Package: mlflow-skinny
MLflow Skinny: A Lightweight Machine Learning Lifecycle Platform ClientMLflow 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 deploymentExecution 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 themlflow.sklearncomponent of MLflow Models, installscikit-learn,numpyandpandas.To use SQL-based metadata storage, installsqlalchemy,alembic, andsqlparse.To use serving-based features, installflaskandpandas.MLflow: A Machine Learning Lifecycle PlatformMLflow is a platform to streamline machine learning development, including tracking experiments, packaging code
into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be
used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you
currently run ML code (e.g. in notebooks, standalone applications or the cloud). MLflow’s current components are:MLflow Tracking: An API to log parameters, code, and
results in machine learning experiments and compare them using an interactive UI.MLflow Projects: A code packaging format for reproducible
runs using Conda and Docker, so you can share your ML code with others.MLflow Models: A model packaging format and tools that let
you easily deploy the same model (from any ML library) to batch and real-time scoring on platforms such as
Docker, Apache Spark, Azure ML and AWS SageMaker.MLflow Model Registry: A centralized model store, set of APIs, and UI, to collaboratively manage the full lifecycle of MLflow Models.Job StatusesInstallingInstall MLflow from PyPI viapip install mlflowMLflow requirescondato be on thePATHfor the projects feature.Nightly snapshots of MLflow master are also availablehere.Install a lower dependency subset of MLflow from PyPI viapip installmlflow-skinnyExtra dependencies can be added per desired scenario.
For example,pip installmlflow-skinnypandas numpyallows for mlflow.pyfunc.log_model support.DocumentationOfficial documentation for MLflow can be found athttps://mlflow.org/docs/latest/index.html.RoadmapThe current MLflow Roadmap is available athttps://github.com/mlflow/mlflow/milestone/3. We are
seeking contributions to all of our roadmap items with thehelp wantedlabel. Please see theContributingsection for more information.CommunityFor help or questions about MLflow usage (e.g. “how do I do X?”) see thedocsorStack Overflow.To report a bug, file a documentation issue, or submit a feature request, please open a GitHub issue.For release announcements and other discussions, please subscribe to our mailing list (mlflow-users@googlegroups.com)
or join us onSlack.Running a Sample App With the Tracking APIThe programs inexamplesuse the MLflow Tracking API. For instance, run:python examples/quickstart/mlflow_tracking.pyThis program will useMLflow Tracking API,
which logs tracking data in./mlruns. This can then be viewed with the Tracking UI.Launching the Tracking UIThe MLflow Tracking UI will show runs logged in./mlrunsathttp://localhost:5000.
Start it with:mlflow uiNote:Runningmlflow uifrom within a clone of MLflow is not recommended - doing so will
run the dev UI from source. We recommend running the UI from a different working directory,
specifying a backend store via the--backend-store-urioption. Alternatively, see
instructions for running the dev UI in thecontributor guide.Running a Project from a URIThemlflow runcommand lets you run a project packaged with a MLproject file from a local path
or a Git URI:mlflow run examples/sklearn_elasticnet_wine -P alpha=0.4

mlflow run https://github.com/mlflow/mlflow-example.git -P alpha=0.4Seeexamples/sklearn_elasticnet_winefor a sample project with an MLproject file.Saving and Serving ModelsTo illustrate managing models, themlflow.sklearnpackage can log scikit-learn models as
MLflow artifacts and then load them again for serving. There is an example training application inexamples/sklearn_logistic_regression/train.pythat you can run as follows:$ python examples/sklearn_logistic_regression/train.py
Score: 0.666
Model saved in run <run-id>

$ mlflow models serve --model-uri runs:/<run-id>/model

$ curl -d '{"dataframe_split": {"columns":[0],"index":[0,1],"data":[[1],[-1]]}}' -H 'Content-Type: application/json'  localhost:5000/invocationsNote:If using MLflow skinny (pip installmlflow-skinny) for model serving, additional
required dependencies (namely,flask) will need to be installed for the MLflow server to function.Official MLflow Docker ImageThe official MLflow Docker image is available on GitHub Container Registry athttps://ghcr.io/mlflow/mlflow.exportCR_PAT=YOUR_TOKENecho$CR_PAT|dockerloginghcr.io-uUSERNAME--password-stdin# Pull the latest versiondockerpullghcr.io/mlflow/mlflow# Pull 2.2.1dockerpullghcr.io/mlflow/mlflow:v2.2.1ContributingWe happily welcome contributions to MLflow. We are also seeking contributions to items on theMLflow Roadmap. Please see ourcontribution guideto learn more about contributing to MLflow.Core MembersMLflow is currently maintained by the following core members with significant contributions from hundreds of exceptionally talented community members.Ben WilsonCorey ZumarDaniel LokGabriel FuHarutaka KawamuraSerena RuanWeichen XuYuki Watanabe