MLFlow integration for Keras-Tuner

mlflow_keras_tuner.py

"""mlflow integration for KerasTuner

Copyright: Soundsensing AS, 2021
License: MIT
"""

import uuid

import structlog
log = structlog.get_logger()

import mlflow
import keras_tuner

def get_run_id(run):
    if run is None:
        return None

    return run.info.run_id


class MlflowLogger(object):
    """KerasTuner Logger for integrating with mlflow

    Each KerasTuner trial is a parent mlflow run,
    and then each execution is a child

    XXX: assumes that executions are done sequentially and non-concurrently
    """

    def __init__(self):
        self.search_run = None
        self.search_id = None
        self.trial_run = None
        self.trial_id = None
        self.trial_state = None
        self.execution_run = None
        self.execution_id = 0
    
    def register_tuner(self, tuner_state):
        """Called at start of search"""

        log.debug('mlflow-logger-search-start')

        self.search_id = str(uuid.uuid4())
        # Register a top-level run
        self.search_run = mlflow.start_run(nested=False, run_name=f'search-{self.search_id[0:8]}')

    def exit(self):
        """Called at end of a search"""

        log.debug('mlflow-logger-search-end')
        self.seach_run = None
        self.search_id = None

    def register_trial(self, trial_id, trial_state):
        """Called at beginning of trial"""

        log.debug('mlflow-logger-trial-start',
                trial_id=trial_id,
                active_run_id=get_run_id(mlflow.active_run()),
        )

        assert self.search_run is not None
        assert self.trial_run is None
        assert self.execution_run is None
        assert self.execution_id == 0

        self.trial_id = trial_id
        self.trial_state = trial_state

        # Start a new run, under the search run
        self.trial_run = mlflow.start_run(nested=True,
            run_name=f'trial-{self.trial_id[0:8]}-{self.search_id[0:8]}'
        )

        # For now, only register these on each execution
        #hyperparams = self.trial_state['hyperparameters']['values']
        #mlflow.log_params(hyperparams)

    def report_trial_state(self, trial_id, trial_state):
        """Called at end of trial"""

        log.debug('mlflow-logger-trial-end',
            trial_id=trial_id,
            active_run_id=get_run_id(mlflow.active_run()),
        )

        assert self.search_run is not None
        assert self.trial_run is not None
        assert self.execution_run is None

        # Start a new run, under the search run
        mlflow.end_run() ## XXX: no way to specify the id?

        self.trial_run = None
        self.trial_id = None
        self.trial_state = None
        self.execution_id = 0

    def register_execution(self):
        log.debug('mlflow-logger-execution-start',
            active_run_id=get_run_id(mlflow.active_run()),
        )

        assert self.search_run is not None
        assert self.trial_run is not None
        assert self.execution_run is None

        self.execution_run = mlflow.start_run(nested=True,
            run_name=f'exec-{self.execution_id}-{self.trial_id[0:8]}-{self.search_id[0:8]}',
        )
        self.execution_id += 1

        # register hyperparameters from the trial
        hyperparams = self.trial_state['hyperparameters']['values']
        mlflow.log_params(hyperparams)

    def report_execution_state(self, histories):
        log.debug('mlflow-logger-execution-end',
            active_run_id=get_run_id(mlflow.active_run()),
        )
        assert self.search_run is not None
        assert self.trial_run is not None
        assert self.execution_run is not None

        mlflow.end_run() ## XXX: no way to specify the id?

        self.execution_run = None


class FakeHistories():
    def __init__(self, metrics={}):
        self.history = metrics

class LoggerTunerMixin():
    def __init__(self, *args, **kwargs):
        if kwargs.get('logger') is None:
            kwargs['logger'] = MlflowLogger()

        self.on_exception =  kwargs.get('on_exception', 'pass')

        return super(LoggerTunerMixin, self).__init__(*args, **kwargs)

    # Hack in registration for each model training "execution"
    def _build_and_fit_model(self, trial, *args, **kwargs):

        # log start of execution
        if self.logger:
            self.logger.register_execution()

        histories = None
        try:
            # call the original function
            histories = super(LoggerTunerMixin, self)._build_and_fit_model(trial, *args, **kwargs)
        except Exception as e:
            if self.on_exception == 'pass':
                o = self.oracle.objective
                value = float('inf') if o.direction == 'min' else float('-inf')
                histories = FakeHistories({o.name: value})
            else:
                raise e

        # log end of execution
        if self.logger:
            self.logger.report_execution_state(histories)

        return histories

# Integrate with keras tuners
class RandomSearch(LoggerTunerMixin, keras_tuner.RandomSearch):
    pass
class BayesianOptimization(LoggerTunerMixin, keras_tuner.BayesianOptimization):
    pass
class SklearnTuner(LoggerTunerMixin, keras_tuner.SklearnTuner):
    pass
class Hyperband(LoggerTunerMixin, keras_tuner.Hyperband):
    pass

Could you provide an example on how you would use it with mlflow?

One imports these overridden classes, and uses that to instantiate as the search.

    from mlflow_keras_tuner import RandomSearch
    tuner = RandomSearch(
        .... ordinary parameters ...
    )

I have to add mlflow.tensorflow.autolog() for it to log my loss as well.

@metalglove ah yes, good point.
And anything one want logged that is not part of the autolog, one should use the mlflow tracking API for (mlflow.log_*).
https://mlflow.org/docs/latest/tracking.html#logging-functions

@jonnor I am getting this error ModuleNotFoundError: No module named 'structlog', am I missing something?

Yes you need to install the "structlog" module (with pip)

…

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@metalglove ah yes, good point. And anything one want logged that is not part of the autolog, one should use the mlflow tracking API for (mlflow.log_*). https://mlflow.org/docs/latest/tracking.html#logging-functions

I'm trying to log a plot of a confusion matrix as an mlflow artifact. Where should I add that bit of code? And any suggestions for how I can retrieve the model from the active trial to run model.predict?