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Analyse de modèle à l'aide de TFX Pipeline et TensorFlow Model Analysis

Dans ce didacticiel basé sur un bloc-notes, nous allons créer et exécuter un pipeline TFX qui crée un modèle de classification simple et analyse ses performances sur plusieurs exécutions. Ce portable est basé sur le pipeline TFX nous avons construit en simple TFX Pipeline Tutorial . Si vous n'avez pas encore lu ce didacticiel, vous devriez le lire avant de continuer avec ce bloc-notes.

Lorsque vous modifiez votre modèle ou que vous l'entraînez avec un nouvel ensemble de données, vous devez vérifier si votre modèle s'est amélioré ou s'il s'est dégradé. La simple vérification des métriques de haut niveau comme la précision peut ne pas suffire. Chaque modèle entraîné doit être évalué avant d'être mis en production.

Nous ajouterons un Evaluator composant au pipeline créé dans le tutoriel précédent. Le composant Evaluator effectue une analyse approfondie de vos modèles et compare le nouveau modèle à une référence pour déterminer qu'ils sont « assez bons ». Il est mis en œuvre à l' aide du modèle d' analyse tensorflow bibliothèque.

S'il vous plaît voir Comprendre TFX Pipelines pour en savoir plus sur les différents concepts TFX.

D'installation

Le processus de configuration est le même que le didacticiel précédent.

Nous devons d'abord installer le package Python TFX et télécharger le jeu de données que nous utiliserons pour notre modèle.

Pip de mise à niveau

Pour éviter de mettre à niveau Pip dans un système lors de l'exécution locale, assurez-vous que nous exécutons dans Colab. Les systèmes locaux peuvent bien sûr être mis à niveau séparément.

try:
  import colab
  !pip install --upgrade pip
except:
  pass

Installer TFX

pip install -U tfx

As-tu redémarré le runtime ?

Si vous utilisez Google Colab, la première fois que vous exécutez la cellule ci-dessus, vous devez redémarrer le runtime en cliquant au-dessus du bouton "RESTART RUNTIME" ou en utilisant le menu "Runtime> Restart runtime ...". Cela est dû à la façon dont Colab charge les packages.

Vérifiez les versions TensorFlow et TFX.

import tensorflow as tf
print('TensorFlow version: {}'.format(tf.__version__))
from tfx import v1 as tfx
print('TFX version: {}'.format(tfx.__version__))
2021-07-24 09:18:35.619355: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0
TensorFlow version: 2.5.0
TFX version: 1.0.0

Configurer des variables

Certaines variables sont utilisées pour définir un pipeline. Vous pouvez personnaliser ces variables comme vous le souhaitez. Par défaut, toutes les sorties du pipeline seront générées sous le répertoire actuel.

import os

PIPELINE_NAME = "penguin-tfma"

# Output directory to store artifacts generated from the pipeline.
PIPELINE_ROOT = os.path.join('pipelines', PIPELINE_NAME)
# Path to a SQLite DB file to use as an MLMD storage.
METADATA_PATH = os.path.join('metadata', PIPELINE_NAME, 'metadata.db')
# Output directory where created models from the pipeline will be exported.
SERVING_MODEL_DIR = os.path.join('serving_model', PIPELINE_NAME)

from absl import logging
logging.set_verbosity(logging.INFO)  # Set default logging level.

Préparer des exemples de données

Nous allons utiliser le même ensemble de données Palmer Penguins .

Il y a quatre caractéristiques numériques dans cet ensemble de données qui ont déjà été normalisées pour avoir une plage [0,1]. Nous allons construire un modèle de classification qui prédit les species de manchots.

Étant donné que TFX ExampleGen lit les entrées d'un répertoire, nous devons créer un répertoire et y copier l'ensemble de données.

import urllib.request
import tempfile

DATA_ROOT = tempfile.mkdtemp(prefix='tfx-data')  # Create a temporary directory.
_data_url = 'https://raw.githubusercontent.com/tensorflow/tfx/master/tfx/examples/penguin/data/labelled/penguins_processed.csv'
_data_filepath = os.path.join(DATA_ROOT, "data.csv")
urllib.request.urlretrieve(_data_url, _data_filepath)
('/tmp/tfx-datal2xni_a_/data.csv', <http.client.HTTPMessage at 0x7fa6a715c710>)

Créer un pipeline

Nous ajouterons un Evaluator composant au pipeline que nous avons créé dans le simple TFX Pipeline Tutorial .

Une composante Evaluator nécessite des données à partir d' une entrée ExampleGen composant et un modèle à partir d' un Trainer composant et un tfma.EvalConfig objet. Nous pouvons éventuellement fournir un modèle de base qui peut être utilisé pour comparer les métriques avec le modèle nouvellement formé.

Un évaluateur crée deux types d'artefacts de sortie, ModelEvaluation et ModelBlessing . ModelEvaluation contient le résultat d'évaluation détaillé qui peut être étudié et visualisé plus avant avec la bibliothèque TFMA. ModelBlessing contient un résultat booléen indiquant si le modèle a passé des critères donnés et peut être utilisé dans des composants ultérieurs comme un Pusher en tant que signal.

Écrire le code d'entraînement du modèle

Nous utiliserons le même code de modèle que dans le simple TFX Pipeline Tutorial .

_trainer_module_file = 'penguin_trainer.py'
%%writefile {_trainer_module_file}

# Copied from https://www.tensorflow.org/tfx/tutorials/tfx/penguin_simple

from typing import List
from absl import logging
import tensorflow as tf
from tensorflow import keras
from tensorflow_transform.tf_metadata import schema_utils

from tfx.components.trainer.executor import TrainerFnArgs
from tfx.components.trainer.fn_args_utils import DataAccessor
from tfx_bsl.tfxio import dataset_options
from tensorflow_metadata.proto.v0 import schema_pb2

_FEATURE_KEYS = [
    'culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm', 'body_mass_g'
]
_LABEL_KEY = 'species'

_TRAIN_BATCH_SIZE = 20
_EVAL_BATCH_SIZE = 10

# Since we're not generating or creating a schema, we will instead create
# a feature spec.  Since there are a fairly small number of features this is
# manageable for this dataset.
_FEATURE_SPEC = {
    **{
        feature: tf.io.FixedLenFeature(shape=[1], dtype=tf.float32)
           for feature in _FEATURE_KEYS
       },
    _LABEL_KEY: tf.io.FixedLenFeature(shape=[1], dtype=tf.int64)
}


def _input_fn(file_pattern: List[str],
              data_accessor: DataAccessor,
              schema: schema_pb2.Schema,
              batch_size: int = 200) -> tf.data.Dataset:
  """Generates features and label for training.

  Args:
    file_pattern: List of paths or patterns of input tfrecord files.
    data_accessor: DataAccessor for converting input to RecordBatch.
    schema: schema of the input data.
    batch_size: representing the number of consecutive elements of returned
      dataset to combine in a single batch

  Returns:
    A dataset that contains (features, indices) tuple where features is a
      dictionary of Tensors, and indices is a single Tensor of label indices.
  """
  return data_accessor.tf_dataset_factory(
      file_pattern,
      dataset_options.TensorFlowDatasetOptions(
          batch_size=batch_size, label_key=_LABEL_KEY),
      schema=schema).repeat()


def _build_keras_model() -> tf.keras.Model:
  """Creates a DNN Keras model for classifying penguin data.

  Returns:
    A Keras Model.
  """
  # The model below is built with Functional API, please refer to
  # https://www.tensorflow.org/guide/keras/overview for all API options.
  inputs = [keras.layers.Input(shape=(1,), name=f) for f in _FEATURE_KEYS]
  d = keras.layers.concatenate(inputs)
  for _ in range(2):
    d = keras.layers.Dense(8, activation='relu')(d)
  outputs = keras.layers.Dense(3)(d)

  model = keras.Model(inputs=inputs, outputs=outputs)
  model.compile(
      optimizer=keras.optimizers.Adam(1e-2),
      loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
      metrics=[keras.metrics.SparseCategoricalAccuracy()])

  model.summary(print_fn=logging.info)
  return model


# TFX Trainer will call this function.
def run_fn(fn_args: TrainerFnArgs):
  """Train the model based on given args.

  Args:
    fn_args: Holds args used to train the model as name/value pairs.
  """

  # This schema is usually either an output of SchemaGen or a manually-curated
  # version provided by pipeline author. A schema can also derived from TFT
  # graph if a Transform component is used. In the case when either is missing,
  # `schema_from_feature_spec` could be used to generate schema from very simple
  # feature_spec, but the schema returned would be very primitive.
  schema = schema_utils.schema_from_feature_spec(_FEATURE_SPEC)

  train_dataset = _input_fn(
      fn_args.train_files,
      fn_args.data_accessor,
      schema,
      batch_size=_TRAIN_BATCH_SIZE)
  eval_dataset = _input_fn(
      fn_args.eval_files,
      fn_args.data_accessor,
      schema,
      batch_size=_EVAL_BATCH_SIZE)

  model = _build_keras_model()
  model.fit(
      train_dataset,
      steps_per_epoch=fn_args.train_steps,
      validation_data=eval_dataset,
      validation_steps=fn_args.eval_steps)

  # The result of the training should be saved in `fn_args.serving_model_dir`
  # directory.
  model.save(fn_args.serving_model_dir, save_format='tf')
Writing penguin_trainer.py

Écrire une définition de pipeline

Nous allons définir une fonction pour créer un pipeline TFX. En plus de la composante Evaluator nous l' avons mentionné ci - dessus, nous allons ajouter un nœud appelé Resolver . Pour vérifier qu'un nouveau modèle s'améliore par rapport au modèle précédent, nous devons le comparer à un modèle publié précédent, appelé référence. ML métadonnées (MLMD) suit tous les artefacts précédents du pipeline et Resolver peut trouver ce qui était le dernier modèle béni - un modèle passé avec succès Evaluator - de MLMD en utilisant une classe de stratégie appelée LatestBlessedModelStrategy .

import tensorflow_model_analysis as tfma

def _create_pipeline(pipeline_name: str, pipeline_root: str, data_root: str,
                     module_file: str, serving_model_dir: str,
                     metadata_path: str) -> tfx.dsl.Pipeline:
  """Creates a three component penguin pipeline with TFX."""
  # Brings data into the pipeline.
  example_gen = tfx.components.CsvExampleGen(input_base=data_root)

  # Uses user-provided Python function that trains a model.
  trainer = tfx.components.Trainer(
      module_file=module_file,
      examples=example_gen.outputs['examples'],
      train_args=tfx.proto.TrainArgs(num_steps=100),
      eval_args=tfx.proto.EvalArgs(num_steps=5))

  # NEW: Get the latest blessed model for Evaluator.
  model_resolver = tfx.dsl.Resolver(
      strategy_class=tfx.dsl.experimental.LatestBlessedModelStrategy,
      model=tfx.dsl.Channel(type=tfx.types.standard_artifacts.Model),
      model_blessing=tfx.dsl.Channel(
          type=tfx.types.standard_artifacts.ModelBlessing)).with_id(
              'latest_blessed_model_resolver')

  # NEW: Uses TFMA to compute evaluation statistics over features of a model and
  #   perform quality validation of a candidate model (compared to a baseline).

  eval_config = tfma.EvalConfig(
      model_specs=[tfma.ModelSpec(label_key='species')],
      slicing_specs=[
          # An empty slice spec means the overall slice, i.e. the whole dataset.
          tfma.SlicingSpec(),
          # Calculate metrics for each penguin species.
          tfma.SlicingSpec(feature_keys=['species']),
          ],
      metrics_specs=[
          tfma.MetricsSpec(per_slice_thresholds={
              'sparse_categorical_accuracy':
                  tfma.config.PerSliceMetricThresholds(thresholds=[
                      tfma.PerSliceMetricThreshold(
                          slicing_specs=[tfma.SlicingSpec()],
                          threshold=tfma.MetricThreshold(
                              value_threshold=tfma.GenericValueThreshold(
                                   lower_bound={'value': 0.6}),
                              # Change threshold will be ignored if there is no
                              # baseline model resolved from MLMD (first run).
                              change_threshold=tfma.GenericChangeThreshold(
                                  direction=tfma.MetricDirection.HIGHER_IS_BETTER,
                                  absolute={'value': -1e-10}))
                       )]),
          })],
      )
  evaluator = tfx.components.Evaluator(
      examples=example_gen.outputs['examples'],
      model=trainer.outputs['model'],
      baseline_model=model_resolver.outputs['model'],
      eval_config=eval_config)

  # Checks whether the model passed the validation steps and pushes the model
  # to a file destination if check passed.
  pusher = tfx.components.Pusher(
      model=trainer.outputs['model'],
      model_blessing=evaluator.outputs['blessing'], # Pass an evaluation result.
      push_destination=tfx.proto.PushDestination(
          filesystem=tfx.proto.PushDestination.Filesystem(
              base_directory=serving_model_dir)))

  components = [
      example_gen,
      trainer,

      # Following two components were added to the pipeline.
      model_resolver,
      evaluator,

      pusher,
  ]

  return tfx.dsl.Pipeline(
      pipeline_name=pipeline_name,
      pipeline_root=pipeline_root,
      metadata_connection_config=tfx.orchestration.metadata
      .sqlite_metadata_connection_config(metadata_path),
      components=components)

Nous devons fournir les informations suivantes à l'Évaluateur via eval_config :

  • Métriques supplémentaires à configurer (si vous voulez plus de métriques que celles définies dans le modèle).
  • Tranches à configurer
  • Seuils de validation du modèle pour vérifier si la validation doit être incluse

Parce que SparseCategoricalAccuracy était déjà inclus dans le model.compile() appel, il sera inclus dans l'analyse automatiquement. Nous n'ajoutons donc aucune métrique supplémentaire ici. SparseCategoricalAccuracy sera utilisé pour décider si le modèle est assez bon, aussi.

Nous calculons les métriques pour l'ensemble de données et pour chaque espèce de manchot. SlicingSpec précise la façon dont nous regroupons les mesures déclarées.

Il y a deux seuils qu'un nouveau modèle doit franchir, l'un est un seuil absolu de 0,6 et l'autre est un seuil relatif qu'il devrait être supérieur au modèle de référence. Lorsque vous exécutez le pipeline pour la première fois, la change_threshold sera ignorée et seule la value_threshold sera vérifiée. Si vous exécutez le pipeline plus d'une fois, le Resolver trouverez un modèle de la course précédente et il sera utilisé comme modèle de référence pour la comparaison.

Voir Guide composante Evaluator pour plus d' informations.

Exécuter le pipeline

Nous utiliserons LocalDagRunner comme dans le tutoriel précédent.

tfx.orchestration.LocalDagRunner().run(
  _create_pipeline(
      pipeline_name=PIPELINE_NAME,
      pipeline_root=PIPELINE_ROOT,
      data_root=DATA_ROOT,
      module_file=_trainer_module_file,
      serving_model_dir=SERVING_MODEL_DIR,
      metadata_path=METADATA_PATH))
INFO:absl:Generating ephemeral wheel package for '/tmpfs/src/temp/docs/tutorials/tfx/penguin_trainer.py' (including modules: ['penguin_trainer']).
INFO:absl:User module package has hash fingerprint version 1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmp/tmpk93hah65/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmpp80w9y0j', '--dist-dir', '/tmp/tmp05l5gny8']
INFO:absl:Successfully built user code wheel distribution at 'pipelines/penguin-tfma/_wheels/tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703-py3-none-any.whl'; target user module is 'penguin_trainer'.
INFO:absl:Full user module path is 'penguin_trainer@pipelines/penguin-tfma/_wheels/tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703-py3-none-any.whl'
INFO:absl:Running pipeline:
 pipeline_info {
  id: "penguin-tfma"
}
nodes {
  pipeline_node {
    node_info {
      type {
        name: "tfx.components.example_gen.csv_example_gen.component.CsvExampleGen"
      }
      id: "CsvExampleGen"
    }
    contexts {
      contexts {
        type {
          name: "pipeline"
        }
        name {
          field_value {
            string_value: "penguin-tfma"
          }
        }
      }
      contexts {
        type {
          name: "pipeline_run"
        }
        name {
          field_value {
            string_value: "2021-07-24T09:18:40.745586"
          }
        }
      }
      contexts {
        type {
          name: "node"
        }
        name {
          field_value {
            string_value: "penguin-tfma.CsvExampleGen"
          }
        }
      }
    }
    outputs {
      outputs {
        key: "examples"
        value {
          artifact_spec {
            type {
              name: "Examples"
              properties {
                key: "span"
                value: INT
              }
              properties {
                key: "split_names"
                value: STRING
              }
              properties {
                key: "version"
                value: INT
              }
            }
          }
        }
      }
    }
    parameters {
      parameters {
        key: "input_base"
        value {
          field_value {
            string_value: "/tmp/tfx-datal2xni_a_"
          }
        }
      }
      parameters {
        key: "input_config"
        value {
          field_value {
            string_value: "{\n  \"splits\": [\n    {\n      \"name\": \"single_split\",\n      \"pattern\": \"*\"\n    }\n  ]\n}"
          }
        }
      }
      parameters {
        key: "output_config"
        value {
          field_value {
            string_value: "{\n  \"split_config\": {\n    \"splits\": [\n      {\n        \"hash_buckets\": 2,\n        \"name\": \"train\"\n      },\n      {\n        \"hash_buckets\": 1,\n        \"name\": \"eval\"\n      }\n    ]\n  }\n}"
          }
        }
      }
      parameters {
        key: "output_data_format"
        value {
          field_value {
            int_value: 6
          }
        }
      }
    }
    downstream_nodes: "Evaluator"
    downstream_nodes: "Trainer"
    execution_options {
      caching_options {
      }
    }
  }
}
nodes {
  pipeline_node {
    node_info {
      type {
        name: "tfx.dsl.components.common.resolver.Resolver"
      }
      id: "latest_blessed_model_resolver"
    }
    contexts {
      contexts {
        type {
          name: "pipeline"
        }
        name {
          field_value {
            string_value: "penguin-tfma"
          }
        }
      }
      contexts {
        type {
          name: "pipeline_run"
        }
        name {
          field_value {
            string_value: "2021-07-24T09:18:40.745586"
          }
        }
      }
      contexts {
        type {
          name: "node"
        }
        name {
          field_value {
            string_value: "penguin-tfma.latest_blessed_model_resolver"
          }
        }
      }
    }
    inputs {
      inputs {
        key: "model"
        value {
          channels {
            context_queries {
              type {
                name: "pipeline"
              }
              name {
                field_value {
                  string_value: "penguin-tfma"
                }
              }
            }
            artifact_query {
              type {
                name: "Model"
              }
            }
          }
        }
      }
      inputs {
        key: "model_blessing"
        value {
          channels {
            context_queries {
              type {
                name: "pipeline"
              }
              name {
                field_value {
                  string_value: "penguin-tfma"
                }
              }
            }
            artifact_query {
              type {
                name: "ModelBlessing"
              }
            }
          }
        }
      }
      resolver_config {
        resolver_steps {
          class_path: "tfx.dsl.input_resolution.strategies.latest_blessed_model_strategy.LatestBlessedModelStrategy"
          config_json: "{}"
          input_keys: "model"
          input_keys: "model_blessing"
        }
      }
    }
    downstream_nodes: "Evaluator"
    execution_options {
      caching_options {
      }
    }
  }
}
nodes {
  pipeline_node {
    node_info {
      type {
        name: "tfx.components.trainer.component.Trainer"
      }
      id: "Trainer"
    }
    contexts {
      contexts {
        type {
          name: "pipeline"
        }
        name {
          field_value {
            string_value: "penguin-tfma"
          }
        }
      }
      contexts {
        type {
          name: "pipeline_run"
        }
        name {
          field_value {
            string_value: "2021-07-24T09:18:40.745586"
          }
        }
      }
      contexts {
        type {
          name: "node"
        }
        name {
          field_value {
            string_value: "penguin-tfma.Trainer"
          }
        }
      }
    }
    inputs {
      inputs {
        key: "examples"
        value {
          channels {
            producer_node_query {
              id: "CsvExampleGen"
            }
            context_queries {
              type {
                name: "pipeline"
              }
              name {
                field_value {
                  string_value: "penguin-tfma"
                }
              }
            }
            context_queries {
              type {
                name: "pipeline_run"
              }
              name {
                field_value {
                  string_value: "2021-07-24T09:18:40.745586"
                }
              }
            }
            context_queries {
              type {
                name: "node"
              }
              name {
                field_value {
                  string_value: "penguin-tfma.CsvExampleGen"
                }
              }
            }
            artifact_query {
              type {
                name: "Examples"
              }
            }
            output_key: "examples"
          }
        }
      }
    }
    outputs {
      outputs {
        key: "model"
        value {
          artifact_spec {
            type {
              name: "Model"
            }
          }
        }
      }
      outputs {
        key: "model_run"
        value {
          artifact_spec {
            type {
              name: "ModelRun"
            }
          }
        }
      }
    }
    parameters {
      parameters {
        key: "custom_config"
        value {
          field_value {
            string_value: "null"
          }
        }
      }
      parameters {
        key: "eval_args"
        value {
          field_value {
            string_value: "{\n  \"num_steps\": 5\n}"
          }
        }
      }
      parameters {
        key: "module_path"
        value {
          field_value {
            string_value: "penguin_trainer@pipelines/penguin-tfma/_wheels/tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703-py3-none-any.whl"
          }
        }
      }
      parameters {
        key: "train_args"
        value {
          field_value {
            string_value: "{\n  \"num_steps\": 100\n}"
          }
        }
      }
    }
    upstream_nodes: "CsvExampleGen"
    downstream_nodes: "Evaluator"
    downstream_nodes: "Pusher"
    execution_options {
      caching_options {
      }
    }
  }
}
nodes {
  pipeline_node {
    node_info {
      type {
        name: "tfx.components.evaluator.component.Evaluator"
      }
      id: "Evaluator"
    }
    contexts {
      contexts {
        type {
          name: "pipeline"
        }
        name {
          field_value {
            string_value: "penguin-tfma"
          }
        }
      }
      contexts {
        type {
          name: "pipeline_run"
        }
        name {
          field_value {
            string_value: "2021-07-24T09:18:40.745586"
          }
        }
      }
      contexts {
        type {
          name: "node"
        }
        name {
          field_value {
            string_value: "penguin-tfma.Evaluator"
          }
        }
      }
    }
    inputs {
      inputs {
        key: "baseline_model"
        value {
          channels {
            producer_node_query {
              id: "latest_blessed_model_resolver"
            }
            context_queries {
              type {
                name: "pipeline"
              }
              name {
                field_value {
                  string_value: "penguin-tfma"
                }
              }
            }
            context_queries {
              type {
                name: "pipeline_run"
              }
              name {
                field_value {
                  string_value: "2021-07-24T09:18:40.745586"
                }
              }
            }
            context_queries {
              type {
                name: "node"
              }
              name {
                field_value {
                  string_value: "penguin-tfma.latest_blessed_model_resolver"
                }
              }
            }
            artifact_query {
              type {
                name: "Model"
              }
            }
            output_key: "model"
          }
        }
      }
      inputs {
        key: "examples"
        value {
          channels {
            producer_node_query {
              id: "CsvExampleGen"
            }
            context_queries {
              type {
                name: "pipeline"
              }
              name {
                field_value {
                  string_value: "penguin-tfma"
                }
              }
            }
            context_queries {
              type {
                name: "pipeline_run"
              }
              name {
                field_value {
                  string_value: "2021-07-24T09:18:40.745586"
                }
              }
            }
            context_queries {
              type {
                name: "node"
              }
              name {
                field_value {
                  string_value: "penguin-tfma.CsvExampleGen"
                }
              }
            }
            artifact_query {
              type {
                name: "Examples"
              }
            }
            output_key: "examples"
          }
        }
      }
      inputs {
        key: "model"
        value {
          channels {
            producer_node_query {
              id: "Trainer"
            }
            context_queries {
              type {
                name: "pipeline"
              }
              name {
                field_value {
                  string_value: "penguin-tfma"
                }
              }
            }
            context_queries {
              type {
                name: "pipeline_run"
              }
              name {
                field_value {
                  string_value: "2021-07-24T09:18:40.745586"
                }
              }
            }
            context_queries {
              type {
                name: "node"
              }
              name {
                field_value {
                  string_value: "penguin-tfma.Trainer"
                }
              }
            }
            artifact_query {
              type {
                name: "Model"
              }
            }
            output_key: "model"
          }
        }
      }
    }
    outputs {
      outputs {
        key: "blessing"
        value {
          artifact_spec {
            type {
              name: "ModelBlessing"
            }
          }
        }
      }
      outputs {
        key: "evaluation"
        value {
          artifact_spec {
            type {
              name: "ModelEvaluation"
            }
          }
        }
      }
    }
    parameters {
      parameters {
        key: "eval_config"
        value {
          field_value {
            string_value: "{\n  \"metrics_specs\": [\n    {\n      \"per_slice_thresholds\": {\n        \"sparse_categorical_accuracy\": {\n          \"thresholds\": [\n            {\n              \"slicing_specs\": [\n                {}\n              ],\n              \"threshold\": {\n                \"change_threshold\": {\n                  \"absolute\": -1e-10,\n                  \"direction\": \"HIGHER_IS_BETTER\"\n                },\n                \"value_threshold\": {\n                  \"lower_bound\": 0.6\n                }\n              }\n            }\n          ]\n        }\n      }\n    }\n  ],\n  \"model_specs\": [\n    {\n      \"label_key\": \"species\"\n    }\n  ],\n  \"slicing_specs\": [\n    {},\n    {\n      \"feature_keys\": [\n        \"species\"\n      ]\n    }\n  ]\n}"
          }
        }
      }
      parameters {
        key: "example_splits"
        value {
          field_value {
            string_value: "null"
          }
        }
      }
    }
    upstream_nodes: "CsvExampleGen"
    upstream_nodes: "Trainer"
    upstream_nodes: "latest_blessed_model_resolver"
    downstream_nodes: "Pusher"
    execution_options {
      caching_options {
      }
    }
  }
}
nodes {
  pipeline_node {
    node_info {
      type {
        name: "tfx.components.pusher.component.Pusher"
      }
      id: "Pusher"
    }
    contexts {
      contexts {
        type {
          name: "pipeline"
        }
        name {
          field_value {
            string_value: "penguin-tfma"
          }
        }
      }
      contexts {
        type {
          name: "pipeline_run"
        }
        name {
          field_value {
            string_value: "2021-07-24T09:18:40.745586"
          }
        }
      }
      contexts {
        type {
          name: "node"
        }
        name {
          field_value {
            string_value: "penguin-tfma.Pusher"
          }
        }
      }
    }
    inputs {
      inputs {
        key: "model"
        value {
          channels {
            producer_node_query {
              id: "Trainer"
            }
            context_queries {
              type {
                name: "pipeline"
              }
              name {
                field_value {
                  string_value: "penguin-tfma"
                }
              }
            }
            context_queries {
              type {
                name: "pipeline_run"
              }
              name {
                field_value {
                  string_value: "2021-07-24T09:18:40.745586"
                }
              }
            }
            context_queries {
              type {
                name: "node"
              }
              name {
                field_value {
                  string_value: "penguin-tfma.Trainer"
                }
              }
            }
            artifact_query {
              type {
                name: "Model"
              }
            }
            output_key: "model"
          }
        }
      }
      inputs {
        key: "model_blessing"
        value {
          channels {
            producer_node_query {
              id: "Evaluator"
            }
            context_queries {
              type {
                name: "pipeline"
              }
              name {
                field_value {
                  string_value: "penguin-tfma"
                }
              }
            }
            context_queries {
              type {
                name: "pipeline_run"
              }
              name {
                field_value {
                  string_value: "2021-07-24T09:18:40.745586"
                }
              }
            }
            context_queries {
              type {
                name: "node"
              }
              name {
                field_value {
                  string_value: "penguin-tfma.Evaluator"
                }
              }
            }
            artifact_query {
              type {
                name: "ModelBlessing"
              }
            }
            output_key: "blessing"
          }
        }
      }
    }
    outputs {
      outputs {
        key: "pushed_model"
        value {
          artifact_spec {
            type {
              name: "PushedModel"
            }
          }
        }
      }
    }
    parameters {
      parameters {
        key: "custom_config"
        value {
          field_value {
            string_value: "null"
          }
        }
      }
      parameters {
        key: "push_destination"
        value {
          field_value {
            string_value: "{\n  \"filesystem\": {\n    \"base_directory\": \"serving_model/penguin-tfma\"\n  }\n}"
          }
        }
      }
    }
    upstream_nodes: "Evaluator"
    upstream_nodes: "Trainer"
    execution_options {
      caching_options {
      }
    }
  }
}
runtime_spec {
  pipeline_root {
    field_value {
      string_value: "pipelines/penguin-tfma"
    }
  }
  pipeline_run_id {
    field_value {
      string_value: "2021-07-24T09:18:40.745586"
    }
  }
}
execution_mode: SYNC
deployment_config {
  type_url: "type.googleapis.com/tfx.orchestration.IntermediateDeploymentConfig"
  value: "\n\207\001\n\tEvaluator\022z\nHtype.googleapis.com/tfx.orchestration.executable_spec.BeamExecutableSpec\022.\n,\n*tfx.components.evaluator.executor.Executor\n\236\001\n\rCsvExampleGen\022\214\001\nHtype.googleapis.com/tfx.orchestration.executable_spec.BeamExecutableSpec\022@\n>\n<tfx.components.example_gen.csv_example_gen.executor.Executor\n\206\001\n\006Pusher\022|\nOtype.googleapis.com/tfx.orchestration.executable_spec.PythonClassExecutableSpec\022)\n\'tfx.components.pusher.executor.Executor\n\220\001\n\007Trainer\022\204\001\nOtype.googleapis.com/tfx.orchestration.executable_spec.PythonClassExecutableSpec\0221\n/tfx.components.trainer.executor.GenericExecutor\022\230\001\n\rCsvExampleGen\022\206\001\nOtype.googleapis.com/tfx.orchestration.executable_spec.PythonClassExecutableSpec\0223\n1tfx.components.example_gen.driver.FileBasedDriver*[\n0type.googleapis.com/ml_metadata.ConnectionConfig\022\'\032%\n!metadata/penguin-tfma/metadata.db\020\003"
}

INFO:absl:Using deployment config:
 executor_specs {
  key: "CsvExampleGen"
  value {
    beam_executable_spec {
      python_executor_spec {
        class_path: "tfx.components.example_gen.csv_example_gen.executor.Executor"
      }
    }
  }
}
executor_specs {
  key: "Evaluator"
  value {
    beam_executable_spec {
      python_executor_spec {
        class_path: "tfx.components.evaluator.executor.Executor"
      }
    }
  }
}
executor_specs {
  key: "Pusher"
  value {
    python_class_executable_spec {
      class_path: "tfx.components.pusher.executor.Executor"
    }
  }
}
executor_specs {
  key: "Trainer"
  value {
    python_class_executable_spec {
      class_path: "tfx.components.trainer.executor.GenericExecutor"
    }
  }
}
custom_driver_specs {
  key: "CsvExampleGen"
  value {
    python_class_executable_spec {
      class_path: "tfx.components.example_gen.driver.FileBasedDriver"
    }
  }
}
metadata_connection_config {
  sqlite {
    filename_uri: "metadata/penguin-tfma/metadata.db"
    connection_mode: READWRITE_OPENCREATE
  }
}

INFO:absl:Using connection config:
 sqlite {
  filename_uri: "metadata/penguin-tfma/metadata.db"
  connection_mode: READWRITE_OPENCREATE
}

INFO:absl:Component CsvExampleGen is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.components.example_gen.csv_example_gen.component.CsvExampleGen"
  }
  id: "CsvExampleGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-tfma"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-07-24T09:18:40.745586"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-tfma.CsvExampleGen"
      }
    }
  }
}
outputs {
  outputs {
    key: "examples"
    value {
      artifact_spec {
        type {
          name: "Examples"
          properties {
            key: "span"
            value: INT
          }
          properties {
            key: "split_names"
            value: STRING
          }
          properties {
            key: "version"
            value: INT
          }
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "input_base"
    value {
      field_value {
        string_value: "/tmp/tfx-datal2xni_a_"
      }
    }
  }
  parameters {
    key: "input_config"
    value {
      field_value {
        string_value: "{\n  \"splits\": [\n    {\n      \"name\": \"single_split\",\n      \"pattern\": \"*\"\n    }\n  ]\n}"
      }
    }
  }
  parameters {
    key: "output_config"
    value {
      field_value {
        string_value: "{\n  \"split_config\": {\n    \"splits\": [\n      {\n        \"hash_buckets\": 2,\n        \"name\": \"train\"\n      },\n      {\n        \"hash_buckets\": 1,\n        \"name\": \"eval\"\n      }\n    ]\n  }\n}"
      }
    }
  }
  parameters {
    key: "output_data_format"
    value {
      field_value {
        int_value: 6
      }
    }
  }
}
downstream_nodes: "Evaluator"
downstream_nodes: "Trainer"
execution_options {
  caching_options {
  }
}

INFO:absl:MetadataStore with DB connection initialized
running bdist_wheel
running build
running build_py
creating build
creating build/lib
copying penguin_trainer.py -> build/lib
installing to /tmp/tmpp80w9y0j
running install
running install_lib
copying build/lib/penguin_trainer.py -> /tmp/tmpp80w9y0j
running install_egg_info
running egg_info
creating tfx_user_code_Trainer.egg-info
writing tfx_user_code_Trainer.egg-info/PKG-INFO
writing dependency_links to tfx_user_code_Trainer.egg-info/dependency_links.txt
writing top-level names to tfx_user_code_Trainer.egg-info/top_level.txt
writing manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'
reading manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'
writing manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'
Copying tfx_user_code_Trainer.egg-info to /tmp/tmpp80w9y0j/tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703-py3.7.egg-info
running install_scripts
creating /tmp/tmpp80w9y0j/tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703.dist-info/WHEEL
creating '/tmp/tmp05l5gny8/tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703-py3-none-any.whl' and adding '/tmp/tmpp80w9y0j' to it
adding 'penguin_trainer.py'
adding 'tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703.dist-info/METADATA'
adding 'tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703.dist-info/WHEEL'
adding 'tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703.dist-info/top_level.txt'
adding 'tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703.dist-info/RECORD'
removing /tmp/tmpp80w9y0j
2021-07-24 09:18:40.762782: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
INFO:absl:select span and version = (0, None)
INFO:absl:latest span and version = (0, None)
2021-07-24 09:18:40.770868: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
2021-07-24 09:18:40.778302: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
2021-07-24 09:18:40.785346: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
INFO:absl:MetadataStore with DB connection initialized
2021-07-24 09:18:40.799126: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
INFO:absl:Going to run a new execution 1
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=1, input_dict={}, output_dict=defaultdict(<class 'list'>, {'examples': [Artifact(artifact: uri: "pipelines/penguin-tfma/CsvExampleGen/examples/1"
custom_properties {
  key: "input_fingerprint"
  value {
    string_value: "split:single_split,num_files:1,total_bytes:25648,xor_checksum:1627118320,sum_checksum:1627118320"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfma:2021-07-24T09:18:40.745586:CsvExampleGen:examples:0"
  }
}
custom_properties {
  key: "span"
  value {
    int_value: 0
  }
}
, artifact_type: name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
)]}), exec_properties={'output_data_format': 6, 'input_config': '{\n  "splits": [\n    {\n      "name": "single_split",\n      "pattern": "*"\n    }\n  ]\n}', 'output_config': '{\n  "split_config": {\n    "splits": [\n      {\n        "hash_buckets": 2,\n        "name": "train"\n      },\n      {\n        "hash_buckets": 1,\n        "name": "eval"\n      }\n    ]\n  }\n}', 'input_base': '/tmp/tfx-datal2xni_a_', 'span': 0, 'version': None, 'input_fingerprint': 'split:single_split,num_files:1,total_bytes:25648,xor_checksum:1627118320,sum_checksum:1627118320'}, execution_output_uri='pipelines/penguin-tfma/CsvExampleGen/.system/executor_execution/1/executor_output.pb', stateful_working_dir='pipelines/penguin-tfma/CsvExampleGen/.system/stateful_working_dir/2021-07-24T09:18:40.745586', tmp_dir='pipelines/penguin-tfma/CsvExampleGen/.system/executor_execution/1/.temp/', pipeline_node=node_info {
  type {
    name: "tfx.components.example_gen.csv_example_gen.component.CsvExampleGen"
  }
  id: "CsvExampleGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-tfma"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-07-24T09:18:40.745586"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-tfma.CsvExampleGen"
      }
    }
  }
}
outputs {
  outputs {
    key: "examples"
    value {
      artifact_spec {
        type {
          name: "Examples"
          properties {
            key: "span"
            value: INT
          }
          properties {
            key: "split_names"
            value: STRING
          }
          properties {
            key: "version"
            value: INT
          }
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "input_base"
    value {
      field_value {
        string_value: "/tmp/tfx-datal2xni_a_"
      }
    }
  }
  parameters {
    key: "input_config"
    value {
      field_value {
        string_value: "{\n  \"splits\": [\n    {\n      \"name\": \"single_split\",\n      \"pattern\": \"*\"\n    }\n  ]\n}"
      }
    }
  }
  parameters {
    key: "output_config"
    value {
      field_value {
        string_value: "{\n  \"split_config\": {\n    \"splits\": [\n      {\n        \"hash_buckets\": 2,\n        \"name\": \"train\"\n      },\n      {\n        \"hash_buckets\": 1,\n        \"name\": \"eval\"\n      }\n    ]\n  }\n}"
      }
    }
  }
  parameters {
    key: "output_data_format"
    value {
      field_value {
        int_value: 6
      }
    }
  }
}
downstream_nodes: "Evaluator"
downstream_nodes: "Trainer"
execution_options {
  caching_options {
  }
}
, pipeline_info=id: "penguin-tfma"
, pipeline_run_id='2021-07-24T09:18:40.745586')
INFO:absl:Generating examples.
WARNING:apache_beam.runners.interactive.interactive_environment:Dependencies required for Interactive Beam PCollection visualization are not available, please use: `pip install apache-beam[interactive]` to install necessary dependencies to enable all data visualization features.
INFO:absl:Processing input csv data /tmp/tfx-datal2xni_a_/* to TFExample.
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.
INFO:absl:Examples generated.
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 1 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'examples': [Artifact(artifact: uri: "pipelines/penguin-tfma/CsvExampleGen/examples/1"
custom_properties {
  key: "input_fingerprint"
  value {
    string_value: "split:single_split,num_files:1,total_bytes:25648,xor_checksum:1627118320,sum_checksum:1627118320"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfma:2021-07-24T09:18:40.745586:CsvExampleGen:examples:0"
  }
}
custom_properties {
  key: "span"
  value {
    int_value: 0
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.0.0"
  }
}
, artifact_type: name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
)]}) for execution 1
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component CsvExampleGen is finished.
INFO:absl:Component latest_blessed_model_resolver is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.dsl.components.common.resolver.Resolver"
  }
  id: "latest_blessed_model_resolver"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-tfma"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-07-24T09:18:40.745586"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-tfma.latest_blessed_model_resolver"
      }
    }
  }
}
inputs {
  inputs {
    key: "model"
    value {
      channels {
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "penguin-tfma"
            }
          }
        }
        artifact_query {
          type {
            name: "Model"
          }
        }
      }
    }
  }
  inputs {
    key: "model_blessing"
    value {
      channels {
        context_queries {
          type {
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            field_value {
              string_value: "penguin-tfma"
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        artifact_query {
          type {
            name: "ModelBlessing"
          }
        }
      }
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  resolver_config {
    resolver_steps {
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      config_json: "{}"
      input_keys: "model"
      input_keys: "model_blessing"
    }
  }
}
downstream_nodes: "Evaluator"
execution_options {
  caching_options {
  }
}

INFO:absl:Running as an resolver node.
INFO:absl:MetadataStore with DB connection initialized
WARNING:absl:Artifact type Model is not found in MLMD.
WARNING:absl:Artifact type ModelBlessing is not found in MLMD.
2021-07-24 09:18:42.009488: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
INFO:absl:Component latest_blessed_model_resolver is finished.
INFO:absl:Component Trainer is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.components.trainer.component.Trainer"
  }
  id: "Trainer"
}
contexts {
  contexts {
    type {
      name: "pipeline"
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    name {
      field_value {
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  contexts {
    type {
      name: "pipeline_run"
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    name {
      field_value {
        string_value: "2021-07-24T09:18:40.745586"
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  contexts {
    type {
      name: "node"
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    name {
      field_value {
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inputs {
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        context_queries {
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        artifact_query {
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        }
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    }
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}
outputs {
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      artifact_spec {
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parameters {
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    }
  }
  parameters {
    key: "train_args"
    value {
      field_value {
        string_value: "{\n  \"num_steps\": 100\n}"
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upstream_nodes: "CsvExampleGen"
downstream_nodes: "Evaluator"
downstream_nodes: "Pusher"
execution_options {
  caching_options {
  }
}

INFO:absl:MetadataStore with DB connection initialized
2021-07-24 09:18:42.031739: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 3
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=3, input_dict={'examples': [Artifact(artifact: id: 1
type_id: 6
uri: "pipelines/penguin-tfma/CsvExampleGen/examples/1"
properties {
  key: "split_names"
  value {
    string_value: "[\"train\", \"eval\"]"
  }
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custom_properties {
  key: "input_fingerprint"
  value {
    string_value: "split:single_split,num_files:1,total_bytes:25648,xor_checksum:1627118320,sum_checksum:1627118320"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfma:2021-07-24T09:18:40.745586:CsvExampleGen:examples:0"
  }
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custom_properties {
  key: "payload_format"
  value {
    string_value: "FORMAT_TF_EXAMPLE"
  }
}
custom_properties {
  key: "span"
  value {
    int_value: 0
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.0.0"
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state: LIVE
create_time_since_epoch: 1627118321993
last_update_time_since_epoch: 1627118321993
, artifact_type: id: 6
name: "Examples"
properties {
  key: "span"
  value: INT
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properties {
  key: "split_names"
  value: STRING
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properties {
  key: "version"
  value: INT
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)]}, output_dict=defaultdict(<class 'list'>, {'model_run': [Artifact(artifact: uri: "pipelines/penguin-tfma/Trainer/model_run/3"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfma:2021-07-24T09:18:40.745586:Trainer:model_run:0"
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, artifact_type: name: "ModelRun"
)], 'model': [Artifact(artifact: uri: "pipelines/penguin-tfma/Trainer/model/3"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfma:2021-07-24T09:18:40.745586:Trainer:model:0"
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  type {
    name: "tfx.components.trainer.component.Trainer"
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  id: "Trainer"
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contexts {
  contexts {
    type {
      name: "pipeline"
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    name {
      field_value {
        string_value: "penguin-tfma"
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    }
  }
  contexts {
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      name: "pipeline_run"
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      field_value {
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  contexts {
    type {
      name: "node"
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    name {
      field_value {
        string_value: "penguin-tfma.Trainer"
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inputs {
  inputs {
    key: "examples"
    value {
      channels {
        producer_node_query {
          id: "CsvExampleGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "penguin-tfma"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2021-07-24T09:18:40.745586"
            }
          }
        }
        context_queries {
          type {
            name: "node"
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          name {
            field_value {
              string_value: "penguin-tfma.CsvExampleGen"
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          }
        }
        artifact_query {
          type {
            name: "Examples"
          }
        }
        output_key: "examples"
      }
    }
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}
outputs {
  outputs {
    key: "model"
    value {
      artifact_spec {
        type {
          name: "Model"
        }
      }
    }
  }
  outputs {
    key: "model_run"
    value {
      artifact_spec {
        type {
          name: "ModelRun"
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      }
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}
parameters {
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  parameters {
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      field_value {
        string_value: "{\n  \"num_steps\": 5\n}"
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  parameters {
    key: "module_path"
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      field_value {
        string_value: "penguin_trainer@pipelines/penguin-tfma/_wheels/tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703-py3-none-any.whl"
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  parameters {
    key: "train_args"
    value {
      field_value {
        string_value: "{\n  \"num_steps\": 100\n}"
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upstream_nodes: "CsvExampleGen"
downstream_nodes: "Evaluator"
downstream_nodes: "Pusher"
execution_options {
  caching_options {
  }
}
, pipeline_info=id: "penguin-tfma"
, pipeline_run_id='2021-07-24T09:18:40.745586')
INFO:absl:Train on the 'train' split when train_args.splits is not set.
INFO:absl:Evaluate on the 'eval' split when eval_args.splits is not set.
INFO:absl:udf_utils.get_fn {'eval_args': '{\n  "num_steps": 5\n}', 'module_path': 'penguin_trainer@pipelines/penguin-tfma/_wheels/tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703-py3-none-any.whl', 'custom_config': 'null', 'train_args': '{\n  "num_steps": 100\n}'} 'run_fn'
INFO:absl:Installing 'pipelines/penguin-tfma/_wheels/tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmp16wizcz0', 'pipelines/penguin-tfma/_wheels/tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703-py3-none-any.whl']
Processing ./pipelines/penguin-tfma/_wheels/tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703-py3-none-any.whl
INFO:absl:Successfully installed 'pipelines/penguin-tfma/_wheels/tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703-py3-none-any.whl'.
INFO:absl:Training model.
INFO:absl:Feature body_mass_g has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_depth_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
  size: 1
}
. Setting to DenseTensor.
Installing collected packages: tfx-user-code-Trainer
Successfully installed tfx-user-code-Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703
2021-07-24 09:18:43.636182: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1
2021-07-24 09:18:43.640445: E tensorflow/stream_executor/cuda/cuda_driver.cc:328] failed call to cuInit: CUDA_ERROR_SYSTEM_DRIVER_MISMATCH: system has unsupported display driver / cuda driver combination
2021-07-24 09:18:43.640487: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: kokoro-gcp-ubuntu-prod-559609198
2021-07-24 09:18:43.640499: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: kokoro-gcp-ubuntu-prod-559609198
2021-07-24 09:18:43.640672: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:200] libcuda reported version is: 470.57.2
2021-07-24 09:18:43.640711: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:204] kernel reported version is: 465.27.0
2021-07-24 09:18:43.640723: E tensorflow/stream_executor/cuda/cuda_diagnostics.cc:313] kernel version 465.27.0 does not match DSO version 470.57.2 -- cannot find working devices in this configuration
2021-07-24 09:18:43.641058: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
INFO:absl:Feature body_mass_g has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_depth_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature body_mass_g has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_depth_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature body_mass_g has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_depth_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Model: "model"
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Layer (type)                    Output Shape         Param #     Connected to                     
INFO:absl:==================================================================================================
INFO:absl:culmen_length_mm (InputLayer)   [(None, 1)]          0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:culmen_depth_mm (InputLayer)    [(None, 1)]          0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:flipper_length_mm (InputLayer)  [(None, 1)]          0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:body_mass_g (InputLayer)        [(None, 1)]          0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:concatenate (Concatenate)       (None, 4)            0           culmen_length_mm[0][0]           
INFO:absl:                                                                 culmen_depth_mm[0][0]            
INFO:absl:                                                                 flipper_length_mm[0][0]          
INFO:absl:                                                                 body_mass_g[0][0]                
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense (Dense)                   (None, 8)            40          concatenate[0][0]                
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_1 (Dense)                 (None, 8)            72          dense[0][0]                      
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_2 (Dense)                 (None, 3)            27          dense_1[0][0]                    
INFO:absl:==================================================================================================
INFO:absl:Total params: 139
INFO:absl:Trainable params: 139
INFO:absl:Non-trainable params: 0
INFO:absl:__________________________________________________________________________________________________
43/100 [===========>..................] - ETA: 0s - loss: 0.9585 - sparse_categorical_accuracy: 0.5698
2021-07-24 09:18:44.142686: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)
2021-07-24 09:18:44.144352: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2000179999 Hz
100/100 [==============================] - 1s 3ms/step - loss: 0.6612 - sparse_categorical_accuracy: 0.7130 - val_loss: 0.3181 - val_sparse_categorical_accuracy: 0.8400
2021-07-24 09:18:44.750716: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
INFO:tensorflow:Assets written to: pipelines/penguin-tfma/Trainer/model/3/Format-Serving/assets
INFO:tensorflow:Assets written to: pipelines/penguin-tfma/Trainer/model/3/Format-Serving/assets
INFO:absl:Training complete. Model written to pipelines/penguin-tfma/Trainer/model/3/Format-Serving. ModelRun written to pipelines/penguin-tfma/Trainer/model_run/3
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 3 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'model_run': [Artifact(artifact: uri: "pipelines/penguin-tfma/Trainer/model_run/3"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfma:2021-07-24T09:18:40.745586:Trainer:model_run:0"
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}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.0.0"
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, artifact_type: name: "ModelRun"
)], 'model': [Artifact(artifact: uri: "pipelines/penguin-tfma/Trainer/model/3"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfma:2021-07-24T09:18:40.745586:Trainer:model:0"
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}
custom_properties {
  key: "tfx_version"
  value {
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, artifact_type: name: "Model"
)]}) for execution 3
INFO:absl:MetadataStore with DB connection initialized
2021-07-24 09:18:45.297260: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
2021-07-24 09:18:45.302616: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
INFO:absl:Component Trainer is finished.
INFO:absl:Component Evaluator is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.components.evaluator.component.Evaluator"
  }
  id: "Evaluator"
}
contexts {
  contexts {
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  contexts {
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  contexts {
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    name {
      field_value {
        string_value: "penguin-tfma.Evaluator"
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inputs {
  inputs {
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    value {
      channels {
        producer_node_query {
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        context_queries {
          type {
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        context_queries {
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            name: "pipeline_run"
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          name {
            field_value {
              string_value: "2021-07-24T09:18:40.745586"
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        context_queries {
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            name: "node"
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          name {
            field_value {
              string_value: "penguin-tfma.latest_blessed_model_resolver"
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          }
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        artifact_query {
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        output_key: "model"
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  inputs {
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    value {
      channels {
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        context_queries {
          type {
            name: "pipeline"
          }
          name {
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        context_queries {
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            name: "pipeline_run"
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          name {
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              string_value: "2021-07-24T09:18:40.745586"
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        context_queries {
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          name {
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        context_queries {
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        artifact_query {
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outputs {
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    value {
      artifact_spec {
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      }
    }
  }
  outputs {
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      }
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  }
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parameters {
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    }
  }
  parameters {
    key: "example_splits"
    value {
      field_value {
        string_value: "null"
      }
    }
  }
}
upstream_nodes: "CsvExampleGen"
upstream_nodes: "Trainer"
upstream_nodes: "latest_blessed_model_resolver"
downstream_nodes: "Pusher"
execution_options {
  caching_options {
  }
}

INFO:absl:MetadataStore with DB connection initialized
2021-07-24 09:18:45.326840: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 4
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=4, input_dict={'examples': [Artifact(artifact: id: 1
type_id: 6
uri: "pipelines/penguin-tfma/CsvExampleGen/examples/1"
properties {
  key: "split_names"
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    string_value: "[\"train\", \"eval\"]"
  }
}
custom_properties {
  key: "input_fingerprint"
  value {
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  }
}
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfma:2021-07-24T09:18:40.745586:CsvExampleGen:examples:0"
  }
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custom_properties {
  key: "payload_format"
  value {
    string_value: "FORMAT_TF_EXAMPLE"
  }
}
custom_properties {
  key: "span"
  value {
    int_value: 0
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.0.0"
  }
}
state: LIVE
create_time_since_epoch: 1627118321993
last_update_time_since_epoch: 1627118321993
, artifact_type: id: 6
name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
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}
properties {
  key: "version"
  value: INT
}
)], 'baseline_model': [], 'model': [Artifact(artifact: id: 3
type_id: 10
uri: "pipelines/penguin-tfma/Trainer/model/3"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfma:2021-07-24T09:18:40.745586:Trainer:model:0"
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}
custom_properties {
  key: "tfx_version"
  value {
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state: LIVE
create_time_since_epoch: 1627118325306
last_update_time_since_epoch: 1627118325306
, artifact_type: id: 10
name: "Model"
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custom_properties {
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, artifact_type: name: "ModelBlessing"
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custom_properties {
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    string_value: "penguin-tfma:2021-07-24T09:18:40.745586:Evaluator:evaluation:0"
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  type {
    name: "tfx.components.evaluator.component.Evaluator"
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  id: "Evaluator"
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contexts {
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  contexts {
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  inputs {
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        context_queries {
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        }
        context_queries {
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        context_queries {
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          name {
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          }
        }
        artifact_query {
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        artifact_query {
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outputs {
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parameters {
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  }
  parameters {
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    value {
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    }
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}
upstream_nodes: "CsvExampleGen"
upstream_nodes: "Trainer"
upstream_nodes: "latest_blessed_model_resolver"
downstream_nodes: "Pusher"
execution_options {
  caching_options {
  }
}
, pipeline_info=id: "penguin-tfma"
, pipeline_run_id='2021-07-24T09:18:40.745586')
INFO:absl:udf_utils.get_fn {'example_splits': 'null', 'eval_config': '{\n  "metrics_specs": [\n    {\n      "per_slice_thresholds": {\n        "sparse_categorical_accuracy": {\n          "thresholds": [\n            {\n              "slicing_specs": [\n                {}\n              ],\n              "threshold": {\n                "change_threshold": {\n                  "absolute": -1e-10,\n                  "direction": "HIGHER_IS_BETTER"\n                },\n                "value_threshold": {\n                  "lower_bound": 0.6\n                }\n              }\n            }\n          ]\n        }\n      }\n    }\n  ],\n  "model_specs": [\n    {\n      "label_key": "species"\n    }\n  ],\n  "slicing_specs": [\n    {},\n    {\n      "feature_keys": [\n        "species"\n      ]\n    }\n  ]\n}'} 'custom_eval_shared_model'
ERROR:absl:There are change thresholds, but the baseline is missing. This is allowed only when rubber stamping (first run).
INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
  label_key: "species"
}
slicing_specs {
}
slicing_specs {
  feature_keys: "species"
}
metrics_specs {
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    key: "sparse_categorical_accuracy"
    value {
      thresholds {
        slicing_specs {
        }
        threshold {
          value_threshold {
            lower_bound {
              value: 0.6
            }
          }
        }
      }
    }
  }
}

INFO:absl:Using pipelines/penguin-tfma/Trainer/model/3/Format-Serving as  model.
INFO:absl:The 'example_splits' parameter is not set, using 'eval' split.
INFO:absl:Evaluating model.
INFO:absl:udf_utils.get_fn {'example_splits': 'null', 'eval_config': '{\n  "metrics_specs": [\n    {\n      "per_slice_thresholds": {\n        "sparse_categorical_accuracy": {\n          "thresholds": [\n            {\n              "slicing_specs": [\n                {}\n              ],\n              "threshold": {\n                "change_threshold": {\n                  "absolute": -1e-10,\n                  "direction": "HIGHER_IS_BETTER"\n                },\n                "value_threshold": {\n                  "lower_bound": 0.6\n                }\n              }\n            }\n          ]\n        }\n      }\n    }\n  ],\n  "model_specs": [\n    {\n      "label_key": "species"\n    }\n  ],\n  "slicing_specs": [\n    {},\n    {\n      "feature_keys": [\n        "species"\n      ]\n    }\n  ]\n}'} 'custom_extractors'
INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
  label_key: "species"
}
slicing_specs {
}
slicing_specs {
  feature_keys: "species"
}
metrics_specs {
  model_names: ""
  per_slice_thresholds {
    key: "sparse_categorical_accuracy"
    value {
      thresholds {
        slicing_specs {
        }
        threshold {
          value_threshold {
            lower_bound {
              value: 0.6
            }
          }
        }
      }
    }
  }
}

INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
  label_key: "species"
}
slicing_specs {
}
slicing_specs {
  feature_keys: "species"
}
metrics_specs {
  model_names: ""
  per_slice_thresholds {
    key: "sparse_categorical_accuracy"
    value {
      thresholds {
        slicing_specs {
        }
        threshold {
          value_threshold {
            lower_bound {
              value: 0.6
            }
          }
        }
      }
    }
  }
}

INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
  label_key: "species"
}
slicing_specs {
}
slicing_specs {
  feature_keys: "species"
}
metrics_specs {
  model_names: ""
  per_slice_thresholds {
    key: "sparse_categorical_accuracy"
    value {
      thresholds {
        slicing_specs {
        }
        threshold {
          value_threshold {
            lower_bound {
              value: 0.6
            }
          }
        }
      }
    }
  }
}

WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
INFO:absl:Evaluation complete. Results written to pipelines/penguin-tfma/Evaluator/evaluation/4.
INFO:absl:Checking validation results.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/writers/metrics_plots_and_validations_writer.py:113: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.
Instructions for updating:
Use eager execution and: 
`tf.data.TFRecordDataset(path)`
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/writers/metrics_plots_and_validations_writer.py:113: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.
Instructions for updating:
Use eager execution and: 
`tf.data.TFRecordDataset(path)`
INFO:absl:Blessing result True written to pipelines/penguin-tfma/Evaluator/blessing/4.
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 4 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'blessing': [Artifact(artifact: uri: "pipelines/penguin-tfma/Evaluator/blessing/4"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfma:2021-07-24T09:18:40.745586:Evaluator:blessing:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.0.0"
  }
}
, artifact_type: name: "ModelBlessing"
)], 'evaluation': [Artifact(artifact: uri: "pipelines/penguin-tfma/Evaluator/evaluation/4"
custom_properties {
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  value {
    string_value: "penguin-tfma:2021-07-24T09:18:40.745586:Evaluator:evaluation:0"
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}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.0.0"
  }
}
, artifact_type: name: "ModelEvaluation"
)]}) for execution 4
INFO:absl:MetadataStore with DB connection initialized
2021-07-24 09:18:49.996620: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
INFO:absl:Component Evaluator is finished.
2021-07-24 09:18:50.001672: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
INFO:absl:Component Pusher is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.components.pusher.component.Pusher"
  }
  id: "Pusher"
}
contexts {
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  contexts {
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      }
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  contexts {
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    name {
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  inputs {
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    value {
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        context_queries {
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          name {
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        }
        artifact_query {
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        output_key: "blessing"
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}
outputs {
  outputs {
    key: "pushed_model"
    value {
      artifact_spec {
        type {
          name: "PushedModel"
        }
      }
    }
  }
}
parameters {
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      }
    }
  }
  parameters {
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    value {
      field_value {
        string_value: "{\n  \"filesystem\": {\n    \"base_directory\": \"serving_model/penguin-tfma\"\n  }\n}"
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  }
}
upstream_nodes: "Evaluator"
upstream_nodes: "Trainer"
execution_options {
  caching_options {
  }
}

INFO:absl:MetadataStore with DB connection initialized
2021-07-24 09:18:50.024654: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 5
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=5, input_dict={'model': [Artifact(artifact: id: 3
type_id: 10
uri: "pipelines/penguin-tfma/Trainer/model/3"
custom_properties {
  key: "name"
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}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.0.0"
  }
}
state: LIVE
create_time_since_epoch: 1627118325306
last_update_time_since_epoch: 1627118325306
, artifact_type: id: 10
name: "Model"
)], 'model_blessing': [Artifact(artifact: id: 4
type_id: 12
uri: "pipelines/penguin-tfma/Evaluator/blessing/4"
custom_properties {
  key: "blessed"
  value {
    int_value: 1
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}
custom_properties {
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  value {
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}
custom_properties {
  key: "current_model_id"
  value {
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custom_properties {
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    string_value: "penguin-tfma:2021-07-24T09:18:40.745586:Evaluator:blessing:0"
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}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.0.0"
  }
}
state: LIVE
create_time_since_epoch: 1627118330005
last_update_time_since_epoch: 1627118330005
, artifact_type: id: 12
name: "ModelBlessing"
)]}, output_dict=defaultdict(<class 'list'>, {'pushed_model': [Artifact(artifact: uri: "pipelines/penguin-tfma/Pusher/pushed_model/5"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfma:2021-07-24T09:18:40.745586:Pusher:pushed_model:0"
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}
, artifact_type: name: "PushedModel"
)]}), exec_properties={'push_destination': '{\n  "filesystem": {\n    "base_directory": "serving_model/penguin-tfma"\n  }\n}', 'custom_config': 'null'}, execution_output_uri='pipelines/penguin-tfma/Pusher/.system/executor_execution/5/executor_output.pb', stateful_working_dir='pipelines/penguin-tfma/Pusher/.system/stateful_working_dir/2021-07-24T09:18:40.745586', tmp_dir='pipelines/penguin-tfma/Pusher/.system/executor_execution/5/.temp/', pipeline_node=node_info {
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contexts {
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    }
    name {
      field_value {
        string_value: "penguin-tfma"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-07-24T09:18:40.745586"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-tfma.Pusher"
      }
    }
  }
}
inputs {
  inputs {
    key: "model"
    value {
      channels {
        producer_node_query {
          id: "Trainer"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "penguin-tfma"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2021-07-24T09:18:40.745586"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "penguin-tfma.Trainer"
            }
          }
        }
        artifact_query {
          type {
            name: "Model"
          }
        }
        output_key: "model"
      }
    }
  }
  inputs {
    key: "model_blessing"
    value {
      channels {
        producer_node_query {
          id: "Evaluator"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "penguin-tfma"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2021-07-24T09:18:40.745586"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "penguin-tfma.Evaluator"
            }
          }
        }
        artifact_query {
          type {
            name: "ModelBlessing"
          }
        }
        output_key: "blessing"
      }
    }
  }
}
outputs {
  outputs {
    key: "pushed_model"
    value {
      artifact_spec {
        type {
          name: "PushedModel"
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "custom_config"
    value {
      field_value {
        string_value: "null"
      }
    }
  }
  parameters {
    key: "push_destination"
    value {
      field_value {
        string_value: "{\n  \"filesystem\": {\n    \"base_directory\": \"serving_model/penguin-tfma\"\n  }\n}"
      }
    }
  }
}
upstream_nodes: "Evaluator"
upstream_nodes: "Trainer"
execution_options {
  caching_options {
  }
}
, pipeline_info=id: "penguin-tfma"
, pipeline_run_id='2021-07-24T09:18:40.745586')
INFO:absl:Model version: 1627118330
INFO:absl:Model written to serving path serving_model/penguin-tfma/1627118330.
INFO:absl:Model pushed to pipelines/penguin-tfma/Pusher/pushed_model/5.
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 5 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'pushed_model': [Artifact(artifact: uri: "pipelines/penguin-tfma/Pusher/pushed_model/5"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfma:2021-07-24T09:18:40.745586:Pusher:pushed_model:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.0.0"
  }
}
, artifact_type: name: "PushedModel"
)]}) for execution 5
INFO:absl:MetadataStore with DB connection initialized
2021-07-24 09:18:50.055003: W ml_metadata/metadata_store/rdbms_metadata_access_object.cc:623] No property is defined for the Type
INFO:absl:Component Pusher is finished.

Une fois le pipeline terminé, vous devriez pouvoir voir quelque chose comme ce qui suit :

INFO:absl:Blessing result True written to pipelines/penguin-tfma/Evaluator/blessing/4.

Ou vous pouvez également vérifier manuellement le répertoire de sortie où sont stockés les artefacts générés. Si vous visitez pipelines/penguin-tfma/Evaluator/blessing/ avec un broswer de fichier, vous pouvez voir un fichier avec un nom BLESSED ou NOT_BLESSED en fonction du résultat de l' évaluation.

Si le résultat de la bénédiction est False , Pusher refusera de pousser le modèle à l' serving_model_dir , parce que le modèle est pas assez bon pour être utilisé dans la production.

Vous pouvez éventuellement réexécuter le pipeline avec différentes configurations d'évaluation. Même si vous exécutez le pipeline avec exactement la même configuration et ensemble de données, le modèle formé pourrait être légèrement différente en raison du caractère aléatoire inhérent à la formation du modèle qui peut conduire à un NOT_BLESSED modèle.

Examiner les sorties du pipeline

Vous pouvez utiliser TFMA pour étudier et visualiser le résultat de l'évaluation dans l'artefact ModelEvaluation.

Obtenir le résultat de l'analyse à partir des artefacts de sortie

Vous pouvez utiliser les API MLMD pour localiser ces sorties par programmation. Tout d'abord, nous allons définir quelques fonctions utilitaires pour rechercher les artefacts de sortie qui viennent d'être produits.

from ml_metadata.proto import metadata_store_pb2
# Non-public APIs, just for showcase.
from tfx.orchestration.portable.mlmd import execution_lib

# TODO(b/171447278): Move these functions into the TFX library.

def get_latest_artifacts(metadata, pipeline_name, component_id):
  """Output artifacts of the latest run of the component."""
  context = metadata.store.get_context_by_type_and_name(
      'node', f'{pipeline_name}.{component_id}')
  executions = metadata.store.get_executions_by_context(context.id)
  latest_execution = max(executions,
                         key=lambda e:e.last_update_time_since_epoch)
  return execution_lib.get_artifacts_dict(metadata, latest_execution.id, 
                                          metadata_store_pb2.Event.OUTPUT)

Nous pouvons trouver la dernière exécution du Evaluator composant et obtenir des artefacts de sortie de celui - ci.

# Non-public APIs, just for showcase.
from tfx.orchestration.metadata import Metadata
from tfx.types import standard_component_specs

metadata_connection_config = tfx.orchestration.metadata.sqlite_metadata_connection_config(
    METADATA_PATH)

with Metadata(metadata_connection_config) as metadata_handler:
  # Find output artifacts from MLMD.
  evaluator_output = get_latest_artifacts(metadata_handler, PIPELINE_NAME,
                                          'Evaluator')
  eval_artifact = evaluator_output[standard_component_specs.EVALUATION_KEY][0]
INFO:absl:MetadataStore with DB connection initialized

Evaluator retourne toujours un artefact d'évaluation, et nous pouvons le visualiser en utilisant la bibliothèque tensorflow modèle d' analyse. Par exemple, le code suivant affichera les mesures de précision pour chaque espèce de manchot.

import tensorflow_model_analysis as tfma

eval_result = tfma.load_eval_result(eval_artifact.uri)
tfma.view.render_slicing_metrics(eval_result, slicing_column='species')
SlicingMetricsViewer(config={'weightedExamplesColumn': 'example_count'}, data=[{'slice': 'species:0', 'metrics…

Si vous choisissez dans « sparse_categorical_accuracy » Show la liste déroulante, vous pouvez voir les valeurs de précision par espèce. Vous voudrez peut-être ajouter plus de tranches et vérifier si votre modèle est bon pour toutes les distributions et s'il y a un biais possible.

Prochaines étapes

En savoir plus sur l' analyse du modèle à tensorflow modèle tutoriel bibliothèque analyse .

Vous pouvez trouver plus de ressources sur https://www.tensorflow.org/tfx/tutorials

S'il vous plaît voir Comprendre TFX Pipelines pour en savoir plus sur les différents concepts TFX.