docs/source/examples/mlflow.rst
MLflow <https://mlflow.org/>_ is an open-source platform, purpose-built to assist machine learning practitioners and teams in handling the complexities of the machine learning process. MLflow focuses on the full lifecycle for machine learning projects, ensuring that each phase is manageable, traceable, and reproducible.
This document explains how to serve and deploy an MLflow model with BentoML.
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<a href="https://github.com/bentoml/BentoMLflow" style="margin-left: 5px; vertical-align: middle;">Source Code</a>
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<a href="#bentocloud" style="margin-left: 5px; vertical-align: middle;">Deploy to BentoCloud</a>
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<a href="#localserving" style="margin-left: 5px; vertical-align: middle;">Serve with BentoML</a>
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The example uses scikit-learn for demo purposes. You can submit a classification request to the endpoint like this:
.. code-block:: bash
{ "input_data": [[5.9,3,5.1,1.8]] }
Expected output:
.. code-block:: bash
["virginica"]
In addition to scikit-learn, both MLflow and BentoML support a wide variety of other frameworks, such as PyTorch, TensorFlow and XGBoost.
This example is ready for quick deployment and scaling on BentoCloud. With a single command, you get a production-grade application with fast autoscaling, secure deployment in your cloud, and comprehensive observability.
.. image:: ../../_static/img/examples/mlflow/mlflow-model-on-bentocloud.png :alt: Screenshot of MLflow Iris classifier model deployed on BentoCloud showing the prediction interface with input data and classification results
You can find the source code in GitHub <https://github.com/bentoml/BentoMLflow>_. Below is a breakdown of the key code implementations within this project.
save_model.py ^^^^^^^^^^^^^
This example uses the scikit-learn framework to train a classification model and saves it with MLflow.
.. code-block:: python
:caption: save_model.py
from sklearn.datasets import load_iris
from sklearn.neighbors import KNeighborsClassifier
from pathlib import Path
import mlflow.sklearn
iris = load_iris()
X_train = iris.data[:, :4]
Y_train = iris.target
model_uri = Path("models", "IrisClf")
model = KNeighborsClassifier()
model.fit(X_train, Y_train)
mlflow.sklearn.save_model(model, model_uri.resolve())
After training, use the bentoml.mlflow.import_model API to save the model to the BentoML :doc:Model Store </build-with-bentoml/model-loading-and-management>, a local directory to store and manage models. You can retrieve this model later in other services to run predictions.
.. code-block:: bash
import bentoml
# model_uri can be any URI that refers to an MLflow model
# Use local path for demonstration
bentoml.mlflow.import_model("iris", model_uri)
To verify that the model has been successfully saved, run:
.. code-block:: bash
$ bentoml models list
Tag Module Size Creation Time
iris:74px7hboeo25fjjt bentoml.mlflow 10.07 KiB 2024-06-19 10:09:21
test.py ^^^^^^^
To ensure that the saved model works correctly, try loading it and running a prediction:
.. code-block:: python
:caption: test.py
import numpy as np
import bentoml
# Load the latest version of iris model
iris_model = bentoml.mlflow.load_model("iris:latest")
input_data = np.array([[5.9, 3, 5.1, 1.8]])
res = iris_model.predict(input_data)
print(res)
Expected result:
.. code-block:: bash
[2] # The model thinks the category seems to be Virginica.
service.py ^^^^^^^^^^
The service.py file is where you define the serving logic and expose the model as a web service.
.. code-block:: python
:caption: service.py
import bentoml
import numpy as np
target_names = ['setosa', 'versicolor', 'virginica']
@bentoml.service(
resources={"cpu": "2"},
traffic={"timeout": 10},
)
class IrisClassifier:
# Declare the model as a class variable
bento_model = bentoml.models.BentoModel("iris:latest")
def __init__(self):
self.model = bentoml.mlflow.load_model(self.bento_model)
@bentoml.api
def predict(self, input_data: np.ndarray) -> list[str]:
preds = self.model.predict(input_data)
return [target_names[i] for i in preds]
The Service code:
@bentoml.service decorator to define a BentoML :doc:Service </build-with-bentoml/services>. Optionally, you can set additional :doc:configurations </reference/bentoml/configurations> like resource allocation on BentoCloud and traffic timeout.@bentoml.api decorator to expose the predict function as an API endpoint.The @bentoml.service decorator also allows you to :doc:define the runtime environment </build-with-bentoml/runtime-environment> for a Bento, the unified distribution format in BentoML. A Bento is packaged with all the source code, Python dependencies, model references, and environment setup, making it easy to deploy consistently across different environments.
Here is an example:
.. code-block:: python
:caption: service.py
my_image = bentoml.images.Image(python_version="3.11") \
.python_packages("mlflow", "scikit-learn")
@bentoml.service(
image=my_image, # Apply the specifications
...
)
class IrisClassifier:
...
You can run this example project <https://github.com/bentoml/BentoMLflow>_ on BentoCloud, or serve it locally, containerize it as an OCI-compliant image and deploy it anywhere.
.. _BentoCloud:
BentoCloud ^^^^^^^^^^
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BentoCloud provides fast and scalable infrastructure for building and scaling AI applications with BentoML in the cloud.
Install the dependencies and :doc:log in to BentoCloud </scale-with-bentocloud/manage-api-tokens> through the BentoML CLI. If you don't have a BentoCloud account, sign up here for free <https://www.bentoml.com/>_.
.. code-block:: bash
pip install bentoml mlflow scikit-learn
bentoml cloud login
Clone the repository.
.. code-block:: bash
git clone https://github.com/bentoml/BentoMLflow.git cd BentoMLflow
Train and save the MLflow model to the BentoML Model Store.
.. code-block:: bash
python3 save_model.py
Deploy the Service to BentoCloud.
.. code-block:: bash
bentoml deploy service.py:IrisClassifier
Once it is up and running, you can call the endpoint in the following ways:
.. tab-set::
.. tab-item:: BentoCloud Playground
.. image:: ../../_static/img/examples/mlflow/mlflow-model-on-bentocloud.png
:alt: Screenshot of MLflow Iris classifier model deployed on BentoCloud showing the prediction interface with input data and classification results
.. tab-item:: Python client
Create a :doc:`BentoML client </build-with-bentoml/clients>` to call the endpoint. Make sure you replace the Deployment URL with your own on BentoCloud. Refer to :ref:`scale-with-bentocloud/deployment/call-deployment-endpoints:obtain the endpoint url` for details.
.. code-block:: python
import bentoml
with bentoml.SyncHTTPClient("https://iris-classifier-bdbe-e3c1c7db.mt-guc1.bentoml.ai") as client:
result = client.predict(
input_data=[
[5.9, 3, 5.1, 1.8]
],
)
print(result)
.. tab-item:: CURL
Make sure you replace the Deployment URL with your own on BentoCloud. Refer to :ref:`scale-with-bentocloud/deployment/call-deployment-endpoints:obtain the endpoint url` for details.
.. code-block:: bash
curl -X 'POST' \
'https://iris-classifier-bdbe-e3c1c7db.mt-guc1.bentoml.ai/predict' \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"input_data": [
[5.9, 3, 5.1, 1.8]
]
}'
To make sure the Deployment automatically scales within a certain replica range, add the scaling flags:
.. code-block:: bash
bentoml deploy --scaling-min 0 --scaling-max 3 # Set your desired count
If it's already deployed, update its allowed replicas as follows:
.. code-block:: bash
bentoml deployment update <deployment-name> --scaling-min 0 --scaling-max 3 # Set your desired count
For more information, see :doc:how to configure concurrency and autoscaling </scale-with-bentocloud/scaling/autoscaling>.
.. _LocalServing:
Local serving ^^^^^^^^^^^^^
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BentoML allows you to run and test your code locally, so that you can quickly validate your code with local compute resources.
Clone the project repository and install the dependencies.
.. code-block:: bash
git clone https://github.com/bentoml/BentoMLflow.git cd BentoMLflow
pip install bentoml mlflow scikit-learn
Train and save the model to the BentoML Model Store.
.. code-block:: bash
python3 save_model.py
Serve it locally.
.. code-block:: bash
bentoml serve service.py:IrisClassifier
Visit or send API requests to http://localhost:3000 <http://localhost:3000/>_.
For custom deployment in your own infrastructure, use BentoML to :doc:generate an OCI-compliant image </get-started/packaging-for-deployment>.