צור צינור TFX עבור הנתונים שלך עם תבנית Penguin

הצג באתר TensorFlow.org

הפעל בגוגל קולאב

צפה במקור ב-GitHub

הורד מחברת

מבוא

מסמך זה יספק הנחיות ליצירת צינור TensorFlow Extended (TFX) עבור מערך הנתונים שלך באמצעות תבנית פינגווין אשר מסופקת עם חבילת TFX Python. צינור שנוצר ישתמש בתחילה במערך הנתונים של Palmer Penguins , אך אנו נשנה את הצינור עבור מערך הנתונים שלך.

דרישות מוקדמות

• לינוקס / MacOS
• Python 3.6-3.8
• מחברת Jupyter

שלב 1. העתק את התבנית המוגדרת מראש לספריית הפרויקט שלך.

בשלב זה, ניצור ספריה וקבצים של פרויקט צינור עובד על ידי העתקת קבצים מתבנית פינגווין ב-TFX. אתה יכול לחשוב על זה כעל פיגום לפרויקט צינור ה-TFX שלך.

עדכן את פיפ

אם אנחנו פועלים ב-Colab, עלינו לוודא שיש לנו את הגרסה העדכנית ביותר של Pip. ניתן כמובן לעדכן מערכות מקומיות בנפרד.

import sys
if 'google.colab' in sys.modules:
  !pip install --upgrade pip

התקן את החבילה הנדרשת

ראשית, התקן את TFX ו-TensorFlow Model Analysis (TFMA).

pip install -U tfx tensorflow-model-analysis

בוא נבדוק את הגרסאות של TFX.

import tensorflow as tf
import tensorflow_model_analysis as tfma
import tfx

print('TF version: {}'.format(tf.__version__))
print('TFMA version: {}'.format(tfma.__version__))
print('TFX version: {}'.format(tfx.__version__))

TF version: 2.7.1
TFMA version: 0.37.0
TFX version: 1.6.0

אנחנו מוכנים ליצור צינור.

הגדר את PROJECT_DIR ליעד המתאים לסביבה שלך. ערך ברירת המחדל הוא ~/imported/${PIPELINE_NAME} המתאים לסביבת Google Cloud AI Platform Notebook .

אתה יכול לתת לצינור שלך שם אחר על ידי שינוי ה- PIPELINE_NAME למטה. זה יהפוך גם לשם של ספריית הפרויקט שבה יוכנסו הקבצים שלך.

PIPELINE_NAME="my_pipeline"
import os
# Set this project directory to your new tfx pipeline project.
PROJECT_DIR=os.path.join(os.path.expanduser("~"), "imported", PIPELINE_NAME)

העתק קבצי תבניות.

TFX כולל את תבנית penguin עם חבילת TFX python. תבנית penguin מכילה הוראות רבות להכנסת מערך הנתונים שלך לצנרת וזו המטרה של מדריך זה.

הפקודה tfx template copy CLI מעתיקה קבצי תבנית מוגדרים מראש לספריית הפרויקט שלך.

# Set `PATH` to include user python binary directory and a directory containing `skaffold`.
PATH=%env PATH
%env PATH={PATH}:/home/jupyter/.local/bin

!tfx template copy \
  --pipeline-name={PIPELINE_NAME} \
  --destination-path={PROJECT_DIR} \
  --model=penguin

env: PATH=/tmpfs/src/tf_docs_env/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin:/opt/puppetlabs/bin:/opt/android-studio/current/bin:/usr/local/go/bin:/usr/local/go/packages/bin:/opt/kubernetes/client/bin/:/home/kbuilder/.local/bin:/home/jupyter/.local/bin
CLI
Copying penguin pipeline template
kubeflow_runner.py -> /home/kbuilder/imported/my_pipeline/kubeflow_runner.py
configs.py -> /home/kbuilder/imported/my_pipeline/pipeline/configs.py
pipeline.py -> /home/kbuilder/imported/my_pipeline/pipeline/pipeline.py
__init__.py -> /home/kbuilder/imported/my_pipeline/pipeline/__init__.py
model.py -> /home/kbuilder/imported/my_pipeline/models/model.py
features.py -> /home/kbuilder/imported/my_pipeline/models/features.py
features_test.py -> /home/kbuilder/imported/my_pipeline/models/features_test.py
preprocessing_test.py -> /home/kbuilder/imported/my_pipeline/models/preprocessing_test.py
preprocessing.py -> /home/kbuilder/imported/my_pipeline/models/preprocessing.py
model_test.py -> /home/kbuilder/imported/my_pipeline/models/model_test.py
__init__.py -> /home/kbuilder/imported/my_pipeline/models/__init__.py
constants.py -> /home/kbuilder/imported/my_pipeline/models/constants.py
local_runner.py -> /home/kbuilder/imported/my_pipeline/local_runner.py
__init__.py -> /home/kbuilder/imported/my_pipeline/__init__.py

שנה את ההקשר של ספריית העבודה במחברת זו לספריית הפרויקט.

%cd {PROJECT_DIR}

/home/kbuilder/imported/my_pipeline

עיין בקבצי המקור שהועתקו

תבנית TFX מספקת קבצי פיגום בסיסיים לבניית צינור, כולל קוד מקור של Python ונתונים לדוגמה. תבנית penguin משתמשת באותו מערך נתונים ומודל ML של Palmer Penguins כמו הדוגמה של Penguin .

הנה מבוא קצר לכל אחד מקבצי Python.

• pipeline - ספרייה זו מכילה את ההגדרה של הצינור
  • configs.py - מגדיר קבועים נפוצים עבור רצי צינורות
  • pipeline.py - מגדיר רכיבי TFX וצינור
• models - ספרייה זו מכילה הגדרות מודל ML
  • features.py , features_test.py - מגדיר תכונות עבור הדגם
  • preprocessing.py , preprocessing_test.py - מגדיר שגרות עיבוד מקדים לנתונים
  • constants.py - מגדיר קבועים של המודל
  • model.py , model_test.py - מגדיר מודל ML באמצעות מסגרות ML כמו TensorFlow
• local_runner.py - הגדר רץ לסביבה מקומית המשתמש במנוע תזמור מקומי
• kubeflow_runner.py - הגדר רץ עבור מנוע התזמור של Kubeflow Pipelines

כברירת מחדל, התבנית כוללת רק רכיבי TFX סטנדרטיים. אם אתה צריך כמה פעולות מותאמות אישית, אתה יכול ליצור רכיבים מותאמים אישית עבור הצינור שלך. אנא עיין במדריך הרכיבים המותאם אישית של TFX לפרטים.

קבצי בדיקת יחידות.

ייתכן שתבחין שיש כמה קבצים עם _test.py בשמם. אלו הן בדיקות יחידה של הצינור ומומלץ להוסיף בדיקות יחידות נוספות ככל שתטמיעו צינורות משלכם. אתה יכול להריץ בדיקות יחידה על ידי אספקת שם המודול של קבצי הבדיקה עם דגל -m . בדרך כלל תוכל לקבל שם מודול על ידי מחיקת סיומת .py והחלפת / עם . . לדוגמה:

import sys
!{sys.executable} -m models.features_test

Running tests under Python 3.7.5: /tmpfs/src/tf_docs_env/bin/python
[ RUN      ] FeaturesTest.testLabelKey
INFO:tensorflow:time(__main__.FeaturesTest.testLabelKey): 0.0s
I0203 11:08:46.306882 140258321348416 test_util.py:2309] time(__main__.FeaturesTest.testLabelKey): 0.0s
[       OK ] FeaturesTest.testLabelKey
[ RUN      ] FeaturesTest.test_session
[  SKIPPED ] FeaturesTest.test_session
----------------------------------------------------------------------
Ran 2 tests in 0.001s

OK (skipped=1)

צור צינור TFX בסביבה מקומית.

TFX תומך במספר מנועי תזמור להפעלת צינורות. נשתמש במנוע תזמור מקומי. מנוע התזמור המקומי פועל ללא תלות נוספת, והוא מתאים לפיתוח וניפוי באגים מכיוון שהוא פועל על סביבה מקומית ולא תלוי באשכולות מחשוב מרוחקים.

אנו נשתמש ב- local_runner.py כדי להפעיל את הצינור שלך באמצעות מתזמר מקומי. עליך ליצור צינור לפני הפעלתו. אתה יכול ליצור צינור עם פקודת pipeline create .

tfx pipeline create --engine=local --pipeline_path=local_runner.py

CLI
Creating pipeline
INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Excluding no splits because exclude_splits is not set.
Pipeline "my_pipeline" created successfully.

הפקודה pipeline create רושמת את הצינור שלך שהוגדר ב- local_runner.py מבלי להפעיל אותו בפועל.

אתה תפעיל את הצינור שנוצר עם הפקודה run create בשלבים הבאים.

שלב 2. הכנס את הנתונים שלך לצינור.

הצינור הראשוני קולט את מערך הנתונים של הפינגווין הכלול בתבנית. אתה צריך להכניס את הנתונים שלך לצינור, ורוב הצינורות של TFX מתחילים ברכיב ExampleGen.

בחר ExampleGen

ניתן לאחסן את הנתונים שלך בכל מקום שהצינור שלך יכול לגשת אליו, במערכת קבצים מקומית או מבוזרת, או במערכת המאפשרת שאילתות. TFX מספק רכיבים שונים של ExampleGen כדי להביא את הנתונים שלך לתוך צינור TFX. אתה יכול לבחור אחד מבין רכיבי יצירת הדוגמאות הבאות.

• CsvExampleGen: קורא קבצי CSV בספרייה. משמש בדוגמה של פינגווין ובדוגמה של מונית בשיקגו .
• ImportExampleGen: לוקח קבצי TFRecord עם פורמט נתונים לדוגמה של TF. משמש בדוגמאות של MNIST .
• FileBasedExampleGen עבור פורמט Avro או פרקט .
• BigQueryExampleGen : קורא נתונים ב-Google Cloud BigQuery ישירות. משמש בדוגמאות למוניות בשיקגו .

אתה יכול גם ליצור ExampleGen משלך, לדוגמה, tfx כולל ExecampleGen מותאם אישית שמשתמש ב-Presto כמקור נתונים. עיין במדריך למידע נוסף על אופן השימוש והפיתוח של מבצעים מותאמים אישית.

לאחר שתחליט באיזה ExampleGen להשתמש, תצטרך לשנות את הגדרת הצינור כדי להשתמש בנתונים שלך.

1. שנה את ה- DATA_PATH ב- local_runner.py והגדר אותו למיקום הקבצים שלך.

   • אם יש לך קבצים בסביבה מקומית, ציין את הנתיב. זוהי האפשרות הטובה ביותר לפיתוח או איתור באגים של צינור.
   • אם הקבצים מאוחסנים ב-GCS, אתה יכול להשתמש בנתיב שמתחיל ב- gs://{bucket_name}/... . אנא ודא שאתה יכול לגשת ל-GCS מהמסוף שלך, למשל, באמצעות gsutil . אנא עקוב אחר מדריך ההרשאות ב-Google Cloud במידת הצורך.
   • אם אתה רוצה להשתמש ב-ExampleGen מבוסס שאילתות כמו BigQueryExampleGen, אתה צריך הצהרת Query כדי לבחור נתונים ממקור הנתונים. יש עוד כמה דברים שעליך להגדיר כדי להשתמש ב-Google Cloud BigQuery כמקור נתונים.
   • ב- pipeline/configs.py :
     • שנה את GOOGLE_CLOUD_PROJECT ואת GCS_BUCKET_NAME לפרויקט ה-GCP ושם הדלי שלך. הדלי צריך להתקיים לפני שנפעיל את הצינור.
     • בטל הערה למשתנה BIG_QUERY_WITH_DIRECT_RUNNER_BEAM_PIPELINE_ARGS .
     • בטל הערה והגדר משתנה BIG_QUERY_QUERY השאילתה שלך .
   • ב- local_runner.py :
     • הערה את ארגומנט data_path וארגומנט query ביטול הערה במקום זאת ב- pipeline.create_pipeline() .
   • ב- pipeline/pipeline.py :
     • הערה את ארגומנט data_path וארגומנט query ביטול הערה ב- create_pipeline() .
     • השתמש ב- BigQueryExampleGen במקום ב-CsvExampleGen.

2. החלף את CsvExampleGen הקיים למחלקה שלך ב-ExampleGen ב- pipeline/pipeline.py . לכל מחלקה של ExampleGen יש חתימה שונה. אנא עיין במדריך רכיבי ExampleGen לפרטים נוספים. אל תשכח לייבא מודולים נדרשים עם הצהרות import ב- pipeline/pipeline.py .

הצינור הראשוני מורכב מארבעה רכיבים, ExampleGen , StatisticsGen , SchemaGen ו- ExampleValidator . אנחנו לא צריכים לשנות שום דבר עבור StatisticsGen , SchemaGen ו- ExampleValidator . בואו נריץ את הצינור בפעם הראשונה.

# Update and run the pipeline.
!tfx pipeline update --engine=local --pipeline_path=local_runner.py \
 && tfx run create --engine=local --pipeline_name={PIPELINE_NAME}

CLI
Updating pipeline
INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Excluding no splits because exclude_splits is not set.
Pipeline "my_pipeline" updated successfully.
CLI
Creating a run for pipeline: my_pipeline
INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Excluding no splits because exclude_splits is not set.
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: "SchemaGen"
  value {
    python_class_executable_spec {
      class_path: "tfx.components.schema_gen.executor.Executor"
    }
  }
}
executor_specs {
  key: "StatisticsGen"
  value {
    beam_executable_spec {
      python_executor_spec {
        class_path: "tfx.components.statistics_gen.executor.Executor"
      }
    }
  }
}
custom_driver_specs {
  key: "CsvExampleGen"
  value {
    python_class_executable_spec {
      class_path: "tfx.components.example_gen.driver.FileBasedDriver"
    }
  }
}
metadata_connection_config {
  database_connection_config {
    sqlite {
      filename_uri: "./tfx_metadata/my_pipeline/metadata.db"
      connection_mode: READWRITE_OPENCREATE
    }
  }
}

INFO:absl:Using connection config:
 sqlite {
  filename_uri: "./tfx_metadata/my_pipeline/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: "my_pipeline"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2022-02-03T11:09:12.120566"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "my_pipeline.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
          }
          base_type: DATASET
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "input_base"
    value {
      field_value {
        string_value: "/home/kbuilder/imported/my_pipeline/data"
      }
    }
  }
  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: "StatisticsGen"
execution_options {
  caching_options {
  }
}

INFO:absl:MetadataStore with DB connection initialized
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
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=1, input_dict={}, output_dict=defaultdict(<class 'list'>, {'examples': [Artifact(artifact: uri: "./tfx_pipeline_output/my_pipeline/CsvExampleGen/examples/1"
custom_properties {
  key: "input_fingerprint"
  value {
    string_value: "split:single_split,num_files:1,total_bytes:25648,xor_checksum:1643886522,sum_checksum:1643886522"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:09:12.120566:CsvExampleGen:examples:0"
  }
}
custom_properties {
  key: "span"
  value {
    int_value: 0
  }
}
, artifact_type: name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
base_type: DATASET
)]}), exec_properties={'input_config': '{\n  "splits": [\n    {\n      "name": "single_split",\n      "pattern": "*"\n    }\n  ]\n}', 'output_data_format': 6, '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_base': '/home/kbuilder/imported/my_pipeline/data', 'span': 0, 'version': None, 'input_fingerprint': 'split:single_split,num_files:1,total_bytes:25648,xor_checksum:1643886522,sum_checksum:1643886522'}, execution_output_uri='./tfx_pipeline_output/my_pipeline/CsvExampleGen/.system/executor_execution/1/executor_output.pb', stateful_working_dir='./tfx_pipeline_output/my_pipeline/CsvExampleGen/.system/stateful_working_dir/2022-02-03T11:09:12.120566', tmp_dir='./tfx_pipeline_output/my_pipeline/CsvExampleGen/.system/executor_execution/1/.temp/', pipeline_node=node_info {
  type {
    name: "tfx.components.example_gen.csv_example_gen.component.CsvExampleGen"
  }
  id: "CsvExampleGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "my_pipeline"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2022-02-03T11:09:12.120566"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "my_pipeline.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
          }
          base_type: DATASET
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "input_base"
    value {
      field_value {
        string_value: "/home/kbuilder/imported/my_pipeline/data"
      }
    }
  }
  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: "StatisticsGen"
execution_options {
  caching_options {
  }
}
, pipeline_info=id: "my_pipeline"
, pipeline_run_id='2022-02-03T11:09:12.120566')
INFO:absl:Generating examples.
INFO:absl:Processing input csv data /home/kbuilder/imported/my_pipeline/data/* to TFExample.
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
E0203 11:09:12.848598153    5127 fork_posix.cc:70]           Fork support is only compatible with the epoll1 and poll polling strategies
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:Value type <class 'NoneType'> of key version in exec_properties is not supported, going to drop it
INFO:absl:Value type <class 'list'> of key _beam_pipeline_args in exec_properties is not supported, going to drop it
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: "./tfx_pipeline_output/my_pipeline/CsvExampleGen/examples/1"
custom_properties {
  key: "input_fingerprint"
  value {
    string_value: "split:single_split,num_files:1,total_bytes:25648,xor_checksum:1643886522,sum_checksum:1643886522"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:09:12.120566:CsvExampleGen:examples:0"
  }
}
custom_properties {
  key: "span"
  value {
    int_value: 0
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.6.0"
  }
}
, artifact_type: name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
base_type: DATASET
)]}) for execution 1
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component CsvExampleGen is finished.
INFO:absl:Component StatisticsGen is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.components.statistics_gen.component.StatisticsGen"
    base_type: PROCESS
  }
  id: "StatisticsGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "my_pipeline"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2022-02-03T11:09:12.120566"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "my_pipeline.StatisticsGen"
      }
    }
  }
}
inputs {
  inputs {
    key: "examples"
    value {
      channels {
        producer_node_query {
          id: "CsvExampleGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "my_pipeline"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2022-02-03T11:09:12.120566"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "my_pipeline.CsvExampleGen"
            }
          }
        }
        artifact_query {
          type {
            name: "Examples"
            base_type: DATASET
          }
        }
        output_key: "examples"
      }
      min_count: 1
    }
  }
}
outputs {
  outputs {
    key: "statistics"
    value {
      artifact_spec {
        type {
          name: "ExampleStatistics"
          properties {
            key: "span"
            value: INT
          }
          properties {
            key: "split_names"
            value: STRING
          }
          base_type: STATISTICS
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "exclude_splits"
    value {
      field_value {
        string_value: "[]"
      }
    }
  }
}
upstream_nodes: "CsvExampleGen"
downstream_nodes: "SchemaGen"
execution_options {
  caching_options {
  }
}

INFO:absl:MetadataStore with DB connection initialized
INFO:absl:MetadataStore with DB connection initialized
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: "./tfx_pipeline_output/my_pipeline/CsvExampleGen/examples/1"
properties {
  key: "split_names"
  value {
    string_value: "[\"train\", \"eval\"]"
  }
}
custom_properties {
  key: "file_format"
  value {
    string_value: "tfrecords_gzip"
  }
}
custom_properties {
  key: "input_fingerprint"
  value {
    string_value: "split:single_split,num_files:1,total_bytes:25648,xor_checksum:1643886522,sum_checksum:1643886522"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:09:12.120566:CsvExampleGen:examples:0"
  }
}
custom_properties {
  key: "payload_format"
  value {
    string_value: "FORMAT_TF_EXAMPLE"
  }
}
custom_properties {
  key: "span"
  value {
    int_value: 0
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.6.0"
  }
}
state: LIVE
create_time_since_epoch: 1643886553302
last_update_time_since_epoch: 1643886553302
, artifact_type: id: 15
name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
base_type: DATASET
)]}, output_dict=defaultdict(<class 'list'>, {'statistics': [Artifact(artifact: uri: "./tfx_pipeline_output/my_pipeline/StatisticsGen/statistics/2"
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:09:12.120566:StatisticsGen:statistics:0"
  }
}
, artifact_type: name: "ExampleStatistics"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
base_type: STATISTICS
)]}), exec_properties={'exclude_splits': '[]'}, execution_output_uri='./tfx_pipeline_output/my_pipeline/StatisticsGen/.system/executor_execution/2/executor_output.pb', stateful_working_dir='./tfx_pipeline_output/my_pipeline/StatisticsGen/.system/stateful_working_dir/2022-02-03T11:09:12.120566', tmp_dir='./tfx_pipeline_output/my_pipeline/StatisticsGen/.system/executor_execution/2/.temp/', pipeline_node=node_info {
  type {
    name: "tfx.components.statistics_gen.component.StatisticsGen"
    base_type: PROCESS
  }
  id: "StatisticsGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "my_pipeline"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2022-02-03T11:09:12.120566"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "my_pipeline.StatisticsGen"
      }
    }
  }
}
inputs {
  inputs {
    key: "examples"
    value {
      channels {
        producer_node_query {
          id: "CsvExampleGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "my_pipeline"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2022-02-03T11:09:12.120566"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "my_pipeline.CsvExampleGen"
            }
          }
        }
        artifact_query {
          type {
            name: "Examples"
            base_type: DATASET
          }
        }
        output_key: "examples"
      }
      min_count: 1
    }
  }
}
outputs {
  outputs {
    key: "statistics"
    value {
      artifact_spec {
        type {
          name: "ExampleStatistics"
          properties {
            key: "span"
            value: INT
          }
          properties {
            key: "split_names"
            value: STRING
          }
          base_type: STATISTICS
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "exclude_splits"
    value {
      field_value {
        string_value: "[]"
      }
    }
  }
}
upstream_nodes: "CsvExampleGen"
downstream_nodes: "SchemaGen"
execution_options {
  caching_options {
  }
}
, pipeline_info=id: "my_pipeline"
, pipeline_run_id='2022-02-03T11:09:12.120566')
INFO:absl:Generating statistics for split train.
INFO:absl:Statistics for split train written to ./tfx_pipeline_output/my_pipeline/StatisticsGen/statistics/2/Split-train.
INFO:absl:Generating statistics for split eval.
INFO:absl:Statistics for split eval written to ./tfx_pipeline_output/my_pipeline/StatisticsGen/statistics/2/Split-eval.
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
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'>, {'statistics': [Artifact(artifact: uri: "./tfx_pipeline_output/my_pipeline/StatisticsGen/statistics/2"
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:09:12.120566:StatisticsGen:statistics:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.6.0"
  }
}
, artifact_type: name: "ExampleStatistics"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
base_type: STATISTICS
)]}) for execution 2
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component StatisticsGen is finished.
INFO:absl:Component SchemaGen is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.components.schema_gen.component.SchemaGen"
    base_type: PROCESS
  }
  id: "SchemaGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "my_pipeline"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2022-02-03T11:09:12.120566"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "my_pipeline.SchemaGen"
      }
    }
  }
}
inputs {
  inputs {
    key: "statistics"
    value {
      channels {
        producer_node_query {
          id: "StatisticsGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "my_pipeline"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2022-02-03T11:09:12.120566"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "my_pipeline.StatisticsGen"
            }
          }
        }
        artifact_query {
          type {
            name: "ExampleStatistics"
            base_type: STATISTICS
          }
        }
        output_key: "statistics"
      }
      min_count: 1
    }
  }
}
outputs {
  outputs {
    key: "schema"
    value {
      artifact_spec {
        type {
          name: "Schema"
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "exclude_splits"
    value {
      field_value {
        string_value: "[]"
      }
    }
  }
  parameters {
    key: "infer_feature_shape"
    value {
      field_value {
        int_value: 1
      }
    }
  }
}
upstream_nodes: "StatisticsGen"
execution_options {
  caching_options {
  }
}

INFO:absl:MetadataStore with DB connection initialized
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={'statistics': [Artifact(artifact: id: 2
type_id: 17
uri: "./tfx_pipeline_output/my_pipeline/StatisticsGen/statistics/2"
properties {
  key: "split_names"
  value {
    string_value: "[\"train\", \"eval\"]"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:09:12.120566:StatisticsGen:statistics:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.6.0"
  }
}
state: LIVE
create_time_since_epoch: 1643886556588
last_update_time_since_epoch: 1643886556588
, artifact_type: id: 17
name: "ExampleStatistics"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
base_type: STATISTICS
)]}, output_dict=defaultdict(<class 'list'>, {'schema': [Artifact(artifact: uri: "./tfx_pipeline_output/my_pipeline/SchemaGen/schema/3"
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:09:12.120566:SchemaGen:schema:0"
  }
}
, artifact_type: name: "Schema"
)]}), exec_properties={'infer_feature_shape': 1, 'exclude_splits': '[]'}, execution_output_uri='./tfx_pipeline_output/my_pipeline/SchemaGen/.system/executor_execution/3/executor_output.pb', stateful_working_dir='./tfx_pipeline_output/my_pipeline/SchemaGen/.system/stateful_working_dir/2022-02-03T11:09:12.120566', tmp_dir='./tfx_pipeline_output/my_pipeline/SchemaGen/.system/executor_execution/3/.temp/', pipeline_node=node_info {
  type {
    name: "tfx.components.schema_gen.component.SchemaGen"
    base_type: PROCESS
  }
  id: "SchemaGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "my_pipeline"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2022-02-03T11:09:12.120566"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "my_pipeline.SchemaGen"
      }
    }
  }
}
inputs {
  inputs {
    key: "statistics"
    value {
      channels {
        producer_node_query {
          id: "StatisticsGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "my_pipeline"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2022-02-03T11:09:12.120566"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "my_pipeline.StatisticsGen"
            }
          }
        }
        artifact_query {
          type {
            name: "ExampleStatistics"
            base_type: STATISTICS
          }
        }
        output_key: "statistics"
      }
      min_count: 1
    }
  }
}
outputs {
  outputs {
    key: "schema"
    value {
      artifact_spec {
        type {
          name: "Schema"
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "exclude_splits"
    value {
      field_value {
        string_value: "[]"
      }
    }
  }
  parameters {
    key: "infer_feature_shape"
    value {
      field_value {
        int_value: 1
      }
    }
  }
}
upstream_nodes: "StatisticsGen"
execution_options {
  caching_options {
  }
}
, pipeline_info=id: "my_pipeline"
, pipeline_run_id='2022-02-03T11:09:12.120566')
INFO:absl:Processing schema from statistics for split train.
INFO:absl:Processing schema from statistics for split eval.
INFO:absl:Schema written to ./tfx_pipeline_output/my_pipeline/SchemaGen/schema/3/schema.pbtxt.
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'>, {'schema': [Artifact(artifact: uri: "./tfx_pipeline_output/my_pipeline/SchemaGen/schema/3"
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:09:12.120566:SchemaGen:schema:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.6.0"
  }
}
, artifact_type: name: "Schema"
)]}) for execution 3
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component SchemaGen is finished.

אתה אמור לראות "Component Example Validator הסתיים." אם הצינור פעל בהצלחה.

בדוק את תפוקת הצינור.

צינור TFX מייצר שני סוגים של פלט, חפצים ומטא נתונים DB(MLMD) המכיל מטא נתונים של חפצים וביצוע צינורות. המיקום לפלט מוגדר ב- local_runner.py . כברירת מחדל, חפצים מאוחסנים תחת ספריית tfx_pipeline_output ומטא נתונים מאוחסנים כמסד נתונים sqlite תחת ספריית tfx_metadata .

אתה יכול להשתמש בממשקי API של MLMD כדי לבחון את הפלטים האלה. ראשית, נגדיר כמה פונקציות שירות לחיפוש חפצי פלט שזה עתה נוצרו.

import tensorflow as tf
import tfx
from ml_metadata import errors
from ml_metadata.proto import metadata_store_pb2
from tfx.types import artifact_utils

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

def get_latest_executions(store, pipeline_name, component_id = None):
  """Fetch all pipeline runs."""
  if component_id is None:  # Find entire pipeline runs.
    run_contexts = [
        c for c in store.get_contexts_by_type('run')
        if c.properties['pipeline_name'].string_value == pipeline_name
    ]
  else:  # Find specific component runs.
    run_contexts = [
        c for c in store.get_contexts_by_type('component_run')
        if c.properties['pipeline_name'].string_value == pipeline_name and
           c.properties['component_id'].string_value == component_id
    ]
  if not run_contexts:
    return []
  # Pick the latest run context.
  latest_context = max(run_contexts,
                       key=lambda c: c.last_update_time_since_epoch)
  return store.get_executions_by_context(latest_context.id)

def get_latest_artifacts(store, pipeline_name, component_id = None):
  """Fetch all artifacts from latest pipeline execution."""
  executions = get_latest_executions(store, pipeline_name, component_id)

  # Fetch all artifacts produced from the given executions.
  execution_ids = [e.id for e in executions]
  events = store.get_events_by_execution_ids(execution_ids)
  artifact_ids = [
      event.artifact_id for event in events
      if event.type == metadata_store_pb2.Event.OUTPUT
  ]
  return store.get_artifacts_by_id(artifact_ids)

def find_latest_artifacts_by_type(store, artifacts, artifact_type):
  """Get the latest artifacts of a specified type."""
  # Get type information from MLMD
  try:
    artifact_type = store.get_artifact_type(artifact_type)
  except errors.NotFoundError:
    return []
  # Filter artifacts with type.
  filtered_artifacts = [aritfact for aritfact in artifacts
                        if aritfact.type_id == artifact_type.id]
  # Convert MLMD artifact data into TFX Artifact instances.
  return [artifact_utils.deserialize_artifact(artifact_type, artifact)
      for artifact in filtered_artifacts]


from tfx.orchestration.experimental.interactive import visualizations

def visualize_artifacts(artifacts):
  """Visualizes artifacts using standard visualization modules."""
  for artifact in artifacts:
    visualization = visualizations.get_registry().get_visualization(
        artifact.type_name)
    if visualization:
      visualization.display(artifact)

from tfx.orchestration.experimental.interactive import standard_visualizations
standard_visualizations.register_standard_visualizations()

import pprint

from tfx.orchestration import metadata
from tfx.types import artifact_utils
from tfx.types import standard_artifacts

def preview_examples(artifacts):
  """Preview a few records from Examples artifacts."""
  pp = pprint.PrettyPrinter()
  for artifact in artifacts:
    print("==== Examples artifact:{}({})".format(artifact.name, artifact.uri))
    for split in artifact_utils.decode_split_names(artifact.split_names):
      print("==== Reading from split:{}".format(split))
      split_uri = artifact_utils.get_split_uri([artifact], split)

      # Get the list of files in this directory (all compressed TFRecord files)
      tfrecord_filenames = [os.path.join(split_uri, name)
                            for name in os.listdir(split_uri)]
      # Create a `TFRecordDataset` to read these files
      dataset = tf.data.TFRecordDataset(tfrecord_filenames,
                                        compression_type="GZIP")
      # Iterate over the first 2 records and decode them.
      for tfrecord in dataset.take(2):
        serialized_example = tfrecord.numpy()
        example = tf.train.Example()
        example.ParseFromString(serialized_example)
        pp.pprint(example)

import local_runner

metadata_connection_config = metadata.sqlite_metadata_connection_config(
              local_runner.METADATA_PATH)

כעת אנו יכולים לקרוא מטא נתונים של חפצי פלט מ-MLMD.

with metadata.Metadata(metadata_connection_config) as metadata_handler:
    # Search all aritfacts from the previous pipeline run.
    artifacts = get_latest_artifacts(metadata_handler.store, PIPELINE_NAME)
    # Find artifacts of Examples type.
    examples_artifacts = find_latest_artifacts_by_type(
        metadata_handler.store, artifacts,
        standard_artifacts.Examples.TYPE_NAME)
    # Find artifacts generated from StatisticsGen.
    stats_artifacts = find_latest_artifacts_by_type(
        metadata_handler.store, artifacts,
        standard_artifacts.ExampleStatistics.TYPE_NAME)
    # Find artifacts generated from SchemaGen.
    schema_artifacts = find_latest_artifacts_by_type(
        metadata_handler.store, artifacts,
        standard_artifacts.Schema.TYPE_NAME)
    # Find artifacts generated from ExampleValidator.
    anomalies_artifacts = find_latest_artifacts_by_type(
        metadata_handler.store, artifacts,
        standard_artifacts.ExampleAnomalies.TYPE_NAME)

כעת נוכל לבחון תפוקות מכל רכיב. Tensorflow Data Validation (TFDV) משמש ב- StatisticsGen , SchemaGen ו- ExampleValidator , וניתן להשתמש ב-TFDV כדי להמחיש פלטים ממרכיבים אלה.

במדריך זה, נשתמש בשיטות מסייעות להדמיה ב-TFX המשתמשות ב-TFDV באופן פנימי כדי להציג את ההדמיה. אנא עיין במדריך לרכיבי TFX כדי ללמוד עוד על כל רכיב.

בדוק את טופס הפלט ExampleGen

בואו נבחן פלט מ-ExampleGen. תסתכל על שתי הדוגמאות הראשונות עבור כל פיצול:

preview_examples(examples_artifacts)

כברירת מחדל, TFX ExampleGen מחלק דוגמאות לשני פיצולים, train ו- eval , אבל אתה יכול להתאים את תצורת הפיצול שלך .

בדוק את הפלט מ-StatisticsGen

visualize_artifacts(stats_artifacts)

נתונים סטטיסטיים אלה מסופקים ל- SchemaGen כדי לבנות סכימה של נתונים באופן אוטומטי.

בדוק פלט מ- SchemaGen

visualize_artifacts(schema_artifacts)

סכימה זו מוסקת אוטומטית מהפלט של StatisticsGen. אנו נשתמש בסכימה שנוצרה במדריך זה, אך תוכל גם לשנות ולהתאים אישית את הסכימה .

בדוק פלט מ-ExampleValidator

visualize_artifacts(anomalies_artifacts)

אם נמצאו חריגות כלשהן, אתה יכול לבדוק את הנתונים שלך שכל הדוגמאות עוקבות אחר ההנחות שלך. פלטים מרכיבים אחרים כמו StatistcsGen עשויים להיות שימושיים. חריגות שנמצאו אינן חוסמות את ביצוע הצינור.

אתה יכול לראות את התכונות הזמינות מהפלטים של SchemaGen . אם ניתן להשתמש בתכונות שלך לבניית מודל ML ב- Trainer ישירות, אתה יכול לדלג על השלב הבא וללכת לשלב 4. אחרת תוכל לבצע עבודת הנדסת תכונות בשלב הבא. יש צורך ברכיב Transform כאשר נדרשות פעולות מעבר מלא כמו חישוב ממוצעים, במיוחד כאשר אתה צריך לשנות קנה מידה.

שלב 3. (אופציונלי) הנדסת תכונות עם רכיב טרנספורמציה.

בשלב זה, תגדיר עבודות הנדסת תכונה שונות אשר ישמשו את רכיב Transform בצנרת. למידע נוסף, ראה מדריך רכיב טרנספורמציה .

זה הכרחי רק אם קוד האימון שלך דורש תכונות נוספות שאינן זמינות בפלט של ExampleGen. אחרת, אל תהסס לעבור קדימה לשלב הבא של השימוש ב-Trainer.

הגדירו תכונות של הדגם

models/features.py מכיל קבועים להגדרת תכונות עבור המודל כולל שמות תכונות, גודל אוצר המילים וכן הלאה. כברירת מחדל לתבנית penguin יש שני מחירים, FEATURE_KEYS ו- LABEL_KEY , מכיוון שמודל penguin שלנו פותר בעיית סיווג באמצעות למידה מפוקחת וכל התכונות הן תכונות מספריות רציפות. ראה הגדרות תכונה מדוגמה של מונית שיקגו לדוגמא נוספת.

יישם עיבוד מקדים להדרכה / הגשה ב-preprocessing_fn().

הנדסת תכונות בפועל מתרחשת בפונקציה preprocessing_fn() ב- models/preprocessing.py .

ב- preprocessing_fn אתה יכול להגדיר סדרה של פונקציות המבצעות מניפולציה על dict קלט של טנסורים כדי לייצר את dict הפלט של טנסורים. ישנן פונקציות עוזרות כמו scale_to_0_1 ו- compute_and_apply_vocabulary ב- TensorFlow Transform API או שאתה יכול פשוט להשתמש בפונקציות TensorFlow רגילות. כברירת מחדל, תבנית penguin כוללת שימושים לדוגמה בפונקציית tft.scale_to_z_score לנרמול ערכי תכונה.

עיין במדריך Tensflow Transform למידע נוסף על עריכת preprocessing_fn .

הוסף רכיב טרנספורמציה לצינור.

אם ה-preprocessing_fn שלך מוכן, הוסף את רכיב Transform לצינור.

1. בקובץ pipeline/pipeline.py , בטל את ההערה # components.append(transform) כדי להוסיף את הרכיב לצינור.

אתה יכול לעדכן את הצינור ולהפעיל שוב.

!tfx pipeline update --engine=local --pipeline_path=local_runner.py \
 && tfx run create --engine=local --pipeline_name={PIPELINE_NAME}

CLI
Updating pipeline
INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Excluding no splits because exclude_splits is not set.
Pipeline "my_pipeline" updated successfully.
CLI
Creating a run for pipeline: my_pipeline
INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Excluding no splits because exclude_splits is not set.
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: "SchemaGen"
  value {
    python_class_executable_spec {
      class_path: "tfx.components.schema_gen.executor.Executor"
    }
  }
}
executor_specs {
  key: "StatisticsGen"
  value {
    beam_executable_spec {
      python_executor_spec {
        class_path: "tfx.components.statistics_gen.executor.Executor"
      }
    }
  }
}
custom_driver_specs {
  key: "CsvExampleGen"
  value {
    python_class_executable_spec {
      class_path: "tfx.components.example_gen.driver.FileBasedDriver"
    }
  }
}
metadata_connection_config {
  database_connection_config {
    sqlite {
      filename_uri: "./tfx_metadata/my_pipeline/metadata.db"
      connection_mode: READWRITE_OPENCREATE
    }
  }
}

INFO:absl:Using connection config:
 sqlite {
  filename_uri: "./tfx_metadata/my_pipeline/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: "my_pipeline"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2022-02-03T11:09:37.055994"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "my_pipeline.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
          }
          base_type: DATASET
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "input_base"
    value {
      field_value {
        string_value: "/home/kbuilder/imported/my_pipeline/data"
      }
    }
  }
  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: "StatisticsGen"
execution_options {
  caching_options {
  }
}

INFO:absl:MetadataStore with DB connection initialized
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 4
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=4, input_dict={}, output_dict=defaultdict(<class 'list'>, {'examples': [Artifact(artifact: uri: "./tfx_pipeline_output/my_pipeline/CsvExampleGen/examples/4"
custom_properties {
  key: "input_fingerprint"
  value {
    string_value: "split:single_split,num_files:1,total_bytes:25648,xor_checksum:1643886522,sum_checksum:1643886522"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:09:37.055994:CsvExampleGen:examples:0"
  }
}
custom_properties {
  key: "span"
  value {
    int_value: 0
  }
}
, artifact_type: name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
base_type: DATASET
)]}), exec_properties={'output_file_format': 5, 'input_base': '/home/kbuilder/imported/my_pipeline/data', '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}', 'output_data_format': 6, 'input_config': '{\n  "splits": [\n    {\n      "name": "single_split",\n      "pattern": "*"\n    }\n  ]\n}', 'span': 0, 'version': None, 'input_fingerprint': 'split:single_split,num_files:1,total_bytes:25648,xor_checksum:1643886522,sum_checksum:1643886522'}, execution_output_uri='./tfx_pipeline_output/my_pipeline/CsvExampleGen/.system/executor_execution/4/executor_output.pb', stateful_working_dir='./tfx_pipeline_output/my_pipeline/CsvExampleGen/.system/stateful_working_dir/2022-02-03T11:09:37.055994', tmp_dir='./tfx_pipeline_output/my_pipeline/CsvExampleGen/.system/executor_execution/4/.temp/', pipeline_node=node_info {
  type {
    name: "tfx.components.example_gen.csv_example_gen.component.CsvExampleGen"
  }
  id: "CsvExampleGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "my_pipeline"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2022-02-03T11:09:37.055994"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "my_pipeline.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
          }
          base_type: DATASET
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "input_base"
    value {
      field_value {
        string_value: "/home/kbuilder/imported/my_pipeline/data"
      }
    }
  }
  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: "StatisticsGen"
execution_options {
  caching_options {
  }
}
, pipeline_info=id: "my_pipeline"
, pipeline_run_id='2022-02-03T11:09:37.055994')
INFO:absl:Generating examples.
INFO:absl:Processing input csv data /home/kbuilder/imported/my_pipeline/data/* to TFExample.
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
E0203 11:09:37.596944686    5287 fork_posix.cc:70]           Fork support is only compatible with the epoll1 and poll polling strategies
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:Value type <class 'NoneType'> of key version in exec_properties is not supported, going to drop it
INFO:absl:Value type <class 'list'> of key _beam_pipeline_args in exec_properties is not supported, going to drop it
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'>, {'examples': [Artifact(artifact: uri: "./tfx_pipeline_output/my_pipeline/CsvExampleGen/examples/4"
custom_properties {
  key: "input_fingerprint"
  value {
    string_value: "split:single_split,num_files:1,total_bytes:25648,xor_checksum:1643886522,sum_checksum:1643886522"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:09:37.055994:CsvExampleGen:examples:0"
  }
}
custom_properties {
  key: "span"
  value {
    int_value: 0
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.6.0"
  }
}
, artifact_type: name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
base_type: DATASET
)]}) for execution 4
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component CsvExampleGen is finished.
INFO:absl:Component StatisticsGen is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.components.statistics_gen.component.StatisticsGen"
    base_type: PROCESS
  }
  id: "StatisticsGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "my_pipeline"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2022-02-03T11:09:37.055994"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "my_pipeline.StatisticsGen"
      }
    }
  }
}
inputs {
  inputs {
    key: "examples"
    value {
      channels {
        producer_node_query {
          id: "CsvExampleGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "my_pipeline"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2022-02-03T11:09:37.055994"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "my_pipeline.CsvExampleGen"
            }
          }
        }
        artifact_query {
          type {
            name: "Examples"
            base_type: DATASET
          }
        }
        output_key: "examples"
      }
      min_count: 1
    }
  }
}
outputs {
  outputs {
    key: "statistics"
    value {
      artifact_spec {
        type {
          name: "ExampleStatistics"
          properties {
            key: "span"
            value: INT
          }
          properties {
            key: "split_names"
            value: STRING
          }
          base_type: STATISTICS
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "exclude_splits"
    value {
      field_value {
        string_value: "[]"
      }
    }
  }
}
upstream_nodes: "CsvExampleGen"
downstream_nodes: "SchemaGen"
execution_options {
  caching_options {
  }
}

INFO:absl:MetadataStore with DB connection initialized
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={'examples': [Artifact(artifact: id: 4
type_id: 15
uri: "./tfx_pipeline_output/my_pipeline/CsvExampleGen/examples/4"
properties {
  key: "split_names"
  value {
    string_value: "[\"train\", \"eval\"]"
  }
}
custom_properties {
  key: "file_format"
  value {
    string_value: "tfrecords_gzip"
  }
}
custom_properties {
  key: "input_fingerprint"
  value {
    string_value: "split:single_split,num_files:1,total_bytes:25648,xor_checksum:1643886522,sum_checksum:1643886522"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:09:37.055994:CsvExampleGen:examples:0"
  }
}
custom_properties {
  key: "payload_format"
  value {
    string_value: "FORMAT_TF_EXAMPLE"
  }
}
custom_properties {
  key: "span"
  value {
    int_value: 0
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.6.0"
  }
}
state: LIVE
create_time_since_epoch: 1643886578210
last_update_time_since_epoch: 1643886578210
, artifact_type: id: 15
name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
base_type: DATASET
)]}, output_dict=defaultdict(<class 'list'>, {'statistics': [Artifact(artifact: uri: "./tfx_pipeline_output/my_pipeline/StatisticsGen/statistics/5"
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:09:37.055994:StatisticsGen:statistics:0"
  }
}
, artifact_type: name: "ExampleStatistics"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
base_type: STATISTICS
)]}), exec_properties={'exclude_splits': '[]'}, execution_output_uri='./tfx_pipeline_output/my_pipeline/StatisticsGen/.system/executor_execution/5/executor_output.pb', stateful_working_dir='./tfx_pipeline_output/my_pipeline/StatisticsGen/.system/stateful_working_dir/2022-02-03T11:09:37.055994', tmp_dir='./tfx_pipeline_output/my_pipeline/StatisticsGen/.system/executor_execution/5/.temp/', pipeline_node=node_info {
  type {
    name: "tfx.components.statistics_gen.component.StatisticsGen"
    base_type: PROCESS
  }
  id: "StatisticsGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "my_pipeline"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2022-02-03T11:09:37.055994"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "my_pipeline.StatisticsGen"
      }
    }
  }
}
inputs {
  inputs {
    key: "examples"
    value {
      channels {
        producer_node_query {
          id: "CsvExampleGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "my_pipeline"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2022-02-03T11:09:37.055994"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "my_pipeline.CsvExampleGen"
            }
          }
        }
        artifact_query {
          type {
            name: "Examples"
            base_type: DATASET
          }
        }
        output_key: "examples"
      }
      min_count: 1
    }
  }
}
outputs {
  outputs {
    key: "statistics"
    value {
      artifact_spec {
        type {
          name: "ExampleStatistics"
          properties {
            key: "span"
            value: INT
          }
          properties {
            key: "split_names"
            value: STRING
          }
          base_type: STATISTICS
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "exclude_splits"
    value {
      field_value {
        string_value: "[]"
      }
    }
  }
}
upstream_nodes: "CsvExampleGen"
downstream_nodes: "SchemaGen"
execution_options {
  caching_options {
  }
}
, pipeline_info=id: "my_pipeline"
, pipeline_run_id='2022-02-03T11:09:37.055994')
INFO:absl:Generating statistics for split train.
INFO:absl:Statistics for split train written to ./tfx_pipeline_output/my_pipeline/StatisticsGen/statistics/5/Split-train.
INFO:absl:Generating statistics for split eval.
INFO:absl:Statistics for split eval written to ./tfx_pipeline_output/my_pipeline/StatisticsGen/statistics/5/Split-eval.
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
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'>, {'statistics': [Artifact(artifact: uri: "./tfx_pipeline_output/my_pipeline/StatisticsGen/statistics/5"
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:09:37.055994:StatisticsGen:statistics:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.6.0"
  }
}
, artifact_type: name: "ExampleStatistics"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
base_type: STATISTICS
)]}) for execution 5
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component StatisticsGen is finished.
INFO:absl:Component SchemaGen is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.components.schema_gen.component.SchemaGen"
    base_type: PROCESS
  }
  id: "SchemaGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "my_pipeline"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2022-02-03T11:09:37.055994"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "my_pipeline.SchemaGen"
      }
    }
  }
}
inputs {
  inputs {
    key: "statistics"
    value {
      channels {
        producer_node_query {
          id: "StatisticsGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "my_pipeline"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2022-02-03T11:09:37.055994"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "my_pipeline.StatisticsGen"
            }
          }
        }
        artifact_query {
          type {
            name: "ExampleStatistics"
            base_type: STATISTICS
          }
        }
        output_key: "statistics"
      }
      min_count: 1
    }
  }
}
outputs {
  outputs {
    key: "schema"
    value {
      artifact_spec {
        type {
          name: "Schema"
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "exclude_splits"
    value {
      field_value {
        string_value: "[]"
      }
    }
  }
  parameters {
    key: "infer_feature_shape"
    value {
      field_value {
        int_value: 1
      }
    }
  }
}
upstream_nodes: "StatisticsGen"
execution_options {
  caching_options {
  }
}

INFO:absl:MetadataStore with DB connection initialized
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 6
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=6, input_dict={'statistics': [Artifact(artifact: id: 5
type_id: 17
uri: "./tfx_pipeline_output/my_pipeline/StatisticsGen/statistics/5"
properties {
  key: "split_names"
  value {
    string_value: "[\"train\", \"eval\"]"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:09:37.055994:StatisticsGen:statistics:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.6.0"
  }
}
state: LIVE
create_time_since_epoch: 1643886581527
last_update_time_since_epoch: 1643886581527
, artifact_type: id: 17
name: "ExampleStatistics"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
base_type: STATISTICS
)]}, output_dict=defaultdict(<class 'list'>, {'schema': [Artifact(artifact: uri: "./tfx_pipeline_output/my_pipeline/SchemaGen/schema/6"
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:09:37.055994:SchemaGen:schema:0"
  }
}
, artifact_type: name: "Schema"
)]}), exec_properties={'exclude_splits': '[]', 'infer_feature_shape': 1}, execution_output_uri='./tfx_pipeline_output/my_pipeline/SchemaGen/.system/executor_execution/6/executor_output.pb', stateful_working_dir='./tfx_pipeline_output/my_pipeline/SchemaGen/.system/stateful_working_dir/2022-02-03T11:09:37.055994', tmp_dir='./tfx_pipeline_output/my_pipeline/SchemaGen/.system/executor_execution/6/.temp/', pipeline_node=node_info {
  type {
    name: "tfx.components.schema_gen.component.SchemaGen"
    base_type: PROCESS
  }
  id: "SchemaGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "my_pipeline"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2022-02-03T11:09:37.055994"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "my_pipeline.SchemaGen"
      }
    }
  }
}
inputs {
  inputs {
    key: "statistics"
    value {
      channels {
        producer_node_query {
          id: "StatisticsGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "my_pipeline"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2022-02-03T11:09:37.055994"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "my_pipeline.StatisticsGen"
            }
          }
        }
        artifact_query {
          type {
            name: "ExampleStatistics"
            base_type: STATISTICS
          }
        }
        output_key: "statistics"
      }
      min_count: 1
    }
  }
}
outputs {
  outputs {
    key: "schema"
    value {
      artifact_spec {
        type {
          name: "Schema"
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "exclude_splits"
    value {
      field_value {
        string_value: "[]"
      }
    }
  }
  parameters {
    key: "infer_feature_shape"
    value {
      field_value {
        int_value: 1
      }
    }
  }
}
upstream_nodes: "StatisticsGen"
execution_options {
  caching_options {
  }
}
, pipeline_info=id: "my_pipeline"
, pipeline_run_id='2022-02-03T11:09:37.055994')
INFO:absl:Processing schema from statistics for split train.
INFO:absl:Processing schema from statistics for split eval.
INFO:absl:Schema written to ./tfx_pipeline_output/my_pipeline/SchemaGen/schema/6/schema.pbtxt.
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 6 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'schema': [Artifact(artifact: uri: "./tfx_pipeline_output/my_pipeline/SchemaGen/schema/6"
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:09:37.055994:SchemaGen:schema:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.6.0"
  }
}
, artifact_type: name: "Schema"
)]}) for execution 6
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component SchemaGen is finished.

אם הצינור פעל בהצלחה, אתה אמור לראות את "המרת הרכיבים הסתיימה". איפשהו ביומן. מכיוון שרכיב Transform ורכיב ExampleValidator אינם תלויים זה בזה, סדר הביצועים אינו קבוע. עם זאת, כל אחד מה- Transform ו- ExampleValidator יכול להיות הרכיב האחרון בביצוע הצינור.

בדוק פלט מ-Transform

רכיב טרנספורמציה יוצר שני סוגים של פלטים, גרף Tensorflow ודוגמאות שעברו טרנספורמציה. הדוגמאות שעברו טרנספורמציה הן מסוג Exemples artifact שמיוצר גם על ידי ExampleGen, אך זו מכילה ערכי תכונה שעברו טרנספורמציה במקום זאת.

אתה יכול לבחון אותם כפי שעשינו בשלב הקודם.

with metadata.Metadata(metadata_connection_config) as metadata_handler:
    # Search all aritfacts from the previous run of Transform component.
    artifacts = get_latest_artifacts(metadata_handler.store,
                                     PIPELINE_NAME, "Transform")
    # Find artifacts of Examples type.
    transformed_examples_artifacts = find_latest_artifacts_by_type(
        metadata_handler.store, artifacts,
        standard_artifacts.Examples.TYPE_NAME)

preview_examples(transformed_examples_artifacts)

שלב 4. אמן את הדגם שלך עם רכיב טריינר.

נבנה מודל ML באמצעות רכיב Trainer . ראה מדריך רכיבי מאמן למידע נוסף. עליך לספק את קוד הדגם שלך לרכיב ה-Trainer.

הגדר את הדגם שלך.

בתבנית פינגווין, models.model.run_fn משמש כארגומנט run_fn עבור רכיב Trainer . המשמעות היא run_fn() ב- models/model.py תיקרא כאשר רכיב ה- Trainer יפעל. אתה יכול לראות את הקוד לבניית מודל DNN פשוט באמצעות keras API בקוד נתון. ראה TensorFlow 2.x במדריך TFX למידע נוסף על שימוש ב-Keras API ב-TFX.

ב- run_fn זה, עליך לבנות מודל ולשמור אותו בספרייה המופנית על ידי fn_args.serving_model_dir אשר מוגדרת על ידי הרכיב. אתה יכול להשתמש בארגומנטים אחרים ב- fn_args שמועברים ל- run_fn . ראה קודים קשורים לרשימה המלאה של ארגומנטים ב- fn_args .

הגדר את התכונות שלך ב- models/features.py והשתמש בהם לפי הצורך. אם שינית את התכונות שלך בשלב 3, עליך להשתמש בתכונות שהשתנו כקלט למודל שלך.

הוסף רכיב Trainer לצינור.

אם ה-run_fn שלך מוכן, הוסף את רכיב Trainer לצינור.

1. בקובץ pipeline/pipeline.py , בטל את ההערה # components.append(trainer) כדי להוסיף את הרכיב לצינור.

טיעונים עבור רכיב המאמן עשויים להיות תלויים אם אתה משתמש ברכיב Transform או לא.

• אם אינך משתמש ברכיב Transform , אינך צריך לשנות את הארגומנטים.

• אם אתה משתמש ברכיב Transform , עליך לשנות ארגומנטים בעת יצירת מופע של רכיב Trainer .

  • שנה ארגומנט examples ל- examples=transform.outputs['transformed_examples'], . אנחנו צריכים להשתמש בדוגמאות שעברו שינוי לאימון.
  • הוסף ארגומנט transform_graph כמו transform_graph=transform.outputs['transform_graph'], . גרף זה מכיל גרף TensorFlow עבור פעולות ההמרה.
  • לאחר השינויים לעיל, הקוד ליצירת רכיבי Trainer ייראה כמו הבא.

  # If you use a Transform component.
  trainer = Trainer(
      run_fn=run_fn,
      examples=transform.outputs['transformed_examples'],
      transform_graph=transform.outputs['transform_graph'],
      schema=schema_gen.outputs['schema'],
      ...
  

אתה יכול לעדכן את הצינור ולהפעיל שוב.

!tfx pipeline update --engine=local --pipeline_path=local_runner.py \
 && tfx run create --engine=local --pipeline_name={PIPELINE_NAME}

CLI
Updating pipeline
INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Excluding no splits because exclude_splits is not set.
Pipeline "my_pipeline" updated successfully.
CLI
Creating a run for pipeline: my_pipeline
INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Excluding no splits because exclude_splits is not set.
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: "SchemaGen"
  value {
    python_class_executable_spec {
      class_path: "tfx.components.schema_gen.executor.Executor"
    }
  }
}
executor_specs {
  key: "StatisticsGen"
  value {
    beam_executable_spec {
      python_executor_spec {
        class_path: "tfx.components.statistics_gen.executor.Executor"
      }
    }
  }
}
custom_driver_specs {
  key: "CsvExampleGen"
  value {
    python_class_executable_spec {
      class_path: "tfx.components.example_gen.driver.FileBasedDriver"
    }
  }
}
metadata_connection_config {
  database_connection_config {
    sqlite {
      filename_uri: "./tfx_metadata/my_pipeline/metadata.db"
      connection_mode: READWRITE_OPENCREATE
    }
  }
}

INFO:absl:Using connection config:
 sqlite {
  filename_uri: "./tfx_metadata/my_pipeline/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: "my_pipeline"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2022-02-03T11:10:00.469382"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "my_pipeline.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
          }
          base_type: DATASET
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "input_base"
    value {
      field_value {
        string_value: "/home/kbuilder/imported/my_pipeline/data"
      }
    }
  }
  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: "StatisticsGen"
execution_options {
  caching_options {
  }
}

INFO:absl:MetadataStore with DB connection initialized
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 7
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=7, input_dict={}, output_dict=defaultdict(<class 'list'>, {'examples': [Artifact(artifact: uri: "./tfx_pipeline_output/my_pipeline/CsvExampleGen/examples/7"
custom_properties {
  key: "input_fingerprint"
  value {
    string_value: "split:single_split,num_files:1,total_bytes:25648,xor_checksum:1643886522,sum_checksum:1643886522"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:10:00.469382:CsvExampleGen:examples:0"
  }
}
custom_properties {
  key: "span"
  value {
    int_value: 0
  }
}
, artifact_type: name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
base_type: DATASET
)]}), exec_properties={'input_base': '/home/kbuilder/imported/my_pipeline/data', '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}', 'output_data_format': 6, 'output_file_format': 5, 'input_config': '{\n  "splits": [\n    {\n      "name": "single_split",\n      "pattern": "*"\n    }\n  ]\n}', 'span': 0, 'version': None, 'input_fingerprint': 'split:single_split,num_files:1,total_bytes:25648,xor_checksum:1643886522,sum_checksum:1643886522'}, execution_output_uri='./tfx_pipeline_output/my_pipeline/CsvExampleGen/.system/executor_execution/7/executor_output.pb', stateful_working_dir='./tfx_pipeline_output/my_pipeline/CsvExampleGen/.system/stateful_working_dir/2022-02-03T11:10:00.469382', tmp_dir='./tfx_pipeline_output/my_pipeline/CsvExampleGen/.system/executor_execution/7/.temp/', pipeline_node=node_info {
  type {
    name: "tfx.components.example_gen.csv_example_gen.component.CsvExampleGen"
  }
  id: "CsvExampleGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "my_pipeline"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2022-02-03T11:10:00.469382"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "my_pipeline.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
          }
          base_type: DATASET
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "input_base"
    value {
      field_value {
        string_value: "/home/kbuilder/imported/my_pipeline/data"
      }
    }
  }
  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: "StatisticsGen"
execution_options {
  caching_options {
  }
}
, pipeline_info=id: "my_pipeline"
, pipeline_run_id='2022-02-03T11:10:00.469382')
INFO:absl:Generating examples.
INFO:absl:Processing input csv data /home/kbuilder/imported/my_pipeline/data/* to TFExample.
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
E0203 11:10:01.173700221    5436 fork_posix.cc:70]           Fork support is only compatible with the epoll1 and poll polling strategies
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:Value type <class 'NoneType'> of key version in exec_properties is not supported, going to drop it
INFO:absl:Value type <class 'list'> of key _beam_pipeline_args in exec_properties is not supported, going to drop it
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 7 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'examples': [Artifact(artifact: uri: "./tfx_pipeline_output/my_pipeline/CsvExampleGen/examples/7"
custom_properties {
  key: "input_fingerprint"
  value {
    string_value: "split:single_split,num_files:1,total_bytes:25648,xor_checksum:1643886522,sum_checksum:1643886522"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:10:00.469382:CsvExampleGen:examples:0"
  }
}
custom_properties {
  key: "span"
  value {
    int_value: 0
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.6.0"
  }
}
, artifact_type: name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
base_type: DATASET
)]}) for execution 7
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component CsvExampleGen is finished.
INFO:absl:Component StatisticsGen is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.components.statistics_gen.component.StatisticsGen"
    base_type: PROCESS
  }
  id: "StatisticsGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "my_pipeline"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2022-02-03T11:10:00.469382"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "my_pipeline.StatisticsGen"
      }
    }
  }
}
inputs {
  inputs {
    key: "examples"
    value {
      channels {
        producer_node_query {
          id: "CsvExampleGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "my_pipeline"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2022-02-03T11:10:00.469382"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "my_pipeline.CsvExampleGen"
            }
          }
        }
        artifact_query {
          type {
            name: "Examples"
            base_type: DATASET
          }
        }
        output_key: "examples"
      }
      min_count: 1
    }
  }
}
outputs {
  outputs {
    key: "statistics"
    value {
      artifact_spec {
        type {
          name: "ExampleStatistics"
          properties {
            key: "span"
            value: INT
          }
          properties {
            key: "split_names"
            value: STRING
          }
          base_type: STATISTICS
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "exclude_splits"
    value {
      field_value {
        string_value: "[]"
      }
    }
  }
}
upstream_nodes: "CsvExampleGen"
downstream_nodes: "SchemaGen"
execution_options {
  caching_options {
  }
}

INFO:absl:MetadataStore with DB connection initialized
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 8
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=8, input_dict={'examples': [Artifact(artifact: id: 7
type_id: 15
uri: "./tfx_pipeline_output/my_pipeline/CsvExampleGen/examples/7"
properties {
  key: "split_names"
  value {
    string_value: "[\"train\", \"eval\"]"
  }
}
custom_properties {
  key: "file_format"
  value {
    string_value: "tfrecords_gzip"
  }
}
custom_properties {
  key: "input_fingerprint"
  value {
    string_value: "split:single_split,num_files:1,total_bytes:25648,xor_checksum:1643886522,sum_checksum:1643886522"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:10:00.469382:CsvExampleGen:examples:0"
  }
}
custom_properties {
  key: "payload_format"
  value {
    string_value: "FORMAT_TF_EXAMPLE"
  }
}
custom_properties {
  key: "span"
  value {
    int_value: 0
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.6.0"
  }
}
state: LIVE
create_time_since_epoch: 1643886601629
last_update_time_since_epoch: 1643886601629
, artifact_type: id: 15
name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
base_type: DATASET
)]}, output_dict=defaultdict(<class 'list'>, {'statistics': [Artifact(artifact: uri: "./tfx_pipeline_output/my_pipeline/StatisticsGen/statistics/8"
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:10:00.469382:StatisticsGen:statistics:0"
  }
}
, artifact_type: name: "ExampleStatistics"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
base_type: STATISTICS
)]}), exec_properties={'exclude_splits': '[]'}, execution_output_uri='./tfx_pipeline_output/my_pipeline/StatisticsGen/.system/executor_execution/8/executor_output.pb', stateful_working_dir='./tfx_pipeline_output/my_pipeline/StatisticsGen/.system/stateful_working_dir/2022-02-03T11:10:00.469382', tmp_dir='./tfx_pipeline_output/my_pipeline/StatisticsGen/.system/executor_execution/8/.temp/', pipeline_node=node_info {
  type {
    name: "tfx.components.statistics_gen.component.StatisticsGen"
    base_type: PROCESS
  }
  id: "StatisticsGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "my_pipeline"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2022-02-03T11:10:00.469382"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "my_pipeline.StatisticsGen"
      }
    }
  }
}
inputs {
  inputs {
    key: "examples"
    value {
      channels {
        producer_node_query {
          id: "CsvExampleGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "my_pipeline"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2022-02-03T11:10:00.469382"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "my_pipeline.CsvExampleGen"
            }
          }
        }
        artifact_query {
          type {
            name: "Examples"
            base_type: DATASET
          }
        }
        output_key: "examples"
      }
      min_count: 1
    }
  }
}
outputs {
  outputs {
    key: "statistics"
    value {
      artifact_spec {
        type {
          name: "ExampleStatistics"
          properties {
            key: "span"
            value: INT
          }
          properties {
            key: "split_names"
            value: STRING
          }
          base_type: STATISTICS
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "exclude_splits"
    value {
      field_value {
        string_value: "[]"
      }
    }
  }
}
upstream_nodes: "CsvExampleGen"
downstream_nodes: "SchemaGen"
execution_options {
  caching_options {
  }
}
, pipeline_info=id: "my_pipeline"
, pipeline_run_id='2022-02-03T11:10:00.469382')
INFO:absl:Generating statistics for split train.
INFO:absl:Statistics for split train written to ./tfx_pipeline_output/my_pipeline/StatisticsGen/statistics/8/Split-train.
INFO:absl:Generating statistics for split eval.
INFO:absl:Statistics for split eval written to ./tfx_pipeline_output/my_pipeline/StatisticsGen/statistics/8/Split-eval.
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 8 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'statistics': [Artifact(artifact: uri: "./tfx_pipeline_output/my_pipeline/StatisticsGen/statistics/8"
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:10:00.469382:StatisticsGen:statistics:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.6.0"
  }
}
, artifact_type: name: "ExampleStatistics"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
base_type: STATISTICS
)]}) for execution 8
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component StatisticsGen is finished.
INFO:absl:Component SchemaGen is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.components.schema_gen.component.SchemaGen"
    base_type: PROCESS
  }
  id: "SchemaGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "my_pipeline"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2022-02-03T11:10:00.469382"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "my_pipeline.SchemaGen"
      }
    }
  }
}
inputs {
  inputs {
    key: "statistics"
    value {
      channels {
        producer_node_query {
          id: "StatisticsGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "my_pipeline"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2022-02-03T11:10:00.469382"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "my_pipeline.StatisticsGen"
            }
          }
        }
        artifact_query {
          type {
            name: "ExampleStatistics"
            base_type: STATISTICS
          }
        }
        output_key: "statistics"
      }
      min_count: 1
    }
  }
}
outputs {
  outputs {
    key: "schema"
    value {
      artifact_spec {
        type {
          name: "Schema"
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "exclude_splits"
    value {
      field_value {
        string_value: "[]"
      }
    }
  }
  parameters {
    key: "infer_feature_shape"
    value {
      field_value {
        int_value: 1
      }
    }
  }
}
upstream_nodes: "StatisticsGen"
execution_options {
  caching_options {
  }
}

INFO:absl:MetadataStore with DB connection initialized
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 9
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=9, input_dict={'statistics': [Artifact(artifact: id: 8
type_id: 17
uri: "./tfx_pipeline_output/my_pipeline/StatisticsGen/statistics/8"
properties {
  key: "split_names"
  value {
    string_value: "[\"train\", \"eval\"]"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:10:00.469382:StatisticsGen:statistics:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.6.0"
  }
}
state: LIVE
create_time_since_epoch: 1643886605023
last_update_time_since_epoch: 1643886605023
, artifact_type: id: 17
name: "ExampleStatistics"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
base_type: STATISTICS
)]}, output_dict=defaultdict(<class 'list'>, {'schema': [Artifact(artifact: uri: "./tfx_pipeline_output/my_pipeline/SchemaGen/schema/9"
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:10:00.469382:SchemaGen:schema:0"
  }
}
, artifact_type: name: "Schema"
)]}), exec_properties={'exclude_splits': '[]', 'infer_feature_shape': 1}, execution_output_uri='./tfx_pipeline_output/my_pipeline/SchemaGen/.system/executor_execution/9/executor_output.pb', stateful_working_dir='./tfx_pipeline_output/my_pipeline/SchemaGen/.system/stateful_working_dir/2022-02-03T11:10:00.469382', tmp_dir='./tfx_pipeline_output/my_pipeline/SchemaGen/.system/executor_execution/9/.temp/', pipeline_node=node_info {
  type {
    name: "tfx.components.schema_gen.component.SchemaGen"
    base_type: PROCESS
  }
  id: "SchemaGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "my_pipeline"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2022-02-03T11:10:00.469382"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "my_pipeline.SchemaGen"
      }
    }
  }
}
inputs {
  inputs {
    key: "statistics"
    value {
      channels {
        producer_node_query {
          id: "StatisticsGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "my_pipeline"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2022-02-03T11:10:00.469382"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "my_pipeline.StatisticsGen"
            }
          }
        }
        artifact_query {
          type {
            name: "ExampleStatistics"
            base_type: STATISTICS
          }
        }
        output_key: "statistics"
      }
      min_count: 1
    }
  }
}
outputs {
  outputs {
    key: "schema"
    value {
      artifact_spec {
        type {
          name: "Schema"
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "exclude_splits"
    value {
      field_value {
        string_value: "[]"
      }
    }
  }
  parameters {
    key: "infer_feature_shape"
    value {
      field_value {
        int_value: 1
      }
    }
  }
}
upstream_nodes: "StatisticsGen"
execution_options {
  caching_options {
  }
}
, pipeline_info=id: "my_pipeline"
, pipeline_run_id='2022-02-03T11:10:00.469382')
INFO:absl:Processing schema from statistics for split train.
INFO:absl:Processing schema from statistics for split eval.
INFO:absl:Schema written to ./tfx_pipeline_output/my_pipeline/SchemaGen/schema/9/schema.pbtxt.
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 9 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'schema': [Artifact(artifact: uri: "./tfx_pipeline_output/my_pipeline/SchemaGen/schema/9"
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:10:00.469382:SchemaGen:schema:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.6.0"
  }
}
, artifact_type: name: "Schema"
)]}) for execution 9
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component SchemaGen is finished.

כאשר ביצוע זה פועל בהצלחה, כעת יצרת והרצת את צינור ה-TFX הראשון שלך עבור הדגם שלך. מזל טוב!

הדגם החדש שלך ימוקם במקום כלשהו תחת ספריית הפלט, אך עדיף שיהיה מודל במיקום קבוע או בשירות מחוץ לצינור TFX אשר מכיל תוצאות ביניים רבות. אפילו טוב יותר עם הערכה מתמשכת של המודל הבנוי שהוא קריטי במערכות ייצור ML. נראה כיצד פועלות הערכה ופריסה מתמשכת ב-TFX בשלב הבא.

שלב 5. (אופציונלי) הערך את המודל עם Evaluator ופרסם עם pusher.

רכיב ה- Evaluator באופן רציף כל דגם שנבנה מ- Trainer , ו- Pusher מעתיק את המודל למיקום מוגדר מראש במערכת הקבצים או אפילו ל- Google Cloud AI Platform Models .

מוסיף רכיב Evaluator לצינור.

בקובץ pipeline/pipeline.py :

1. בטל את # components.append(model_resolver) כדי להוסיף פותר המודל העדכני ביותר לצינור. ניתן להשתמש ב-Evaluator כדי להשוות מודל למודל בסיס ישן שעבר את Evaluator ברצף הצינור האחרון. LatestBlessedModelResolver מוצא את המודל האחרון שעבר את Evaluator.
2. הגדר את tfma.MetricsSpec מתאים לדגם שלך. ההערכה עשויה להיות שונה עבור כל מודל ML. בתבנית הפינגווין, נעשה שימוש SparseCategoricalAccuracy מכיוון שאנו פותרים בעיית סיווג מרובת קטגוריות. אתה גם צריך לציין את tfma.SliceSpec כדי לנתח את המודל שלך עבור פרוסות ספציפיות. לפרטים נוספים, ראה מדריך רכיבי Evaluator .
3. בטל את ההערה # components.append(evaluator) כדי להוסיף את הרכיב לצינור.

אתה יכול לעדכן את הצינור ולהפעיל שוב.

# Update and run the pipeline.
!tfx pipeline update --engine=local --pipeline_path=local_runner.py \
 && tfx run create --engine=local --pipeline_name={PIPELINE_NAME}

CLI
Updating pipeline
INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Excluding no splits because exclude_splits is not set.
Pipeline "my_pipeline" updated successfully.
CLI
Creating a run for pipeline: my_pipeline
INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Excluding no splits because exclude_splits is not set.
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: "SchemaGen"
  value {
    python_class_executable_spec {
      class_path: "tfx.components.schema_gen.executor.Executor"
    }
  }
}
executor_specs {
  key: "StatisticsGen"
  value {
    beam_executable_spec {
      python_executor_spec {
        class_path: "tfx.components.statistics_gen.executor.Executor"
      }
    }
  }
}
custom_driver_specs {
  key: "CsvExampleGen"
  value {
    python_class_executable_spec {
      class_path: "tfx.components.example_gen.driver.FileBasedDriver"
    }
  }
}
metadata_connection_config {
  database_connection_config {
    sqlite {
      filename_uri: "./tfx_metadata/my_pipeline/metadata.db"
      connection_mode: READWRITE_OPENCREATE
    }
  }
}

INFO:absl:Using connection config:
 sqlite {
  filename_uri: "./tfx_metadata/my_pipeline/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: "my_pipeline"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2022-02-03T11:10:24.358660"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "my_pipeline.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
          }
          base_type: DATASET
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "input_base"
    value {
      field_value {
        string_value: "/home/kbuilder/imported/my_pipeline/data"
      }
    }
  }
  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: "StatisticsGen"
execution_options {
  caching_options {
  }
}

INFO:absl:MetadataStore with DB connection initialized
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 10
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=10, input_dict={}, output_dict=defaultdict(<class 'list'>, {'examples': [Artifact(artifact: uri: "./tfx_pipeline_output/my_pipeline/CsvExampleGen/examples/10"
custom_properties {
  key: "input_fingerprint"
  value {
    string_value: "split:single_split,num_files:1,total_bytes:25648,xor_checksum:1643886522,sum_checksum:1643886522"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:10:24.358660:CsvExampleGen:examples:0"
  }
}
custom_properties {
  key: "span"
  value {
    int_value: 0
  }
}
, artifact_type: name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
base_type: DATASET
)]}), 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}', 'output_data_format': 6, 'input_base': '/home/kbuilder/imported/my_pipeline/data', 'input_config': '{\n  "splits": [\n    {\n      "name": "single_split",\n      "pattern": "*"\n    }\n  ]\n}', 'span': 0, 'version': None, 'input_fingerprint': 'split:single_split,num_files:1,total_bytes:25648,xor_checksum:1643886522,sum_checksum:1643886522'}, execution_output_uri='./tfx_pipeline_output/my_pipeline/CsvExampleGen/.system/executor_execution/10/executor_output.pb', stateful_working_dir='./tfx_pipeline_output/my_pipeline/CsvExampleGen/.system/stateful_working_dir/2022-02-03T11:10:24.358660', tmp_dir='./tfx_pipeline_output/my_pipeline/CsvExampleGen/.system/executor_execution/10/.temp/', pipeline_node=node_info {
  type {
    name: "tfx.components.example_gen.csv_example_gen.component.CsvExampleGen"
  }
  id: "CsvExampleGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "my_pipeline"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2022-02-03T11:10:24.358660"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "my_pipeline.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
          }
          base_type: DATASET
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "input_base"
    value {
      field_value {
        string_value: "/home/kbuilder/imported/my_pipeline/data"
      }
    }
  }
  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: "StatisticsGen"
execution_options {
  caching_options {
  }
}
, pipeline_info=id: "my_pipeline"
, pipeline_run_id='2022-02-03T11:10:24.358660')
INFO:absl:Generating examples.
INFO:absl:Processing input csv data /home/kbuilder/imported/my_pipeline/data/* to TFExample.
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
E0203 11:10:24.894390124    5584 fork_posix.cc:70]           Fork support is only compatible with the epoll1 and poll polling strategies
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:Value type <class 'NoneType'> of key version in exec_properties is not supported, going to drop it
INFO:absl:Value type <class 'list'> of key _beam_pipeline_args in exec_properties is not supported, going to drop it
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 10 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'examples': [Artifact(artifact: uri: "./tfx_pipeline_output/my_pipeline/CsvExampleGen/examples/10"
custom_properties {
  key: "input_fingerprint"
  value {
    string_value: "split:single_split,num_files:1,total_bytes:25648,xor_checksum:1643886522,sum_checksum:1643886522"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:10:24.358660:CsvExampleGen:examples:0"
  }
}
custom_properties {
  key: "span"
  value {
    int_value: 0
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.6.0"
  }
}
, artifact_type: name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
base_type: DATASET
)]}) for execution 10
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component CsvExampleGen is finished.
INFO:absl:Component StatisticsGen is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.components.statistics_gen.component.StatisticsGen"
    base_type: PROCESS
  }
  id: "StatisticsGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "my_pipeline"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2022-02-03T11:10:24.358660"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "my_pipeline.StatisticsGen"
      }
    }
  }
}
inputs {
  inputs {
    key: "examples"
    value {
      channels {
        producer_node_query {
          id: "CsvExampleGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "my_pipeline"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2022-02-03T11:10:24.358660"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "my_pipeline.CsvExampleGen"
            }
          }
        }
        artifact_query {
          type {
            name: "Examples"
            base_type: DATASET
          }
        }
        output_key: "examples"
      }
      min_count: 1
    }
  }
}
outputs {
  outputs {
    key: "statistics"
    value {
      artifact_spec {
        type {
          name: "ExampleStatistics"
          properties {
            key: "span"
            value: INT
          }
          properties {
            key: "split_names"
            value: STRING
          }
          base_type: STATISTICS
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "exclude_splits"
    value {
      field_value {
        string_value: "[]"
      }
    }
  }
}
upstream_nodes: "CsvExampleGen"
downstream_nodes: "SchemaGen"
execution_options {
  caching_options {
  }
}

INFO:absl:MetadataStore with DB connection initialized
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 11
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=11, input_dict={'examples': [Artifact(artifact: id: 10
type_id: 15
uri: "./tfx_pipeline_output/my_pipeline/CsvExampleGen/examples/10"
properties {
  key: "split_names"
  value {
    string_value: "[\"train\", \"eval\"]"
  }
}
custom_properties {
  key: "file_format"
  value {
    string_value: "tfrecords_gzip"
  }
}
custom_properties {
  key: "input_fingerprint"
  value {
    string_value: "split:single_split,num_files:1,total_bytes:25648,xor_checksum:1643886522,sum_checksum:1643886522"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:10:24.358660:CsvExampleGen:examples:0"
  }
}
custom_properties {
  key: "payload_format"
  value {
    string_value: "FORMAT_TF_EXAMPLE"
  }
}
custom_properties {
  key: "span"
  value {
    int_value: 0
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.6.0"
  }
}
state: LIVE
create_time_since_epoch: 1643886625515
last_update_time_since_epoch: 1643886625515
, artifact_type: id: 15
name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
base_type: DATASET
)]}, output_dict=defaultdict(<class 'list'>, {'statistics': [Artifact(artifact: uri: "./tfx_pipeline_output/my_pipeline/StatisticsGen/statistics/11"
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:10:24.358660:StatisticsGen:statistics:0"
  }
}
, artifact_type: name: "ExampleStatistics"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
base_type: STATISTICS
)]}), exec_properties={'exclude_splits': '[]'}, execution_output_uri='./tfx_pipeline_output/my_pipeline/StatisticsGen/.system/executor_execution/11/executor_output.pb', stateful_working_dir='./tfx_pipeline_output/my_pipeline/StatisticsGen/.system/stateful_working_dir/2022-02-03T11:10:24.358660', tmp_dir='./tfx_pipeline_output/my_pipeline/StatisticsGen/.system/executor_execution/11/.temp/', pipeline_node=node_info {
  type {
    name: "tfx.components.statistics_gen.component.StatisticsGen"
    base_type: PROCESS
  }
  id: "StatisticsGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "my_pipeline"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2022-02-03T11:10:24.358660"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "my_pipeline.StatisticsGen"
      }
    }
  }
}
inputs {
  inputs {
    key: "examples"
    value {
      channels {
        producer_node_query {
          id: "CsvExampleGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "my_pipeline"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2022-02-03T11:10:24.358660"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "my_pipeline.CsvExampleGen"
            }
          }
        }
        artifact_query {
          type {
            name: "Examples"
            base_type: DATASET
          }
        }
        output_key: "examples"
      }
      min_count: 1
    }
  }
}
outputs {
  outputs {
    key: "statistics"
    value {
      artifact_spec {
        type {
          name: "ExampleStatistics"
          properties {
            key: "span"
            value: INT
          }
          properties {
            key: "split_names"
            value: STRING
          }
          base_type: STATISTICS
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "exclude_splits"
    value {
      field_value {
        string_value: "[]"
      }
    }
  }
}
upstream_nodes: "CsvExampleGen"
downstream_nodes: "SchemaGen"
execution_options {
  caching_options {
  }
}
, pipeline_info=id: "my_pipeline"
, pipeline_run_id='2022-02-03T11:10:24.358660')
INFO:absl:Generating statistics for split train.
INFO:absl:Statistics for split train written to ./tfx_pipeline_output/my_pipeline/StatisticsGen/statistics/11/Split-train.
INFO:absl:Generating statistics for split eval.
INFO:absl:Statistics for split eval written to ./tfx_pipeline_output/my_pipeline/StatisticsGen/statistics/11/Split-eval.
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 11 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'statistics': [Artifact(artifact: uri: "./tfx_pipeline_output/my_pipeline/StatisticsGen/statistics/11"
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:10:24.358660:StatisticsGen:statistics:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.6.0"
  }
}
, artifact_type: name: "ExampleStatistics"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
base_type: STATISTICS
)]}) for execution 11
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component StatisticsGen is finished.
INFO:absl:Component SchemaGen is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.components.schema_gen.component.SchemaGen"
    base_type: PROCESS
  }
  id: "SchemaGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "my_pipeline"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2022-02-03T11:10:24.358660"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "my_pipeline.SchemaGen"
      }
    }
  }
}
inputs {
  inputs {
    key: "statistics"
    value {
      channels {
        producer_node_query {
          id: "StatisticsGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "my_pipeline"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2022-02-03T11:10:24.358660"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "my_pipeline.StatisticsGen"
            }
          }
        }
        artifact_query {
          type {
            name: "ExampleStatistics"
            base_type: STATISTICS
          }
        }
        output_key: "statistics"
      }
      min_count: 1
    }
  }
}
outputs {
  outputs {
    key: "schema"
    value {
      artifact_spec {
        type {
          name: "Schema"
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "exclude_splits"
    value {
      field_value {
        string_value: "[]"
      }
    }
  }
  parameters {
    key: "infer_feature_shape"
    value {
      field_value {
        int_value: 1
      }
    }
  }
}
upstream_nodes: "StatisticsGen"
execution_options {
  caching_options {
  }
}

INFO:absl:MetadataStore with DB connection initialized
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 12
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=12, input_dict={'statistics': [Artifact(artifact: id: 11
type_id: 17
uri: "./tfx_pipeline_output/my_pipeline/StatisticsGen/statistics/11"
properties {
  key: "split_names"
  value {
    string_value: "[\"train\", \"eval\"]"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:10:24.358660:StatisticsGen:statistics:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.6.0"
  }
}
state: LIVE
create_time_since_epoch: 1643886628941
last_update_time_since_epoch: 1643886628941
, artifact_type: id: 17
name: "ExampleStatistics"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
base_type: STATISTICS
)]}, output_dict=defaultdict(<class 'list'>, {'schema': [Artifact(artifact: uri: "./tfx_pipeline_output/my_pipeline/SchemaGen/schema/12"
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:10:24.358660:SchemaGen:schema:0"
  }
}
, artifact_type: name: "Schema"
)]}), exec_properties={'infer_feature_shape': 1, 'exclude_splits': '[]'}, execution_output_uri='./tfx_pipeline_output/my_pipeline/SchemaGen/.system/executor_execution/12/executor_output.pb', stateful_working_dir='./tfx_pipeline_output/my_pipeline/SchemaGen/.system/stateful_working_dir/2022-02-03T11:10:24.358660', tmp_dir='./tfx_pipeline_output/my_pipeline/SchemaGen/.system/executor_execution/12/.temp/', pipeline_node=node_info {
  type {
    name: "tfx.components.schema_gen.component.SchemaGen"
    base_type: PROCESS
  }
  id: "SchemaGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "my_pipeline"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2022-02-03T11:10:24.358660"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "my_pipeline.SchemaGen"
      }
    }
  }
}
inputs {
  inputs {
    key: "statistics"
    value {
      channels {
        producer_node_query {
          id: "StatisticsGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "my_pipeline"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2022-02-03T11:10:24.358660"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "my_pipeline.StatisticsGen"
            }
          }
        }
        artifact_query {
          type {
            name: "ExampleStatistics"
            base_type: STATISTICS
          }
        }
        output_key: "statistics"
      }
      min_count: 1
    }
  }
}
outputs {
  outputs {
    key: "schema"
    value {
      artifact_spec {
        type {
          name: "Schema"
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "exclude_splits"
    value {
      field_value {
        string_value: "[]"
      }
    }
  }
  parameters {
    key: "infer_feature_shape"
    value {
      field_value {
        int_value: 1
      }
    }
  }
}
upstream_nodes: "StatisticsGen"
execution_options {
  caching_options {
  }
}
, pipeline_info=id: "my_pipeline"
, pipeline_run_id='2022-02-03T11:10:24.358660')
INFO:absl:Processing schema from statistics for split train.
INFO:absl:Processing schema from statistics for split eval.
INFO:absl:Schema written to ./tfx_pipeline_output/my_pipeline/SchemaGen/schema/12/schema.pbtxt.
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 12 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'schema': [Artifact(artifact: uri: "./tfx_pipeline_output/my_pipeline/SchemaGen/schema/12"
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:10:24.358660:SchemaGen:schema:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.6.0"
  }
}
, artifact_type: name: "Schema"
)]}) for execution 12
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component SchemaGen is finished.

בחן את הפלט של Evaluator

שלב זה דורש הרחבה למחברת Jupyter של TensorFlow Model Analysis(TFMA). שימו לב שהגרסה של תוסף המחברת TFMA צריכה להיות זהה לגרסה של חבילת TFMA python.

הפקודה הבאה תתקין את סיומת המחברת TFMA מהרישום של NPM. ההשלמה עשויה להימשך מספר דקות.

# Install TFMA notebook extension.
jupyter labextension install tensorflow_model_analysis@{tfma.__version__}

usage: jupyter [-h] [--version] [--config-dir] [--data-dir] [--runtime-dir]
               [--paths] [--json] [--debug]
               [subcommand]

Jupyter: Interactive Computing

positional arguments:
  subcommand     the subcommand to launch

optional arguments:
  -h, --help     show this help message and exit
  --version      show the versions of core jupyter packages and exit
  --config-dir   show Jupyter config dir
  --data-dir     show Jupyter data dir
  --runtime-dir  show Jupyter runtime dir
  --paths        show all Jupyter paths. Add --json for machine-readable
                 format.
  --json         output paths as machine-readable json
  --debug        output debug information about paths

Available subcommands: bundlerextension console dejavu execute kernel
kernelspec migrate nbconvert nbextension notebook qtconsole run
serverextension troubleshoot trust

Jupyter command `jupyter-labextension` not found.

אם ההתקנה הושלמה, אנא טען מחדש את הדפדפן שלך כדי שהתוסף ייכנס לתוקף.

with metadata.Metadata(metadata_connection_config) as metadata_handler:
  # Search all aritfacts from the previous pipeline run.
  artifacts = get_latest_artifacts(metadata_handler.store, PIPELINE_NAME)
  model_evaluation_artifacts = find_latest_artifacts_by_type(
      metadata_handler.store, artifacts,
      standard_artifacts.ModelEvaluation.TYPE_NAME)

if model_evaluation_artifacts:
  tfma_result = tfma.load_eval_result(model_evaluation_artifacts[0].uri)
  tfma.view.render_slicing_metrics(tfma_result)

מוסיף רכיב Pusher לצינור.

אם המודל נראה מבטיח, עלינו לפרסם את המודל. רכיב ה-Pusher יכול לפרסם את המודל למיקום במערכת הקבצים או ל-GCP AI Platform Models באמצעות מבצע מותאם אישית .

רכיב ה- Evaluator באופן רציף כל דגם שנבנה מ- Trainer , ו- Pusher מעתיק את המודל למיקום מוגדר מראש במערכת הקבצים או אפילו ל- Google Cloud AI Platform Models .

1. ב- local_runner.py , הגדר את SERVING_MODEL_DIR לספרייה לפרסום.
2. בקובץ pipeline/pipeline.py , בטל את ההערה # components.append(pusher) כדי להוסיף Pusher לצינור.

אתה יכול לעדכן את הצינור ולהפעיל שוב.

# Update and run the pipeline.
!tfx pipeline update --engine=local --pipeline_path=local_runner.py \
 && tfx run create --engine=local --pipeline_name={PIPELINE_NAME}

CLI
Updating pipeline
INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Excluding no splits because exclude_splits is not set.
Pipeline "my_pipeline" updated successfully.
CLI
Creating a run for pipeline: my_pipeline
INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Excluding no splits because exclude_splits is not set.
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: "SchemaGen"
  value {
    python_class_executable_spec {
      class_path: "tfx.components.schema_gen.executor.Executor"
    }
  }
}
executor_specs {
  key: "StatisticsGen"
  value {
    beam_executable_spec {
      python_executor_spec {
        class_path: "tfx.components.statistics_gen.executor.Executor"
      }
    }
  }
}
custom_driver_specs {
  key: "CsvExampleGen"
  value {
    python_class_executable_spec {
      class_path: "tfx.components.example_gen.driver.FileBasedDriver"
    }
  }
}
metadata_connection_config {
  database_connection_config {
    sqlite {
      filename_uri: "./tfx_metadata/my_pipeline/metadata.db"
      connection_mode: READWRITE_OPENCREATE
    }
  }
}

INFO:absl:Using connection config:
 sqlite {
  filename_uri: "./tfx_metadata/my_pipeline/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: "my_pipeline"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2022-02-03T11:10:48.556314"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "my_pipeline.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
          }
          base_type: DATASET
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "input_base"
    value {
      field_value {
        string_value: "/home/kbuilder/imported/my_pipeline/data"
      }
    }
  }
  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: "StatisticsGen"
execution_options {
  caching_options {
  }
}

INFO:absl:MetadataStore with DB connection initialized
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 13
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=13, input_dict={}, output_dict=defaultdict(<class 'list'>, {'examples': [Artifact(artifact: uri: "./tfx_pipeline_output/my_pipeline/CsvExampleGen/examples/13"
custom_properties {
  key: "input_fingerprint"
  value {
    string_value: "split:single_split,num_files:1,total_bytes:25648,xor_checksum:1643886522,sum_checksum:1643886522"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:10:48.556314:CsvExampleGen:examples:0"
  }
}
custom_properties {
  key: "span"
  value {
    int_value: 0
  }
}
, artifact_type: name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
base_type: DATASET
)]}), exec_properties={'input_config': '{\n  "splits": [\n    {\n      "name": "single_split",\n      "pattern": "*"\n    }\n  ]\n}', 'output_config': '{\n  "split_config": {\n    "splits": [\n      {\n        "hash_buckets": 2,\n        "name": "train"\n      },\n      {\n        "hash_buckets": 1,\n        "name": "eval"\n      }\n    ]\n  }\n}', 'output_file_format': 5, 'output_data_format': 6, 'input_base': '/home/kbuilder/imported/my_pipeline/data', 'span': 0, 'version': None, 'input_fingerprint': 'split:single_split,num_files:1,total_bytes:25648,xor_checksum:1643886522,sum_checksum:1643886522'}, execution_output_uri='./tfx_pipeline_output/my_pipeline/CsvExampleGen/.system/executor_execution/13/executor_output.pb', stateful_working_dir='./tfx_pipeline_output/my_pipeline/CsvExampleGen/.system/stateful_working_dir/2022-02-03T11:10:48.556314', tmp_dir='./tfx_pipeline_output/my_pipeline/CsvExampleGen/.system/executor_execution/13/.temp/', pipeline_node=node_info {
  type {
    name: "tfx.components.example_gen.csv_example_gen.component.CsvExampleGen"
  }
  id: "CsvExampleGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "my_pipeline"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2022-02-03T11:10:48.556314"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "my_pipeline.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
          }
          base_type: DATASET
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "input_base"
    value {
      field_value {
        string_value: "/home/kbuilder/imported/my_pipeline/data"
      }
    }
  }
  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: "StatisticsGen"
execution_options {
  caching_options {
  }
}
, pipeline_info=id: "my_pipeline"
, pipeline_run_id='2022-02-03T11:10:48.556314')
INFO:absl:Generating examples.
INFO:absl:Processing input csv data /home/kbuilder/imported/my_pipeline/data/* to TFExample.
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
E0203 11:10:49.163841363    5734 fork_posix.cc:70]           Fork support is only compatible with the epoll1 and poll polling strategies
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:Value type <class 'NoneType'> of key version in exec_properties is not supported, going to drop it
INFO:absl:Value type <class 'list'> of key _beam_pipeline_args in exec_properties is not supported, going to drop it
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 13 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'examples': [Artifact(artifact: uri: "./tfx_pipeline_output/my_pipeline/CsvExampleGen/examples/13"
custom_properties {
  key: "input_fingerprint"
  value {
    string_value: "split:single_split,num_files:1,total_bytes:25648,xor_checksum:1643886522,sum_checksum:1643886522"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:10:48.556314:CsvExampleGen:examples:0"
  }
}
custom_properties {
  key: "span"
  value {
    int_value: 0
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.6.0"
  }
}
, artifact_type: name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
base_type: DATASET
)]}) for execution 13
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component CsvExampleGen is finished.
INFO:absl:Component StatisticsGen is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.components.statistics_gen.component.StatisticsGen"
    base_type: PROCESS
  }
  id: "StatisticsGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "my_pipeline"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2022-02-03T11:10:48.556314"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "my_pipeline.StatisticsGen"
      }
    }
  }
}
inputs {
  inputs {
    key: "examples"
    value {
      channels {
        producer_node_query {
          id: "CsvExampleGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "my_pipeline"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2022-02-03T11:10:48.556314"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "my_pipeline.CsvExampleGen"
            }
          }
        }
        artifact_query {
          type {
            name: "Examples"
            base_type: DATASET
          }
        }
        output_key: "examples"
      }
      min_count: 1
    }
  }
}
outputs {
  outputs {
    key: "statistics"
    value {
      artifact_spec {
        type {
          name: "ExampleStatistics"
          properties {
            key: "span"
            value: INT
          }
          properties {
            key: "split_names"
            value: STRING
          }
          base_type: STATISTICS
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "exclude_splits"
    value {
      field_value {
        string_value: "[]"
      }
    }
  }
}
upstream_nodes: "CsvExampleGen"
downstream_nodes: "SchemaGen"
execution_options {
  caching_options {
  }
}

INFO:absl:MetadataStore with DB connection initialized
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 14
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=14, input_dict={'examples': [Artifact(artifact: id: 13
type_id: 15
uri: "./tfx_pipeline_output/my_pipeline/CsvExampleGen/examples/13"
properties {
  key: "split_names"
  value {
    string_value: "[\"train\", \"eval\"]"
  }
}
custom_properties {
  key: "file_format"
  value {
    string_value: "tfrecords_gzip"
  }
}
custom_properties {
  key: "input_fingerprint"
  value {
    string_value: "split:single_split,num_files:1,total_bytes:25648,xor_checksum:1643886522,sum_checksum:1643886522"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:10:48.556314:CsvExampleGen:examples:0"
  }
}
custom_properties {
  key: "payload_format"
  value {
    string_value: "FORMAT_TF_EXAMPLE"
  }
}
custom_properties {
  key: "span"
  value {
    int_value: 0
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.6.0"
  }
}
state: LIVE
create_time_since_epoch: 1643886649739
last_update_time_since_epoch: 1643886649739
, artifact_type: id: 15
name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
base_type: DATASET
)]}, output_dict=defaultdict(<class 'list'>, {'statistics': [Artifact(artifact: uri: "./tfx_pipeline_output/my_pipeline/StatisticsGen/statistics/14"
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:10:48.556314:StatisticsGen:statistics:0"
  }
}
, artifact_type: name: "ExampleStatistics"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
base_type: STATISTICS
)]}), exec_properties={'exclude_splits': '[]'}, execution_output_uri='./tfx_pipeline_output/my_pipeline/StatisticsGen/.system/executor_execution/14/executor_output.pb', stateful_working_dir='./tfx_pipeline_output/my_pipeline/StatisticsGen/.system/stateful_working_dir/2022-02-03T11:10:48.556314', tmp_dir='./tfx_pipeline_output/my_pipeline/StatisticsGen/.system/executor_execution/14/.temp/', pipeline_node=node_info {
  type {
    name: "tfx.components.statistics_gen.component.StatisticsGen"
    base_type: PROCESS
  }
  id: "StatisticsGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "my_pipeline"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2022-02-03T11:10:48.556314"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "my_pipeline.StatisticsGen"
      }
    }
  }
}
inputs {
  inputs {
    key: "examples"
    value {
      channels {
        producer_node_query {
          id: "CsvExampleGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "my_pipeline"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2022-02-03T11:10:48.556314"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "my_pipeline.CsvExampleGen"
            }
          }
        }
        artifact_query {
          type {
            name: "Examples"
            base_type: DATASET
          }
        }
        output_key: "examples"
      }
      min_count: 1
    }
  }
}
outputs {
  outputs {
    key: "statistics"
    value {
      artifact_spec {
        type {
          name: "ExampleStatistics"
          properties {
            key: "span"
            value: INT
          }
          properties {
            key: "split_names"
            value: STRING
          }
          base_type: STATISTICS
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "exclude_splits"
    value {
      field_value {
        string_value: "[]"
      }
    }
  }
}
upstream_nodes: "CsvExampleGen"
downstream_nodes: "SchemaGen"
execution_options {
  caching_options {
  }
}
, pipeline_info=id: "my_pipeline"
, pipeline_run_id='2022-02-03T11:10:48.556314')
INFO:absl:Generating statistics for split train.
INFO:absl:Statistics for split train written to ./tfx_pipeline_output/my_pipeline/StatisticsGen/statistics/14/Split-train.
INFO:absl:Generating statistics for split eval.
INFO:absl:Statistics for split eval written to ./tfx_pipeline_output/my_pipeline/StatisticsGen/statistics/14/Split-eval.
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 14 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'statistics': [Artifact(artifact: uri: "./tfx_pipeline_output/my_pipeline/StatisticsGen/statistics/14"
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:10:48.556314:StatisticsGen:statistics:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.6.0"
  }
}
, artifact_type: name: "ExampleStatistics"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
base_type: STATISTICS
)]}) for execution 14
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component StatisticsGen is finished.
INFO:absl:Component SchemaGen is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.components.schema_gen.component.SchemaGen"
    base_type: PROCESS
  }
  id: "SchemaGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "my_pipeline"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2022-02-03T11:10:48.556314"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "my_pipeline.SchemaGen"
      }
    }
  }
}
inputs {
  inputs {
    key: "statistics"
    value {
      channels {
        producer_node_query {
          id: "StatisticsGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "my_pipeline"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2022-02-03T11:10:48.556314"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "my_pipeline.StatisticsGen"
            }
          }
        }
        artifact_query {
          type {
            name: "ExampleStatistics"
            base_type: STATISTICS
          }
        }
        output_key: "statistics"
      }
      min_count: 1
    }
  }
}
outputs {
  outputs {
    key: "schema"
    value {
      artifact_spec {
        type {
          name: "Schema"
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "exclude_splits"
    value {
      field_value {
        string_value: "[]"
      }
    }
  }
  parameters {
    key: "infer_feature_shape"
    value {
      field_value {
        int_value: 1
      }
    }
  }
}
upstream_nodes: "StatisticsGen"
execution_options {
  caching_options {
  }
}

INFO:absl:MetadataStore with DB connection initialized
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 15
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=15, input_dict={'statistics': [Artifact(artifact: id: 14
type_id: 17
uri: "./tfx_pipeline_output/my_pipeline/StatisticsGen/statistics/14"
properties {
  key: "split_names"
  value {
    string_value: "[\"train\", \"eval\"]"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:10:48.556314:StatisticsGen:statistics:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.6.0"
  }
}
state: LIVE
create_time_since_epoch: 1643886653128
last_update_time_since_epoch: 1643886653128
, artifact_type: id: 17
name: "ExampleStatistics"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
base_type: STATISTICS
)]}, output_dict=defaultdict(<class 'list'>, {'schema': [Artifact(artifact: uri: "./tfx_pipeline_output/my_pipeline/SchemaGen/schema/15"
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:10:48.556314:SchemaGen:schema:0"
  }
}
, artifact_type: name: "Schema"
)]}), exec_properties={'infer_feature_shape': 1, 'exclude_splits': '[]'}, execution_output_uri='./tfx_pipeline_output/my_pipeline/SchemaGen/.system/executor_execution/15/executor_output.pb', stateful_working_dir='./tfx_pipeline_output/my_pipeline/SchemaGen/.system/stateful_working_dir/2022-02-03T11:10:48.556314', tmp_dir='./tfx_pipeline_output/my_pipeline/SchemaGen/.system/executor_execution/15/.temp/', pipeline_node=node_info {
  type {
    name: "tfx.components.schema_gen.component.SchemaGen"
    base_type: PROCESS
  }
  id: "SchemaGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "my_pipeline"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2022-02-03T11:10:48.556314"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "my_pipeline.SchemaGen"
      }
    }
  }
}
inputs {
  inputs {
    key: "statistics"
    value {
      channels {
        producer_node_query {
          id: "StatisticsGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "my_pipeline"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2022-02-03T11:10:48.556314"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "my_pipeline.StatisticsGen"
            }
          }
        }
        artifact_query {
          type {
            name: "ExampleStatistics"
            base_type: STATISTICS
          }
        }
        output_key: "statistics"
      }
      min_count: 1
    }
  }
}
outputs {
  outputs {
    key: "schema"
    value {
      artifact_spec {
        type {
          name: "Schema"
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "exclude_splits"
    value {
      field_value {
        string_value: "[]"
      }
    }
  }
  parameters {
    key: "infer_feature_shape"
    value {
      field_value {
        int_value: 1
      }
    }
  }
}
upstream_nodes: "StatisticsGen"
execution_options {
  caching_options {
  }
}
, pipeline_info=id: "my_pipeline"
, pipeline_run_id='2022-02-03T11:10:48.556314')
INFO:absl:Processing schema from statistics for split train.
INFO:absl:Processing schema from statistics for split eval.
INFO:absl:Schema written to ./tfx_pipeline_output/my_pipeline/SchemaGen/schema/15/schema.pbtxt.
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 15 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'schema': [Artifact(artifact: uri: "./tfx_pipeline_output/my_pipeline/SchemaGen/schema/15"
custom_properties {
  key: "name"
  value {
    string_value: "my_pipeline:2022-02-03T11:10:48.556314:SchemaGen:schema:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.6.0"
  }
}
, artifact_type: name: "Schema"
)]}) for execution 15
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component SchemaGen is finished.

אתה אמור להיות מסוגל למצוא את הדגם החדש שלך ב- SERVING_MODEL_DIR .

שלב 6. (אופציונלי) פרוס את הצינור שלך ל-Kubeflow Pipelines ב-GCP.

כפי שהוזכר קודם לכן, local_runner.py טוב למטרות איתור באגים או פיתוח, אך אינו הפתרון הטוב ביותר לעומסי עבודה בייצור. בשלב זה, נפרוס את הצינור ל-Kubeflow Pipelines ב-Google Cloud.

הכנה

אנחנו צריכים את חבילת kfp python ותוכנית skaffold כדי לפרוס צינור לאשכול של Kubeflow Pipelines.

pip install --upgrade -q kfp

# Download skaffold and set it executable.
curl -Lo skaffold https://storage.googleapis.com/skaffold/releases/latest/skaffold-linux-amd64 && chmod +x skaffold

אתה צריך להעביר skaffold בינארי למקום שבו הקונכייה שלך יכולה למצוא אותו. או שאתה יכול לציין את הנתיב ל-skaffold כאשר אתה מפעיל את tfx בינארי עם דגל --skaffold-cmd .

# Move skaffold binary into your path
mv skaffold /home/jupyter/.local/bin/

mv: cannot move 'skaffold' to '/home/jupyter/.local/bin/': No such file or directory

אתה צריך גם אשכול Kubeflow Pipelines כדי להפעיל את הצינור. אנא עקוב אחר שלב 1 ו-2 במדריך TFX ב-Cloud AI Platform Pipelines .

כאשר האשכול שלך מוכן, פתח את לוח המחוונים של הצינורות על ידי לחיצה על פתח את לוח המחוונים של Pipelines בדף הצינורות של מסוף הענן של Google . כתובת האתר של דף זה היא ENDPOINT כדי לבקש הפעלת צינור. ערך נקודת הקצה הוא כל מה שנמצא בכתובת האתר אחרי https://, עד וכולל googleusercontent.com. שים את נקודת הקצה שלך לבלוק הקוד הבא.

ENDPOINT='' # Enter your ENDPOINT here.

כדי להפעיל את הקוד שלנו באשכול Kubeflow Pipelines, עלינו לארוז את הקוד שלנו בתמונת קונטיינר. התמונה תיבנה באופן אוטומטי בזמן פריסת הצינור שלנו, ואתה רק צריך להגדיר שם ורישום קונטיינר עבור התמונה שלך. בדוגמה שלנו, נשתמש ב- Google Container registry , ונקרא לו tfx-pipeline .

# Read GCP project id from env.
shell_output=!gcloud config list --format 'value(core.project)' 2>/dev/null
GOOGLE_CLOUD_PROJECT=shell_output[0]

# Docker image name for the pipeline image.
CUSTOM_TFX_IMAGE='gcr.io/' + GOOGLE_CLOUD_PROJECT + '/tfx-pipeline'

הגדר את מיקום הנתונים.

הנתונים שלך צריכים להיות נגישים מאשכול Kubeflow Pipelines. אם השתמשת בנתונים בסביבה המקומית שלך, ייתכן שתצטרך להעלות אותם לאחסון מרוחק כמו Google Cloud Storage. לדוגמה, אנו יכולים להעלות נתוני פינגווין לדלי ברירת מחדל שנוצר באופן אוטומטי כאשר נפרס אשכול Kubeflow Pipelines כמו להלן.

gsutil cp data/data.csv gs://{GOOGLE_CLOUD_PROJECT}-kubeflowpipelines-default/tfx-template/data/penguin/

Copying file://data/data.csv [Content-Type=text/csv]...
NotFoundException: 404 The destination bucket gs://tf-benchmark-dashboard-kubeflowpipelines-default does not exist or the write to the destination must be restarted

עדכן את מיקום הנתונים המאוחסן ב- DATA_PATH ב- kubeflow_runner.py .

אם אתה משתמש ב-BigQueryExampleGen, אין צורך להעלות את קובץ הנתונים, אך אנא ודא ש- kubeflow_runner.py משתמש באותה query beam_pipeline_args עבור הפונקציה pipeline.create_pipeline() .

פרוס את הצינור.

אם הכל מוכן, אתה יכול ליצור צינור באמצעות הפקודה tfx pipeline create .

!tfx pipeline create  \
--engine=kubeflow \
--pipeline-path=kubeflow_runner.py \
--endpoint={ENDPOINT} \
--build-target-image={CUSTOM_TFX_IMAGE}

CLI
[Error] --build-target-image flag was DELETED. You should specify the build target image at the `KubeflowDagRunnerConfig` class instead, and use --build-image flag without argument to build a container image when creating or updating a pipeline.

כעת התחל ריצת ביצוע עם הצינור החדש שנוצר באמצעות הפקודה tfx run create .

tfx run create --engine=kubeflow --pipeline-name={PIPELINE_NAME} --endpoint={ENDPOINT}

CLI
Creating a run for pipeline: my_pipeline
Failed to load kube config.
Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/urllib3/connection.py", line 175, in _new_conn
    (self._dns_host, self.port), self.timeout, **extra_kw
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/urllib3/util/connection.py", line 95, in create_connection
    raise err
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/urllib3/util/connection.py", line 85, in create_connection
    sock.connect(sa)
ConnectionRefusedError: [Errno 111] Connection refused

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/urllib3/connectionpool.py", line 710, in urlopen
    chunked=chunked,
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/urllib3/connectionpool.py", line 398, in _make_request
    conn.request(method, url, **httplib_request_kw)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/urllib3/connection.py", line 239, in request
    super(HTTPConnection, self).request(method, url, body=body, headers=headers)
  File "/usr/lib/python3.7/http/client.py", line 1256, in request
    self._send_request(method, url, body, headers, encode_chunked)
  File "/usr/lib/python3.7/http/client.py", line 1302, in _send_request
    self.endheaders(body, encode_chunked=encode_chunked)
  File "/usr/lib/python3.7/http/client.py", line 1251, in endheaders
    self._send_output(message_body, encode_chunked=encode_chunked)
  File "/usr/lib/python3.7/http/client.py", line 1030, in _send_output
    self.send(msg)
  File "/usr/lib/python3.7/http/client.py", line 970, in send
    self.connect()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/urllib3/connection.py", line 205, in connect
    conn = self._new_conn()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/urllib3/connection.py", line 187, in _new_conn
    self, "Failed to establish a new connection: %s" % e
urllib3.exceptions.NewConnectionError: <urllib3.connection.HTTPConnection object at 0x7ff729e34190>: Failed to establish a new connection: [Errno 111] Connection refused

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/tmpfs/src/tf_docs_env/bin/tfx", line 8, in <module>
    sys.exit(cli_group())
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/click/core.py", line 829, in __call__
    return self.main(*args, **kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/click/core.py", line 782, in main
    rv = self.invoke(ctx)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/click/core.py", line 1259, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/click/core.py", line 1259, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/click/core.py", line 1066, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/click/core.py", line 610, in invoke
    return callback(*args, **kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/click/decorators.py", line 73, in new_func
    return ctx.invoke(f, obj, *args, **kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/click/core.py", line 610, in invoke
    return callback(*args, **kwargs)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tfx/tools/cli/commands/run.py", line 94, in create_run
    handler = handler_factory.create_handler(ctx.flags_dict)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tfx/tools/cli/handler/handler_factory.py", line 93, in create_handler
    return kubeflow_handler.KubeflowHandler(flags_dict)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tfx/tools/cli/handler/kubeflow_handler.py", line 62, in __init__
    namespace=self.flags_dict[labels.NAMESPACE])
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/kfp/_client.py", line 197, in __init__
    if not self._context_setting['namespace'] and self.get_kfp_healthz(
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/kfp/_client.py", line 411, in get_kfp_healthz
    response = self._healthz_api.get_healthz()
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/kfp_server_api/api/healthz_service_api.py", line 63, in get_healthz
    return self.get_healthz_with_http_info(**kwargs)  # noqa: E501
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/kfp_server_api/api/healthz_service_api.py", line 148, in get_healthz_with_http_info
    collection_formats=collection_formats)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/kfp_server_api/api_client.py", line 369, in call_api
    _preload_content, _request_timeout, _host)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/kfp_server_api/api_client.py", line 185, in __call_api
    _request_timeout=_request_timeout)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/kfp_server_api/api_client.py", line 393, in request
    headers=headers)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/kfp_server_api/rest.py", line 234, in GET
    query_params=query_params)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/kfp_server_api/rest.py", line 212, in request
    headers=headers)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/urllib3/request.py", line 75, in request
    method, url, fields=fields, headers=headers, **urlopen_kw
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/urllib3/request.py", line 96, in request_encode_url
    return self.urlopen(method, url, **extra_kw)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/urllib3/poolmanager.py", line 375, in urlopen
    response = conn.urlopen(method, u.request_uri, **kw)
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/urllib3/connectionpool.py", line 826, in urlopen
    **response_kw
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/urllib3/connectionpool.py", line 826, in urlopen
    **response_kw
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/urllib3/connectionpool.py", line 826, in urlopen
    **response_kw
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/urllib3/connectionpool.py", line 786, in urlopen
    method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]
  File "/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/urllib3/util/retry.py", line 592, in increment
    raise MaxRetryError(_pool, url, error or ResponseError(cause))
urllib3.exceptions.MaxRetryError: HTTPConnectionPool(host='localhost', port=80): Max retries exceeded with url: /apis/v1beta1/healthz (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7ff729e34190>: Failed to establish a new connection: [Errno 111] Connection refused'))

לחלופין, אתה יכול גם להפעיל את הצינור בלוח המחוונים של Kubeflow Pipelines. ההרצה החדשה תופיע תחת Experiments בלוח המחוונים של Kubeflow Pipelines. לחיצה על הניסוי תאפשר לך לעקוב אחר ההתקדמות ולדמיין את החפצים שנוצרו במהלך ריצת הביצוע.

אם אתה מעוניין להפעיל את הצינור שלך ב-Kubeflow Pipelines, מצא הנחיות נוספות ב- TFX ב-Cloud AI Platform Pipelines .

ניקיון

כדי לנקות את כל המשאבים של Google Cloud המשמשים בשלב זה, אתה יכול למחוק את פרויקט Google Cloud שבו השתמשת עבור המדריך.

לחלופין, תוכל לנקות משאבים בודדים על ידי ביקור בכל קונסולות:

• Google Cloud Storage
• רישום המכולות של גוגל
• Google Kubernetes Engine