Metadata-Version: 2.1
Name: fromconfig-mlflow
Version: 0.2.0
Summary: # FromConfig MlFlow
Home-page: https://github.com/criteo/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/criteo/fromconfig-mlflow/workflows/Continuous%20integration/badge.svg)](https://github.com/criteo/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.
        
        Set the `MLFLOW_TRACKING_URI` environment variable
        
        ```bash
        export MLFLOW_TRACKING_URI=http://127.0.0.1:5000
        ```
        
        Given the following module
        
        ```python
        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)
        ```
        
        and config files
        
        `config.yaml`
        
        ```yaml
        model:
          _attr_: foo.Model
          learning_rate: "${params.learning_rate}"
        ```
        
        `params.yaml`
        
        ```yaml
        params:
          learning_rate: 0.001
        ```
        
        Run
        
        ```bash
        fromconfig config.yaml params.yaml --launcher.log=mlflow - model - train
        ```
        
        which prints
        
        ```
        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 parameters and configs.
        
        This example can be found in [`docs/examples/quickstart`](docs/examples/quickstart).
        
        You can also use a `launcher.yaml` file
        
        ```yaml
        # Configure mlflow
        mlflow:
          # tracking_uri: "http://127.0.0.1:5000"
          # experiment_name: "test-experiment"
          # run_name: test
          # artifact_location: "path/to/artifacts"
        
        # Configure launcher (only change the log step)
        launcher:
          log: mlflow
        ```
        
        by running
        
        ```bash
        fromconfig config.yaml params.yaml launcher.yaml - model - train
        ```
        
        <a id="usage-reference"></a>
        ## Usage Reference
        
        <a id="options"></a>
        ### Options
        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)
        
        You can also set the following attributes
        
        - log_artifacts : bool, optional
              If True, save config and command as artifacts.
        - log_parameters : bool, optional
              If True, log flattened config as parameters.
        - path_command : str, optional
              Name for the command file
        - path_config : str, optional
              Name for the config file.
        - set_env_vars : bool, optional
              If True, set MlFlow environment variables.
        - set_run_id : bool, optional
              If True, the run_id is overridden in the config.
        - ignore_keys : Iterable[str], optional
              If given, don't log some parameters that have some substrings.
        - include_keys : Iterable[str], optional
              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"`.
        
        
        <a id="examples"></a>
        ## Examples
        
        <a id="multi"></a>
        ### Multi
        
        In this example, we show how to call and configure multiple launches of the `MlFlowLauncher`. We first log the non-parsed configs, then parse, then log both the parsed configs and the flattened parameters.
        
        Re-using the [quickstart](#quickstart) code, modify the `launcher.yaml` file
        
        ```yaml
        # Configure logging
        logging:
          level: 20
        
        # Configure mlflow
        mlflow:
          # tracking_uri: "http://127.0.0.1:5000"
          # experiment_name: "test-experiment"
          # run_name: test
          # artifact_location: "path/to/artifacts"
        
        launcher:
          parse:
            - _attr_: fromconfig_mlflow.MlFlowLauncher  # Log non-parsed config
              log_artifacts: true
              log_params: false
              path_config: "config.yaml"
              path_command: "config_launch.sh"
            - parser  # Parse config
            - _attr_: fromconfig_mlflow.MlFlowLauncher  # Log parsed config and parameters
              log_artifacts: true
              log_params: true
              path_config: "parsed.yaml"
              path_command: "parsed_launch.sh"
              include_keys:  # Only parameters that start with model will be logged as parameters
                - model
        
        ```
        
        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.yaml`
        - the parsed config, saved as `parsed.yaml`
        
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
