieved_context"}}

result = mlflow.evaluate(
    model,
    eval_dataset,
    predictions="answer",
    targets="targets",
    extra_metrics=[mymetric],
    evaluator_config=config,
)

However, you can also avoid using col_mapping if the parameter of eval_fn is the same as the output column name of the model.

def eval_fn(predictions, targets, retrieved_context):
    scores = (predictions == targets) + retrieved_context
    return MetricValue(
        scores=list(scores),
        aggregate_results={"mean": np.mean(scores), "sum": np.sum(scores)},
    )


mymetric = make_metric(eval_fn=eval_fn, greater_is_better=False, name="mymetric")


def model(x):
    return pd.DataFrame({"retrieved_context": x["inputs"] + 1, "answer": x["inputs"]})


eval_dataset = pd.DataFrame(
    {
        "targets": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0],
        "inputs": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0],
    }
)

result = mlflow.evaluate(
    model,
    eval_dataset,
    predictions="answer",
    targets="targets",
    extra_metrics=[mymetric],
)

col_mapping also allows you to pass additional parameters to the extra metric function, in this case passing a value k.

def eval_fn(predictions, targets, k):
    scores = k * (predictions == targets)
    return MetricValue(scores=list(scores), aggregate_results={"mean": np.mean(scores)})


weighted_mymetric = make_metric(eval_fn=eval_fn, greater_is_better=False)


def model(x):
    return x["inputs"]


eval_dataset = pd.DataFrame(
    {
        "targets": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0],
        "inputs": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0],
    }
)

config = {"col_mapping": {"k": 5}}
mlflow.evaluate(
    model,
    eval_dataset,
    targets="targets",
    extra_metrics=[weighted_mymetric],
    evaluator_config=config,
)

You can also add the name of other metrics as an argument to the extra metric function, which will pass in the MetricValue calculated for that metric.

def eval_fn(predictions, targets, retrieved_context):
    scores = (predictions == targets) + retrieved_context
    return MetricValue(
        scores=list(scores),
        aggregate_results={"mean": np.mean(scores), "sum": np.sum(scores)},
    )


mymetric = make_metric(eval_fn=eval_fn, greater_is_better=False, name="mymetric")


def eval_fn_2(predictions, targets, mymetric):
    scores = ["true" if score else "false" for score in mymetric.scores]
    return MetricValue(
        scores=list(scores),
    )


mymetric2 = make_metric(eval_fn=eval_fn_2, greater_is_better=False, name="mymetric2")


def model(x):
    return pd.DataFrame({"retrieved_context": x["inputs"] + 1, "answer": x["inputs"]})


eval_dataset = pd.DataFrame(
    {
        "targets": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0],
        "inputs": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0],
    }
)

result = mlflow.evaluate(
    model,
    eval_dataset,
    predictions="answer",
    targets="targets",
    extra_metrics=[mymetric, mymetric2],
)

The following short example from the MLflow GitHub Repository uses mlflow.evaluate() with an extra metric function to evaluate the performance of a regressor on the California Housing Dataset.

import os

import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split

import mlflow
from mlflow.models import infer_signature, make_metric

# loading the California housing dataset
cali_housing = fetch_california_housing(as_frame=True)

# split the dataset into train and test partitions
X_train, X_test, y_train, y_test = train_test_split(
    cali_housing.data, cali_housing.target, test_size=0.2, random_state=123
)

# train the model
lin_reg = LinearRegression().fit(X_train, y_train)

# Infer model signature
predictions = lin_reg.predict(X_train)
signature = infer_signature(X_train, predictions)

# creating the evaluation dataframe
eval_data = X_test.copy()
eval_data["target"] = y_test


def squared_diff_plus_one(eval_df, _builtin_metrics):
    """
    This example custom metric function creates a metric based on the ``prediction`` and
    ``target`` columns in ``eval_df`.
    """
    return np.sum(np.abs(eval_df["prediction"] - eval_df["target"] + 1) ** 2)


def sum_on_target_divided_by_two(_eval_df, builtin_metrics):
    """
    This example custom metric function creates a metric derived from existing metrics in
    ``builtin_metrics``.
    """
    return builtin_metrics["sum_on_target"] / 2


def prediction_target_scatter(eval_df, _builtin_metrics, artifacts_dir):
    """
    This example custom artifact generates and saves a scatter plot to ``artifacts_dir`` that
    visualizes the relationship between the predictions and targets for the given model to a
    file as an image artifact.
    """
    plt.scatter(eval_df["prediction"], eval_df["target"])
    plt.xlabel("Targets")
    plt.ylabel("Predictions")
    plt.title("Targets vs. Predictions")
    plot_path = os.path.join(artifacts_dir, "example_scatter_plot.png")
    plt.savefig(plot_path)
    return {"example_scatter_plot_artifact": plot_path}


with mlflow.start_run() as run:
    mlflow.sklearn.log_model(lin_reg, "model", signature=signature)
    model_uri = mlflow.get_artifact_uri("model")
    result = mlflow.evaluate(
        model=model_uri,
        data=eval_data,
        targets="target",
        model_type="regressor",
        evaluators=["default"],
        extra_metrics=[
            make_metric(
                eval_fn=squared_diff_plus_one,
                greater_is_better=False,
            ),
            make_metric(
                eval_fn=sum_on_target_divided_by_two,
                greater_is_better=True,
            ),
        ],
        custom_artifacts=[prediction_target_scatter],
    )

print(f"metrics:\n{result.metrics}")
print(f"artifacts:\n{result.artifacts}")

For a more comprehensive extra metrics usage example, refer to this example from the MLflow GitHub Repository.

Evaluating with a Function

As of MLflow 2.8.0, mlflow.evaluate() supports evaluating a python function without requiring logging the model to MLflow. This is useful when you don’t want to log the model and just want to evaluate it. The requirements for the function’s input and output are the same as the requirements for a model’s input and output.

The following example uses mlflow.evaluate() to evaluate a function:

import shap
import xgboost
from sklearn.model_selection import train_test_split

import mlflow

# Load the UCI Adult Dataset
X, y = shap.datasets.adult()

# Split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

# Fit an XGBoost binary classifier on the training data split
model = xgboost.XGBClassifier().fit(X_train, y_train)

# Build the Evaluation Dataset from the test set
eval_data = X_test
eval_data["label"] = y_test


# Define a function that calls the model's predict method
def fn(X):
    return model.predict(X)


with mlflow.start_run() as run:
    # Evaluate the function without logging the model
    result = mlflow.evaluate(
        fn,
        eval_data,
        targets="label",
        model_type="classifier",
        evaluators=["default"],
    )

print(f"metrics:\n{result.metrics}")
print(f"artifacts:\n{result.artifacts}")

Evaluating with a Static Dataset

As of MLflow 2.8.0, mlflow.evaluate() supports evaluating a static dataset without specifying a model. This is useful when you save the model output to a column in a Pandas DataFrame or an MLflow PandasDataset, and want to evaluate the static dataset without re-running the model.

If you are using a Pandas DataFrame, you must specify the column name that contains the model output using the top-level predictions parameter in mlflow.evaluate():

# Assume that the model output is saved to the pandas_df["model_output"] column
mlflow.evaluate(data=pandas_df, predictions="model_output", ...)

If you are using an MLflow PandasDataset, you must specify the column name that contains the model output using the predictions parameter in mlflow.data.from_pandas(), and specify None for the predictions parameter in mlflow.evaluate():

# Assume that the model output is saved to the pandas_df["model_output"] column
dataset = mlflow.data.from_pandas(pandas_df, predictions="model_output")
mlflow.evaluate(data=pandas_df, predictions=None, ...)

When your model has multiple outputs, you must specify one column among the model output columns as the predictions column. The other output columns of the model will be treated as “input” columns. For example, if your model has two outputs named retrieved_context and answer, you can specify answer as the predictions column. The retrieved_context column will be treated as an “input” column when calculating the metrics.

The following example uses mlflow.evaluate() to evaluate a static dataset:

import shap
import xgboost
from sklearn.model_selection import train_test_split

import mlflow

# Load the UCI Adult Dataset
X, y = shap.datasets.adult()

# Split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

# Fit an XGBoost binary classifier on the training data split
model = xgboost.XGBClassifier().fit(X_train, y_train)

# Build the Evaluation Dataset from the test set
y_test_pred = model.predict(X=X_test)
eval_data = X_test
eval_data["label"] = y_test
eval_data["predictions"] = y_test_pred


with mlflow.start_run() as run:
    # Evaluate the static dataset without providing a model
    result = mlflow.evaluate(
        data=eval_data,
        targets="label",
        predictions="predictions",
        model_type="classifier",
    )

print(f"metrics:\n{result.metrics}")
print(f"artifacts:\n{result.artifacts}")

Performing Model Validation

Attention

MLflow 2.18.0 has moved the model validation functionality from the mlflow.evaluate() API to a dedicated mlflow.validate_evaluation_results() API. The relevant parameters, such as baseline_model, are deprecated and will be removed from the older API in future versions.

With the mlflow.validate_evaluation_results() API, you can validate metrics generated during model evaluation to assess the quality of your model against a baseline. To achieve this, first evaluate both the candidate and baseline models using mlflow.evaluate() (or load persisted evaluation results from local storage). Then, pass the results along with a validation_thresholds dictionary that maps metric names to mlflow.models.MetricThreshold objects. If your model fails to meet the specified thresholds, mlflow.validate_evaluation_results() will raise a ModelValidationFailedException with details about the validation failure.

More information on model evaluation behavior and outputs can be found in the mlflow.evaluate() API documentation.

Validating a Model Agasint a Baseline Model

Below is an example of how to validate a candidate model’s performance against a baseline using predefined thresholds.

import xgboost
import shap
from sklearn.model_selection import train_test_split
from sklearn.dummy import DummyClassifier
import mlflow
from mlflow.models import MetricThreshold

# load UCI Adult Data Set; segment it into training and test sets
X, y = shap.datasets.adult()
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.33, random_state=42
)

# construct an evaluation dataset from the test set
eval_data = X_test
eval_data["label"] = y_test

# Log and evaluate the candidate model
candidate_model = xgboost.XGBClassifier().fit(X_train, y_train)

with mlflow.start_run(run_name="candidate") as run:
    candidate_model_uri = mlflow.sklearn.log_model(
        candidate_model, "candidate_model", signature=signature
    ).model_uri

    candidate_result = mlflow.evaluate(
        candidate_model_uri,
        eval_data,
        targets="label",
        model_type="classifier",
    )

# Log and evaluate the baseline model
baseline_model = DummyClassifier(strategy="uniform").fit(X_train, y_train)

with mlflow.start_run(run_name="baseline") as run:
    baseline_model_uri = mlflow.sklearn.log_model(
        baseline_model, "baseline_model", signature=signature
    ).model_uri

    baseline_result = mlflow.evaluate(
        baseline_model_uri,
        eval_data,
        targets="label",
        model_type="classifier",
    )

# Define criteria for model to be validated against
thresholds = {
    "accuracy_score": MetricThreshold(
        threshold=0.8,  # accuracy should be >=0.8
        min_absolute_change=0.05,  # accuracy should be at least 0.05 greater than baseline model accuracy
        min_relative_change=0.05,  # accuracy should be at least 5 percent greater than baseline model accuracy
        greater_is_better=True,
    ),
}

# Validate the candidate model agaisnt baseline
mlflow.validate_evaluation_results(
    candidate_result=candidate_result,
    baseline_result=baseline_result,
    validation_thresholds=thresholds,
)

Refer to mlflow.models.MetricThreshold to see details on how the thresholds are specified and checked.

The logged output within the MLflow UI for the comprehensive example is shown below.

eval_importance_compare_img

Note

Model validation results are not included in the active MLflow run.

Validating a Model Against Static Thresholds

The mlflow.validate_evaluation_results() API can also be used to validate a candidate model against static thresholds, instead of comparing it to a baseline model, by passing None to the baseline_result parameter.

import mlflow
from mlflow.models import MetricThreshold

thresholds = {
    "accuracy_score": MetricThreshold(
        threshold=0.8,  # accuracy should be >=0.8
        greater_is_better=True,
    ),
}

# Validate the candidate model agaisnt static threshold
mlflow.validate_evaluation_results(
    candidate_result=candidate_result,
    baseline_result=None,
    validation_thresholds=thresholds,
)

Reusing Baseline Result For Multiple Validations

The mlflow.models.EvaluationResult object returned by mlflow.evaluate() can be saved to and loaded from local storage. This feature allows you to reuse the same baseline result across different candidate models, which is particularly useful for automating model quality monitoring.

import mlflow
from mlflow.models.evaluation import EvaluationResult

baseline_result = mlflow.evaluate(
    baseline_model_uri,
    eval_data,
    targets="label",
    model_type="classifier",
)
baseline_result.save("RESULT_PATH")

# Load the evaluation result for validation
baseline_result = EvaluationResult.load("RESULT_PATH")

Note

There are plugins that support in-depth model validation with features that are not supported directly in MLflow. To learn more, see:

Note

Differences in the computation of Area under Curve Precision Recall score (metric name precision_recall_auc) between multi and binary classifiers:

Multiclass classifier models, when evaluated, utilize the standard scoring metric from sklearn: sklearn.metrics.roc_auc_score to calculate the area under the precision recall curve. This algorithm performs a linear interpolation calculation utilizing the trapezoidal rule to estimate the area under the precision recall curve. It is well-suited for use in evaluating multi-class classification models to provide a single numeric value of the quality of fit.

Binary classifier models, on the other hand, use the sklearn.metrics.average_precision_score to avoid the shortcomings of the roc_auc_score implementation when applied to heavily imbalanced classes in binary classification. Usage of the roc_auc_score for imbalanced datasets can give a misleading result (optimistically better than the model’s actual ability to accurately predict the minority class membership).

For additional information on the topic of why different algorithms are employed for this, as well as links to the papers that informed the implementation of these metrics within the sklearn.metrics module, refer to the documentation.

For simplicity purposes, both methodologies evaluation metric results (whether for multi-class or binary classification) are unified in the single metric: precision_recall_auc.

Model Validation with Giskard’s plugin

To extend the validation capabilities of MLflow and anticipate issues before they go to production, a plugin has been built by Giskard allowing users to:

See the following plugin example notebooks for a demo:

For more information on the plugin, see the giskard-mlflow docs.

Model Validation with Trubrics’ plugin

To extend the validation capabilities of MLflow, a plugin has been built by Trubrics allowing users:

  • to use a large number of out-of-the-box validations

  • to validate a run with any custom python functions

  • to view all validation results in a .json file, for diagnosis of why an MLflow run could have failed

See the plugin example notebook for a demo.

For more information on the plugin, see the trubrics-mlflow docs.

Model Customization

While MLflow’s built-in model persistence utilities are convenient for packaging models from various popular ML libraries in MLflow Model format, they do not cover every use case. For example, you may want to use a model from an ML library that is not explicitly supported by MLflow’s built-in flavors. Alternatively, you may want to package custom inference code and data to create an MLflow Model. Fortunately, MLflow provides two solutions that can be used to accomplish these tasks: Custom Python Models and Custom Flavors.

Custom Python Models

The mlflow.pyfunc module provides save_model() and log_model() utilities for creating MLflow Models with the python_function flavor that contain user-specified code and artifact (file) dependencies. These artifact dependencies may include serialized models produced by any Python ML library.

Because these custom models contain the python_function flavor, they can be deployed to any of MLflow’s supported production environments, such as SageMaker, AzureML, or local REST endpoints.

The following examples demonstrate how you can use the mlflow.pyfunc module to create custom Python models. For additional information about model customization with MLflow’s python_function utilities, see the python_function custom models documentation.

Example: Creating a custom “add n” model

This example defines a class for a custom model that adds a specified numeric value, n, to all columns of a Pandas DataFrame input. Then, it uses the mlflow.pyfunc APIs to save an instance of this model with n = 5 in MLflow Model format. Finally, it loads the model in python_function format and uses it to evaluate a sample input.

import mlflow.pyfunc


# Define the model class
class AddN(mlflow.pyfunc.PythonModel):
    def __init__(self, n):
        self.n = n

    def predict(self, context, model_input, params=None):
        return model_input.apply(lambda column: column + self.n)


# Construct and save the model
model_path = "add_n_model"
add5_model = AddN(n=5)
mlflow.pyfunc.save_model(path=model_path, python_model=add5_model)

# Load the model in `python_function` format
loaded_model = mlflow.pyfunc.load_model(model_path)

# Evaluate the model
import pandas as pd

model_input = pd.DataFrame([range(10)])
model_output = loaded_model.predict(model_input)
assert model_output.equals(pd.DataFrame([range(5, 15)]))

Example: Saving an XGBoost model in MLflow format

This example begins by training and saving a gradient boosted tree model using the XGBoost library. Next, it defines a wrapper class around the XGBoost model that conforms to MLflow’s python_function inference API. Then, it uses the wrapper class and the saved XGBoost model to construct an MLflow Model that performs inference using the gradient boosted tree. Finally, it loads the MLflow Model in python_function format and uses it to evaluate test data.

# Load training and test datasets
from sys import version_info
import xgboost as xgb
from sklearn import datasets
from sklearn.model_selection import train_test_split

PYTHON_VERSION = f"{version_info.major}.{version_info.minor}.{version_info.micro}"
iris = datasets.load_iris()
x = iris.data[:, 2:]
y = iris.target
x_train, x_test, y_train, _ = train_test_split(x, y, test_size=0.2, random_state=42)
dtrain = xgb.DMatrix(x_train, label=y_train)

# Train and save an XGBoost model
xgb_model = xgb.train(params={"max_depth": 10}, dtrain=dtrain, num_boost_round=10)
xgb_model_path = "xgb_model.pth"
xgb_model.save_model(xgb_model_path)

# Create an `artifacts` dictionary that assigns a unique name to the saved XGBoost model file.
# This dictionary will be passed to `mlflow.pyfunc.save_model`, which will copy the model file
# into the new MLflow Model's directory.
artifacts = {"xgb_model": xgb_model_path}

# Define the model class
import mlflow.pyfunc


class XGBWrapper(mlflow.pyfunc.PythonModel):
    def load_context(self, context):
        import xgboost as xgb

        self.xgb_model = xgb.Booster()
        self.xgb_model.load_model(context.artifacts["xgb_model"])

    def predict(self, context, model_input, params=None):
        input_matrix = xgb.DMatrix(model_input.values)
        return self.xgb_model.predict(input_matrix)


# Create a Conda environment for the new MLflow Model that contains all necessary dependencies.
import cloudpickle

conda_env = {
    "channels": ["defaults"],
    "dependencies": [
        f"python={PYTHON_VERSION}",
        "pip",
        {
            "pip": [
                f"mlflow=={mlflow.__version__}",
                f"xgboost=={xgb.__version__}",
                f"cloudpickle=={cloudpickle.__version__}",
            ],
        },
    ],
    "name": "xgb_env",
}

# Save the MLflow Model
mlflow_pyfunc_model_path = "xgb_mlflow_pyfunc"
mlflow.pyfunc.save_model(
    path=mlflow_pyfunc_model_path,
    python_model=XGBWrapper(),
    artifacts=artifacts,
    conda_env=conda_env,
)

# Load the model in `python_function` format
loaded_model = mlflow.pyfunc.load_model(mlflow_pyfunc_model_path)

# Evaluate the model
import pandas as pd

test_predictions = loaded_model.predict(pd.DataFrame(x_test))
print(test_predictions)

Example: Logging a transformers model with hf:/ schema to avoid copying large files

This example shows how to use a special schema hf:/ to log a transformers model from huggingface hub directly. This is useful when the model is too large and especially when you want to serve the model directly, but it doesn’t save extra space if you want to download and test the model locally.

import mlflow
from mlflow.models import infer_signature
import numpy as np
import transformers


# Define a custom PythonModel
class QAModel(mlflow.pyfunc.PythonModel):
    def load_context(self, context):
        """
        This method initializes the tokenizer and language model
        using the specified snapshot location from model context.
        """
        snapshot_location = context.artifacts["bert-tiny-model"]
        # Initialize tokenizer and language model
        tokenizer = transformers.AutoTokenizer.from_pretrained(snapshot_location)
        model = transformers.BertForQuestionAnswering.from_pretrained(snapshot_location)
        self.pipeline = transformers.pipeline(
            task="question-answering", model=model, tokenizer=tokenizer
        )

    def predict(self, context, model_input, params=None):
        question = model_input["question"][0]
        if isinstance(question, np.ndarray):
            question = question.item()
        ctx = model_input["context"][0]
        if isinstance(ctx, np.ndarray):
            ctx = ctx.item()
        return self.pipeline(question=question, context=ctx)


# Log the model
data = {"question": "Who's house?", "context": "The house is owned by Run."}
pyfunc_artifact_path = "question_answering_model"
with mlflow.start_run() as run:
    model_info = mlflow.pyfunc.log_model(
        artifact_path=pyfunc_artifact_path,
        python_model=QAModel(),
        artifacts={"bert-tiny-model": "hf:/prajjwal1/bert-tiny"},
        input_example=data,
        signature=infer_signature(data, ["Run"]),
        extra_pip_requirements=["torch", "accelerate", "transformers", "numpy"],
    )

Custom Flavors

You can also create custom MLflow Models by writing a custom flavor.

As discussed in the Model API and Storage Format sections, an MLflow Model is defined by a directory of files that contains an MLmodel configuration file. This MLmodel file describes various model attributes, including the flavors in which the model can be interpreted. The MLmodel file contains an entry for each flavor name; each entry is a YAML-formatted collection of flavor-specific attributes.

To create a new flavor to support a custom model, you define the set of flavor-specific attributes to include in the MLmodel configuration file, as well as the code that can interpret the contents of the model directory and the flavor’s attributes. A detailed example of constructing a custom model flavor and its usage is shown below. New custom flavors not considered for official inclusion into MLflow should be introduced as separate GitHub repositories with documentation provided in the Community Model Flavors page.

Example: Creating a custom “sktime” flavor

This example illustrates the creation of a custom flavor for sktime time series library. The library provides a unified interface for multiple learning tasks including time series forecasting. While the custom flavor in this example is specific in terms of the sktime inference API and model serialization format, its interface design is similar to many of the existing built-in flavors. Particularly, the interface for utilizing the custom model loaded as a python_function flavor for generating predictions uses a single-row Pandas DataFrame configuration argument to expose the paramters of the sktime inference API. The complete code for this example is included in the flavor.py module of the sktime example directory.

Let’s examine the custom flavor module in more detail. The first step is to import several modules inluding sktime library, various MLflow utilities as well as the MLflow pyfunc module which is required to add the pyfunc specification to the MLflow model configuration. Note also the import of the flavor module itself. This will be passed to the mlflow.models.Model.log() method to log the model as an artifact to the current MLflow run.

import logging
import os
import pickle

import flavor
import mlflow
import numpy as np
import pandas as pd
import sktime
import yaml
from mlflow import pyfunc
from mlflow.exceptions import MlflowException
from mlflow.models import Model
from mlflow.models.model import MLMODEL_FILE_NAME
from mlflow.models.utils import _save_example
from mlflow.protos.databricks_pb2 import INTERNAL_ERROR, INVALID_PARAMETER_VALUE
from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS
from mlflow.tracking.artifact_utils import _download_artifact_from_uri
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,
    _validate_env_arguments,
)
from mlflow.utils.file_utils import write_to
from mlflow.utils.model_utils import (
    _add_code_from_conf_to_system_path,
    _get_flavor_configuration,
    _validate_and_copy_code_paths,
    _validate_and_prepare_target_save_path,
)
from mlflow.utils.requirements_utils import _get_pinned_requirement
from sktime.utils.multiindex import flatten_multiindex

_logger = logging.getLogger(__name__)

We continue by defining a set of important variables used throughout the code that follows.

The flavor name needs to be provided for every custom flavor and should reflect the name of the library to be supported. It is saved as part of the flavor-specific attributes to the MLmodel configuration file. This example also defines some sktime specific variables. For illustration purposes, only a subset of the available predict methods to be exposed via the _SktimeModelWrapper class is included when loading the model in its python_function flavor (additional methods could be added in a similar fashion). Additionaly, the model serialization formats, namely pickle (default) and cloudpickle, are defined. Note that both serialization modules require using the same python environment (version) in whatever environment this model is used for inference to ensure that the model will load with the appropriate version of pickle (cloudpickle).

FLAVOR_NAME = "sktime"

SKTIME_PREDICT = "predict"
SKTIME_PREDICT_INTERVAL = "predict_interval"
SKTIME_PREDICT_QUANTILES = "predict_quantiles"
SKTIME_PREDICT_VAR = "predict_var"
SUPPORTED_SKTIME_PREDICT_METHODS = [
    SKTIME_PREDICT,
    SKTIME_PREDICT_INTERVAL,
    SKTIME_PREDICT_QUANTILES,
    SKTIME_PREDICT_VAR,
]

SERIALIZATION_FORMAT_PICKLE = "pickle"
SERIALIZATION_FORMAT_CLOUDPICKLE = "cloudpickle"
SUPPORTED_SERIALIZATION_FORMATS = [
    SERIALIZATION_FORMAT_PICKLE,
    SERIALIZATION_FORMAT_CLOUDPICKLE,
]

Similar to the MLflow built-in flavors, a custom flavor logs the model in MLflow format via the save_model() and log_model() functions. In the save_model() function, the sktime model is saved to a specified output directory. Additionally, save_model() leverages the mlflow.models.Model.add_flavor() and mlflow.models.Model.save() methods to produce the MLmodel configuration containing the sktime and the python_function flavor. The resulting configuration has several flavor-specific attributes, such as the flavor name and sktime_version, which denotes the version of the sktime library that was used to train the model. An example of the output directoy for the custom sktime model is shown below:

# Directory written by flavor.save_model(model, "my_model")
my_model/
├── MLmodel
├── conda.yaml
├── model.pkl
├── python_env.yaml
└── requirements.txt

And its YAML-formatted MLmodel file describes the two flavors:

flavors:
  python_function:
    env:
      conda: conda.yaml
      virtualenv: python_env.yaml
    loader_module: flavor
    model_path: model.pkl
    python_version: 3.8.15
  sktime:
    code: null
    pickled_model: model.pkl
    serialization_format: pickle
    sktime_version: 0.16.0

The save_model() function also provides flexibility to add additional paramters which can be added as flavor-specific attributes to the model configuration. In this example there is only one flavor-specific parameter for specifying the model serialization format. All other paramters are non-flavor specific (for a detailed description of these parameters take a look at mlflow.sklearn.save_model). Note: When creating your own custom flavor, be sure rename the sktime_model parameter in both the save_model() and log_model() functions to reflect the name of your custom model flavor.

def save_model(
    sktime_model,
    path,
    conda_env=None,
    code_paths=None,
    mlflow_model=None,
    signature=None,
    input_example=None,
    pip_requirements=None,
    extra_pip_requirements=None,
    serialization_format=SERIALIZATION_FORMAT_PICKLE,
):
    _validate_env_arguments(conda_env, pip_requirements, extra_pip_requirements)

    if serialization_format not in SUPPORTED_SERIALIZATION_FORMATS:
        raise MlflowException(
            message=(
                f"Unrecognized serialization format: {serialization_format}. "
                "Please specify one of the following supported formats: "
                "{SUPPORTED_SERIALIZATION_FORMATS}."
            ),
            error_code=INVALID_PARAMETER_VALUE,
        )

    _validate_and_prepare_target_save_path(path)
    code_dir_subpath = _validate_and_copy_code_paths(code_paths, path)

    if mlflow_model is None:
        mlflow_model = Model()
    if signature is not None:
        mlflow_model.signature = signature
    if input_example is not None:
        _save_example(mlflow_model, input_example, path)

    model_data_subpath = "model.pkl"
    model_data_path = os.path.join(path, model_data_subpath)
    _save_model(
        sktime_model, model_data_path, serialization_format=serialization_format
    )

    pyfunc.add_to_model(
        mlflow_model,
        loader_module="flavor",
        model_path=model_data_subpath,
        conda_env=_CONDA_ENV_FILE_NAME,
        python_env=_PYTHON_ENV_FILE_NAME,
        code=code_dir_subpath,
    )

    mlflow_model.add_flavor(
        FLAVOR_NAME,
        pickled_model=model_data_subpath,
        sktime_version=sktime.__version__,
        serialization_format=serialization_format,
        code=code_dir_subpath,
    )
    mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME))

    if conda_env is None:
        if pip_requirements is None:
            include_cloudpickle = (
                serialization_format == SERIALIZATION_FORMAT_CLOUDPICKLE
            )
            default_reqs = get_default_pip_requirements(include_cloudpickle)
            inferred_reqs = mlflow.models.infer_pip_requirements(
                path, 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)

    if pip_constraints:
        write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME), "\n".join(pip_constraints))

    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 _save_model(model, path, serialization_format):
    with open(path, "wb") as out:
        if serialization_format == SERIALIZATION_FORMAT_PICKLE:
            pickle.dump(model, out)
        else:
            import cloudpickle

            cloudpickle.dump(model, out)

The save_model() function also writes the model dependencies to a requirements.txt and conda.yaml file in the model output directory. For this purpose the set of pip dependecies produced by this flavor need to be added to the get_default_pip_requirements() function. In this example only the minimum required dependencies are provided. In practice, additional requirements needed for preprocessing or post-processing steps could be included. Note that for any custom flavor, the mlflow.models.infer_pip_requirements() method in the save_model() function will return the default requirements defined in get_default_pip_requirements() as package imports are only inferred for built-in flavors.

def get_default_pip_requirements(include_cloudpickle=False):
    pip_deps = [_get_pinned_requirement("sktime")]
    if include_cloudpickle:
        pip_deps += [_get_pinned_requirement("cloudpickle")]

    return pip_deps


def get_default_conda_env(include_cloudpickle=False):
    return _mlflow_conda_env(
        additional_pip_deps=get_default_pip_requirements(include_cloudpickle)
    )

Next, we add the log_model() function. This function is little more than a wrapper around the mlflow.models.Model.log() method to enable logging our custom model as an artifact to the curren MLflow run. Any flavor-specific parameters (e.g. serialization_format) introduced in the save_model() function also need to be added in the log_model() function. We also need to pass the flavor module to the mlflow.models.Model.log() method which internally calls the save_model() function from above to persist the model.

def log_model(
    sktime_model,
    artifact_path,
    conda_env=None,
    code_paths=None,
    registered_model_name=None,
    signature=None,
    input_example=None,
    await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS,
    pip_requirements=None,
    extra_pip_requirements=None,
    serialization_format=SERIALIZATION_FORMAT_PICKLE,
    **kwargs,
):
    return Model.log(
        artifact_path=artifact_path,
        flavor=flavor,
        registered_model_name=registered_model_name,
        sktime_model=sktime_model,
        conda_env=conda_env,
        code_paths=code_paths,
        signature=signature,
        input_example=input_example,
        await_registration_for=await_registration_for,
        pip_requirements=pip_requirements,
        extra_pip_requirements=extra_pip_requirements,
        serialization_format=serialization_format,
        **kwargs,
    )

To interpret model directories produced by save_model(), the custom flavor must also define a load_model() function. The load_model() function reads the MLmodel configuration from the specified model directory and uses the configuration attributes to load and return the sktime model from its serialized representation.

def load_model(model_uri, dst_path=None):
    local_model_path = _download_artifact_from_uri(
        artifact_uri=model_uri, output_path=dst_path
    )
    flavor_conf = _get_flavor_configuration(
        model_path=local_model_path, flavor_name=FLAVOR_NAME
    )
    _add_code_from_conf_to_system_path(local_model_path, flavor_conf)
    sktime_model_file_path = os.path.join(
        local_model_path, flavor_conf["pickled_model"]
    )
    serialization_format = flavor_conf.get(
        "serialization_format", SERIALIZATION_FORMAT_PICKLE
    )
    return _load_model(
        path=sktime_model_file_path, serialization_format=serialization_format
    )


def _load_model(path, serialization_format):
    with open(path, "rb") as pickled_model:
        if serialization_format == SERIALIZATION_FORMAT_PICKLE:
            return pickle.load(pickled_model)
        elif serialization_format == SERIALIZATION_FORMAT_CLOUDPICKLE:
            import cloudpickle

            return cloudpickle.load(pickled_model)

The _load_pyfunc() function will be called by the mlflow.pyfunc.load_model() method to load the custom model flavor as a pyfunc type. The MLmodel flavor configuration is used to pass any flavor-specific attributes to the _load_model() function (i.e., the path to the python_function flavor in the model directory and the model serialization format).

def _load_pyfunc(path):
    try:
        sktime_flavor_conf = _get_flavor_configuration(
            model_path=path, flavor_name=FLAVOR_NAME
        )
        serialization_format = sktime_flavor_conf.get(
            "serialization_format", SERIALIZATION_FORMAT_PICKLE
        )
    except MlflowException:
        _logger.warning(
            "Could not find sktime flavor configuration during model "
            "loading process. Assuming 'pickle' serialization format."
        )
        serialization_format = SERIALIZATION_FORMAT_PICKLE

    pyfunc_flavor_conf = _get_flavor_configuration(
        model_path=path, flavor_name=pyfunc.FLAVOR_NAME
    )
    path = os.path.join(path, pyfunc_flavor_conf["model_path"])

    return _SktimeModelWrapper(
        _load_model(path, serialization_format=serialization_format)
    )

The final step is to create the model wrapper class defining the python_function flavor. The design of the wrapper class determines how the flavor’s inference API is exposed when making predictions using the python_function flavor. Just like the built-in flavors, the predict() method of the sktime wrapper class accepts a single-row Pandas DataFrame configuration argument. For an example of how to construct this configuration DataFrame refer to the usage example in the next section. A detailed description of the supported paramaters and input formats is provided in the flavor.py module docstrings.

class _SktimeModelWrapper:
    def __init__(self, sktime_model):
        self.sktime_model = sktime_model

    def predict(self, dataframe, params=None) -> pd.DataFrame:
        df_schema = dataframe.columns.values.tolist()

        if len(dataframe) > 1:
            raise MlflowException(
                f"The provided prediction pd.DataFrame contains {len(dataframe)} rows. "
                "Only 1 row should be supplied.",
                error_code=INVALID_PARAMETER_VALUE,
            )

        # Convert the configuration dataframe into a dictionary to simplify the
        # extraction of parameters passed to the sktime predcition methods.
        attrs = dataframe.to_dict(orient="index").get(0)
        predict_method = attrs.get("predict_method")

        if not predict_method:
            raise MlflowException(
                f"The provided prediction configuration pd.DataFrame columns ({df_schema}) do not "
                "contain the required column `predict_method` for specifying the prediction method.",
                error_code=INVALID_PARAMETER_VALUE,
            )

        if predict_method not in SUPPORTED_SKTIME_PREDICT_METHODS:
            raise MlflowException(
                "Invalid `predict_method` value."
                f"The supported prediction methods are {SUPPORTED_SKTIME_PREDICT_METHODS}",
                error_code=INVALID_PARAMETER_VALUE,
            )

        # For inference parameters 'fh', 'X', 'coverage', 'alpha', and 'cov'
        # the respective sktime default value is used if the value was not
        # provided in the configuration dataframe.
        fh = attrs.get("fh", None)

        # Any model that is trained with exogenous regressor elements will need
        # to provide `X` entries as a numpy ndarray to the predict method.
        X = attrs.get("X", None)

        # When the model is served via REST API the exogenous regressor must be
        # provided as a list to the configuration DataFrame to be JSON serializable.
        # Below we convert the list back to ndarray type as required by sktime
        # predict methods.
        if isinstance(X, list):
            X = np.array(X)

        # For illustration purposes only a subset of the available sktime prediction
        # methods is exposed. Additional methods (e.g. predict_proba) could be added
        # in a similar fashion.
        if predict_method == SKTIME_PREDICT:
            predictions = self.sktime_model.predict(fh=fh, X=X)

        if predict_method == SKTIME_PREDICT_INTERVAL:
            coverage = attrs.get("coverage", 0.9)
            predictions = self.sktime_model.predict_interval(
                fh=fh, X=X, coverage=coverage
            )

        if predict_method == SKTIME_PREDICT_QUANTILES:
            alpha = attrs.get("alpha", None)
            predictions = self.sktime_model.predict_quantiles(fh=fh, X=X, alpha=alpha)

        if predict_method == SKTIME_PREDICT_VAR:
            cov = attrs.get("cov", False)
            predictions = self.sktime_model.predict_var(fh=fh, X=X, cov=cov)

        # Methods predict_interval() and predict_quantiles() return a pandas
        # MultiIndex column structure. As MLflow signature inference does not
        # support MultiIndex column structure the columns must be flattened.
        if predict_method in [SKTIME_PREDICT_INTERVAL, SKTIME_PREDICT_QUANTILES]:
            predictions.columns = flatten_multiindex(predictions)

        return predictions

Example: Using the custom “sktime” flavor

This example trains a sktime NaiveForecaster model using the Longley dataset for forecasting with exogenous variables. It shows a custom model type implementation that logs the training hyper-parameters, evaluation metrics and the trained model as an artifact. The single-row configuration DataFrame for this example defines an interval forecast with nominal coverage values [0.9,0.95], a future forecast horizon of four periods, and an exogenous regressor.

import json

import flavor
import pandas as pd
from sktime.datasets import load_longley
from sktime.forecasting.model_selection import temporal_train_test_split
from sktime.forecasting.naive import NaiveForecaster
from sktime.performance_metrics.forecasting import (
    mean_absolute_error,
    mean_absolute_percentage_error,
)

import mlflow

ARTIFACT_PATH = "model"

with mlflow.start_run() as run:
    y, X = load_longley()
    y_train, y_test, X_train, X_test = temporal_train_test_split(y, X)

    forecaster = NaiveForecaster()
    forecaster.fit(
        y_train,
        X=X_train,
    )

    # Extract parameters
    parameters = forecaster.get_params()

    # Evaluate model
    y_pred = forecaster.predict(fh=[1, 2, 3, 4], X=X_test)
    metrics = {
        "mae": mean_absolute_error(y_test, y_pred),
        "mape": mean_absolute_percentage_error(y_test, y_pred),
    }

    print(f"Parameters: \n{json.dumps(parameters, indent=2)}")
    print(f"Metrics: \n{json.dumps(metrics, indent=2)}")

    # Log parameters and metrics
    mlflow.log_params(parameters)
    mlflow.log_metrics(metrics)

    # Log model using custom model flavor with pickle serialization (default).
    flavor.log_model(
        sktime_model=forecaster,
        artifact_path=ARTIFACT_PATH,
        serialization_format="pickle",
    )
    model_uri = mlflow.get_artifact_uri(ARTIFACT_PATH)

# Load model in native sktime flavor and pyfunc flavor
loaded_model = flavor.load_model(model_uri=model_uri)
loaded_pyfunc = flavor.pyfunc.load_model(model_uri=model_uri)

# Convert test data to 2D numpy array so it can be passed to pyfunc predict using
# a single-row Pandas DataFrame configuration argument
X_test_array = X_test.to_numpy()

# Create configuration DataFrame
predict_conf = pd.DataFrame(
    [
        {
            "fh": [1, 2, 3, 4],
            "predict_method": "predict_interval",
            "coverage": [0.9, 0.95],
            "X": X_test_array,
        }
    ]
)

# Generate interval forecasts with native sktime flavor and pyfunc flavor
print(
    f"\nNative sktime 'predict_interval':\n${loaded_model.predict_interval(fh=[1, 2, 3], X=X_test, coverage=[0.9, 0.95])}"
)
print(f"\nPyfunc 'predict_interval':\n${loaded_pyfunc.predict(predict_conf)}")

# Print the run id wich is used for serving the model to a local REST API endpoint
print(f"\nMLflow run id:\n{run.info.run_id}")

When opening the MLflow runs detail page the serialized model artifact will show up, such as:

_images/tracking_artifact_ui_custom_flavor.png

To serve the model to a local REST API endpoint run the following MLflow CLI command substituting the run id printed during execution of the previous block (for more details refer to the Deploy MLflow models section):

mlflow models serve -m runs:/<run_id>/model --env-manager local --host 127.0.0.1

An example of requesting a prediction from the served model is shown below. The exogenous regressor needs to be provided as a list to be JSON serializable. The wrapper instance will convert the list back to numpy ndarray type as required by sktime inference API.

import pandas as pd
import requests

from sktime.datasets import load_longley
from sktime.forecasting.model_selection import temporal_train_test_split

y, X = load_longley()
y_train, y_test, X_train, X_test = temporal_train_test_split(y, X)

# Define local host and endpoint url
host = "127.0.0.1"
url = f"http://{host}:5000/invocations"

# Create configuration DataFrame
X_test_list = X_test.to_numpy().tolist()
predict_conf = pd.DataFrame(
    [
        {
            "fh": [1, 2, 3, 4],
            "predict_method": "predict_interval",
            "coverage": [0.9, 0.95],
            "X": X_test_list,
        }
    ]
)

# Create dictionary with pandas DataFrame in the split orientation
json_data = {"dataframe_split": predict_conf.to_dict(orient="split")}

# Score model
response = requests.post(url, json=json_data)
print(f"\nPyfunc 'predict_interval':\n${response.json()}")

Built-In Deployment Tools

This information is moved to MLflow Deployment page.

Export a python_function model as an Apache Spark UDF

You can output a python_function model as an Apache Spark UDF, which can be uploaded to a Spark cluster and used to score the model.

Example

from pyspark.sql.functions import struct
from pyspark.sql import SparkSession

spark = SparkSession.builder.getOrCreate()
pyfunc_udf = mlflow.pyfunc.spark_udf(spark, "<path-to-model>")
df = spark_df.withColumn("prediction", pyfunc_udf(struct([...])))

If a model contains a signature, the UDF can be called without specifying column name arguments. In this case, the UDF will be called with column names from signature, so the evaluation dataframe’s column names must match the model signature’s column names.

Example

from pyspark.sql import SparkSession

spark = SparkSession.builder.getOrCreate()
pyfunc_udf = mlflow.pyfunc.spark_udf(spark, "<path-to-model-with-signature>")
df = spark_df.withColumn("prediction", pyfunc_udf())

If a model contains a signature with tensor spec inputs, you will need to pass a column of array type as a corresponding UDF argument. The values in this column must be comprised of one-dimensional arrays. The UDF will reshape the array values to the required shape with ‘C’ order (i.e. read / write the elements using C-like index order) and cast the values as the required tensor spec type. For example, assuming a model requires input ‘a’ of shape (-1, 2, 3) and input ‘b’ of shape (-1, 4, 5). In order to perform inference on this data, we need to prepare a Spark DataFrame with column ‘a’ containing arrays of length 6 and column ‘b’ containing arrays of length 20. We can then invoke the UDF like following example code:

Example

from pyspark.sql import SparkSession

spark = SparkSession.builder.getOrCreate()
# Assuming the model requires input 'a' of shape (-1, 2, 3) and input 'b' of shape (-1, 4, 5)
model_path = "<path-to-model-requiring-multidimensional-inputs>"
pyfunc_udf = mlflow.pyfunc.spark_udf(spark, model_path)
# The `spark_df` has column 'a' containing arrays of length 6 and
# column 'b' containing arrays of length 20
df = spark_df.withColumn("prediction", pyfunc_udf(struct("a", "b")))

The resulting UDF is based on Spark’s Pandas UDF and is currently limited to producing either a single value, an array of values, or a struct containing multiple field values of the same type per observation. By default, we return the first numeric column as a double. You can control what result is returned by supplying result_type argument. The following values are supported:

  • 'int' or IntegerType: The leftmost integer that can fit in int32 result is returned or an exception is raised if there are none.

  • 'long' or LongType: The leftmost long integer that can fit in int64 result is returned or an exception is raised if there are none.

  • ArrayType (IntegerType | LongType): Return all integer columns that can fit into the requested size.

  • 'float' or FloatType: The leftmost numeric result cast to float32 is returned or an exception is raised if there are no numeric columns.

  • 'double' or DoubleType: The leftmost numeric result cast to double is returned or an exception is raised if there are no numeric columns.

  • ArrayType ( FloatType | DoubleType ): Return all numeric columns cast to the requested type. An exception is raised if there are no numeric columns.

  • 'string' or StringType: Result is the leftmost column cast as string.

  • ArrayType ( StringType ): Return all columns cast as string.

  • 'bool' or 'boolean' or BooleanType: The leftmost column cast to bool is returned or an exception is raised if the values cannot be coerced.

  • 'field1 FIELD1_TYPE, field2 FIELD2_TYPE, ...': A struct type containing multiple fields separated by comma, each field type must be one of types listed above.

Example

from pyspark.sql import SparkSession

spark = SparkSession.builder.getOrCreate()
# Suppose the PyFunc model `predict` method returns a dict like:
# `{'prediction': 1-dim_array, 'probability': 2-dim_array}`
# You can supply result_type to be a struct type containing
# 2 fields 'prediction' and 'probability' like following.
pyfunc_udf = mlflow.pyfunc.spark_udf(
    spark, "<path-to-model>", result_type="prediction float, probability: array<float>"
)
df = spark_df.withColumn("prediction", pyfunc_udf())

Example

from pyspark.sql.types import ArrayType, FloatType
from pyspark.sql.functions import struct
from pyspark.sql import SparkSession

spark = SparkSession.builder.getOrCreate()
pyfunc_udf = mlflow.pyfunc.spark_udf(
    spark, "path/to/model", result_type=ArrayType(FloatType())
)
# The prediction column will contain all the numeric columns returned by the model as floats
df = spark_df.withColumn("prediction", pyfunc_udf(struct("name", "age")))

If you want to use conda to restore the python environment that was used to train the model, set the env_manager argument when calling mlflow.pyfunc.spark_udf().

Example

from pyspark.sql.types import ArrayType, FloatType
from pyspark.sql.functions import struct
from pyspark.sql import SparkSession

spark = SparkSession.builder.getOrCreate()
pyfunc_udf = mlflow.pyfunc.spark_udf(
    spark,
    "path/to/model",
    result_type=ArrayType(FloatType()),
    env_manager="conda",  # Use conda to restore the environment used in training
)
df = spark_df.withColumn("prediction", pyfunc_udf(struct("name", "age")))

If you want to call :py:func:`mlflow.pyfunc.spark_udf through Databricks connect in remote client, you need to build the model environment in Databricks runtime first.

Example

from mlflow.pyfunc import build_model_env

# Build the model env and save it as an archive file to the provided UC volume directory
# and print the saved model env archive file path (like '/Volumes/.../.../XXXXX.tar.gz')
print(build_model_env(model_uri, "/Volumes/..."))

# print the cluster id. Databricks Connect client needs to use the cluster id.
print(spark.conf.get("spark.databricks.clusterUsageTags.clusterId"))

Once you have pre-built the model environment, you can run :py:func:`mlflow.pyfunc.spark_udf with ‘prebuilt_model_env’ parameter through Databricks connect in remote client,

Example

from databricks.connect import DatabricksSession

spark = DatabricksSession.builder.remote(
    host=os.environ["DATABRICKS_HOST"],
    token=os.environ["DATABRICKS_TOKEN"],
    cluster_id="<cluster id>",  # get cluster id by spark.conf.get("spark.databricks.clusterUsageTags.clusterId")
).getOrCreate()

# The path generated by `build_model_env` in Databricks runtime.
model_env_uc_uri = "dbfs:/Volumes/.../.../XXXXX.tar.gz"
pyfunc_udf = mlflow.pyfunc.spark_udf(
    spark, model_uri, prebuilt_env_uri=model_env_uc_uri
)

Deployment to Custom Targets

In addition to the built-in deployment tools, MLflow provides a pluggable mlflow.deployments Python API and mlflow deployments CLI for deploying models to custom targets and environments. To deploy to a custom target, you must first install an appropriate third-party Python plugin. See the list of known community-maintained plugins here.

Commands

The mlflow deployments CLI contains the following commands, which can also be invoked programmatically using the mlflow.deployments Python API:

  • Create: Deploy an MLflow model to a specified custom target

  • Delete: Delete a deployment

  • Update: Update an existing deployment, for example to deploy a new model version or change the deployment’s configuration (e.g. increase replica count)

  • List: List IDs of all deployments

  • Get: Print a detailed description of a particular deployment

  • Run Local: Deploy the model locally for testing

  • Help: Show the help string for the specified target

For more info, see:

mlflow deployments --help
mlflow deployments create --help
mlflow deployments delete --help
mlflow deployments update --help
mlflow deployments list --help
mlflow deployments get --help
mlflow deployments run-local --help
mlflow deployments help --help

Community Model Flavors

Go to the Community Model Flavors page to get an overview of other useful MLflow flavors, which are developed and maintained by the MLflow community.