MLflow Export Import

This repo is deprecated. Please use: https://github.com/mlflow/mlflow-export-import

This package provides tools to export and import MLflow objects (runs, experiments or registered models) from one MLflow tracking server (Databricks workspace) to another. See the Databricks MLflow Object Relationships slide deck.

Useful Links

Point tools README
- export_experiment API
- export_model API
- export_run API
- import_experiment API
- import_model API
- import_run API
Bulk tools README
Databricks notebooks for object export/import.

Architecture

Overview

Why use MLflow Export Import?

Share and collaborate with other data scientists in the same or another tracking server.
- For example, clone a favorite experiment from another user in your own workspace.
Migrate experiments to another tracking server.
- For example, promote a registered model version (and its associated run) from the development to the production tracking server.
Backup your MLflow objects.
Disaster recovery.

Migration modes

Source tracking server	Destination tracking server	Note
Open source	Open source	common
Open source	Databricks	less common
Databricks	Databricks	common
Databricks	Open source	rare

Two migration tool contexts

Open source MLflow Python CLI scripts - this page.
Databricks notebooks.

Two sets of migration tools

Point tools. Low-level tools to copy individual MLflow objects and have fine-grained control over the target names. For example, if you wish to clone an experiment in the same tracking server (workspace), use these tools.
Bulk tools. High-level tools to copy an entire tracking server or the web of MLflow objects (runs and experiments) associated with registered models. Full object referential integrity is maintained as well as the original MLflow object names.
- For registered models it exports:
- All the latest versions of a model.
- The run associated with the version.
- The experiment that the run belongs to.

Other

Miscellanous tools

Limitations

General Limitations

Nested runs are only supported when you import an experiment. For a run, it is still a TODO.
If the run linked to a registered model version does not exist (has been deleted) the version is not exported since when importing MLflowClient.create_model_version requires a run ID.

Databricks Limitations

Exporting Notebook Revisions

The notebook revision associated with the run can be exported. It is stored as an artifact in the notebooks folder under the run's artifacts root.
You can save the notebook in the suppported SOURCE, HTML, JUPYTER and DBC formats.

Importing Notebooks

Partial functionality due to Databricks API limitations.
The Databricks API does not support:
- Importing a notebook with its entire revision history.
- Linking an imported run with a given notebook revision.
When you import a run, the link to its source notebook revision ID will not exist and thus the UI will point to a dead link.
As a convenience, the import tools allows you to import the exported notebook into Databricks. For more details, see:
- README_point - Import run
- README_point - Import experiment
The imported notebook cannot be attached to the run that created it.
If you have several runs that point to different revisions of the same notebook, each imported run will be attached a different notebook.
You must export a notebook in the SOURCE format for the notebook to be imported.

Used ID

When importing a run or experiment, for open source MLflow you can specify the user owner. For Databricks import you cannot - the owner will be based on the personal access token (PAT) of the import user.

Common options details

notebook-formats - If exporting a Databricks run, the run's notebook revision can be saved in the specified formats (comma-delimited argument). Each format is saved in the notebooks folder of the run's artifact root directory as notebook.{format}. Supported formats are SOURCE, HTML, JUPYTER and DBC. See Databricks Export Format documentation.

use-src-user-id - Set the destination user ID to the source user ID. Source user ID is ignored when importing into Databricks since the user is automatically picked up from your Databricks access token.

export-metadata-tags - Creates metadata tags (starting with mlflow_export_import.metadata) that contain export information. These are the source mlflow tags in addition to other information. This is useful for provenance and auditing purposes in regulated industries.

Name                                          Value
mlflow_export_import.metadata.timestamp       1551037752
mlflow_export_import.metadata.timestamp_nice  2019-02-24 19:49:12
mlflow_export_import.metadata.experiment_id   2
mlflow_export_import.metadata.experiment-name sklearn_wine
mlflow_export_import.metadata.run-id          50fa90e751eb4b3f9ba9cef0efe8ea30
mlflow_export_import.metadata.tracking_uri    http://localhost:5000

Setup

Supports python 3.7.6 or above.

Local setup

First create a virtual environment.

python -m venv mlflow-export-import
source mlflow-export-import/bin/activate

There are two different ways to install the package.

1. Install from github directly

pip install git+https:///github.com/amesar/mlflow-export-import/#egg=mlflow-export-import

2. Install from github clone

git clone https://github.com/amesar/mlflow-export-import
cd mlflow-export-import
pip install -e .

Databricks setup

There are two different ways to install the package.

1. Install package in notebook

Install notebook-scoped libraries with %pip.

pip install git+https:///github.com/amesar/mlflow-export-import/#egg=mlflow-export-import

2. Install package as a wheel on cluster

Build the wheel artifact, upload it to DBFS and then install it on your cluster.

python setup.py bdist_wheel
databricks fs cp dist/mlflow_export_import-1.0.0-py3-none-any.whl {MY_DBFS_PATH}

Databricks MLflow usage

To run the tools externally (from your laptop) against a Databricks tracking server (workspace) set the following environment variables.

export MLFLOW_TRACKING_URI=databricks
export DATABRICKS_HOST=https://mycompany.cloud.databricks.com
export DATABRICKS_TOKEN=MY_TOKEN

For full details see Access the MLflow tracking server from outside Databricks.

Running tools

The main tool scripts can be executed either as a standard Python script or console script.

Python console scripts (such as export-run, import-run, etc.) are provided as a convenience. For a list of scripts see setup.py.

This allows you to use:

export-experiment --help

instead of:

python -u -m mlflow_export_import.experiment.export_experiment --help

amesar / mlflow-export-import

readme