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setup.py
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setup.py
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# Copyright 2018 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
try:
from setuptools import setup
except ImportError:
from distutils.core import setup
import json
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("mlrun-setup")
def version():
try:
with open("mlrun/utils/version/version.json") as version_file:
version_metadata = json.load(version_file)
return version_metadata["version"]
except (ValueError, KeyError, FileNotFoundError):
# When installing un-released version (e.g. by doing
# pip install git+https://github.com/mlrun/mlrun@development)
# it won't have a version file, so adding some sane default
logger.warning("Failed resolving version. Ignoring and using 0.0.0+unstable")
return "0.0.0+unstable"
def is_ignored(line):
line = line.strip()
return (not line) or (line[0] == "#")
def load_deps(path):
"""Load dependencies from requirements file"""
with open(path) as fp:
deps = []
for line in fp:
if is_ignored(line):
continue
line = line.strip()
# e.g.: git+https://github.com/nuclio/nuclio-jupyter.git@some-branch#egg=nuclio-jupyter
if "#egg=" in line:
_, package = line.split("#egg=")
deps.append(f"{package} @ {line}")
continue
# append package
deps.append(line)
return deps
with open("README.md") as fp:
long_desc = fp.read()
install_requires = list(load_deps("requirements.txt"))
tests_require = list(load_deps("dev-requirements.txt"))
api_deps = list(load_deps("dockerfiles/mlrun-api/requirements.txt"))
# NOTE:
# - These are tested in `automation/package_test/test.py`. If you modify these, make sure to change the
# corresponding line there.
# - We have a copy of these in extras-requirements.txt. If you modify these, make sure to change it there as well
extras_require = {
# from 1.17.107 boto3 requires botocore>=1.20.107,<1.21.0 which
# conflicts with s3fs 2021.8.1 that has aiobotocore~=1.4.0
# which so far (1.4.1) has botocore>=1.20.106,<1.20.107
# boto3 1.17.106 has botocore>=1.20.106,<1.21.0, so we must add botocore explicitly
"s3": [
"boto3~=1.9, <1.17.107",
"botocore>=1.20.106,<1.20.107",
"aiobotocore~=1.4.0",
"s3fs~=2021.8.1",
],
"azure-blob-storage": [
"msrest~=0.6.21",
"azure-core~=1.24",
"azure-storage-blob~=12.13",
"adlfs~=2021.8.1",
],
"azure-key-vault": ["azure-identity~=1.5", "azure-keyvault-secrets~=4.2"],
"bokeh": [
# >=2.4.2 to force having a security fix done in 2.4.2
"bokeh~=2.4, >=2.4.2",
],
# plotly artifact body in 5.12.0 may contain chars that are not encodable in 'latin-1' encoding
# so, it cannot be logged as artifact (raised UnicodeEncode error - ML-3255)
"plotly": ["plotly~=5.4, <5.12.0"],
# used to generate visualization nuclio/serving graph steps
"graphviz": ["graphviz~=0.20.0"],
# google-cloud is mainly used for QA, that is why we are not including it in complete
"google-cloud": [
# because of kfp 1.8.13 requiring google-cloud-storage<2.0.0, >=1.20.0
"google-cloud-storage~=1.20",
# because of storey which isn't compatible with google-cloud-bigquery >3.2, conflicting grpcio
# google-cloud-bigquery 3.3.0 has grpcio >= 1.47.0, < 2.0dev while storey 1.2.2 has grpcio<1.42 and >1.34.0
"google-cloud-bigquery[pandas]~=3.2",
"google-cloud~=0.34",
],
"google-cloud-storage": ["gcsfs~=2021.8.1"],
"google-cloud-bigquery": ["google-cloud-bigquery[pandas]~=3.2"],
"kafka": ["kafka-python~=2.0"],
"redis": ["redis~=4.3"],
}
extras_require["complete"] = sorted(
{
requirement
for extra_key, requirement_list in extras_require.items()
for requirement in requirement_list
# see above why we are excluding google-cloud
if extra_key not in ["bokeh", "google-cloud"]
}
)
extras_require["all"] = sorted(
{
requirement
for extra_key, requirement_list in extras_require.items()
for requirement in requirement_list
}
)
extras_require["api"] = api_deps
complete_api_deps = set(api_deps)
complete_api_deps.update(extras_require["complete"])
extras_require["complete-api"] = sorted(complete_api_deps)
setup(
name="mlrun",
version=version(),
description="Tracking and config of machine learning runs",
long_description=long_desc,
long_description_content_type="text/markdown",
author="Yaron Haviv",
author_email="yaronh@iguazio.com",
license="MIT",
url="https://github.com/mlrun/mlrun",
packages=[
"mlrun",
"mlrun.api",
"mlrun.api.api",
"mlrun.api.api.endpoints",
"mlrun.api.api.endpoints.internal",
"mlrun.api.crud",
"mlrun.api.crud.model_monitoring",
"mlrun.api.db",
"mlrun.api.db.filedb",
"mlrun.api.db.sqldb",
"mlrun.api.db.sqldb.models",
"mlrun.api.migrations_sqlite",
"mlrun.api.migrations_sqlite.versions",
"mlrun.api.migrations_mysql",
"mlrun.api.migrations_mysql.versions",
"mlrun.api.schemas",
"mlrun.api.utils",
"mlrun.api.utils.auth",
"mlrun.api.utils.auth.providers",
"mlrun.api.utils.clients",
"mlrun.api.utils.clients.protocols",
"mlrun.api.utils.db",
"mlrun.api.utils.projects",
"mlrun.api.utils.projects.remotes",
"mlrun.api.utils.singletons",
"mlrun.artifacts",
"mlrun.data_types",
"mlrun.datastore",
"mlrun.datastore.wasbfs",
"mlrun.db",
"mlrun.feature_store",
"mlrun.feature_store.retrieval",
"mlrun.frameworks",
"mlrun.frameworks._common",
"mlrun.frameworks._dl_common",
"mlrun.frameworks._dl_common.loggers",
"mlrun.frameworks._ml_common",
"mlrun.frameworks._ml_common.loggers",
"mlrun.frameworks._ml_common.plans",
"mlrun.frameworks.auto_mlrun",
"mlrun.frameworks.huggingface",
"mlrun.frameworks.lgbm",
"mlrun.frameworks.lgbm.callbacks",
"mlrun.frameworks.lgbm.mlrun_interfaces",
"mlrun.frameworks.onnx",
"mlrun.frameworks.pytorch",
"mlrun.frameworks.pytorch.callbacks",
"mlrun.frameworks.sklearn",
"mlrun.frameworks.tf_keras",
"mlrun.frameworks.tf_keras.callbacks",
"mlrun.frameworks.xgboost",
"mlrun.mlutils",
"mlrun.model_monitoring",
"mlrun.platforms",
"mlrun.projects",
"mlrun.runtimes",
"mlrun.runtimes.mpijob",
"mlrun.runtimes.package",
"mlrun.runtimes.sparkjob",
"mlrun.serving",
"mlrun.utils",
"mlrun.utils.notifications",
"mlrun.utils.notifications.notification",
"mlrun.utils.version",
],
install_requires=install_requires,
extras_require=extras_require,
classifiers=[
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"License :: OSI Approved :: Apache Software License",
"Operating System :: POSIX :: Linux",
"Operating System :: Microsoft :: Windows",
"Operating System :: MacOS",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python",
"Topic :: Software Development :: Libraries :: Python Modules",
"Topic :: Software Development :: Libraries",
],
tests_require=tests_require,
zip_safe=False,
include_package_data=True,
entry_points={"console_scripts": ["mlrun=mlrun.__main__:main"]},
)