Create imgae 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 S2I.

Source-to-Image (S2I) is a toolkit and workflow for building reproducible container images from source code. S2I produces ready-to-run images by injecting source code into a container image and letting the container prepare that source code for execution.

Step 1 - Install s2i

Download and 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-python3:1.19.0-dev

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

• Your model's dependencies and environment, which can be described using either of:

  • requirements.txt

  • setup.py

  • environment.yml

• .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 further details see details on creating your python class

Dependencies

You can describe your model's dependencies using either of: requirements.txt, setup.py or environment.yml.

requirements.txt

Populate a requirements.txt with any software dependencies your code requires. These will be installed via pip when creating the image.

setup.py

Similar to a requirements.txt file, you can also describe your model's dependencies using a setup.py file:

from setuptools import setup

setup(
  name="my-model",
  # ...
  install_requires=[
    "scikit-learn",
  ]
)

environment.yml

Describe your Conda environment using an environment.yml file:

name: my-conda-environment
channels:
  - defaults
dependencies:
  - python=3.6
  - scikit-learn=0.19.1

During image creation, s2i will create your Conda environment, fetching all the required dependencies. At run time, the created Conda environment will get activated at startup.

.s2i/environment

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

MODEL_NAME=MyModel
SERVICE_TYPE=MODEL

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

See below for the possible keys and values for this file.

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

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-python3:1.19.0-dev <my-image-name>

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

s2i build https://github.com/seldonio/seldon-core.git --context-dir=wrappers/s2i/python/test/model-template-app seldonio/seldon-core-s2i-python3:1.19.0-dev seldon-core-template-model

The above s2i build invocation:

• uses the GitHub repo: https://github.com/seldonio/seldon-core.git 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 https://github.com/seldonio/seldon-core.git
cd seldon-core
s2i build wrappers/s2i/python/test/model-template-app seldonio/seldon-core-s2i-python3:1.19.0-dev seldon-core-template-model

For more help see:

s2i usage seldonio/seldon-core-s2i-python3:1.19.0-dev
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.

MODEL_NAME

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

SERVICE_TYPE

The service type being created. Available options are:

• MODEL

• ROUTER

• TRANSFORMER

• COMBINER

• OUTLIER_DETECTOR

EXTRA_INDEX_URL

.. Warning:: EXTRA_INDEX_URL is recommended to be passed as argument to s2i command rather than adding in .s2i/environment as a practice of avoiding checking in credentials in the code.

For installing packages from private/self-hosted PyPi registry.

PIP_TRUSTED_HOST

For adding private/self-hosted unsecured PyPi registry by adding it to pip trusted-host.

s2i build \
   -e EXTRA_INDEX_URL=https://<pypi-user>:<pypi-auth>@mypypi.example.com/simple \
   -e PIP_TRUSTED_HOST=mypypi.example.com \
   <src-folder> \
   seldonio/seldon-core-s2i-python3:1.19.0-dev \
   <my-image-name>

PAYLOAD_PASSTHROUGH

If enabled, the Python server won't try to decode the request payload nor encode the response back. That means that the predict() method of your SeldonComponent model will receive the payload as-is and it will be responsible to decode it. Likewise, the return value of predict() must be a serialised response.

By default, this option will be disabled.

Creating different service types

MODEL

• A minimal skeleton for model source code

• Example using SKLearn Spacy NLP

• Example with SKLearn Iris Classifier

ROUTER

• Description of routers in Seldon Core

• A minimal skeleton for router source code

TRANSFORMER

• A minimal skeleton for transformer source code

• Example transformers

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": [
      {
        "name": "grpc_endpoint",
        "type": "STRING",
        "value": "localhost:8000"
      },
      {
        "name": "model_name",
        "type": "STRING",
        "value": "mnist-model"
      },
      {
        "name": "model_output",
        "type": "STRING",
        "value": "scores"
      },
      {
        "name": "model_input",
        "type": "STRING",
        "value": "images"
      },
      {
        "name": "signature_name",
        "type": "STRING",
        "value": "predict_images"
      }
    ]
  }
}

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

Local Python Dependencies

from version 0.5

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-python3:1.19.0-dev <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 Request Tags

from version 0.3

To add custom request tags 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}