Python utilities for easily configurable machine learning.
This project utilizes the excellent tutorial How to Publish an Open-Source Python Package to PyPI
from pyconfigurableml.entry import run
def main(config, log):
# TODO: put your logic here.
pass
if __name__ == '__main__':
# The main function will be called with appropriate configuration
# object and logger.
run(main, __file__)
# Alternative approach. Will only load configuration + run main if
# __name__ == '__main__'.
run(main, __file__, __name__)You may also use this library to configure a set of unit tests. If you're using PyTest,
from pyconfigurableml.entry import run_no_parse_args
def custom_setup_logic(config, log):
# TODO: put your logic here.
pass
def setup_module(module):
# You probably don't want to attempt to parse command line arguments
# inside unit tests.
run_no_parse_args(custom_setup_logic, __file__)In addition to using pyconfigurableml to parse and inject configuration into
your main method, you may also configure the library itself by adding some
information under a pyconfigurableml field in your config file.
First, to enable these extras, install them:
pip install pyconfigurableml[azure,munch]
Then, add subfields following the example below.
# You may insert your other configuration here as usual.
# This section is for configuration specific to this library.
pyconfigurableml:
azure:
# Replace URLs to Azure Key Vault secrets with the secret values. The code
# must be running in an environment with access to those key vaults.
resolve_secret_identifiers: True
# (Optional) Azure Active Directory tenant ID to use when initializing a
# "default Azure credential" object.
tenant: 2b9d773f-f2b1-43e7-8a53-bbe28bbb0c6b
# Ensure files downloaded from the configured URLs exist at these paths
# (relative to working directory). This library does not (yet) handle
# authenticating against files stored in enterprise storage accounts (e.g.,
# Azure blob storage).
files:
.data/resnet.tar.gz: https://github.com/onnx/models/raw/master/vision/classification/resnet/model/resnet18-v2-7.tar.gz
.data/labels.txt: https://raw.githubusercontent.com/onnx/models/master/vision/classification/synset.txt
# Dictionary mapping logger names to minimum levels. This is convenient for
# suppressing overly verbose logs from consumed libraries.
logging:
azure.core.pipeline.policies.http_logging_policy: WARN
# If this flag is set to true, the library will convert the configuration
# object into a "JavaScript-style" object, i.e. a['b'] may be accessed via
# a.b.
munch: True