fromconfig-mlflow 0.1.1

# FromConfig MlFlow

FromConfig MlFlow

A fromconfig Launcher for MlFlow support.

• Install
• Quickstart
• Usage Reference
  • Options
• Examples
  • Multi

Install

pip install fromconfig_mlflow

Quickstart

Once installed, the launcher is available with the name mlflow.

Start a local MlFlow server with

mlflow server

You should see

[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.

Given the following module

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}")

and config files

# config.yaml
model:
  _attr_: foo.Model
  learning_rate: "@params.learning_rate"

# params.yaml
params:
  learning_rate: 0.001

# launcher.yaml
mlflow:
  tracking_uri: "http://127.0.0.1:5000"
logging:
  level: 20
launcher:
  log:
    - logging
    - mlflow

Run

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

which prints

INFO:fromconfig.launcher.logger:- launcher.log[0]: logging
INFO:fromconfig.launcher.logger:- launcher.log[1]: mlflow
INFO:fromconfig.launcher.logger:- params.learning_rate: 0.001
INFO:fromconfig.launcher.logger:- mlflow.tracking_uri: http://127.0.0.1:5000
INFO:fromconfig.launcher.logger:- logging.level: 20
INFO:fromconfig.launcher.logger:- model._attr_: foo.Model
INFO:fromconfig.launcher.logger:- model.learning_rate: 0.001
INFO:fromconfig_mlflow.launcher:Started new run: http://127.0.0.1:5000/experiments/0/runs/e5ae42827da041fc989aca024040c725
INFO:fromconfig_mlflow.launcher:Logging artifacts config.json and launch.txt
INFO:fromconfig_mlflow.launcher:Logging parameters
Training model with learning_rate 0.001

If you navigate to http://127.0.0.1:5000/experiments/0/runs/40ea35e951e942bc9f0b9c792c4ce1e7 you should see your parameters and configs.

This example can be found in docs/examples/quickstart.

Usage Reference

Options

To configure MlFlow, add a mlflow entry to your config.

You can 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, if you wish to call the mlflow launcher multiple times during the launch (for example once before the parser, and once after), you need to configure the different launches with the special launches key (otherwise only the first launch will actually log artifacts and parameters).

The launches key should be a list of dictionaries with the following parameters

• log_artifacts: if True (default), will log the artifacts (the config and command given to the launcher)
• log_parameters: if True (default) will log a flattened view of the parameters
• path_config: if given, will write the config as an artifact with that name (default is config.json)
• path_command: if given, will write the command as an artifact with that name (default is launch.txt, using the .txt extension because you can preview it on MlFlow).

See the multi example.

Examples

Multi

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

mlflow:
  tracking_uri: "http://127.0.0.1:5000"
  launches:
    -
      log_artifacts: true
      log_parameters: false
      path_config: "config.json"
      path_command: "launch_config.txt"
    -
      log_artifacts: true
      log_parameters: true
      path_config: "parsed.json"
      path_command: "launch_parsed.json"
logging:
  level: 20
launcher:
  parse:
    - mlflow
    - parser
  log:
    - logging
    - mlflow

and run

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

If you navigate to the MlFlow run, 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.json
• the parsed config, saved as parsed.json