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setup.py
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setup.py
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import imp
import os
from setuptools import setup, find_packages
from os import path
# io.open is needed for projects that support Python 2.7
# It ensures open() defaults to text mode with universal newlines,
# and accepts an argument to specify the text encoding
# Python 3 only projects can skip this import
from io import open
version = imp.load_source('mlapp.version', os.path.join('mlapp', 'version.py')).VERSION
here = path.abspath(path.dirname(__file__))
# Get the long description from the README file
with open(path.join(here, 'README.md'), encoding='utf-8') as f:
long_description = f.read()
# Arguments marked as "Required" below must be included for upload to PyPI.
# Fields marked as "Optional" may be commented out.
with open("requirements.txt", "r", encoding="utf-8") as f:
requires = [x.strip() for x in f if x.strip()]
setup(
name='mlapp', # Required
version=version, # Required
description='IBM Services Framework for ML Applications Python 3 framework for building robust, '
'production-ready machine learning applications. '
'Official ML accelerator within the larger RAD-ML methodology.',
long_description=long_description,
long_description_content_type='text/markdown',
url='https://github.com/ibm/mlapp',
author='IBM',
author_email='tomer.galula@ibm.com, tal.waitzenberg@ibm.com, michael.chein@ibm.com, erez.nardia@ibm.com, '
'annaelle.cohen@ibm.com, katzn@us.ibm.com',
keywords=['mlapp', 'ibm', 'machine-learning', 'auto-ml'],
packages=find_packages(exclude=['tests', 'docs', 'venv']), # Required
license="Apache License 2.0",
classifiers=[
'Intended Audience :: Developers',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
'Programming Language :: Python :: 3.8',
'Programming Language :: Python :: 3.9',
'License :: OSI Approved :: Apache Software License'
],
python_requires='>=3.6',
install_requires=requires,
extras_require={ # Optional
'rabbitmq': ['pika'],
'minio': ['minio'],
'mysql': ['PyMySQL'],
'snowflake': ['snowflake-sqlalchemy'],
'azure-servicebus': ['azure-servicebus<=0.50.3'],
'kafka': ['kafka-python'],
'kafka-kerberos': ['confluent_kafka'],
'boto3': ['boto3'],
'ibm-boto3': ['ibm-cos-sdk'],
'azure-storage-blob': ['azure-storage-blob<=2.1.0'],
'postgres': ['pg8000<=1.16.5'],
'livy': ['livy'],
'mssql': ['pyodbc'],
'pyspark': ['pyspark'],
'aml': [
'azureml-sdk'
],
'sm': [
'sagemaker',
'sagemaker-training'
],
'cp': [
'pika',
'pg8000<=1.16.5',
'minio'
]
},
# package_data={
# 'mlapp': ['mlapp/cli/script.sh'],
# },
# data_files=[('my_data', ['data/data_file'])], # Optional
include_package_data=True,
entry_points={
'console_scripts': [
'mlapp=mlapp.cli:cli',
],
},
project_urls={
'Bug Reports': 'https://github.com/ibm/mlapp/issues',
'Wiki Page': 'https://mlapp-docs.s3-web.us-south.cloud-object-storage.appdomain.cloud',
'Crash Course': 'https://mlapp-docs.s3-web.us-south.cloud-object-storage.appdomain.cloud'
'/crash-course/introduction',
'Source': 'https://github.com/ibm/mlapp'
},
)