mlflow-export-import 1.2.0

Copy MLflow objects (experiments, runs or registered models) to another tracking server

MLflow Export Import

The MLflow Export Import package provides tools to copy MLflow objects (runs, experiments or registered models) from one MLflow tracking server (Databricks workspace) to another. Using the MLflow REST API, the tools export MLflow objects to an intermediate directory and then import them into the target tracking server.

For more details:

• JSON export file format.
• MLflow Object Relationships slide deck.

Architecture

Overview

Why use MLflow Export Import?

• MLOps CI/CD. Migrate runs (or registered models) to another tracking server.
  • Promote a run from the development to the test tracking server.
  • After it passes tests, then promote it to the production tracking server.
• Share and collaborate with other data scientists in the same or other tracking server (Databricks workspace).
  • For example, copy an experiment from one user to another.
• Backup your MLflow objects to external storage so they can be restored if needed.
• Disaster recovery. Save your MLflow objects to external storage so they can be replicated to another tracking server.

MLflow Export Import scenarios

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

MLflow Objects

These are the MLflow objects and their attributes that can be exported.

Object	REST	Python	SQL
Run	link	link	link
Experiment	link	link	link
Registered Model	link	link	link
Registered Model Version	link	link	link

MLflow provides rudimentary capabilities for tracking lineage regarding the original source objects. See README_governance.

Tools Overview

There are two dimensions to the MLflow Export Import tools:

• Export of MLflow objects in single or bulk mode.
• Regular Python scripts or Databricks notebooks.

Single and Bulk Tools

The two export modes are:

• Single tools. Copy a single MLflow object between tracking servers. These tools allow you to specify a different destination object name. For example, if you want to clone the experiment /Mary/Experiments/Iris under a new name, you can specify the target experiment name as /John/Experiments/Iris.

• Bulk tools. High-level tools to copy an entire tracking server or a collection of MLflow objects. There is no option to change destination object names. Full object referential integrity is maintained (e.g. an imported registered model version will point to the imported run that it refers to.

Databricks notebooks simply invoke the corresponding Python classes.

Limitations

See README_limitations.md.

Quick Start

Setup

pip install mlflow-export-import

Export experiment

export MLFLOW_TRACKING_URI=http://localhost:5000

export-experiment \
  --experiment sklearn-wine \
  --output-dir /tmp/export

Import experiment

export MLFLOW_TRACKING_URI=http://localhost:5001

import-experiment \
  --experiment-name sklearn-wine \
  --input-dir /tmp/export

Setup

Supports python 3.8.

Local setup

First create a virtual environment.

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

There are several different ways to install the package.

1. Install from PyPI - recommended

pip install mlflow-export-import

2. Install from github directly

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

3. Install from github clone

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

Databricks notebook setup

There are two different ways to install the package in a Databricks notebook.

1. Install package in notebook

See documentation: Install notebook-scoped libraries with %pip.

%pip install 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.

git clone https://github.com/mlflow/mlflow-export-import
cd mlflow-export-import
python setup.py bdist_wheel
databricks fs cp dist/mlflow_export_import-1.0.0-py3-none-any.whl {MY_DBFS_PATH}

Laptop to Databricks 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

Other

• README_options.md advanced options.
• Miscellanous tools.

Testing

There are two types of tests : open source and Databricks tests. See tests/README for details.

README files

• README.md
• README_single.md
• README_bulk.md
• README_tools.md
• README_limitations.md
• README_options.md
• README_export_format.md
• README_governance.md
• tests/README.md
  • tests/open_source/README.md
  • tests/databricks/README.md
• mlflow_export_import/workflow_api/README.md
• databricks_notebooks/README.md