The A2ML ("Automate AutoML") project is a Python API and set of command line tools to automate Automated Machine Learning tools from multiple vendors. The intention is to provide a common API for all Cloud-oriented AutoML vendors. Data scientists can then train their datasets against multiple AutoML models to get the best possible predictive model. May the best "algorithm/hyperparameter search" win. Full documentation for A2ML is available at a2ml.org
Every AutoML vendor has their own API to manage the datasets and create and manage predictive models. They are similar but not identical APIs. But they share a common set of stages:
Since ITEDPR is hard to remember we refer to this pipeline by its conveniently mnemonic anagram: "PREDIT" (French for "predict"). The A2ML project provides classes which implement this pipeline for various Cloud AutoML providers and a command line interface that invokes stages of the pipeline.
A2ML is distributed as a python package, so to install it:
$ pip install -U a2ml
It will install Auger provider.
To use Azure AutoML:
$ brew install libomp
$ pip install "a2ml[azure]" --ignore-installed onnxruntime
$ apt-get update && apt-get -y install gcc g++ libgomp1
$ pip install "a2ml[azure]"
To use Google Cloud:
$ pip install "a2ml[google]"
To install everything including testing and server code:
$ pip install "a2ml[all]"
To release a new version the flow should be:
VERSION variable in
setup.py to match what you want to release, minus the “v”. By default it would be “
VERSION variable should simply be “0.3.0".
Commit and push the changes above.
git tag v<the-version> (for example: git tag v0.3.0) git push --tags
pip install -U a2ml==0.3.0 docker pull augerai/a2ml:v0.3.0
VERSIONvariable to the next version in the current milestone. For example, "0.3.1.dev0"