Skip to content

Latest commit

 

History

History
495 lines (402 loc) · 16.9 KB

README_single.md

File metadata and controls

495 lines (402 loc) · 16.9 KB
Raw

MLflow Export Import - Single Object Tools

Overview

The Single tools allow you to export and import single MLflow objects between tracking servers (Databricks workspaces). These tools allow you to specify a different destination object name.

For example, if you want to clone the experiment /Mary/Experiment/Iris under a new name, you can specify the target experiment name as /John/Experiment/Iris.

See sample JSON export files here.

Tools

MLflow Object Documentation Code
Registered Model export-model code
import-model code
Model Version export-model-version code
import-model-version code
copy-model-version code
Experiment export-experiment code
import-experiment code
Run export-run code
import-run code
copy-run code

Experiment Tools

Export Experiment

Export an experiment to a directory. Accepts either an experiment ID or name.

Usage

export-experiment --help

Options:
  --experiment TEXT              Experiment name or ID.  [required]
  --output-dir TEXT              Output directory.  [required]
  --run-ids TEXT                 List of run IDs to export (comma seperated).
  --export-permissions BOOLEAN   Export Databricks permissions.  [default:
                                 False]
  --run-start-time TEXT          Only export runs started after this UTC time
                                 (inclusive). Format: YYYY-MM-DD.
  --export-deleted-runs BOOLEAN  Export deleted runs.  [default: False]
  --check-nested-runs BOOLEAN    Check if run in the 'run-ids' option is a
                                 parent of nested runs and export all the
                                 nested runs.  [default: False]
  --notebook-formats TEXT        Databricks notebook formats. Values are
                                 SOURCE, HTML, JUPYTER or DBC (comma
                                 separated).

Examples

Export experiment by experiment ID:
export-experiment \
  --experiment 2 \
  --output-dir out
Export experiment by experiment name:
export-experiment \
  --experiment sklearn-wine \
  --output-dir out
Export selected runs:
export-experiment \
  --experiment sklearn-wine \
  --output-dir out \
  --run-ids 1eea5a4f49184781947d6761b7207b25,1320b260e894469a9b60b415a25e4062
Export selected nested runs:
export-experiment \
  --experiment sklearn-wine \
  --output-dir out \
  --run-ids 1eea5a4f49184781947d6761b7207b25 \
  --check-nested-runs True

Databricks export examples

See Access the MLflow tracking server from outside Databricks.

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

export-experiment \
  --experiment /Users/me@mycompany.com/SklearnWine \
  --output-dir out \
  --notebook-formats DBC,SOURCE

Export directory structure

The export directory contains a JSON export file for the experiment and a subdirectory for each run. The run directory contains a JSON export file containing run metadata and an artifact folder directory.

Sample export directory

+-experiment.json
+-eb66c160957d4a28b11d3f1b968df9cd/
| +-run.json
| +-artifacts/
|   +-plot.png
|   +-model/
|     +-requirements.txt
|     +-python_env.yaml
|     +-model.pkl
|     +-conda.yaml
|     +-MLmodel

Import Experiment

Import an experiment from a directory. Reads the manifest file to import the expirement and its runs.

The experiment will be created if it does not exist in the destination tracking server. If the destination experiment already exists, the source runs will be added to it.

Usage

import-experiment --help

Options:
  --experiment-name TEXT        Destination experiment name  [required]
  --input-dir TEXT              Input directory  [required]
  --import-permissions BOOLEAN  Import Databricks permissions.  [default:
                                False]
  --import-source-tags BOOLEAN  Import source information for registered model
                                and its versions ad tags in destination
                                object.  [default: False]
  --use-src-user-id BOOLEAN     Set the destination user field to the source
                                user field.  Only valid for open source
                                MLflow.  When importing into Databricks, the
                                source user field is ignored since it is
                                automatically picked up from your Databricks
                                access token.  There is no MLflow API endpoint
                                to explicity set the user_id for Run and
                                Registered Model.
  --dst-notebook-dir TEXT       Databricks destination workpsace base
                                directory for notebook. A run ID will be added
                                to contain the run's notebook.
  --help                        Show this message and exit.

Import examples

import-experiment \
  --experiment-name imported_sklearn \
  --input-dir out

Databricks import examples

export MLFLOW_TRACKING_URI=databricks
import-experiment \
  --experiment-name /Users/me@mycompany.com/imported/SklearnWine \
  --input-dir exported_experiments/3532228 \

Run Tools

Export run

Export run to directory.

Usage

export-run --help

Options:
  --run-id TEXT                   Run ID.  [required]
  --output-dir TEXT               Output directory.  [required]
  --notebook-formats TEXT         Notebook formats. Values are SOURCE, HTML,
                                  JUPYTER or DBC (comma seperated).  [default: ]

Run examples

export-run \
  --run-id 50fa90e751eb4b3f9ba9cef0efe8ea30 \
  --output-dir out

Produces a directory with the following structure:

+-run.json
+-artifacts/
| +-model/
|   +-requirements.txt
|   +-python_env.yaml
|   +-model.pkl
|   +-conda.yaml
|   +-MLmodel

Sample run.json files: OSS - Databricks.

Import run

Imports a run from a directory.

Usage

import-run --help

Options:
  --input-dir TEXT              Input directory.  [required]
  --experiment-name TEXT        Destination experiment name.  [required]
  --import-source-tags BOOLEAN  Import source information for registered model
                                and its versions ad tags in destination
                                object.  [default: False]
  --use-src-user-id BOOLEAN     Set the destination user field to the source
                                user field.  Only valid for open source
                                MLflow.  When importing into Databricks, the
                                source user field is ignored since it is
                                automatically picked up from your Databricks
                                access token.  There is no MLflow API endpoint
                                to explicity set the user_id for Run and
                                Registered Model.  [default: False]
  --dst-notebook-dir TEXT       Databricks destination workpsace base
                                directory for notebook. A run ID will be added
                                to contain the run's notebook.
  --mlmodel-fix BOOLEAN         Add correct run ID in destination MLmodel
                                artifact. Can be expensive for deeply nested
                                artifacts.  [default: True]

Import examples

Local import example
export MLFLOW_TRACKING_URI=databricks

import-run \
  --run-id 50fa90e751eb4b3f9ba9cef0efe8ea30 \
  --input out \
  --experiment-name sklearn_wine_imported
Databricks import example
export MLFLOW_TRACKING_URI=databricks

run.import-run \
  --run-id 50fa90e751eb4b3f9ba9cef0efe8ea30 \
  --input out \
  --experiment-name /Users/me@mycompany.com/imported/SklearnWine

Registered Model Tools

Export Registered Model

Export a registered model to a directory. The default is to export all versions of a model including all None and Archived stages. You can specify a list of stages to export.

Source: export_model.py.

Usage

export-model --help

Options:
  --model TEXT                    Registered model name.  [required]
  --output-dir TEXT               Output directory.  [required]
  --stages TEXT                   Stages to export (comma separated). Default
                                  is all stages and all versions. Stages are
                                  Production, Staging, Archived and None.
                                  Mututally exclusive with option --versions.
  --versions TEXT                 Export specified versions (comma separated).
                                  Mututally exclusive with option --stages.
  --export-latest-versions BOOLEAN
                                  Export latest registered model versions
                                  instead of all versions.  [default: False]
  --export-version-model BOOLEAN  Export registered model version's 'cached'
                                  MLflow model.  [default: False]
  --export-deleted-runs BOOLEAN   Export deleted runs.  [default: False]
  --export-permissions BOOLEAN    Export Databricks permissions.  [default:
                                  False]
  --notebook-formats TEXT         Databricks notebook formats. Values are
                                  SOURCE, HTML, JUPYTER or DBC (comma
                                  separated).

Example

export-model \
  --model sklearn_wine \
  --output-dir out \
  --stages Production,Staging

Found 6 versions
Exporting version 3 stage 'Production' with run_id 24aa9cce1388474e9f26d17100724cdd to out/24aa9cce1388474e9f26d17100724cdd
Exporting version 5 stage 'Staging' with run_id 8efd80f59b7946119d8f1838515eea25 to out/8efd80f59b7946119d8f1838515eea25

Output

Output export directory example.

+-749930c36dee49b8aeb45ee9cdfe1abb/
| +-artifacts/
|   +-sklearn-model/
|   | +-model.pkl
|   | +-conda.yaml
|   | +-MLmodel
|   |
+-model.json

Sample model.json files: OSS - Databricks.

{
"mlflow": {
  "registered_model": {
    "name": "sklearn_wine",
    "creation_timestamp": "1587517284168",
    "last_updated_timestamp": "1587572072601",
    "description": "hi my desc",
    "latest_versions": [
      {
        "name": "sklearn_wine",
        "version": "1",
        "creation_timestamp": "1587517284216",
. . .

Import registered model

Import a registered model from a directory.

Source: import_model.py.

Usage

import-model --help

Options:
  --input-dir TEXT              Input directory  [required]
  --model TEXT                  Registered model name.  [required]
  --experiment-name TEXT        Destination experiment name  [required]
  --delete-model BOOLEAN        If the model exists, first delete the model
                                and all its versions.  [default: False]
  --import-permissions BOOLEAN  Import Databricks permissions using the HTTP
                                PATCH method.  [default: False]
  --import-source-tags BOOLEAN  Import source information for registered model
                                and its versions ad tags in destination
                                object.  [default: False]
  --await-creation-for INTEGER  Await creation for specified seconds.

Example

import-model \
  --model sklearn_wine \
  --experiment-name sklearn_wine_imported \
  --input-dir out  \
  --delete-model True

Model to import:
  Name: sklearn_wine
  Description: my model
  2 versions
Deleting 1 versions for model 'sklearn_wine_imported'
  version=2 status=READY stage=Production run-id=f93d5e4d182e4f0aba5493a0fa8d9eb6
Importing versions:
  Version 1:
    current_stage: None:
    Run to import:
      run-id: 749930c36dee49b8aeb45ee9cdfe1abb
      artifact_uri: file:///opt/mlflow/server/mlruns/1/749930c36dee49b8aeb45ee9cdfe1abb/artifacts
      source:       file:///opt/mlflow/server/mlruns/1/749930c36dee49b8aeb45ee9cdfe1abb/artifacts/sklearn-model
      model_path: sklearn-model
      run-id: 749930c36dee49b8aeb45ee9cdfe1abb
    Importing run into experiment 'scratch' from 'out/749930c36dee49b8aeb45ee9cdfe1abb'
    Imported run:
      run-id: 03d0cfae60774ec99f949c42e1575532
      artifact_uri: file:///opt/mlflow/server/mlruns/13/03d0cfae60774ec99f949c42e1575532/artifacts
      source:       file:///opt/mlflow/server/mlruns/13/03d0cfae60774ec99f949c42e1575532/artifacts/sklearn-model
Version: id=1 status=READY state=None
Waited 0.01 seconds

Model Version Tools

Export Model Version

Exports a registered model version and its run. Also exports registered model and experiment metadata (description and tags).

Source: export_model_version.py.

Example

export-model-version \
  --model my_catalog.models.Sklearn_Wine \
  --version 1 \
  --output-dir dbfs:/mnt/mlflow-export-import/Sklearn_Wine

Usage

export-model-version --help

Options:
  --model TEXT                    Registered model name.  [required]
  --version TEXT                  Registered model version.  [required]
  --output-dir TEXT               Output directory.  [required]
  --export-version-model BOOLEAN  Export registered model version's 'cached'
                                  MLflow model.  [default: False]
  --export-permissions BOOLEAN    Export Databricks permissions.  [default:
                                  False]
  --notebook-formats TEXT         Databricks notebook formats. Values are
                                  SOURCE, HTML, JUPYTER or DBC (comma
                                  separated).

Import Model Version

Imports a registered model version and its run.

Optionally import the registered model and experiment metadata (description and tags) only if they are being created for the first time. Will not modify metadata of an existing registered model and experiment.

Source: import_model_version.py.

Example

import-model-version \
  --input-dir dbfs:/mnt/mlflow-export-import/Sklearn_Wine \
  --model my_catalog.models.Sklearn_Wine \
  --create-model True \
  --experiment-name /Users/me@mycompany.com/SklearnWine

Usage

import-model-version --help

Usage: import-model-version
           [OPTIONS]

  Imports a registered model version and its run.

Options:
  --model TEXT                    Registered model name.  [required]
  --experiment-name TEXT          Destination experiment name for the
                                  version's run.  [required]
  --input-dir TEXT                Input directory.  [required]
  --create-model BOOLEAN          Create registered model before creating
                                  model version.  [default: False]
  --import-permissions BOOLEAN    Import Databricks permissions using the HTTP
                                  PATCH method.  [default: False]
  --import-source-tags BOOLEAN    Import source information for registered
                                  model and its versions and tags in
                                  destination object.  [default: False]
  --import-stages-and-aliases BOOLEAN
                                  Import stages and aliases.  [default: False]
  --import-metadata BOOLEAN       Import registered model and experiment
                                  metadata (description and tags).  [default:
                                  False]