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

מדריך פשוט לצינור TFX בעזרת מערך פינגווין

מדריך קצר להפעלת צינור TFX פשוט.

במדריך זה המבוסס על מחברת, ניצור ונפעיל צינור TFX עבור מודל סיווג פשוט. הצינור יורכב משלושה רכיבי TFX חיוניים: ExampleGen, Trainer ו-Pusher. הצינור כולל את זרימת העבודה המינימלית ביותר של ML כמו ייבוא ​​נתונים, אימון מודל וייצוא המודל המאומן.

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

להכין

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-simple"

# 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

נוריד את מערך הנתונים לדוגמה לשימוש בצינור ה-TFX שלנו. בסיס הנתונים שאנו משתמשים הוא במערך הפינגווינים פאלמר אשר משמש גם אחרים דוגמאות TFX .

ישנן ארבע תכונות מספריות במערך הנתונים הזה:

  • culmen_length_mm
  • culmen_depth_mm
  • פליפר_length_mm
  • מסת_גוף_ג

כל התכונות כבר 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-dataijanq9u3/data.csv', <http.client.HTTPMessage at 0x7f487953d110>)

עיין במהירות בקובץ ה-CSV.

head {_data_filepath}
species,culmen_length_mm,culmen_depth_mm,flipper_length_mm,body_mass_g
0,0.2545454545454545,0.6666666666666666,0.15254237288135594,0.2916666666666667
0,0.26909090909090905,0.5119047619047618,0.23728813559322035,0.3055555555555556
0,0.29818181818181805,0.5833333333333334,0.3898305084745763,0.1527777777777778
0,0.16727272727272732,0.7380952380952381,0.3559322033898305,0.20833333333333334
0,0.26181818181818167,0.892857142857143,0.3050847457627119,0.2638888888888889
0,0.24727272727272717,0.5595238095238096,0.15254237288135594,0.2569444444444444
0,0.25818181818181823,0.773809523809524,0.3898305084745763,0.5486111111111112
0,0.32727272727272727,0.5357142857142859,0.1694915254237288,0.1388888888888889
0,0.23636363636363636,0.9642857142857142,0.3220338983050847,0.3055555555555556

אתה אמור להיות מסוגל לראות חמישה ערכים. species הם אחת 0, 1 או 2, וכול תכונות האחרות צריכות ערכים בין 0 ו 1.

צור צינור

צינורות TFX מוגדרים באמצעות ממשקי API של Python. נגדיר צינור המורכב משלושה מרכיבים הבאים.

  • CsvExampleGen: קורא קבצי נתונים וממיר אותם לפורמט פנימי TFX לעיבוד נוסף. ישנם מספר ExampleGen של פורמטים שונים. במדריך זה, נשתמש ב-CsvExampleGen אשר לוקח קלט קובץ CSV.
  • מאמן: מאמן דגם ML. רכיב מאמן דורש קוד הגדרת מודל ממשתמשים. אתה יכול להשתמש TensorFlow APIs לציין איך לאמן מודל ולשמור אותו בפורמט מודל _saved.
  • Pusher: מעתיק את המודל המאומן מחוץ לצינור TFX. רכיב Pusher יכול להיחשב תהליך פריסה של מודל ML המאומן.

לפני הגדרת הצינור בפועל, עלינו לכתוב תחילה קוד דגם עבור רכיב ה-Trainer.

כתוב קוד אימון מודל

ניצור מודל DNN פשוט לסיווג באמצעות TensorFlow Keras API. קוד הדרכה של דגם זה יישמר בקובץ נפרד.

במדריך זה נשתמש Generic מאמן של TFX התומכים מודלים מבוססי Keras. אתה צריך לכתוב קובץ Python המכיל run_fn פונקציה, המהווה את נקודת הכניסה עבור Trainer רכיב.

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

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 import v1 as tfx
from tfx_bsl.public import tfxio
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: tfx.components.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,
      tfxio.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: tfx.components.FnArgs):
  """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

כעת השלמת את כל שלבי ההכנה לבניית צינור TFX.

כתוב הגדרת צינור

אנו מגדירים פונקציה ליצירת צינור TFX. Pipeline אובייקט מייצג צינור TFX אשר ניתן להפעיל באמצעות אחת ממערכות תזמור צינור תומך TFX.

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))

  # Pushes the model to a filesystem destination.
  pusher = tfx.components.Pusher(
      model=trainer.outputs['model'],
      push_destination=tfx.proto.PushDestination(
          filesystem=tfx.proto.PushDestination.Filesystem(
              base_directory=serving_model_dir)))

  # Following three components will be included in the pipeline.
  components = [
      example_gen,
      trainer,
      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)

הפעל את הצינור

TFX תומך במספר מתזמרים להפעלת צינורות. במדריך זה נשתמש LocalDagRunner אשר נכללת צינורות TFX Python חבילה והוא פועל על הסביבה המקומית. לעתים קרובות אנו קוראים לצינורות TFX "DAGs" אשר מייצג גרף א-ציקלי מכוון.

LocalDagRunner מספק חזרות מהירות developemnt וניפוי שגיאות. TFX תומך גם במתזמרים אחרים כולל Kubeflow Pipelines ו- Apache Airflow המתאימים למקרי שימוש בהפקה.

ראה TFX על ענן AI פלטפורמת צנרת או TFX Airflow הדרכה כדי ללמוד עוד על מערכות תזמור אחרות.

עכשיו אנחנו יוצרים LocalDagRunner ולהעביר Pipeline אובייקט שנוצר מפונקציית אנחנו כבר מוגדרות.

הצינור פועל ישירות וניתן לראות יומנים להתקדמות הצינור כולל אימון מודל ML.

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 a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmp/tmp28n_co8j/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmpfb02sbta', '--dist-dir', '/tmp/tmpyu7gi15_']
/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-simple/_wheels/tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc-py3-none-any.whl'; target user module is 'penguin_trainer'.
INFO:absl:Full user module path is 'penguin_trainer@pipelines/penguin-simple/_wheels/tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc-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: "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-simple/metadata.db"
    connection_mode: READWRITE_OPENCREATE
  }
}

INFO:absl:Using connection config:
 sqlite {
  filename_uri: "metadata/penguin-simple/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-simple"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-12-05T10:44:06.706974"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-simple.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-dataijanq9u3"
      }
    }
  }
  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: "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/tmpfb02sbta
running install
running install_lib
copying build/lib/penguin_trainer.py -> /tmp/tmpfb02sbta
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/tmpfb02sbta/tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc-py3.7.egg-info
running install_scripts
creating /tmp/tmpfb02sbta/tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc.dist-info/WHEEL
creating '/tmp/tmpyu7gi15_/tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc-py3-none-any.whl' and adding '/tmp/tmpfb02sbta' to it
adding 'penguin_trainer.py'
adding 'tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc.dist-info/METADATA'
adding 'tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc.dist-info/WHEEL'
adding 'tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc.dist-info/top_level.txt'
adding 'tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc.dist-info/RECORD'
removing /tmp/tmpfb02sbta
WARNING: Logging before InitGoogleLogging() is written to STDERR
I1205 10:44:07.061197 30480 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 10:44:07.067816 30480 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 10:44:07.074599 30480 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 10:44:07.081624 30480 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
INFO:absl:Going to run a new execution 1
I1205 10:44:07.136307 30480 rdbms_metadata_access_object.cc:686] No property is defined for the Type
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-simple/CsvExampleGen/examples/1"
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)]}), exec_properties={'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-dataijanq9u3', 'input_config': '{\n  "splits": [\n    {\n      "name": "single_split",\n      "pattern": "*"\n    }\n  ]\n}', 'output_file_format': 5, 'output_data_format': 6, 'span': 0, 'version': None, 'input_fingerprint': 'split:single_split,num_files:1,total_bytes:25648,xor_checksum:1638701046,sum_checksum:1638701046'}, execution_output_uri='pipelines/penguin-simple/CsvExampleGen/.system/executor_execution/1/executor_output.pb', stateful_working_dir='pipelines/penguin-simple/CsvExampleGen/.system/stateful_working_dir/2021-12-05T10:44:06.706974', tmp_dir='pipelines/penguin-simple/CsvExampleGen/.system/executor_execution/1/.temp/', pipeline_node=node_info {
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contexts {
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outputs {
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      field_value {
        string_value: "/tmp/tfx-dataijanq9u3"
      }
    }
  }
  parameters {
    key: "input_config"
    value {
      field_value {
        string_value: "{\n  \"splits\": [\n    {\n      \"name\": \"single_split\",\n      \"pattern\": \"*\"\n    }\n  ]\n}"
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    key: "output_config"
    value {
      field_value {
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  parameters {
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    value {
      field_value {
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}
downstream_nodes: "Trainer"
execution_options {
  caching_options {
  }
}
, pipeline_info=id: "penguin-simple"
, pipeline_run_id='2021-12-05T10:44:06.706974')
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-dataijanq9u3/* 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-simple/CsvExampleGen/examples/1"
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custom_properties {
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properties {
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INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component CsvExampleGen is finished.
INFO:absl:Component Trainer is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.components.trainer.component.Trainer"
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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 10:44:08.274386 30480 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Going to run a new execution 2
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=2, input_dict={'examples': [Artifact(artifact: id: 1
type_id: 15
uri: "pipelines/penguin-simple/CsvExampleGen/examples/1"
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custom_properties {
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custom_properties {
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custom_properties {
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state: LIVE
create_time_since_epoch: 1638701048257
last_update_time_since_epoch: 1638701048257
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properties {
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inputs {
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        context_queries {
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        context_queries {
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outputs {
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upstream_nodes: "CsvExampleGen"
downstream_nodes: "Pusher"
execution_options {
  caching_options {
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, pipeline_info=id: "penguin-simple"
, pipeline_run_id='2021-12-05T10:44:06.706974')
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 {'custom_config': 'null', 'module_path': 'penguin_trainer@pipelines/penguin-simple/_wheels/tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc-py3-none-any.whl', 'train_args': '{\n  "num_steps": 100\n}', 'eval_args': '{\n  "num_steps": 5\n}'} 'run_fn'
INFO:absl:Installing 'pipelines/penguin-simple/_wheels/tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmp9yk6w_js', 'pipelines/penguin-simple/_wheels/tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc-py3-none-any.whl']
Processing ./pipelines/penguin-simple/_wheels/tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc-py3-none-any.whl
INFO:absl:Successfully installed 'pipelines/penguin-simple/_wheels/tfx_user_code_Trainer-0.0+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc-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+a7e2e8dccbb913b74904edeec5549d868a2ea392bcd84fbc1965aba698dce3fc
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: 0.4074 - sparse_categorical_accuracy: 0.8755 - val_loss: 0.0760 - val_sparse_categorical_accuracy: 0.9800
2021-12-05 10:44:13.263941: 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-simple/Trainer/model/2/Format-Serving/assets
INFO:tensorflow:Assets written to: pipelines/penguin-simple/Trainer/model/2/Format-Serving/assets
INFO:absl:Training complete. Model written to pipelines/penguin-simple/Trainer/model/2/Format-Serving. ModelRun written to pipelines/penguin-simple/Trainer/model_run/2
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 2 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'model': [Artifact(artifact: uri: "pipelines/penguin-simple/Trainer/model/2"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-simple:2021-12-05T10:44:06.706974: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"
)], 'model_run': [Artifact(artifact: uri: "pipelines/penguin-simple/Trainer/model_run/2"
custom_properties {
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custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.4.0"
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}
, artifact_type: name: "ModelRun"
)]}) for execution 2
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component Trainer is finished.
I1205 10:44:13.795414 30480 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Component Pusher is running.
I1205 10:44:13.799805 30480 rdbms_metadata_access_object.cc:686] No property is defined for the Type
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-simple"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-12-05T10:44:06.706974"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-simple.Pusher"
      }
    }
  }
}
inputs {
  inputs {
    key: "model"
    value {
      channels {
        producer_node_query {
          id: "Trainer"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "penguin-simple"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2021-12-05T10:44:06.706974"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "penguin-simple.Trainer"
            }
          }
        }
        artifact_query {
          type {
            name: "Model"
          }
        }
        output_key: "model"
      }
    }
  }
}
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-simple\"\n  }\n}"
      }
    }
  }
}
upstream_nodes: "Trainer"
execution_options {
  caching_options {
  }
}

INFO:absl:MetadataStore with DB connection initialized
I1205 10:44:13.821346 30480 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 3
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=3, input_dict={'model': [Artifact(artifact: id: 2
type_id: 17
uri: "pipelines/penguin-simple/Trainer/model/2"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-simple:2021-12-05T10:44:06.706974:Trainer:model:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.4.0"
  }
}
state: LIVE
create_time_since_epoch: 1638701053803
last_update_time_since_epoch: 1638701053803
, artifact_type: id: 17
name: "Model"
)]}, output_dict=defaultdict(<class 'list'>, {'pushed_model': [Artifact(artifact: uri: "pipelines/penguin-simple/Pusher/pushed_model/3"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-simple:2021-12-05T10:44:06.706974:Pusher:pushed_model:0"
  }
}
, artifact_type: name: "PushedModel"
)]}), exec_properties={'push_destination': '{\n  "filesystem": {\n    "base_directory": "serving_model/penguin-simple"\n  }\n}', 'custom_config': 'null'}, execution_output_uri='pipelines/penguin-simple/Pusher/.system/executor_execution/3/executor_output.pb', stateful_working_dir='pipelines/penguin-simple/Pusher/.system/stateful_working_dir/2021-12-05T10:44:06.706974', tmp_dir='pipelines/penguin-simple/Pusher/.system/executor_execution/3/.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-simple"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-12-05T10:44:06.706974"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-simple.Pusher"
      }
    }
  }
}
inputs {
  inputs {
    key: "model"
    value {
      channels {
        producer_node_query {
          id: "Trainer"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "penguin-simple"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2021-12-05T10:44:06.706974"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "penguin-simple.Trainer"
            }
          }
        }
        artifact_query {
          type {
            name: "Model"
          }
        }
        output_key: "model"
      }
    }
  }
}
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-simple\"\n  }\n}"
      }
    }
  }
}
upstream_nodes: "Trainer"
execution_options {
  caching_options {
  }
}
, pipeline_info=id: "penguin-simple"
, pipeline_run_id='2021-12-05T10:44:06.706974')
WARNING:absl:Pusher is going to push the model without validation. Consider using Evaluator or InfraValidator in your pipeline.
INFO:absl:Model version: 1638701053
INFO:absl:Model written to serving path serving_model/penguin-simple/1638701053.
INFO:absl:Model pushed to pipelines/penguin-simple/Pusher/pushed_model/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'>, {'pushed_model': [Artifact(artifact: uri: "pipelines/penguin-simple/Pusher/pushed_model/3"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-simple:2021-12-05T10:44:06.706974:Pusher:pushed_model:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.4.0"
  }
}
, artifact_type: name: "PushedModel"
)]}) for execution 3
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component Pusher is finished.
I1205 10:44:13.851651 30480 rdbms_metadata_access_object.cc:686] No property is defined for the Type

אתה אמור לראות "INFO:absl:Pusher Component הסתיים." בסוף היומנים אם הצינור הסתיים בהצלחה. מכיוון Pusher רכיב הוא הרכיב האחרון של הצינור.

מרכיב דחפן דוחף את המודל המודרך אל SERVING_MODEL_DIR המהווה את serving_model/penguin-simple הספרייה אם לא שינית את משתנה בשלבים הקודמים. אתה יכול לראות את התוצאה מדפדפן הקבצים בחלונית השמאלית ב-Colab, או באמצעות הפקודה הבאה:

# List files in created model directory.
find {SERVING_MODEL_DIR}
serving_model/penguin-simple
serving_model/penguin-simple/1638701053
serving_model/penguin-simple/1638701053/keras_metadata.pb
serving_model/penguin-simple/1638701053/assets
serving_model/penguin-simple/1638701053/variables
serving_model/penguin-simple/1638701053/variables/variables.data-00000-of-00001
serving_model/penguin-simple/1638701053/variables/variables.index
serving_model/penguin-simple/1638701053/saved_model.pb

הצעדים הבאים

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

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