SKLearn Server

If you have a trained SKLearn model saved as a pickle you can deploy it simply using Seldon's prepackaged SKLearn server.

Prerequisites

Seldon expects that your model has been saved using joblib, and it named as model.joblib. Note that this is the recommended approach to serialise models by the SKLearn project.

Note that, since we are using joblib, it's important that your trained model matches the framework version expected in the inference server.

The expected versions in the latest SKLearn pre-packaged server are as follows:

Package	Version
scikit-learn	0.24.2

To check compatibility requirements for older versions of Seldon Core you can see the compatibility table below.

Usage

To use the pre-packaged SKLearn server, it's enough to declare SKLEARN_SERVER as the implementation for your model. For example, for a saved Iris prediction model, you could do:

apiVersion: machinelearning.seldon.io/v1alpha2
kind: SeldonDeployment
metadata:
  name: sklearn
spec:
  name: iris
  predictors:
  - graph:
      children: []
      implementation: SKLEARN_SERVER
      modelUri: gs://seldon-models/v1.20.0-dev/sklearn/iris
      name: classifier
    name: default
    replicas: 1

You can try a similar example in this worked notebook.

Sklearn inference method

By default the server will call predict_proba on your loaded model/pipeline. If you wish for it to call predict instead you can pass a parameter method and set it to predict. For example:

apiVersion: machinelearning.seldon.io/v1alpha2
kind: SeldonDeployment
metadata:
  name: sklearn
spec:
  name: iris-predict
  predictors:
  - graph:
      children: []
      implementation: SKLEARN_SERVER
      modelUri: gs://seldon-models/v1.20.0-dev/sklearn/iris
      name: classifier
      parameters:
        - name: method
          type: STRING
          value: predict
    name: default
    replicas: 1

Acceptable values for the method parameter are predict, predict_proba, decision_function.

Open Inference Protocol (or V2 protocol)

The SKLearn server can also be used to expose an API compatible with the Open Inference Protocol. Note that, under the hood, it will use the Seldon MLServer runtime.

In order to enable support for the Open Inference Protocol it's enough to specify the protocol of the SeldonDeployment to use v2. For example,

apiVersion: machinelearning.seldon.io/v1alpha2
kind: SeldonDeployment
metadata:
  name: sklearn
spec:
  name: iris-predict
  protocol: v2 # Activate the Open Inference Protocol
  predictors:
  - graph:
      children: []
      implementation: SKLEARN_SERVER
      modelUri: gs://seldon-models/v1.20.0-dev/sklearn/iris
      name: classifier
      parameters:
        - name: method
          type: STRING
          value: predict
    name: default

You can try a similar example in this worked notebook.

Version compatibility

The version of SKLearn used by the pre-packaged inference server will depend on the installed version of Seldon Core. In particular,

Seldon Version	SKLearn Version
>=1.3	0.23.2
<1.3 (latest 1.2.3)	0.20.3

Note that using a different version of SKLearn at training and inference time can cause unexpected issues when it comes to serving.

Using an older version

If you wish to use an older image of the SKLearn inference server, you can override the used image in the componentSpecs. For example, to use version 1.2.3 of the SKLearn server you could do:

apiVersion: machinelearning.seldon.io/v1alpha2
kind: SeldonDeployment
metadata:
  name: sklearn
spec:
  name: iris
  predictors:
  - componentSpecs:
    - spec:
       containers:
       - name: classifier
         image: seldonio/sklearnserver_rest:1.2.3
    graph:
      children: []
      implementation: SKLEARN_SERVER
      modelUri: gs://seldon-models/v1.20.0-dev/sklearn/iris
      name: classifier
    name: default
    replicas: 1
    svcOrchSpec: 
      env: 
      - name: SELDON_LOG_LEVEL
        value: DEBUG

Using a different version of SKLearn

If you wish to use an unsupported version of SKLearn, you can extend the existing SKLearn server to build your own. In particular, you could extend the code in the servers/sklearnserver folder to build a custom image. This image used for the SKLEARN_SERVER implementation can then be overridden in the componentSpecs.

Note that you can also change the image used globally for the SKLearn server by editing the seldon-config configmap. This change would apply to all SeldonDeployments in your cluster leveraging the SKLEARN_SERVER implementation. For example, you could add the following to the configmap:

  SKLEARN_SERVER:
    grpc:
      defaultImageVersion: "1.2.3"
      image: seldonio/sklearnserver_grpc
    rest:
      defaultImageVersion: "1.2.3"
      image: seldonio/sklearnserver_rest