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Databricks MLflow Object Relationships

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Databricks MLflow Object Relationships

  1. 1. Andre Mesarovic Sr. Specialist Solutions Architect 23 November 2022 Object Relationships Databricks
  2. 2. ● Databricks MLflow objects (runs, experiments, registered models and their versions, notebooks) form a complex web of relationships. ● Objects live in different places: workspace objects, DBFS (cloud) and MySQL. ○ A run’s metadata lives in MySQL, its artifacts in cloud and its notebook in the workspace and/or git. ● Experiments have zero or more runs. ● Registered models have 0 or more versions that point to a run’s MLflow model. ● Code that generated a run’s MLflow model: ○ MLflow runs have pointers to a notebook revision that generated the model. ○ Runs will/should have pointers to the git version of a notebook that generated the model. Overview
  3. 3. ● Model is an overloaded term with three meanings: ○ Native model artifact - this is the lowest level and is simply the native flavor’s serialized format. For sklearn it’s a pickle file, for Keras it’s a directory with TensorFlow’s native SaveModel format files. ○ MLflow model - a wrapper around the native model artifact with metadata in the MLmodel file and environment information in conda.yaml and requirements.txt files. ○ Registered model - a bucket of model versions. A model version contains one MLflow model that is cached in the model repository. A version has the following links (expressed as tags): ■ run_id - points to the run that generated the version’s model. ■ source - points to the path of MLflow model in the run that corresponds to the version’s model. ■ workspace_uri - currently missing. Needed if using shared model registry. ML-19472. Model terminology
  4. 4. Model relationships
  5. 5. ● Runs ○ Contains one or more MLflow models ● Experiments ○ Notebook experiments ○ Workspace experiments ● Registered models ○ A registered model contains versions ○ A version points to one run’s MLflow model ○ Native model artifacts - the actual bits that execute predictions that are part of the MLflow model ● Notebooks Databricks MLflow object relationships
  6. 6. Databricks MLflow objects relationships
  7. 7. ● Diagram uses the UML modeling language. ○ *: indicates a many relationship ○ 1: indicates a required one relationship. ○ 0..1: indicates an optional one relationship. ● This is a logical diagram. Not all nuances are captured for simplification. ● The diagram represents a notebook experiment. ● A workspace experiment is not represented in the diagram. Diagram legend
  8. 8. ● A registered model is a bucket for model versions. ● A version has one MLflow model which is linked to the run that generated it. ● The production and staging stage have one "latest" version. ● Registered model versions are cached in the model registry. ● This is a clone of the run's MLflow model that the version points to. ● If source run is in a different workspace we have a lineage reachability problem. See ML-19472 - Add workspace URI field in ModelVersion for a registered model to make run reachable. Registered models
  9. 9. ● An experiment has zero or more runs. ● Two types of experiments: ○ Notebook experiment ■ Relationship of experiment to notebook is one-to-one. ■ Workspace path of the experiment is the same as its notebook. ○ Workspace experiment ■ Relationship of experiment to notebook is one-to-many. ■ Explicitly specify the experiment path with set_experiment method. ■ Different notebooks can create runs in the same experiment. Experiments
  10. 10. ● A run belongs to only one experiment. ● A run is linked to one notebook revision. MLflow notebook tags: ○ mlflow.databricks.notebookRevisionID ○ mlflow.databricks.notebookID ○ mlflow.databricks.notebookPath ● Optionally a run’s notebook can be linked to a git reference. ○ See discussion on Notebook below for details. ● A run can have one or more MLflow models (flavors) such as Sklearn and ONNX. ● Every run has a default Pyfunc flavor which is wrapper around the native model. Runs
  11. 11. MLflow Run Details
  12. 12. ● An MLflow Run has three basic components ○ Metadata (params, metrics, tags) residing in a MySQL database. ○ MLflow model artifact which lives in DBFS (cloud). Note you can also have arbitrary customer artifacts. ○ Link to code: ■ For Databricks, the run points to either: ● Workspace notebook revision ● Repos notebook a pointer to git. ■ For open source the link points to git. MLflow Run Details Legend
  13. 13. ● A notebook has many revisions. ● Optionally, a notebook revision can be checked into git with Databricks Repos. ● Need to capture git reference analogous to the MLflow open source tags: ○ mlflow.source.git.commit ○ mlflow.source.git.repoURL ○ mlflow.gitRepoURL ● See ML-19473 - Add git reference tags to Databricks run if its notebook is synced with Repos ● Two sources of truth for a notebook snapshot that can be confusing: ○ Databricks notebook revision ○ Git version Notebooks
  14. 14. journey! Happy

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