Packaging a Python model for Seldon Core using s2i

In this guide, we illustrate the steps needed to wrap your own python model in a docker image ready for deployment with Seldon Core using source-to-image app s2i.

If you are not familiar with s2i you can read general instructions on using s2i and then follow the steps below.

Step 1 - Install s2i

  • Prerequisites for using s2i are:
    • Docker
    • Git (if building from a remote git repo)

To check everything is working you can run

s2i usage seldonio/seldon-core-s2i-python3:0.18

Step 2 - Create your source code

To use our s2i builder image to package your python model you will need:

  • A python file with a class that runs your model
  • requirements.txt or
  • .s2i/environment - model definitions used by the s2i builder to correctly wrap your model

We will go into detail for each of these steps:

Python file

Your source code should contain a python file which defines a class of the same name as the file. For example, looking at our skeleton python model file at wrappers/s2i/python/test/model-template-app/

class MyModel(object):
    Model template. You can load your model parameters in __init__ from a location accessible at runtime

    def __init__(self):
        Add any initialization parameters. These will be passed at runtime from the graph definition parameters defined in your seldondeployment kubernetes resource manifest.

    def predict(self,X,features_names):
        Return a prediction.

        X : array-like
        feature_names : array of feature names (optional)
        print("Predict called - will run identity function")
        return X
  • The file is called and it defines a class MyModel
  • The class contains a predict method that takes an array (numpy) X and feature_names and returns an array of predictions.
  • You can add any required initialization inside the class init method.
  • Your return array should be at least 2-dimensional.


Populate a requirements.txt with any software dependencies your code requires. These will be installed via pip when creating the image. You can instead provide a if you prefer.


Define the core parameters needed by our python builder image to wrap your model. An example is:


These values can also be provided or overridden on the command line when building the image.

Step 3 - Build your image

Use s2i build to create your Docker image from source code. You will need Docker installed on the machine and optionally git if your source code is in a public git repo. You can choose from three python builder images

  • Python 3.6 : seldonio/seldon-core-s2i-python36:0.18, seldonio/seldon-core-s2i-python3:0.18
  • Python 3.6 plus ONNX support via Intel nGraph : seldonio/seldon-core-s2i-python3-ngraph-onnx:0.1

Using s2i you can build directly from a git repo or from a local source folder. See the s2i docs for further details. The general format is:

s2i build <src-folder> seldonio/seldon-core-s2i-python3:0.18 <my-image-name>

Change to seldonio/seldon-core-s2i-python3 if using python 3.

An example invocation using the test template model inside seldon-core:

s2i build --context-dir=wrappers/s2i/python/test/model-template-app seldonio/seldon-core-s2i-python3:0.18 seldon-core-template-model

The above s2i build invocation:

  • uses the GitHub repo: and the directory wrappers/s2i/python/test/model-template-app inside that repo.
  • uses the builder image seldonio/seldon-core-s2i-python3
  • creates a docker image seldon-core-template-model

For building from a local source folder, an example where we clone the seldon-core repo:

git clone
cd seldon-core
s2i build wrappers/s2i/python/test/model-template-app seldonio/seldon-core-s2i-python3:0.18 seldon-core-template-model

For more help see:

s2i usage seldonio/seldon-core-s2i-python3:0.18
s2i build --help

Using with Keras/Tensorflow Models

To ensure Keras models with the Tensorflow backend work correctly you may need to call _make_predict_function() on your model after it is loaded. This is because Flask may call the prediction request in a separate thread from the one that initialised your model. See the keras issue for further discussion.

Environment Variables

The required environment variables understood by the builder image are explained below. You can provide them in the .s2i/environment file or on the s2i build command line.


The name of the class containing the model. Also the name of the python file which will be imported.


API type to create. Can be REST or GRPC


The service type being created. Available options are:



Set either to 0 or 1. Default is 0. If set to 1 then your model will be saved periodically to redis and loaded from redis (if exists) or created fresh if not.

Advanced Usage

Model Class Arguments

You can add arguments to your component which will be populated from the parameters defined in the SeldonDeloyment when you deploy your image on Kubernetes. For example, our Python TFServing proxy has the class init method signature defined as below:

class TfServingProxy(object):

    def __init__(self,rest_endpoint=None,grpc_endpoint=None,model_name=None,signature_name=None,model_input=None,model_output=None):

These arguments can be set when deploying in a Seldon Deployment. An example can be found in the MNIST TFServing example where the arguments are defined in the SeldonDeployment which is partly show below:

 "graph": {
    "name": "tfserving-proxy",
    "endpoint": {"type" : "REST"},
    "type": "MODEL",
    "children": [],
    "parameters": [

The allowable type values for the parameters are defined in the proto buffer definition.

Local Python Dependencies

from version 0.5-SNAPSHOT

To use a private repository for installing Python dependencies use the following build command:

s2i build -i <python-wheel-folder>:/whl <src-folder> seldonio/seldon-core-s2i-python3:0.18 <my-image-name>

This command will look for local Python wheels in the <python-wheel-folder> and use these before searching PyPI.

Custom Metrics

from version 0.3

To add custom metrics to your response you can define an optional method metrics in your class that returns a list of metric dicts. An example is shown below:

class MyModel(object):

    def predict(self, X, features_names):
        return X

    def metrics(self):
        return [{"type": "COUNTER", "key": "mycounter", "value": 1}]

For more details on custom metrics and the format of the metric dict see here.

There is an example notebook illustrating a model with custom metrics in python.

Custom Meta Data

from version 0.3

To add custom meta data you can add an optional method tags which can return a dict of custom meta tags as shown in the example below:

class MyModel(object):

    def predict(self, X, features_names):
        return X

    def tags(self):
        return {"mytag": 1}