packflow 0.3.0

AI/ML inference SDK for streaming data

Introduction

packflow is a software development kit (SDK) that simplifies the development process and standardizes packaging of AI/ML running on streaming data sources.

Many existing packaging frameworks are catered towards inference APIs and often require custom preprocessing steps. This can be particularly challenging when dealing with data sources that typically generate data one row at a time in key-value pairs (e.g., firewall logs or message streams).

Packflow, however, is optimized to run models on either individual events or batches of events, streamlining development and reducing the need for additional preprocessing. By leveraging Packflow, teams can focus on building and deploying models with custom out-of-the-box workflows and utilities, significantly reducing the time and effort required to onboard new capabilities.

Getting Started

The following instructions quickly walk through how to install Packflow and serve user documentation.

Installing Packflow

Prerequisite Requirements

• Python (version 3.10+)

Installation from PyPI

1. Packflow can be installed directly from PyPI:

pip install packflow

from the root of the Packflow repo.

[!NOTE] If contributing to Packflow, it is recommended to install packflow from source in editable mode: pip install -e .

Packflow Documentation

Packflow documentation is available pre-built in the repository and can be viewed immediately, or built from source for development work. Follow the instructions below to get started with serving the documentation.

Viewing Pre-built Documentation

The simplest way to view the Packflow documentation is to serve the pre-built HTML files included in the repository:

1. Navigate to the pre-built docs folder: cd docs/built/html
2. Start a local web server: python -m http.server 8000
3. Access the documentation in a web browser by navigating to http://127.0.0.1:8000/

[!WARNING] If a "Not Found" error page is received when first accessing the documentation, wait a moment for the server to fully start and refresh the page.

Building Documentation from Source

Prerequisite Requirements

The following are required to build documentation from source:

• Python (version 3.10+)
• Pip
• Packflow (the version corresponding to the docs being served)
• Pandoc[^1] (see Pandoc.org's official installation instructions)
• make Command[^2]

Steps

1. Navigate to the docs folder: cd docs
2. Install Python dependencies for building and hosting the documentation: pip install -r requirements.txt
3. Run make dev to serve the documentation from a working tree with live updates, or make prod-serve to serve static multi-version documentation (requires .git directory with branch/tag history)
4. Access the built documentation in a web browser by navigating to http://127.0.0.1:8000/

[!WARNING] If a "Not Found" error page is received when first accessing the documentation, wait a moment for the server to fully start and refresh the page.

Usage

Packflow provides a flexible base class called an InferenceBackend that allows users to build highly scalable platform- and tool-agnostic inference code, enabling simplified sharing across environments.

Additionally, Packflow's CLI can assist with creating projects, gathering environmental information, and creating distributable code packages for sharing reproducible inference code between disconnected environments.

For detailed information and usage patterns on Packflow, please see the About Packflow and User Guide sections of the official documentation site.

Dummy Inference Backend

To create a dummy Inference Backend, update the inference.py file to the following:

from packflow import InferenceBackend


class Backend(InferenceBackend):
    def execute(self, inputs):
        """
        Simply print 'Hello, world!' then return the input data
        """
        print('Hello, world!')
        return inputs

Load and Run the Inference Backend

In a different Python file or from the command line in the same directory, execute the following:

from packflow.loaders import LocalLoader

backend = LocalLoader('inference:Backend').load()

backend({"sample": "data"})
# >> {"sample": "data"}

Contributing

Contributions to Packflow are welcomed and highly encouraged! Please refer to the CONTRIBUTE.md guide for more information and guidelines for contributing to Packflow.

Authors

Packflow is developed and maintained by Booz Allen Hamilton on behalf of the Federal Government of the United States of America and the Department of War's Chief Digital and Artificial Intelligence Office (CDAO).

License

packflow is distributed under the terms of the MIT license. Please refer to the LICENSE.txt for more information of acceptable usage and distribution of Packflow.

[^1]: Pandoc must be installed separately from the pandoc python package in docs/requirements.txt. [^2]: Installation of make varies by operating system. On MacOS, install xcode-select. On Windows, it is recommend to use Windows Subsystem for Linux (WSL). On Debian/Ubuntu, make can be installed via apt package manager:

sudo apt update
sudo apt install make build-essential