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. They provide a convention to save a model in different “flavors” that can be understood by different downstream tools.
Table of Contents
Each MLflow Model is simply a directory containing arbitrary files, together with an
file in the root of the directory that can define multiple flavors that the model can be viewed
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 sagemaker tool for deploying models to Amazon SageMaker).
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
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
This model can then be used with any tool that supports either the
python_function model flavor. For example, the
mlflow sklearn command can serve a
model with the
mlflow sklearn serve my_model
In addition, the
mlflow sagemaker command-line tool can package and deploy models to AWS
SageMaker as long as they support the
mlflow sagemaker deploy -m my_model [other options]
Apart from a flavors field listing the model flavors, the MLmodel YAML format can contain the following fields:
- Date and time when the model was created, in UTC ISO 8601 format.
- ID of the run that created the model, if the model was saved using MLflow Tracking.
You can save and load MLflow Models in multiple ways. First, MLflow includes integrations with
several common libraries. For example,
load_model functions for Scikit-learn models. Second,
you can use the more general
mlflow.models.Model class to create and write models. This
class has four key functions:
add_flavorto 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.
savesaves the model to a local directory.
log_artifactlogs the model as an artifact in the current run using MLflow Tracking.
Model.loadloads a model from a local directory or from an artifact in a previous run.
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.
The Python Function flavor defines a generic filesystem 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.
The convention for Pyfunc models is to have a
predict method or function with the following
predict(data: pandas.DataFrame) -> pandas.DataFrame | numpy.array
Other MLflow components expect Pyfunc models to follow this convention.
The Pyfunc model format is defined as a directory structure containing all required data, code and configuration:
./dst-path/ ./MLmodel - config <code> - any code packaged with the model (specified in the conf file, see below) <data> - any data packaged with the model (specified in the conf file, see below) <env> - conda environment definition (specified in the conf file, see below)
A Pyfunc model directory must contain an
MLmodel file in its root with “python_function” format and the following
- loader_module [required]: Python module that can load the model. Expected to be a module identifier (e.g. ``mlflow.sklearn``) importable via ``importlib.import_module``. The imported module must contain a function with the following signature: load_pyfunc(path: string) -> <pyfunc model> The path argument is specified by the data parameter and may refer to a file or directory. - code [optional]: A relative path to a directory containing the code packaged with this model. All files and directories inside this directory are added to the Python path prior to importing the model loader. - data [optional]: A relative path to a file or directory containing model data. the path is passed to the model loader. - env [optional]: A relative path to an exported Conda environment. If present this environment will be activated prior to running the model.
>tree example/sklearn_iris/mlruns/run1/outputs/linear-lr ├── MLmodel ├── code │ ├── sklearn_iris.py │ ├── data │ └── model.pkl └── mlflow_env.yml >cat example/sklearn_iris/mlruns/run1/outputs/linear-lr/MLmodel python_function: code: code data: data/model.pkl env: mlflow_env.yml main: sklearn_iris
For more detail see docs at
The sklearn model flavor provides an easy to use interface for handling scikit-learn models with no
external dependencies. It saves and loads models using Python’s pickle module and also generates a valid
Python Function flavor. For more information, see
The TensorFlow model flavor enables logging TensorFlow
Saved Models and loading them back as
Python Function models for inference on Pandas DataFrames. Given a directory containing a saved model, you can log the model to MLflow via
log_saved_model. The saved model can then be loaded for inference via
load_pyfunc(). For more information, see
The Spark model flavor enables exporting Spark MLlib models as MLflow models. Exported models are
saved using Spark MLLib’s native serialization, and can then be loaded back as MLlib models or
Python Function models. When deployed as a Pyfunc, the model will create its own
SparkContext and convert pandas DataFrame input to a Spark DataFrame before scoring. While this is not
the most efficient solution, especially for real-time scoring, it enables users to easily deploy any MLlib PipelineModel
(as long as the PipelineModel has no external JAR dependencies) to any endpoint supported by
MLflow. For more information, see
In general, you can add any flavor you’d like in MLmodel files, either by writing them directly or
building them with the
mlflow.models.Model class. Just choose an arbitrary string name
for your flavor. MLflow’s tools will ignore flavors that they do not understand in the MLmodel file.
MLflow provides tools for deployment on a local machine and several production environments. You can use these tools to easily apply your models in a production environment. Not all deployment methods are available for all model flavors. Deployment is currently supported mostly for the python function format and all compatible formats.
MLflow can deploy models locally as a local REST API endpoint or to directly score csv files. This functionality is a convenient way of testing models before uploading to remote.
Python function flavor can be deployed locally via
mlflow.pyfunc module as
servedeploys model as a local REST api server
predictuses the model to generate prediction for local csv file.
For more info, see:
mlflow pyfunc --help mlflow pyfunc serve --help mlflow pyfunc predict --help
mlflow.azureml module can export
Python Function models as Azure ML compatible models. It
can also be used to directly deploy and serve models on Azure ML, provided the environment has
been correctly set up.
exportexports the model in Azure ML-compatible format. MLFlow will output a directory with the dependencies necessary to deploy the model.
deploydeploys the model directly to Azure ML. You first need to set up your environment to work with the Azure ML CLI. You can do this by starting a shell from the Azure ML Workbench application. You also have to set up all accounts required to run and deploy on Azure ML. Where the model is deployed is dependent on your active Azure ML environment. If the active environment is set up for local deployment, the model will be deployed locally in a Docker container (Docker is required).
Model export example:
mlflow azureml export -m <path-to-model> -o test-output tree test-output test-output ├── create_service.sh - you can use this script to upload the model to Azure ML ├── score.py - main module required by Azure ML └── test-output - dir containing MLFlow model in Python Function flavor
Example model workflow for deployment:
az ml set env <local-env> - set environment to local deployment mlflow azureml deploy <parameters> - deploy locally to test the model az ml set env <cluster-env> - set environment to cluster mlflow azureml deploy <parameters> - deploy to the cloud
For more info, see:
mlflow azureml --help mlflow azureml export --help mlflow azureml deploy --help
mlflow.sagemaker module can deploy
Python Function models on Sagemaker
or locally in a docker container with Sagemaker compatible environment (Docker is required).
Similarly to Azure ML, you have to set up your environment and user accounts first in order to
deploy to Sagemaker with MLflow. Also, in order to export a custom model to Sagemaker, you need a
MLflow-compatible Docker image to be available on Amazon ECR. MLflow provides a default Docker
image defintion;however, it is up to the user to build the actual image and upload it to ECR.
MLflow includes a utility function to perform this step. Once built and uploaded, the MLflow
container can be used for all MLflow models.
build-and-push-containerbuilds an MLFLow Docker image and uploads it to ECR. The calling user has to have the correct permissions set up. The image is built locally and requires Docker to be present on the machine that performs this step.
run_localdeploys the model locally in a Docker container. The image and the environment should be identical to how the model would be run remotely and it is therefore useful for testing the model prior to deployment.
deploydeploys the model on Amazon Sagemaker. MLflow will upload the Python Function model into S3 and start an Amazon Sagemaker endpoint serving the model.
mlflow sagemaker build-and-push-container - build the container (only needs to be called once) mlflow sagemaker run-local -m <path-to-yourmodel> - test the model locally mlflow sagemaker deploy <parameters> - deploy the model to the cloud
For more info, see:
mlflow sagemaker --help mlflow sagemaker build-and-push-container --help mlflow sagemaker run-local --help mlflow sagemaker deploy --help