Metadata-Version: 2.1
Name: fromconfig-mlflow
Version: 0.1.3
Summary: # FromConfig MlFlow
Home-page: https://github.com/guillaumegenthial/fromconfig-mlflow
Author: Criteo
License: UNKNOWN
Description: # FromConfig MlFlow
        [![pypi](https://img.shields.io/pypi/v/fromconfig-mlflow.svg)](https://pypi.python.org/pypi/fromconfig-mlflow)
        [![ci](https://github.com/guillaumegenthial/fromconfig-mlflow/workflows/Continuous%20integration/badge.svg)](https://github.com/guillaumegenthial/fromconfig-mlflow/actions?query=workflow%3A%22Continuous+integration%22)
        
        A [fromconfig](https://github.com/criteo/fromconfig) `Launcher` for [MlFlow](https://www.mlflow.org) support.
        
        <!-- MarkdownTOC -->
        
        - [Install](#install)
        - [Quickstart](#quickstart)
        - [Usage Reference](#usage-reference)
          - [Options](#options)
        - [Examples](#examples)
          - [Multi](#multi)
        
        <!-- /MarkdownTOC -->
        
        
        <a id="install"></a>
        ## Install
        
        ```bash
        pip install fromconfig_mlflow
        ```
        
        <a id="quickstart"></a>
        ## Quickstart
        
        Once installed, the launcher is available with the name `mlflow`.
        
        Start a local MlFlow server with
        
        ```bash
        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
        
        ```python
        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
        
        ```yaml
        # 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
        
        ```bash
        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`](docs/examples/quickstart).
        
        <a id="usage-reference"></a>
        ## Usage Reference
        
        <a id="options"></a>
        ### 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).
        - `include_keys`: if given, only log flattened parameters that have one of these keys as substring. 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"`.
        - `ignore_keys`: if given, parameters that have at least one of the keys as substring will be ignored.
        
        See [the multi example](#multi).
        
        
        <a id="examples"></a>
        ## Examples
        
        <a id="multi"></a>
        ### Multi
        
        Re-using the [quickstart](#quickstart) code, modify the `launcher.yaml` file
        
        ```yaml
        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
        
        ```bash
        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`
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Intended Audience :: Developers
Description-Content-Type: text/markdown
