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setup.py
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from pathlib import Path
from setuptools import find_packages, setup
install_requires = [
"cloudpickle",
"dill",
"requests",
"isort>=5.10",
"pydantic>=1.9.0,<2",
"typer",
"click<8.2",
"rich",
"aiohttp<4",
"aiohttp_swagger<2",
"Jinja2>=3",
"fsspec>=2021.7.0",
"pyparsing<4", # legacy resolver problem
"cached-property",
"entrypoints",
"gitpython",
"python-gitlab",
"flatdict",
"iterative-telemetry>=0.0.6",
"python-multipart",
"importlib-metadata",
]
tests = [
"pytest",
"pytest-cov",
"pytest-lazy-fixture==0.6.3",
"pytest-mock",
"pylint<2.14",
# we use this to suppress pytest-related false positives in our tests.
"pylint-pytest",
# we use this to suppress some messages in tests, eg: foo/bar naming,
# and, protected method calls in our tests
"pylint-plugin-utils",
# we use this to mark tests that needs to be retried
"flaky",
"s3fs",
"boto3",
"botocore",
"adlfs",
"gcsfs",
"testcontainers",
"emoji",
"lxml",
"openpyxl",
"xlrd",
"tables",
"pyarrow",
"skl2onnx",
"dvc[s3]",
"httpx",
"jupyter",
"nbconvert",
"nbloader",
# We're using regex to test requirement version extraction
# edge case, see: https://github.com/iterative/mlem/issues/688
"regex==2023.6.3",
]
extras = {
"tests": tests,
"pandas": ["pandas"],
"numpy": ["numpy"],
"sklearn": ["scikit-learn"],
"scipy": ["scipy"],
"onnx": ["onnx"],
"onnxruntime": [
"protobuf==3.20.1",
"onnxruntime<1.14",
], # TODO - see if it can be merged with onnx
"catboost": ["catboost"],
"xgboost": ["xgboost"],
"lightgbm": ["lightgbm"],
"fastapi": ["uvicorn", "fastapi"],
"prometheus": ["prometheus-fastapi-instrumentator"],
"streamlit": ["uvicorn", "fastapi", "streamlit", "streamlit_pydantic"],
"sagemaker": ["docker", "boto3", "sagemaker"],
"torch": ["torch"],
"tensorflow": ["tensorflow"],
"azure": ["adlfs>=2021.10.0", "azure-identity>=1.4.0", "knack"],
"gs": ["gcsfs>=2021.11.1"],
"hdfs": [
"pyarrow>=1",
"fsspec[arrow]",
],
"s3": ["s3fs[boto3]>=2021.11.1", "aiobotocore[boto3]>2"],
"ssh": ["bcrypt", "sshfs[bcrypt]>=2021.11.2"],
"rmq": ["pika"],
"docker": ["docker"],
"heroku": ["docker", "fastapi", "uvicorn"],
"kubernetes": ["docker", "kubernetes"],
"dvc": ["dvc~=2.0"],
"git": ["pygit2"],
"flyio": ["docker", "fastapi", "uvicorn", "tomlkit"],
"torchvision": ["torchvision"],
"pil": ["Pillow", "numpy"],
}
# add DVC extras
for e in [
"azure",
"gdrive",
"gs",
"hdfs",
"oss",
"s3",
"ssh",
"ssh_gssapi",
"webdav",
"webhdfs",
"webhdfs_kerberos",
]:
extras[f"dvc-{e}"] = [f"dvc[{e}]~=2.0"]
extras["all"] = [_ for e in extras.values() for _ in e]
extras["tests"] += [e for e in extras["all"] if e[: len("dvc[")] != "dvc["]
setup_args = dict( # noqa: C408
name="mlem",
use_scm_version=True,
setup_requires=["setuptools_scm", "fastentrypoints>=0.12"],
description="Version and deploy your models following GitOps principles",
long_description=(Path(__file__).parent / "README.md").read_text(
encoding="utf8"
),
long_description_content_type="text/markdown",
maintainer="Iterative",
maintainer_email="support@mlem.ai",
author="Mikhail Sveshnikov",
author_email="mike0sv@iterative.ai",
download_url="https://github.com/iterative/mlem",
license="Apache License 2.0",
install_requires=install_requires,
extras_require=extras,
keywords="data-science data-version-control machine-learning git mlops"
" developer-tools reproducibility collaboration ai",
python_requires=">=3.6",
classifiers=[
"Development Status :: 4 - Beta",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
],
packages=find_packages(exclude=["tests*"]),
include_package_data=True,
url="https://mlem.ai",
entry_points={
"console_scripts": ["mlem = mlem.cli:app"],
# Additional mechanism for plugins.
# This is the way for mlem to find implementations in installed modules.
# Since mlem has some "optional" implementations,
# we should populate them like this as well
"mlem.contrib": [
"resolver.bitbucket = mlem.contrib.bitbucketfs:BitBucketResolver",
"model_type.callable = mlem.contrib.callable:CallableModelType",
"model_io.pickle = mlem.contrib.callable:PickleModelIO",
"model_type.catboost = mlem.contrib.catboost:CatBoostModel",
"model_io.catboost_io = mlem.contrib.catboost:CatBoostModelIO",
"deployment.docker_container = mlem.contrib.docker.base:DockerContainer",
"deploy_state.docker_container = mlem.contrib.docker.base:DockerContainerState",
"builder.docker_dir = mlem.contrib.docker.base:DockerDirBuilder",
"env.docker = mlem.contrib.docker.base:DockerEnv",
"docker_registry.docker_io = mlem.contrib.docker.base:DockerIORegistry",
"builder.docker = mlem.contrib.docker.base:DockerImageBuilder",
"docker_registry.local = mlem.contrib.docker.base:DockerRegistry",
"docker_registry.remote = mlem.contrib.docker.base:RemoteRegistry",
"artifact.dvc = mlem.contrib.dvc:DVCArtifact",
"storage.dvc = mlem.contrib.dvc:DVCStorage",
"server.fastapi = mlem.contrib.fastapi:FastAPIServer",
"deployment.flyio = mlem.contrib.flyio.meta:FlyioApp",
"deploy_state.flyio = mlem.contrib.flyio.meta:FlyioAppState",
"env.flyio = mlem.contrib.flyio.meta:FlyioEnv",
"resolver.local_git = mlem.contrib.git:LocalGitResolver",
"resolver.github = mlem.contrib.github:GithubResolver",
"resolver.gitlab = mlem.contrib.gitlabfs:GitlabResolver",
"docker_registry.heroku = mlem.contrib.heroku.build:HerokuRemoteRegistry",
"deployment.heroku = mlem.contrib.heroku.meta:HerokuDeployment",
"env.heroku = mlem.contrib.heroku.meta:HerokuEnv",
"deploy_state.heroku = mlem.contrib.heroku.meta:HerokuState",
"server._heroku = mlem.contrib.heroku.server:HerokuServer",
"deployment.kubernetes = mlem.contrib.kubernetes.base:K8sDeployment",
"deploy_state.kubernetes = mlem.contrib.kubernetes.base:K8sDeploymentState",
"env.kubernetes = mlem.contrib.kubernetes.base:K8sEnv",
"k8s_service_type.clusterip = mlem.contrib.kubernetes.service:ClusterIPService",
"k8s_service_type.loadbalancer = mlem.contrib.kubernetes.service:LoadBalancerService",
"k8s_service_type.nodeport = mlem.contrib.kubernetes.service:NodePortService",
"data_reader.lightgbm = mlem.contrib.lightgbm:LightGBMDataReader",
"serializer.lightgbm = mlem.contrib.lightgbm:LightGBMDataSerializer",
"data_type.lightgbm = mlem.contrib.lightgbm:LightGBMDataType",
"data_writer.lightgbm = mlem.contrib.lightgbm:LightGBMDataWriter",
"model_type.lightgbm = mlem.contrib.lightgbm:LightGBMModel",
"model_io.lightgbm_io = mlem.contrib.lightgbm:LightGBMModelIO",
"data_reader.numpy = mlem.contrib.numpy:NumpyArrayReader",
"data_writer.numpy = mlem.contrib.numpy:NumpyArrayWriter",
"serializer.ndarray = mlem.contrib.numpy:NumpyNdarraySerializer",
"data_type.ndarray = mlem.contrib.numpy:NumpyNdarrayType",
"data_reader.numpy_number = mlem.contrib.numpy:NumpyNumberReader",
"serializer.number = mlem.contrib.numpy:NumpyNumberSerializer",
"data_type.number = mlem.contrib.numpy:NumpyNumberType",
"data_writer.numpy_number = mlem.contrib.numpy:NumpyNumberWriter",
"model_io.model_proto = mlem.contrib.onnx:ModelProtoIO",
"model_type.onnx = mlem.contrib.onnx:ONNXModel",
"serializer.dataframe = mlem.contrib.pandas:DataFrameSerializer",
"data_type.dataframe = mlem.contrib.pandas:DataFrameType",
"import.pandas = mlem.contrib.pandas:PandasImport",
"data_reader.pandas = mlem.contrib.pandas:PandasReader",
"data_reader.pandas_series = mlem.contrib.pandas:PandasSeriesReader",
"data_writer.pandas_series = mlem.contrib.pandas:PandasSeriesWriter",
"data_writer.pandas = mlem.contrib.pandas:PandasWriter",
"serializer.series = mlem.contrib.pandas:SeriesSerializer",
"data_type.series = mlem.contrib.pandas:SeriesType",
"serializer.pil_numpy = mlem.contrib.pil:PILImageSerializer",
"builder.pip = mlem.contrib.pip.base:PipBuilder",
"builder.whl = mlem.contrib.pip.base:WhlBuilder",
"middleware.prometheus_fastapi = mlem.contrib.prometheus:PrometheusFastAPIMiddleware",
"client.rmq = mlem.contrib.rabbitmq:RabbitMQClient",
"server.rmq = mlem.contrib.rabbitmq:RabbitMQServer",
"builder.requirements = mlem.contrib.requirements:RequirementsBuilder",
"docker_registry.ecr = mlem.contrib.sagemaker.build:ECRegistry",
"deploy_state.sagemaker = mlem.contrib.sagemaker.meta:SagemakerDeployState",
"deployment.sagemaker = mlem.contrib.sagemaker.meta:SagemakerDeployment",
"env.sagemaker = mlem.contrib.sagemaker.meta:SagemakerEnv",
"server._sagemaker = mlem.contrib.sagemaker.runtime:SageMakerServer",
"client.sagemaker = mlem.contrib.sagemaker.runtime:SagemakerClient",
"data_reader.csr_matrix = mlem.contrib.scipy:ScipyReader",
"data_type.csr_matrix = mlem.contrib.scipy:ScipySparseMatrix",
"serializer.csr_matrix = mlem.contrib.scipy:ScipySparseMatrixSerializer",
"data_writer.csr_matrix = mlem.contrib.scipy:ScipyWriter",
"model_type.sklearn = mlem.contrib.sklearn:SklearnModel",
"model_type.sklearn_pipeline = mlem.contrib.sklearn:SklearnPipelineType",
"model_type.sklearn_transformer = mlem.contrib.sklearn:SklearnTransformer",
"server.streamlit = mlem.contrib.streamlit.server:StreamlitServer",
"model_type.tf_keras = mlem.contrib.tensorflow:TFKerasModel",
"model_io.tf_keras = mlem.contrib.tensorflow:TFKerasModelIO",
"data_type.tf_tensor = mlem.contrib.tensorflow:TFTensorDataType",
"data_reader.tf_tensor = mlem.contrib.tensorflow:TFTensorReader",
"serializer.tf_tensor = mlem.contrib.tensorflow:TFTensorSerializer",
"data_writer.tf_tensor = mlem.contrib.tensorflow:TFTensorWriter",
"model_type.torch = mlem.contrib.torch:TorchModel",
"model_io.torch_io = mlem.contrib.torch:TorchModelIO",
"import.torch = mlem.contrib.torch:TorchModelImport",
"data_type.torch = mlem.contrib.torch:TorchTensorDataType",
"data_reader.torch = mlem.contrib.torch:TorchTensorReader",
"serializer.torch = mlem.contrib.torch:TorchTensorSerializer",
"data_writer.torch = mlem.contrib.torch:TorchTensorWriter",
"serializer.torch_image = mlem.contrib.torchvision:TorchImageSerializer",
"builder.conda = mlem.contrib.venv:CondaBuilder",
"requirement.conda = mlem.contrib.venv:CondaPackageRequirement",
"builder.venv = mlem.contrib.venv:VenvBuilder",
"data_type.xgboost_dmatrix = mlem.contrib.xgboost:DMatrixDataType",
"serializer.xgboost_dmatrix = mlem.contrib.xgboost:DMatrixSerializer",
"model_type.xgboost = mlem.contrib.xgboost:XGBoostModel",
"model_io.xgboost_io = mlem.contrib.xgboost:XGBoostModelIO",
],
"mlem.config": [
"core = mlem.config:MlemConfig",
"bitbucket = mlem.contrib.bitbucketfs:BitbucketConfig",
"docker = mlem.contrib.docker.context:DockerConfig",
"flyio = mlem.contrib.flyio.meta:FlyioConfig",
"heroku = mlem.contrib.heroku.config:HerokuConfig",
"pandas = mlem.contrib.pandas:PandasConfig",
"aws = mlem.contrib.sagemaker.config:AWSConfig",
"sagemaker = mlem.contrib.sagemaker.runtime:SageMakerServerConfig",
"torch = mlem.contrib.torch:TorchConfig",
],
},
zip_safe=False,
)
if __name__ == "__main__":
setup(**setup_args)