Added export to MLflow pyfunc model format

ludwig/api.py

@@ -484,64 +484,65 @@ def train(
                     if not skip_save_model:
                         self.save_config(model_dir)
 
-                train_stats = trainer.train(
-                    self.model,
-                    training_set,
-                    validation_set=validation_set,
-                    test_set=test_set,
-                    save_path=model_dir,
-                )
-
-                self.model, train_trainset_stats, train_valiset_stats, train_testset_stats = train_stats
-                train_stats = {
-                    TRAINING: train_trainset_stats,
-                    VALIDATION: train_valiset_stats,
-                    TEST: train_testset_stats
-                }
-
-                # save training statistics
-                if self.backend.is_coordinator():
-                    if not skip_save_training_statistics:
-                        save_json(training_stats_fn, train_stats)
-
-                # grab the results of the model with highest validation test performance
-                validation_field = trainer.validation_field
-                validation_metric = trainer.validation_metric
-                validation_field_result = train_valiset_stats[validation_field]
-
-                best_function = get_best_function(validation_metric)
-                # results of the model with highest validation test performance
-                if self.backend.is_coordinator() and validation_set is not None:
-                    epoch_best_vali_metric, best_vali_metric = best_function(
-                        enumerate(validation_field_result[validation_metric]),
-                        key=lambda pair: pair[1]
-                    )
-                    logger.info(
-                        'Best validation model epoch: {0}'.format(
-                            epoch_best_vali_metric + 1)
+                try:
+                    train_stats = trainer.train(
+                        self.model,
+                        training_set,
+                        validation_set=validation_set,
+                        test_set=test_set,
+                        save_path=model_dir,
                     )
-                    logger.info(
-                        'Best validation model {0} on validation set {1}: {2}'.format(
-                            validation_metric, validation_field, best_vali_metric
-                        ))
-                    if test_set is not None:
-                        best_vali_metric_epoch_test_metric = train_testset_stats[
-                            validation_field][validation_metric][
-                            epoch_best_vali_metric]
 
+                    self.model, train_trainset_stats, train_valiset_stats, train_testset_stats = train_stats
+                    train_stats = {
+                        TRAINING: train_trainset_stats,
+                        VALIDATION: train_valiset_stats,
+                        TEST: train_testset_stats
+                    }
+
+                    # save training statistics
+                    if self.backend.is_coordinator():
+                        if not skip_save_training_statistics:
+                            save_json(training_stats_fn, train_stats)
+
+                    # grab the results of the model with highest validation test performance
+                    validation_field = trainer.validation_field
+                    validation_metric = trainer.validation_metric
+                    validation_field_result = train_valiset_stats[validation_field]
+
+                    best_function = get_best_function(validation_metric)
+                    # results of the model with highest validation test performance
+                    if self.backend.is_coordinator() and validation_set is not None:
+                        epoch_best_vali_metric, best_vali_metric = best_function(
+                            enumerate(validation_field_result[validation_metric]),
+                            key=lambda pair: pair[1]
+                        )
                         logger.info(
-                            'Best validation model {0} on test set {1}: {2}'.format(
-                                validation_metric,
-                                validation_field,
-                                best_vali_metric_epoch_test_metric
-                            )
+                            'Best validation model epoch: {0}'.format(
+                                epoch_best_vali_metric + 1)
                         )
-                    logger.info(
-                        '\nFinished: {0}_{1}'.format(experiment_name, model_name))
-                    logger.info('Saved to: {0}'.format(output_directory))
-
-                for callback in self.callbacks:
-                    callback.on_train_end(output_directory)
+                        logger.info(
+                            'Best validation model {0} on validation set {1}: {2}'.format(
+                                validation_metric, validation_field, best_vali_metric
+                            ))
+                        if test_set is not None:
+                            best_vali_metric_epoch_test_metric = train_testset_stats[
+                                validation_field][validation_metric][
+                                epoch_best_vali_metric]
+
+                            logger.info(
+                                'Best validation model {0} on test set {1}: {2}'.format(
+                                    validation_metric,
+                                    validation_field,
+                                    best_vali_metric_epoch_test_metric
+                                )
+                            )
+                        logger.info(
+                            '\nFinished: {0}_{1}'.format(experiment_name, model_name))
+                        logger.info('Saved to: {0}'.format(output_directory))
+                finally:
+                    for callback in self.callbacks:
+                        callback.on_train_end(output_directory)
 
                 self.training_set_metadata = training_set_metadata
 

ludwig/callbacks.py

@@ -77,7 +77,7 @@ def on_test_end(self, trainer, progress_tracker, save_path):
     def on_visualize_figure(self, fig):
         pass
 
-    def prepare_ray_tune(self, train_fn, tune_config):
+    def prepare_ray_tune(self, train_fn, tune_config, tune_callbacks):
         """Configures Ray Tune to properly use this callback in each trial."""
         return train_fn, tune_config
 

ludwig/cli.py

@@ -55,6 +55,7 @@ def __init__(self):
    collect_activations   Collects tensors for each datapoint using a pretrained model
    export_savedmodel     Exports Ludwig models to SavedModel
    export_neuropod       Exports Ludwig models to Neuropod
+   export_mlflow         Exports Ludwig models to MLflow
    preprocess            Preprocess data and saves it into HDF5 and JSON format
    synthesize_dataset    Creates synthetic data for tesing purposes
 ''')
@@ -117,6 +118,10 @@ def export_neuropod(self):
         from ludwig import export
         export.cli_export_neuropod(sys.argv[2:])
 
+    def export_mlflow(self):
+        from ludwig import export
+        export.cli_export_mlflow(sys.argv[2:])
+
     def preprocess(self):
         from ludwig import preprocess
         preprocess.cli(sys.argv[2:])

ludwig/contribs/mlflow.py

@@ -1,6 +1,7 @@
 import logging
 import os
 
+from ludwig.api import LudwigModel
 from ludwig.callbacks import Callback
 from ludwig.utils.data_utils import chunk_dict, flatten_dict, to_json_dict
 from ludwig.utils.package_utils import LazyLoader
@@ -57,8 +58,7 @@ def on_train_start(self, config, **kwargs):
         self._log_params({'training': config['training']})
 
     def on_train_end(self, output_directory):
-        for fname in os.listdir(output_directory):
-            mlflow.log_artifact(os.path.join(output_directory, fname))
+        _log_artifacts(output_directory)
         if self.run is not None:
             mlflow.end_run()
 
@@ -68,13 +68,19 @@ def on_epoch_end(self, trainer, progress_tracker, save_path):
             step=progress_tracker.steps
         )
 
+        mlflow.pyfunc.log_model(
+            artifact_path='model',
+            **_export_kwargs(save_path)
+        )
+
     def on_visualize_figure(self, fig):
         # TODO: need to also include a filename for this figure
         # mlflow.log_figure(fig)
         pass
 
-    def prepare_ray_tune(self, train_fn, tune_config):
+    def prepare_ray_tune(self, train_fn, tune_config, tune_callbacks):
         from ray.tune.integration.mlflow import mlflow_mixin
+
         return mlflow_mixin(train_fn), {
             **tune_config,
             'mlflow': {
@@ -92,3 +98,52 @@ def __setstate__(self, d):
         self.__dict__ = d
         if self.tracking_uri:
             mlflow.set_tracking_uri(self.tracking_uri)
+
+
+class LudwigMlflowModel(mlflow.pyfunc.PythonModel):
+    def __init__(self):
+        super().__init__()
+        self._model = None
+
+    def load_context(self, context):
+        self._model = LudwigModel.load(context.artifacts['model'])
+
+    def predict(self, context, model_input):
+        pred_df, _ = self._model.predict(model_input)
+        return pred_df
+
+
+def _export_kwargs(model_path):
+    return dict(
+        python_model=LudwigMlflowModel(),
+        artifacts={
+            'model': model_path,
+        },
+    )
+
+
+def _log_artifacts(output_directory):
+    for fname in os.listdir(output_directory):
+        lpath = os.path.join(output_directory, fname)
+        if fname == 'model':
+            mlflow.pyfunc.log_model(
+                artifact_path='model',
+                **_export_kwargs(lpath)
+            )
+        else:
+            mlflow.log_artifact(lpath)
+
+
+def export_model(model_path, output_path, registered_model_name=None):
+    kwargs = _export_kwargs(model_path)
+    if registered_model_name:
+        mlflow.pyfunc.log_model(
+            artifact_path=output_path,
+            registered_model_name=registered_model_name,
+            **kwargs
+        )
+    else:
+        mlflow.pyfunc.save_model(
+            path=output_path,
+            **kwargs
+        )

ludwig/export.py

@@ -85,6 +85,36 @@ def export_neuropod(
     logger.info('Saved to: {0}'.format(output_path))
 
 
+def export_mlflow(
+        model_path,
+        output_path='mlflow',
+        registered_model_name=None,
+        **kwargs
+):
+    """Exports a model to MLflow
+
+    # Inputs
+
+    :param model_path: (str) filepath to pre-trained model.
+    :param output_path: (str, default: `'mlflow'`)  directory to store the
+        mlflow model.
+    :param registered_model_name: (str, default: `None`) save mlflow under this
+        name in the model registry. Saved locally if `None`.
+
+    # Return
+
+    :returns: (`None`)
+    """
+    logger.info('Model path: {}'.format(model_path))
+    logger.info('Output path: {}'.format(output_path))
+    logger.info('\n')
+
+    from ludwig.contribs import mlflow
+    mlflow.export_model(model_path, output_path, registered_model_name)
+
+    logger.info('Saved to: {0}'.format(output_path))
+
+
 def cli_export_savedmodel(sys_argv):
     parser = argparse.ArgumentParser(
         description='This script loads a pretrained model '
@@ -209,6 +239,71 @@ def cli_export_neuropod(sys_argv):
     export_neuropod(**vars(args))
 
 
+def cli_export_mlflow(sys_argv):
+    parser = argparse.ArgumentParser(
+        description='This script loads a pretrained model '
+                    'and saves it as an MLFlow model.',
+        prog='ludwig export_mlflow',
+        usage='%(prog)s [options]'
+    )
+
+    # ----------------
+    # Model parameters
+    # ----------------
+    parser.add_argument(
+        '-m',
+        '--model_path',
+        help='model to load',
+        required=True
+    )
+    parser.add_argument(
+        '-mn',
+        '--registered_model_name',
+        help='model name to upload to in MLflow model registry',
+        default='mlflow'
+    )
+
+    # -----------------
+    # Output parameters
+    # -----------------
+    parser.add_argument(
+        '-od',
+        '--output_path',
+        type=str,
+        help='path where to save the exported model',
+        required=True
+    )
+
+    # ------------------
+    # Runtime parameters
+    # ------------------
+    parser.add_argument(
+        '-l',
+        '--logging_level',
+        default='info',
+        help='the level of logging to use',
+        choices=['critical', 'error', 'warning', 'info', 'debug', 'notset']
+    )
+
+    add_contrib_callback_args(parser)
+    args = parser.parse_args(sys_argv)
+
+    args.callbacks = args.callbacks or []
+    for callback in args.callbacks:
+        callback.on_cmdline('export_mlflow', *sys_argv)
+
+    args.logging_level = logging_level_registry[args.logging_level]
+    logging.getLogger('ludwig').setLevel(
+        args.logging_level
+    )
+    global logger
+    logger = logging.getLogger('ludwig.export')
+
+    print_ludwig('Export MLFlow', LUDWIG_VERSION)
+
+    export_mlflow(**vars(args))
+
+
 if __name__ == '__main__':
     if len(sys.argv) > 1:
         if sys.argv[1] == 'savedmodel':

ludwig/hyperopt/execution.py

@@ -905,10 +905,12 @@ def run_experiment_trial(config, checkpoint_dir=None):
             return self._run_experiment(config, checkpoint_dir, hyperopt_dict, self.decode_ctx)
 
         tune_config = {}
+        tune_callbacks = []
         for callback in callbacks or []:
             run_experiment_trial, tune_config = callback.prepare_ray_tune(
                 run_experiment_trial,
                 tune_config,
+                tune_callbacks,
             )
 
         register_trainable(
@@ -933,6 +935,7 @@ def run_experiment_trial(config, checkpoint_dir=None):
             mode=mode,
             trial_name_creator=lambda trial: f"trial_{trial.trial_id}",
             trial_dirname_creator=lambda trial: f"trial_{trial.trial_id}",
+            callbacks=tune_callbacks,
         )
 
         ordered_trials = analysis.results_df.sort_values(

tests/integration_tests/scripts/run_train_comet.py

@@ -7,7 +7,7 @@
 # This test runs in an isolated environment to ensure TensorFlow imports are not leaked
 # from previous tests.
 
-# Comet must be imported before the libraries to wraps
+# Comet must be imported before the libraries it wraps
 import comet_ml
 
 import argparse