fromconfig-mlflow 0.4.0

# FromConfig MlFlow <!-- {docsify-ignore} -->

FromConfig MlFlow

A fromconfig Launcher for MlFlow support.

• Install
• Quickstart
• MlFlow server
• Configure MlFlow
• Artifacts and Parameters
• Usage-Reference
  • StartRunLauncher
  • LogArtifactsLauncher
  • LogParamsLauncher

Install

pip install fromconfig_mlflow

Quickstart

To activate MlFlow login, simply add --launcher.log=mlflow to your command

fromconfig config.yaml params.yaml --launcher.log=mlflow - model - train

With

model.py

"""Dummy Model."""

import mlflow


class Model:
    def __init__(self, learning_rate: float):
        self.learning_rate = learning_rate

    def train(self):
        print(f"Training model with learning_rate {self.learning_rate}")
        if mlflow.active_run():
            mlflow.log_metric("learning_rate", self.learning_rate)

config.yaml

model:
  _attr_: model.Model
  learning_rate: "${params.learning_rate}"

params.yaml

params:
  learning_rate: 0.001

It should print

Started run: http://127.0.0.1:5000/experiments/0/runs/7fe650dd99574784aec1e4b18fceb73f
Training model with learning_rate 0.001

If you navigate to http://127.0.0.1:5000/experiments/0/runs/7fe650dd99574784aec1e4b18fceb73f you should see your the logged learning_rate metric.

MlFlow server

To setup a local MlFlow tracking server, run

mlflow server

which should print

[INFO] Starting gunicorn 20.0.4
[INFO] Listening at: http://127.0.0.1:5000

We will assume that the tracking URI is http://127.0.0.1:5000 from now on.

Configure MlFlow

You can set the tracking URI either via an environment variable or via the config.

To set the MLFLOW_TRACKING_URI environment variable

export MLFLOW_TRACKING_URI=http://127.0.0.1:5000

Alternatively, you can set the mlflow.tracking_uri config key either via command line with

fromconfig config.yaml params.yaml --launcher.log=mlflow --mlflow.tracking_uri="http://127.0.0.1:5000" - model - train

or in a config file with

launcher.yaml

# Configure mlflow
mlflow:
  # tracking_uri: "http://127.0.0.1:5000"  # Or set env variable MLFLOW_TRACKING_URI
  # experiment_name: "test-experiment"  # Which experiment to use
  # run_id: 12345  # To restore a previous run
  # run_name: test  # To give a name to your new run
  # artifact_location: "path/to/artifacts"  # Used only when creating a new experiment

# Configure launcher
launcher:
  log: mlflow

and run

fromconfig config.yaml params.yaml launcher.yaml - model - train

Artifacts and Parameters

In this example, we add logging of the config and parameters.

Re-using the quickstart code, modify the launcher.yaml file

# Configure logging
logging:
  level: 20

# Configure mlflow
mlflow:
  # tracking_uri: "http://127.0.0.1:5000"  # Or set env variable MLFLOW_TRACKING_URI
  # experiment_name: "test-experiment"  # Which experiment to use
  # run_id: 12345  # To restore a previous run
  # run_name: test  # To give a name to your new run
  # artifact_location: "path/to/artifacts"  # Used only when creating a new experiment
  # include_keys:  # Only log params that match *model*
  #   - model

# Configure launcher
launcher:
  log:
    - logging
    - mlflow
  parse:
    - mlflow.log_artifacts
    - parser
    - mlflow.log_params

and run

fromconfig config.yaml params.yaml launcher.yaml - model - train

which prints

INFO:fromconfig_mlflow.launcher:Started run: http://127.0.0.1:5000/experiments/0/runs/<MLFLOW_RUN_ID>
Training model with learning_rate 0.001

If you navigate to the MlFlow run URL, you should see

• the parameters, a flattened version of the parsed config (model.learning_rate is 0.001 and not ${params.learning_rate})
• the original config, saved as config.yaml
• the parsed config, saved as parsed.yaml

Usage-Reference

StartRunLauncher

To configure MlFlow, add a mlflow entry to your config and set the following parameters

• run_id: if you wish to restart an existing run
• run_name: if you wish to give a name to your new run
• tracking_uri: to configure the tracking remote
• experiment_name: to use a different experiment than the custom experiment
• artifact_location: the location of the artifacts (config files)

Additionally, the launcher can be initialized with the following attributes

• set_env_vars: if True (default is True), set MLFLOW_RUN_ID and MLFLOW_TRACKING_URI
• set_run_id: if True (default is False), set mlflow.run_id in config.

For example,

# Configure logging
logging:
  level: 20

# Configure mlflow
mlflow:
  # tracking_uri: "http://127.0.0.1:5000"  # Or set env variable MLFLOW_TRACKING_URI
  # experiment_name: "test-experiment"  # Which experiment to use
  # run_id: 12345  # To restore a previous run
  # run_name: test  # To give a name to your new run
  # artifact_location: "path/to/artifacts"  # Used only when creating a new experiment

# Configure Launcher
launcher:
  log:
    - logging
    - _attr_: mlflow
      set_env_vars: true
      set_run_id: true

LogArtifactsLauncher

The launcher can be initialized with the following attributes

• path_command: Name for the command file. If None, don't log the command.
• path_config: Name for the config file. If None, don't log the config.

For example,

# Configure logging
logging:
  level: 20

# Configure mlflow
mlflow:
  # tracking_uri: "http://127.0.0.1:5000"  # Or set env variable MLFLOW_TRACKING_URI
  # experiment_name: "test-experiment"  # Which experiment to use
  # run_id: 12345  # To restore a previous run
  # run_name: test  # To give a name to your new run
  # artifact_location: "path/to/artifacts"  # Used only when creating a new experiment

# Configure launcher
launcher:
  log:
    - logging
    - mlflow
  parse:
    - _attr_: mlflow.log_artifacts
      path_command: launch.sh
      path_config: config.yaml
    - parser
    - _attr_: mlflow.log_artifacts
      path_command: null
      path_config: parsed.yaml

LogParamsLauncher

The launcher will use include_keys and ignore_keys if present in the config in the mlflow key.

• ignore_keys : If given, don't log some parameters that have some substrings.
• include_keys : If given, only log some parameters that have some substrings. Also shorten the flattened parameter to start at the first match. For example, if the config is {"foo": {"bar": 1}} and include_keys=("bar",), then the logged parameter will be "bar".

For example,

# Configure logging
logging:
  level: 20

# Configure mlflow
mlflow:
  # tracking_uri: "http://127.0.0.1:5000"  # Or set env variable MLFLOW_TRACKING_URI
  # experiment_name: "test-experiment"  # Which experiment to use
  # run_id: 12345  # To restore a previous run
  # run_name: test  # To give a name to your new run
  # artifact_location: "path/to/artifacts"  # Used only when creating a new experiment
  include_keys:  # Only log params that match *model*
    - model

# Configure launcher
launcher:
  log:
    - logging
    - mlflow
  parse:
    - parser
    - mlflow.log_params