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Eedeleon/release skinny client (mlflow#4049)
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* Prepare for mlflow skinny release

Signed-off-by: Eduardo de Leon <eddeleon@microsoft.com>

* Add content type back

Signed-off-by: Eduardo de Leon <eddeleon@microsoft.com>

* RST cleanup

* Update README_SKINNY.rst

* Update README_SKINNY.rst

Co-authored-by: Harutaka Kawamura <hkawamura0130@gmail.com>

* Add blank line

Signed-off-by: harupy <17039389+harupy@users.noreply.github.com>

Co-authored-by: dbczumar <39497902+dbczumar@users.noreply.github.com>
Co-authored-by: Harutaka Kawamura <hkawamura0130@gmail.com>
Co-authored-by: harupy <17039389+harupy@users.noreply.github.com>
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4 people authored Feb 6, 2021
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16 changes: 16 additions & 0 deletions README_SKINNY.rst
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=======================================================================
MLflow Skinny: A Lightweight Machine Learning Lifecycle Platform Client
=======================================================================

MLflow Skinny is a lightweight MLflow package without SQL storage, server, UI, or data science dependencies.
MLflow Skinny supports:

* Tracking operations (logging / loading / searching params, metrics, tags + logging / loading artifacts)
* Model registration, search, artifact loading, and transitions
* Execution of GitHub projects within notebook & against a remote target.

Additional dependencies can be installed to leverage the full feature set of MLflow. For example:

* To use the `mlflow.sklearn` component of MLflow Models, install `scikit-learn`, `numpy`, and `pandas`.
* To use SQL-based metadata storage, install `sqlalchemy`, `alembic`, and `sqlparse`.

12 changes: 8 additions & 4 deletions setup.py
Original file line number Diff line number Diff line change
Expand Up @@ -75,11 +75,13 @@ def package_files(directory):
logging.debug("{} env var is set: {}".format(_MLFLOW_SKINNY_ENV_VAR, _is_mlflow_skinny))

setup(
name="mlflow",
name="mlflow" if not _is_mlflow_skinny else "mlflow-skinny",
version=version,
packages=find_packages(exclude=["tests", "tests.*"]),
package_data={"mlflow": js_files + models_container_server_files + alembic_files},
install_requires=SKINNY_REQUIREMENTS if _is_mlflow_skinny else CORE_REQUIREMENTS,
package_data={"mlflow": js_files + models_container_server_files + alembic_files}
if not _is_mlflow_skinny
else {},
install_requires=CORE_REQUIREMENTS if not _is_mlflow_skinny else SKINNY_REQUIREMENTS,
extras_require={
"extras": [
"scikit-learn",
Expand Down Expand Up @@ -107,7 +109,9 @@ def package_files(directory):
zip_safe=False,
author="Databricks",
description="MLflow: A Platform for ML Development and Productionization",
long_description=open("README.rst").read(),
long_description=open("README.rst").read()
if not _is_mlflow_skinny
else open("README_SKINNY.rst").read() + open("README.rst").read(),
long_description_content_type="text/x-rst",
license="Apache License 2.0",
classifiers=["Intended Audience :: Developers", "Programming Language :: Python :: 3.6"],
Expand Down

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