עזרה להגן על שונית המחסום הגדולה עם TensorFlow על Kaggle הצטרפו אתגר

ניתוח מודלים באמצעות צינור TFX וניתוח מודל TensorFlow

במדריך זה המבוסס על מחברת, ניצור ונפעיל צינור TFX אשר יוצר מודל סיווג פשוט ומנתח את הביצועים שלו על פני ריצות מרובות. מחברת זו מבוססת על צינור TFX שבנינו פשוט הדרכת צינור TFX . אם עדיין לא קראת את המדריך הזה, עליך לקרוא אותו לפני שתמשיך עם מחברת זו.

כשאתה מכוון את המודל שלך או מאמן אותו עם מערך נתונים חדש, עליך לבדוק אם המודל שלך השתפר או החמיר. ייתכן שרק בדיקת מדדים ברמה העליונה כמו דיוק לא מספיקה. יש להעריך כל דגם מאומן לפני שהוא נדחף לייצור.

אנחנו נוסיף Evaluator רכיב בצנרת שנוצרה ההדרכה הקודמת. רכיב ה-Evaluator מבצע ניתוח עמוק עבור המודלים שלך ומשווה את המודל החדש מול קו בסיס כדי לקבוע שהם "טובים מספיק". זה מיושם באמצעות ניתוח דגם TensorFlow הספרייה.

אנא ראה הבנת TFX צנרת כדי ללמוד עוד על מושגים שונים TFX.

להכין

תהליך ההגדרה זהה למדריך הקודם.

ראשית עלינו להתקין את חבילת TFX Python ולהוריד את מערך הנתונים בו נשתמש עבור המודל שלנו.

שדרוג פיפ

כדי להימנע משדרוג Pip במערכת בעת הפעלה מקומית, בדוק כדי לוודא שאנו פועלים ב-Colab. ניתן כמובן לשדרג מערכות מקומיות בנפרד.

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

התקן TFX

pip install -U tfx

הפעלת מחדש את זמן הריצה?

אם אתה משתמש ב-Google Colab, בפעם הראשונה שאתה מפעיל את התא שלמעלה, עליך להפעיל מחדש את זמן הריצה על ידי לחיצה מעל לחצן "התחל ריצה מחדש" או שימוש בתפריט "זמן ריצה > הפעל מחדש זמן ריצה...". זה בגלל האופן שבו קולאב טוען חבילות.

בדוק את גרסאות TensorFlow ו-TFX.

import tensorflow as tf
print('TensorFlow version: {}'.format(tf.__version__))
from tfx import v1 as tfx
print('TFX version: {}'.format(tfx.__version__))
TensorFlow version: 2.6.2
TFX version: 1.4.0

הגדר משתנים

ישנם כמה משתנים המשמשים להגדרת צינור. אתה יכול להתאים אישית את המשתנים האלה כרצונך. כברירת מחדל, כל הפלט מהצינור ייווצר תחת הספרייה הנוכחית.

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.

הכן נתונים לדוגמה

נשתמש באותה במערך הפינגווינים פאלמר .

ישנן ארבע תכונות מספריות במערך נתונים זה שכבר נורמלו לטווח [0,1]. אנחנו נבנינו מודל סיווג אשר חוזה את species של פינגווינים.

מכיוון ש-TFX ExampleGen קורא קלט מספריה, עלינו ליצור ספרייה ולהעתיק אליה מערך נתונים.

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-datal5lxy_yw/data.csv', <http.client.HTTPMessage at 0x7fa18a9da150>)

צור צינור

אנחנו נוסיף Evaluator רכיב בצנרת יצרנו את מדריך צינור TFX פשוט .

מעריך רכיב דורש נתון קלט מתוך ExampleGen רכיב דגם מתוך Trainer רכיב לבין tfma.EvalConfig אובייקט. אנו יכולים לספק מודל בסיסי שניתן להשתמש בו כדי להשוות מדדים עם המודל החדש שעבר הכשרה.

מעריכה יוצרת שני סוגים של חפצי פלט, ModelEvaluation ו ModelBlessing . ModelEvaluation מכיל את תוצאת ההערכה המפורטת שניתן לחקור ולהמחיש עוד יותר עם ספריית TFMA. ModelBlessing מכיל תוצאה בוליאנית בין אם המודל עבר קריטריונים נתונים וניתן להשתמש בו ברכיבים מאוחרים יותר כמו Pusher כאות.

כתוב קוד אימון מודל

נשתמש באותו קוד דגם כמו Simple TFX צינור הדרכה .

_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

כתוב הגדרת צינור

נגדיר פונקציה ליצירת צינור TFX. בנוסף לרכיב למעריכים שהזכרנו לעיל, נוסיף צומת אחד יותר נקרא Resolver . כדי לבדוק שדגם חדש משתפר מהדגם הקודם, עלינו להשוות אותו למודל קודם שפורסם, הנקרא baseline. ML Metadata (MLMD) עוקב אחר כול חפצים הקודמים של הצינור Resolver יכול למצוא מה היה המודל המבורך האחרון - מודל עבר למעריכים בהצלחה - מ MLMD באמצעות מח' אסטרטגיה בשם 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.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)

אנחנו צריכים לספק את המידע הבא כדי מעריך באמצעות eval_config :

  • מדדים נוספים להגדרה (אם רוצים יותר מדדים ממה שהוגדר במודל).
  • פרוסות להגדרה
  • ספי אימות מודל כדי לוודא אם יש לכלול אימות

מכיוון SparseCategoricalAccuracy נכללה כבר model.compile() שיחה, זה ייכלל בניתוח אוטומטי. אז אנחנו לא מוסיפים כאן מדדים נוספים. SparseCategoricalAccuracy ישמש להחליט אם המודל הוא מספיק טוב, מדי.

אנו מחשבים את המדדים עבור כל מערך הנתונים ולכל מין פינגווין. SlicingSpec מציין כיצד אנו צוברים את מדדי הכריז.

ישנם שני סף שמודל חדש צריך לעבור, האחד הוא סף מוחלט של 0.6 והשני הוא סף יחסי שהוא צריך להיות גבוה מהמודל הבסיסי. כאשר אתה מפעיל את הצינור בפעם הראשונה, change_threshold יתעלם ורק value_threshold ייבדק. אם אתה מפעיל את הצינור יותר מפעם אחת, את Resolver תמצא מודל מהריצה הקודמת והוא ישמש כמודל בסיס להשוואה.

ראה מדריך למעריכי רכיב לקבלת מידע נוסף.

הפעל את הצינור

נשתמש LocalDagRunner כמו ההדרכה הקודמת.

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/tmpr3anh67s/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmp6s2sw4dj', '--dist-dir', '/tmp/tmp6jr76e54']
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
  setuptools.SetuptoolsDeprecationWarning,
listing git files failed - pretending there aren't any
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: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: "2021-12-05T10:34:23.517028"
      }
    }
  }
  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-datal5lxy_yw"
      }
    }
  }
  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
      }
    }
  }
  parameters {
    key: "output_file_format"
    value {
      field_value {
        int_value: 5
      }
    }
  }
}
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/tmp6s2sw4dj
running install
running install_lib
copying build/lib/penguin_trainer.py -> /tmp/tmp6s2sw4dj
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/tmp6s2sw4dj/tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703-py3.7.egg-info
running install_scripts
creating /tmp/tmp6s2sw4dj/tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703.dist-info/WHEEL
creating '/tmp/tmp6jr76e54/tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703-py3-none-any.whl' and adding '/tmp/tmp6s2sw4dj' 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/tmp6s2sw4dj
WARNING: Logging before InitGoogleLogging() is written to STDERR
I1205 10:34:23.723806 28099 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 10:34:23.730262 28099 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 10:34:23.736788 28099 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 10:34:23.744907 28099 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:select span and version = (0, None)
INFO:absl:latest span and version = (0, None)
INFO:absl:MetadataStore with DB connection initialized
I1205 10:34:23.758380 28099 rdbms_metadata_access_object.cc:686] 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"
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, artifact_type: name: "Examples"
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properties {
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)]}), exec_properties={'output_file_format': 5, '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_config': '{\n  "splits": [\n    {\n      "name": "single_split",\n      "pattern": "*"\n    }\n  ]\n}', 'output_data_format': 6, 'input_base': '/tmp/tfx-datal5lxy_yw', 'span': 0, 'version': None, 'input_fingerprint': 'split:single_split,num_files:1,total_bytes:25648,xor_checksum:1638700463,sum_checksum:1638700463'}, 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/2021-12-05T10:34:23.517028', tmp_dir='pipelines/penguin-tfma/CsvExampleGen/.system/executor_execution/1/.temp/', pipeline_node=node_info {
  type {
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  id: "CsvExampleGen"
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contexts {
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  contexts {
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      name: "node"
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    name {
      field_value {
        string_value: "penguin-tfma.CsvExampleGen"
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outputs {
  outputs {
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    value {
      artifact_spec {
        type {
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          properties {
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          }
          properties {
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          properties {
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parameters {
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  parameters {
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        string_value: "{\n  \"splits\": [\n    {\n      \"name\": \"single_split\",\n      \"pattern\": \"*\"\n    }\n  ]\n}"
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downstream_nodes: "Trainer"
execution_options {
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}
, pipeline_info=id: "penguin-tfma"
, pipeline_run_id='2021-12-05T10:34:23.517028')
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-datal5lxy_yw/* 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 {
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custom_properties {
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custom_properties {
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, artifact_type: name: "Examples"
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properties {
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properties {
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)]}) 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 {
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  id: "latest_blessed_model_resolver"
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contexts {
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downstream_nodes: "Evaluator"
execution_options {
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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.
I1205 10:34:24.899447 28099 rdbms_metadata_access_object.cc:686] 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 {
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  id: "Trainer"
}
contexts {
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  contexts {
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inputs {
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upstream_nodes: "CsvExampleGen"
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execution_options {
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INFO:absl:MetadataStore with DB connection initialized
INFO:absl:MetadataStore with DB connection initialized
I1205 10:34:24.924589 28099 rdbms_metadata_access_object.cc:686] No property is defined for the Type
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: 15
uri: "pipelines/penguin-tfma/CsvExampleGen/examples/1"
properties {
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custom_properties {
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custom_properties {
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state: LIVE
create_time_since_epoch: 1638700464882
last_update_time_since_epoch: 1638700464882
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inputs {
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outputs {
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parameters {
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upstream_nodes: "CsvExampleGen"
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execution_options {
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}
, pipeline_info=id: "penguin-tfma"
, pipeline_run_id='2021-12-05T10:34:23.517028')
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 {'train_args': '{\n  "num_steps": 100\n}', 'custom_config': 'null', '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'} '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/tmpc97ini82', '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
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:__________________________________________________________________________________________________
100/100 [==============================] - 1s 3ms/step - loss: 0.5273 - sparse_categorical_accuracy: 0.8175 - val_loss: 0.2412 - val_sparse_categorical_accuracy: 0.9600
2021-12-05 10:34:29.879208: 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:2021-12-05T10:34:23.517028:Trainer:model_run:0"
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custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.4.0"
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, artifact_type: name: "ModelRun"
)], 'model': [Artifact(artifact: uri: "pipelines/penguin-tfma/Trainer/model/3"
custom_properties {
  key: "name"
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    string_value: "penguin-tfma:2021-12-05T10:34:23.517028:Trainer:model:0"
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custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.4.0"
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, artifact_type: name: "Model"
)]}) for execution 3
INFO:absl:MetadataStore with DB connection initialized
I1205 10:34:30.399760 28099 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 10:34:30.404250 28099 rdbms_metadata_access_object.cc:686] 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 {
    type {
      name: "pipeline"
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    name {
      field_value {
        string_value: "penguin-tfma"
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  contexts {
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  parameters {
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  parameters {
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      field_value {
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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
I1205 10:34:30.428037 28099 rdbms_metadata_access_object.cc:686] 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: 15
uri: "pipelines/penguin-tfma/CsvExampleGen/examples/1"
properties {
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    string_value: "tfrecords_gzip"
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custom_properties {
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  value {
    string_value: "split:single_split,num_files:1,total_bytes:25648,xor_checksum:1638700463,sum_checksum:1638700463"
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custom_properties {
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custom_properties {
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    string_value: "FORMAT_TF_EXAMPLE"
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custom_properties {
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custom_properties {
  key: "tfx_version"
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state: LIVE
create_time_since_epoch: 1638700464882
last_update_time_since_epoch: 1638700464882
, artifact_type: id: 15
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properties {
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)], 'model': [Artifact(artifact: id: 3
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, artifact_type: id: 19
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parameters {
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  parameters {
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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='2021-12-05T10:34:23.517028')
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}', 'fairness_indicator_thresholds': 'null'} 'custom_eval_shared_model'
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 {
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}
metrics_specs {
  per_slice_thresholds {
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    value {
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        threshold {
          value_threshold {
            lower_bound {
              value: 0.6
            }
          }
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  }
}

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}', 'fairness_indicator_thresholds': 'null'} '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:114: 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:114: 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:2021-12-05T10:34:23.517028:Evaluator:blessing:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.4.0"
  }
}
, artifact_type: name: "ModelBlessing"
)], 'evaluation': [Artifact(artifact: uri: "pipelines/penguin-tfma/Evaluator/evaluation/4"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfma:2021-12-05T10:34:23.517028:Evaluator:evaluation:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.4.0"
  }
}
, artifact_type: name: "ModelEvaluation"
)]}) for execution 4
INFO:absl:MetadataStore with DB connection initialized
I1205 10:34:35.040588 28099 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 10:34:35.045548 28099 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Component Evaluator is finished.
INFO:absl:Component Pusher is running.
INFO:absl:Running launcher for 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: "2021-12-05T10:34:23.517028"
      }
    }
  }
  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: "2021-12-05T10:34:23.517028"
            }
          }
        }
        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: "2021-12-05T10:34:23.517028"
            }
          }
        }
        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 {
  }
}

INFO:absl:MetadataStore with DB connection initialized
I1205 10:34:35.068168 28099 rdbms_metadata_access_object.cc:686] 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: 19
uri: "pipelines/penguin-tfma/Trainer/model/3"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfma:2021-12-05T10:34:23.517028:Trainer:model:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.4.0"
  }
}
state: LIVE
create_time_since_epoch: 1638700470409
last_update_time_since_epoch: 1638700470409
, artifact_type: id: 19
name: "Model"
)], 'model_blessing': [Artifact(artifact: id: 4
type_id: 21
uri: "pipelines/penguin-tfma/Evaluator/blessing/4"
custom_properties {
  key: "blessed"
  value {
    int_value: 1
  }
}
custom_properties {
  key: "current_model"
  value {
    string_value: "pipelines/penguin-tfma/Trainer/model/3"
  }
}
custom_properties {
  key: "current_model_id"
  value {
    int_value: 3
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfma:2021-12-05T10:34:23.517028:Evaluator:blessing:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.4.0"
  }
}
state: LIVE
create_time_since_epoch: 1638700475049
last_update_time_since_epoch: 1638700475049
, artifact_type: id: 21
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:2021-12-05T10:34:23.517028:Pusher:pushed_model:0"
  }
}
, artifact_type: name: "PushedModel"
)]}), exec_properties={'custom_config': 'null', 'push_destination': '{\n  "filesystem": {\n    "base_directory": "serving_model/penguin-tfma"\n  }\n}'}, 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/2021-12-05T10:34:23.517028', tmp_dir='pipelines/penguin-tfma/Pusher/.system/executor_execution/5/.temp/', 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: "2021-12-05T10:34:23.517028"
      }
    }
  }
  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: "2021-12-05T10:34:23.517028"
            }
          }
        }
        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: "2021-12-05T10:34:23.517028"
            }
          }
        }
        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='2021-12-05T10:34:23.517028')
INFO:absl:Model version: 1638700475
INFO:absl:Model written to serving path serving_model/penguin-tfma/1638700475.
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:2021-12-05T10:34:23.517028:Pusher:pushed_model:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.4.0"
  }
}
, artifact_type: name: "PushedModel"
)]}) for execution 5
INFO:absl:MetadataStore with DB connection initialized
I1205 10:34:35.098553 28099 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Component Pusher is finished.

כאשר הצינור הושלם, אתה אמור להיות מסוגל לראות משהו כמו הבא:

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

או שאתה יכול גם לבדוק ידנית את ספריית הפלט שבה מאוחסנים החפצים שנוצרו. אם אתה מבקר pipelines/penguin-tfma/Evaluator/blessing/ עם broswer קובץ, אתה יכול לראות קובץ עם שם BLESSED או NOT_BLESSED פי תוצאת ההערכה.

אם תוצאת הברכה היא False , Pusher יסרב לדחוף את המודל serving_model_dir , כי המודל איננו מספיק טוב כדי לשמש בייצור.

אתה יכול להפעיל את הצינור שוב, אולי עם תצורות הערכה שונות. גם אם אתה מפעיל את הצינור עם config ו במערך הזהה, המודל המאומן עשוי להיות שונה מעט בשל האקראיות טמון של אימוני מודל אשר יכול להוביל NOT_BLESSED מודל.

בחן את התפוקות של הצינור

אתה יכול להשתמש ב-TFMA כדי לחקור ולהמחיש את תוצאת ההערכה ב-ModelEvaluation artifact.

קבל תוצאת ניתוח מחפצי פלט

אתה יכול להשתמש בממשקי API של MLMD כדי לאתר את הפלטים הללו באופן פרוגרמטי. ראשית, נגדיר כמה פונקציות שירות כדי לחפש את חפצי הפלט שזה עתה נוצרו.

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])

אנחנו יכולים למצוא את הביצוע האחרון של Evaluator רכיב ולקבל חפצי תוצר שלו.

# 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 תמיד מחזיר חפץ הערכה אחת, ואנחנו יכולים לדמיין אותו באמצעות ספריית ניתוח TensorFlow דגם. לדוגמה, הקוד הבא יציג את מדדי הדיוק עבור כל מין פינגווין.

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…

אם תבחר "sparse_categorical_accuracy" ב Show רשימה נפתחת, אתה יכול לראות את ערכי דיוק לכול מינים. אולי כדאי להוסיף עוד פרוסות ולבדוק אם הדגם שלך מתאים לכל הפצה ואם יש הטיה אפשרית.

הצעדים הבאים

למידע נוסף על ניתוח מודל הדרכת ספריית ניתוח דגם TensorFlow .

אתה יכול למצוא עוד משאבים על https://www.tensorflow.org/tfx/tutorials

אנא ראה הבנת TFX צנרת כדי ללמוד עוד על מושגים שונים TFX.