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Surround is a framework for serving machine learning pipelines in Python

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Surround is a lightweight framework for serving machine learning pipelines in Python. It is designed to be flexible, easy to use and to assist data scientists by focusing them on the problem at hand rather than writing glue code. Surround began as a project at the Applied Artificial Intelligence Institute to address the following problems:

  • The same changes were required again and again to refactor code written by data scientists to make it ready for serving e.g. no standard way to run scripts, no standard way to handle configuration and no standard pipeline architecture.
  • Existing model serving solutions focus on serving the model rather than serving an end-to-end solution. Our machine learning projects require multiple models and glue code to tie these models together.
  • Existing serving approaches do not allow for the evolution of a machine learning pipeline without re-engineering the solution i.e. using a cloud API for the first release before training a custom model much later on.
  • Code was commonly being commented out to run other branches as experimentation was not a first class citizen in the code being written.

Used in projects by:

Installation

Prerequisites

  • Python 3+ (Tested on 3.6.5)
  • Docker (required for running in containers)
  • Tornado (optional, needed if serving via Web)

Use package manager pip to install the latest (stable) version:

$ pip3 install surround

Simple usage

A short explanation is provided in the hello-world example's README file.

import logging
from surround import State, Validator, Estimator, Assembler

class HelloWorld(Estimator):
    def estimate(self, state, config):
        state.text = "Hello world"

    def fit(self, state, config):
        print("No training implemented")

class InputValidator(Validator):
    def validate(self, state, config):
        if state.text:
            raise ValueError("'text' is not None")

class AssemblerState(State):
    text = None

if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO)
    data = AssemblerState()
    assembler = Assembler("Hello world example", InputValidator(), HelloWorld())
    assembler.run(data)
    print("Text is '%s'" % data.text)

Command Line Usage

Surround comes with a range of command line tools to help you create and run Surround pipelines.

To get more information on these tools, run the following command:

$ surround -h

Generating projects

For example you can use the sub-command init to generate a new project:

$ surround init <path-to-dir> --project-name sample --description "Sample description" --require-web

Where a new folder in path-to-dir (current directory if left blank) will be created with the name of the project. In this folder will be a collection of scripts and folders typically needed for a Surround project. For more information on what is generated, see our Getting Started guide.

Running projects

You can then test the genereated pipeline using the run sub-command in the root of the project like so:

$ surround run batchLocal

This will execute the pipeline locally in batch mode. If you want to run the pipeline in a container then use the following:

$ surround run build
$ surround run batch

If you would like to serve your pipeline via Web endpoints (--require-web is required when generating for this option) then you can use:

$ surround run web

Which (by default) will accept input data as JSON via HTTP POST to the endpoint http://localhost:8080/estimate in the following format:

{ "message": "this data will be processed by the pipeline" }

To see a full list of the available tasks just run the following command:

$ surround run

For more information on different run modes and when/how they should be used see both our About and Getting Started pages.

Surround pipeline architecture

The following diagram describes how data flows through a Surround pipeline depending on the mode used when running.

For a more in-depth description of this diagram, see the About page on our website.

Examples

See the examples directory for useful examples on how Surround can be utilized.

Full Documentation

See our website for an in-depth explanation of Surround (in the About page), a Getting Started Guide, and full documentation of the API.

Contributing

For guidance on setting up a development environment and how to make a contribution to Surround, see the contributing guidelines.

License

Surround is released under a BSD-3 license.

Project Status

Surround is currently under heavy development, please submit any issues that occur or suggestions you may have, it is very much appreciated!

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Surround is a framework for serving machine learning pipelines in Python

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