Examples¶
MLRun has many code examples and tutorial Jupyter notebooks with embedded documentation, ranging from examples of basic tasks to full end-to-end use-case applications, including the following. Some of the examples are found in other mlrun GitHub repositories.
Learn MLRun basics — mlrun_basics.ipynb
Convert local runs to Kubernetes jobs and create automated pipelines in a single notebook — mlrun_jobs.ipynb
End-to-end ML pipeline— demos/scikit-learn, including:
Data ingestion and analysis
Model training
Verification
Model deployment
MLRun with scale-out runtimes —
Distributed TensorFlow with Horovod and MPIJob, including data collection and labeling, model training and serving, and implementation of an automated workflow — demos/image-classification-with-distributed-training
Serverless model serving with Nuclio — xgb_serving.ipynb
Dask — mlrun_dask.ipynb
Spark — mlrun_sparkk8s.ipynb
MLRun project and Git life cycle —
Load a project from a remote Git location and run pipelines — load-project.ipynb
Create a new project, functions, and pipelines, and upload to Git — new-project.ipynb
Import and export functions using files or Git — mlrun_export_import.ipynb
Query the MLRun DB — mlrun_db.ipynb
Additional Examples¶
Additional end-to-end use-case applications — mlrun/demos repo
MLRun functions Library — mlrun/functions repo