TFX पाइपलाइन और TensorFlow डेटा सत्यापन का उपयोग करके डेटा सत्यापन

TensorFlow.org पर देखें

Google Colab में चलाएं

GitHub पर स्रोत देखें

नोटबुक डाउनलोड करें

इस नोटबुक-आधारित ट्यूटोरियल में, हम इनपुट डेटा को मान्य करने और एक ML मॉडल बनाने के लिए TFX पाइपलाइन बनाएंगे और चलाएंगे। इस नोटबुक TFX पाइपलाइन हम में बनाया पर आधारित है सरल TFX पाइपलाइन ट्यूटोरियल । यदि आपने अभी तक उस ट्यूटोरियल को नहीं पढ़ा है, तो इस नोटबुक के साथ आगे बढ़ने से पहले आपको इसे पढ़ना चाहिए।

किसी भी डेटा साइंस या एमएल प्रोजेक्ट में पहला काम डेटा को समझना और साफ करना है, जिसमें शामिल हैं:

• प्रत्येक सुविधा के बारे में डेटा प्रकार, वितरण और अन्य जानकारी (जैसे, माध्य मान, या अद्वितीय की संख्या) को समझना
• डेटा का वर्णन करने वाली प्रारंभिक स्कीमा बनाना
• दिए गए स्कीमा के संबंध में डेटा में विसंगतियों और लापता मूल्यों की पहचान करना

इस ट्यूटोरियल में, हम दो TFX पाइपलाइन बनाएंगे।

सबसे पहले, हम डेटासेट का विश्लेषण करने के लिए एक पाइपलाइन बनाएंगे और दिए गए डेटासेट की प्रारंभिक स्कीमा तैयार करेंगे। इस पाइप लाइन के दो नए घटकों, शामिल होंगे StatisticsGen और SchemaGen ।

एक बार हमारे पास डेटा का उचित स्कीमा हो जाने के बाद, हम पिछले ट्यूटोरियल से पाइपलाइन के आधार पर एमएल वर्गीकरण मॉडल को प्रशिक्षित करने के लिए एक पाइपलाइन तैयार करेंगे। इस पाइप लाइन में, हम पहले पाइप लाइन और एक नया घटक, से स्कीमा का उपयोग करेगा ExampleValidator , इनपुट डेटा को मान्य करने के।

तीन नए घटकों, StatisticsGen, SchemaGen और ExampleValidator, डेटा विश्लेषण और सत्यापन के लिए TFX घटक हैं, और वे का उपयोग करके लागू TensorFlow डेटा मान्यता पुस्तकालय।

कृपया देखें TFX पाइपलाइन को समझना TFX में विभिन्न अवधारणाओं के बारे में अधिक जानने के लिए।

सेट अप

हमें सबसे पहले टीएफएक्स पायथन पैकेज को स्थापित करना होगा और डेटासेट डाउनलोड करना होगा जिसका उपयोग हम अपने मॉडल के लिए करेंगे।

पिप अपग्रेड करें

स्थानीय रूप से चलते समय सिस्टम में पिप को अपग्रेड करने से बचने के लिए, यह सुनिश्चित करने के लिए जांचें कि हम कोलाब में चल रहे हैं। स्थानीय प्रणालियों को निश्चित रूप से अलग से अपग्रेड किया जा सकता है।

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

टीएफएक्स स्थापित करें

pip install -U tfx

क्या आपने रनटाइम को पुनरारंभ किया?

यदि आप Google Colab का उपयोग कर रहे हैं, जब आप पहली बार ऊपर सेल चलाते हैं, तो आपको "रनटाइम को पुनरारंभ करें" बटन पर क्लिक करके या "रनटाइम> रनटाइम पुनरारंभ करें ..." मेनू का उपयोग करके रनटाइम को पुनरारंभ करना होगा। ऐसा इसलिए है क्योंकि Colab संकुल को लोड करता है।

TensorFlow और TFX संस्करणों की जाँच करें।

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

TensorFlow version: 2.6.2
TFX version: 1.4.0

चर सेट करें

पाइपलाइन को परिभाषित करने के लिए कुछ चर का उपयोग किया जाता है। आप इन चरों को अपनी इच्छानुसार अनुकूलित कर सकते हैं। डिफ़ॉल्ट रूप से पाइपलाइन से सभी आउटपुट वर्तमान निर्देशिका के तहत उत्पन्न होंगे।

import os

# We will create two pipelines. One for schema generation and one for training.
SCHEMA_PIPELINE_NAME = "penguin-tfdv-schema"
PIPELINE_NAME = "penguin-tfdv"

# Output directory to store artifacts generated from the pipeline.
SCHEMA_PIPELINE_ROOT = os.path.join('pipelines', SCHEMA_PIPELINE_NAME)
PIPELINE_ROOT = os.path.join('pipelines', PIPELINE_NAME)
# Path to a SQLite DB file to use as an MLMD storage.
SCHEMA_METADATA_PATH = os.path.join('metadata', SCHEMA_PIPELINE_NAME,
                                    'metadata.db')
METADATA_PATH = os.path.join('metadata', PIPELINE_NAME, 'metadata.db')

# Output directory where created models from the pipeline will be exported.
SERVING_MODEL_DIR = os.path.join('serving_model', PIPELINE_NAME)

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

उदाहरण डेटा तैयार करें

हम अपने TFX पाइपलाइन में उपयोग के लिए उदाहरण डेटासेट डाउनलोड करेंगे। डाटासेट हम उपयोग कर रहे हैं पामर पेंगुइन डाटासेट जो भी अन्य में प्रयोग किया जाता है TFX उदाहरण ।

इस डेटासेट में चार अंकीय विशेषताएं हैं:

• culmen_length_mm
• कल्मेन_डेप्थ_मिमी
• फ्लिपर_लेंथ_मिमी
• बॉडी_मास_जी

सभी सुविधाओं को पहले से ही [0,1] श्रेणी के लिए सामान्यीकृत किया गया था। हम एक वर्गीकरण मॉडल जो भविष्यवाणी का निर्माण करेगा species पेंगुइन की।

क्योंकि TFX exampleGen घटक एक निर्देशिका से इनपुट पढ़ता है, हमें एक निर्देशिका बनाने और उसमें डेटासेट की प्रतिलिपि बनाने की आवश्यकता है।

import urllib.request
import tempfile

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

('/tmp/tfx-datan3p7t1d2/data.csv', <http.client.HTTPMessage at 0x7f8d2f9f9110>)

सीएसवी फ़ाइल पर एक त्वरित नज़र डालें।

head {_data_filepath}

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

आपको पांच फीचर कॉलम देखने में सक्षम होना चाहिए। species 0, 1 या 2 में से एक है, और अन्य सभी सुविधाओं मान 0 और 1. के बीच हम इस डेटासेट का विश्लेषण करने के लिए एक TFX पाइपलाइन का निर्माण करेगा होना चाहिए।

एक प्रारंभिक स्कीमा उत्पन्न करें

टीएफएक्स पाइपलाइनों को पायथन एपीआई का उपयोग करके परिभाषित किया गया है। हम इनपुट उदाहरणों से स्वचालित रूप से एक स्कीमा उत्पन्न करने के लिए एक पाइपलाइन बनाएंगे। इस स्कीमा की समीक्षा एक मानव द्वारा की जा सकती है और आवश्यकतानुसार समायोजित की जा सकती है। एक बार स्कीमा को अंतिम रूप देने के बाद इसका उपयोग प्रशिक्षण और बाद के कार्यों में उदाहरण सत्यापन के लिए किया जा सकता है।

के अलावा CsvExampleGen जिसमें प्रयोग किया जाता है सरल TFX पाइपलाइन ट्यूटोरियल , हम का उपयोग करेगा StatisticsGen और SchemaGen :

• StatisticsGen डाटासेट के लिए आँकड़े गणना करता है।
• SchemaGen आंकड़ों की जांच करता है और एक प्रारंभिक डेटा स्कीमा पैदा करता है।

प्रत्येक घटक के लिए गाइड देखें या TFX घटकों ट्यूटोरियल इन घटकों के बारे में अधिक जानने के लिए।

एक पाइपलाइन परिभाषा लिखें

हम एक TFX पाइपलाइन बनाने के लिए एक फ़ंक्शन को परिभाषित करते हैं। एक Pipeline वस्तु एक TFX पाइपलाइन जो पाइप लाइन आर्केस्ट्रा प्रणाली है कि TFX का समर्थन करता है में से एक का उपयोग कर चलाया जा सकता है प्रतिनिधित्व करता है।

def _create_schema_pipeline(pipeline_name: str,
                            pipeline_root: str,
                            data_root: str,
                            metadata_path: str) -> tfx.dsl.Pipeline:
  """Creates a pipeline for schema generation."""
  # Brings data into the pipeline.
  example_gen = tfx.components.CsvExampleGen(input_base=data_root)

  # NEW: Computes statistics over data for visualization and schema generation.
  statistics_gen = tfx.components.StatisticsGen(
      examples=example_gen.outputs['examples'])

  # NEW: Generates schema based on the generated statistics.
  schema_gen = tfx.components.SchemaGen(
      statistics=statistics_gen.outputs['statistics'], infer_feature_shape=True)

  components = [
      example_gen,
      statistics_gen,
      schema_gen,
  ]

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

पाइपलाइन चलाएं

हम का उपयोग करेगा LocalDagRunner पिछले ट्यूटोरियल में के रूप में।

tfx.orchestration.LocalDagRunner().run(
  _create_schema_pipeline(
      pipeline_name=SCHEMA_PIPELINE_NAME,
      pipeline_root=SCHEMA_PIPELINE_ROOT,
      data_root=DATA_ROOT,
      metadata_path=SCHEMA_METADATA_PATH))

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 {
  sqlite {
    filename_uri: "metadata/penguin-tfdv-schema/metadata.db"
    connection_mode: READWRITE_OPENCREATE
  }
}

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

INFO:absl:Component CsvExampleGen is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.components.example_gen.csv_example_gen.component.CsvExampleGen"
  }
  id: "CsvExampleGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-tfdv-schema"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-12-05T11:10:06.420329"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-tfdv-schema.CsvExampleGen"
      }
    }
  }
}
outputs {
  outputs {
    key: "examples"
    value {
      artifact_spec {
        type {
          name: "Examples"
          properties {
            key: "span"
            value: INT
          }
          properties {
            key: "split_names"
            value: STRING
          }
          properties {
            key: "version"
            value: INT
          }
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "input_base"
    value {
      field_value {
        string_value: "/tmp/tfx-datan3p7t1d2"
      }
    }
  }
  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
WARNING: Logging before InitGoogleLogging() is written to STDERR
I1205 11:10:06.444468  4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 11:10:06.453292  4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 11:10:06.460209  4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 11:10:06.467104  4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:select span and version = (0, None)
INFO:absl:latest span and version = (0, None)
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 1
I1205 11:10:06.521926  4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=1, input_dict={}, output_dict=defaultdict(<class 'list'>, {'examples': [Artifact(artifact: uri: "pipelines/penguin-tfdv-schema/CsvExampleGen/examples/1"
custom_properties {
  key: "input_fingerprint"
  value {
    string_value: "split:single_split,num_files:1,total_bytes:25648,xor_checksum:1638702606,sum_checksum:1638702606"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfdv-schema:2021-12-05T11:10:06.420329:CsvExampleGen:examples:0"
  }
}
custom_properties {
  key: "span"
  value {
    int_value: 0
  }
}
, artifact_type: name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
)]}), exec_properties={'input_config': '{\n  "splits": [\n    {\n      "name": "single_split",\n      "pattern": "*"\n    }\n  ]\n}', 'output_config': '{\n  "split_config": {\n    "splits": [\n      {\n        "hash_buckets": 2,\n        "name": "train"\n      },\n      {\n        "hash_buckets": 1,\n        "name": "eval"\n      }\n    ]\n  }\n}', 'input_base': '/tmp/tfx-datan3p7t1d2', 'output_file_format': 5, 'output_data_format': 6, 'span': 0, 'version': None, 'input_fingerprint': 'split:single_split,num_files:1,total_bytes:25648,xor_checksum:1638702606,sum_checksum:1638702606'}, execution_output_uri='pipelines/penguin-tfdv-schema/CsvExampleGen/.system/executor_execution/1/executor_output.pb', stateful_working_dir='pipelines/penguin-tfdv-schema/CsvExampleGen/.system/stateful_working_dir/2021-12-05T11:10:06.420329', tmp_dir='pipelines/penguin-tfdv-schema/CsvExampleGen/.system/executor_execution/1/.temp/', pipeline_node=node_info {
  type {
    name: "tfx.components.example_gen.csv_example_gen.component.CsvExampleGen"
  }
  id: "CsvExampleGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-tfdv-schema"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-12-05T11:10:06.420329"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-tfdv-schema.CsvExampleGen"
      }
    }
  }
}
outputs {
  outputs {
    key: "examples"
    value {
      artifact_spec {
        type {
          name: "Examples"
          properties {
            key: "span"
            value: INT
          }
          properties {
            key: "split_names"
            value: STRING
          }
          properties {
            key: "version"
            value: INT
          }
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "input_base"
    value {
      field_value {
        string_value: "/tmp/tfx-datan3p7t1d2"
      }
    }
  }
  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: "penguin-tfdv-schema"
, pipeline_run_id='2021-12-05T11:10:06.420329')
INFO:absl:Generating examples.
WARNING:apache_beam.runners.interactive.interactive_environment:Dependencies required for Interactive Beam PCollection visualization are not available, please use: `pip install apache-beam[interactive]` to install necessary dependencies to enable all data visualization features.
INFO:absl:Processing input csv data /tmp/tfx-datan3p7t1d2/* to TFExample.
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.
INFO:absl:Examples generated.
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 1 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'examples': [Artifact(artifact: uri: "pipelines/penguin-tfdv-schema/CsvExampleGen/examples/1"
custom_properties {
  key: "input_fingerprint"
  value {
    string_value: "split:single_split,num_files:1,total_bytes:25648,xor_checksum:1638702606,sum_checksum:1638702606"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfdv-schema:2021-12-05T11:10:06.420329:CsvExampleGen:examples:0"
  }
}
custom_properties {
  key: "span"
  value {
    int_value: 0
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.4.0"
  }
}
, artifact_type: name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
)]}) for execution 1
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component CsvExampleGen is finished.
INFO:absl:Component StatisticsGen is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.components.statistics_gen.component.StatisticsGen"
  }
  id: "StatisticsGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-tfdv-schema"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-12-05T11:10:06.420329"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-tfdv-schema.StatisticsGen"
      }
    }
  }
}
inputs {
  inputs {
    key: "examples"
    value {
      channels {
        producer_node_query {
          id: "CsvExampleGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "penguin-tfdv-schema"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2021-12-05T11:10:06.420329"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "penguin-tfdv-schema.CsvExampleGen"
            }
          }
        }
        artifact_query {
          type {
            name: "Examples"
          }
        }
        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
          }
        }
      }
    }
  }
}
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
I1205 11:10:08.104562  4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 2
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=2, input_dict={'examples': [Artifact(artifact: id: 1
type_id: 15
uri: "pipelines/penguin-tfdv-schema/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:1638702606,sum_checksum:1638702606"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfdv-schema:2021-12-05T11:10:06.420329: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: "1.4.0"
  }
}
state: LIVE
create_time_since_epoch: 1638702608076
last_update_time_since_epoch: 1638702608076
, artifact_type: id: 15
name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
)]}, output_dict=defaultdict(<class 'list'>, {'statistics': [Artifact(artifact: uri: "pipelines/penguin-tfdv-schema/StatisticsGen/statistics/2"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfdv-schema:2021-12-05T11:10:06.420329:StatisticsGen:statistics:0"
  }
}
, artifact_type: name: "ExampleStatistics"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
)]}), exec_properties={'exclude_splits': '[]'}, execution_output_uri='pipelines/penguin-tfdv-schema/StatisticsGen/.system/executor_execution/2/executor_output.pb', stateful_working_dir='pipelines/penguin-tfdv-schema/StatisticsGen/.system/stateful_working_dir/2021-12-05T11:10:06.420329', tmp_dir='pipelines/penguin-tfdv-schema/StatisticsGen/.system/executor_execution/2/.temp/', pipeline_node=node_info {
  type {
    name: "tfx.components.statistics_gen.component.StatisticsGen"
  }
  id: "StatisticsGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-tfdv-schema"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-12-05T11:10:06.420329"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-tfdv-schema.StatisticsGen"
      }
    }
  }
}
inputs {
  inputs {
    key: "examples"
    value {
      channels {
        producer_node_query {
          id: "CsvExampleGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "penguin-tfdv-schema"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2021-12-05T11:10:06.420329"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "penguin-tfdv-schema.CsvExampleGen"
            }
          }
        }
        artifact_query {
          type {
            name: "Examples"
          }
        }
        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
          }
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "exclude_splits"
    value {
      field_value {
        string_value: "[]"
      }
    }
  }
}
upstream_nodes: "CsvExampleGen"
downstream_nodes: "SchemaGen"
execution_options {
  caching_options {
  }
}
, pipeline_info=id: "penguin-tfdv-schema"
, pipeline_run_id='2021-12-05T11:10:06.420329')
INFO:absl:Generating statistics for split train.
INFO:absl:Statistics for split train written to pipelines/penguin-tfdv-schema/StatisticsGen/statistics/2/Split-train.
INFO:absl:Generating statistics for split eval.
INFO:absl:Statistics for split eval written to pipelines/penguin-tfdv-schema/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: "pipelines/penguin-tfdv-schema/StatisticsGen/statistics/2"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfdv-schema:2021-12-05T11:10:06.420329:StatisticsGen:statistics:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.4.0"
  }
}
, artifact_type: name: "ExampleStatistics"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
)]}) 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"
  }
  id: "SchemaGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-tfdv-schema"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-12-05T11:10:06.420329"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-tfdv-schema.SchemaGen"
      }
    }
  }
}
inputs {
  inputs {
    key: "statistics"
    value {
      channels {
        producer_node_query {
          id: "StatisticsGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "penguin-tfdv-schema"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2021-12-05T11:10:06.420329"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "penguin-tfdv-schema.StatisticsGen"
            }
          }
        }
        artifact_query {
          type {
            name: "ExampleStatistics"
          }
        }
        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
I1205 11:10:10.975282  4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 3
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=3, input_dict={'statistics': [Artifact(artifact: id: 2
type_id: 17
uri: "pipelines/penguin-tfdv-schema/StatisticsGen/statistics/2"
properties {
  key: "split_names"
  value {
    string_value: "[\"train\", \"eval\"]"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfdv-schema:2021-12-05T11:10:06.420329:StatisticsGen:statistics:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.4.0"
  }
}
state: LIVE
create_time_since_epoch: 1638702610957
last_update_time_since_epoch: 1638702610957
, artifact_type: id: 17
name: "ExampleStatistics"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
)]}, output_dict=defaultdict(<class 'list'>, {'schema': [Artifact(artifact: uri: "pipelines/penguin-tfdv-schema/SchemaGen/schema/3"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfdv-schema:2021-12-05T11:10:06.420329:SchemaGen:schema:0"
  }
}
, artifact_type: name: "Schema"
)]}), exec_properties={'exclude_splits': '[]', 'infer_feature_shape': 1}, execution_output_uri='pipelines/penguin-tfdv-schema/SchemaGen/.system/executor_execution/3/executor_output.pb', stateful_working_dir='pipelines/penguin-tfdv-schema/SchemaGen/.system/stateful_working_dir/2021-12-05T11:10:06.420329', tmp_dir='pipelines/penguin-tfdv-schema/SchemaGen/.system/executor_execution/3/.temp/', pipeline_node=node_info {
  type {
    name: "tfx.components.schema_gen.component.SchemaGen"
  }
  id: "SchemaGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-tfdv-schema"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-12-05T11:10:06.420329"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-tfdv-schema.SchemaGen"
      }
    }
  }
}
inputs {
  inputs {
    key: "statistics"
    value {
      channels {
        producer_node_query {
          id: "StatisticsGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "penguin-tfdv-schema"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2021-12-05T11:10:06.420329"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "penguin-tfdv-schema.StatisticsGen"
            }
          }
        }
        artifact_query {
          type {
            name: "ExampleStatistics"
          }
        }
        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: "penguin-tfdv-schema"
, pipeline_run_id='2021-12-05T11:10:06.420329')
INFO:absl:Processing schema from statistics for split train.
INFO:absl:Processing schema from statistics for split eval.
INFO:absl:Schema written to pipelines/penguin-tfdv-schema/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: "pipelines/penguin-tfdv-schema/SchemaGen/schema/3"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfdv-schema:2021-12-05T11:10:06.420329:SchemaGen:schema:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.4.0"
  }
}
, artifact_type: name: "Schema"
)]}) for execution 3
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component SchemaGen is finished.
I1205 11:10:11.010145  4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type

आपको "सूचना: एबीएसएल: घटक स्कीमाजेन समाप्त" देखना चाहिए। यदि पाइपलाइन सफलतापूर्वक समाप्त हो गई।

हम अपने डेटासेट को समझने के लिए पाइपलाइन के आउटपुट की जांच करेंगे।

पाइपलाइन के आउटपुट की समीक्षा करें

जैसा कि पिछले ट्यूटोरियल में बताया गया है, एक TFX पाइपलाइन आउटपुट, कलाकृतियों और एक के दो प्रकार का उत्पादन मेटाडाटा डीबी (MLMD) जो कलाकृतियों और पाइपलाइन फांसी के मेटाडाटा शामिल हैं। हमने उपरोक्त कोशिकाओं में इन आउटपुट के स्थान को परिभाषित किया है। डिफ़ॉल्ट रूप से, कलाकृतियों के तहत जमा हो जाती है pipelines निर्देशिका और मेटाडाटा के तहत एक SQLite डेटाबेस के रूप में संग्रहीत किया जाता है metadata निर्देशिका।

इन आउटपुट को प्रोग्रामेटिक रूप से ढूंढने के लिए आप एमएलएमडी एपीआई का उपयोग कर सकते हैं। सबसे पहले, हम कुछ उपयोगिता कार्यों को परिभाषित करेंगे जो कि अभी उत्पादित आउटपुट कलाकृतियों की खोज के लिए हैं।

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

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

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

# Non-public APIs, just for showcase.
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()

अब हम पाइपलाइन निष्पादन से आउटपुट की जांच कर सकते हैं।

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

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

with Metadata(metadata_connection_config) as metadata_handler:
  # Find output artifacts from MLMD.
  stat_gen_output = get_latest_artifacts(metadata_handler, SCHEMA_PIPELINE_NAME,
                                         'StatisticsGen')
  stats_artifacts = stat_gen_output[standard_component_specs.STATISTICS_KEY]

  schema_gen_output = get_latest_artifacts(metadata_handler,
                                           SCHEMA_PIPELINE_NAME, 'SchemaGen')
  schema_artifacts = schema_gen_output[standard_component_specs.SCHEMA_KEY]

INFO:absl:MetadataStore with DB connection initialized

यह प्रत्येक घटक से आउटपुट की जांच करने का समय है। जैसा कि ऊपर वर्णित, Tensorflow डेटा मान्यता (TFDV) में प्रयोग किया जाता है StatisticsGen और SchemaGen , और TFDV भी इन घटकों से आउटपुट के दृश्य प्रदान करता है।

इस ट्यूटोरियल में, हम TFX में विज़ुअलाइज़ेशन हेल्पर विधियों का उपयोग करेंगे जो विज़ुअलाइज़ेशन दिखाने के लिए आंतरिक रूप से TFDV का उपयोग करते हैं।

स्टैटिस्टिक्सजेन से आउटपुट की जांच करें

# docs-infra: no-execute
visualize_artifacts(stats_artifacts)

आप इनपुट डेटा के लिए विभिन्न आँकड़े देख सकते हैं। इन आंकड़ों के आपूर्ति की जाती है SchemaGen स्वचालित रूप से डेटा की एक प्रारंभिक स्कीमा के निर्माण के लिए।

स्कीमाजेन से आउटपुट की जांच करें

visualize_artifacts(schema_artifacts)

यह स्कीमा स्टैटिस्टिक्सजेन के आउटपुट से स्वचालित रूप से अनुमानित है। आपको 4 FLOAT फीचर और 1 INT फीचर देखने में सक्षम होना चाहिए।

भविष्य में उपयोग के लिए स्कीमा निर्यात करें

हमें जेनरेट किए गए स्कीमा की समीक्षा और परिशोधन करने की आवश्यकता है। एमएल मॉडल प्रशिक्षण के लिए बाद की पाइपलाइनों में उपयोग किए जाने के लिए समीक्षा की गई स्कीमा को जारी रखने की आवश्यकता है। दूसरे शब्दों में, आप वास्तविक उपयोग के मामलों के लिए स्कीमा फ़ाइल को अपने संस्करण नियंत्रण प्रणाली में जोड़ना चाह सकते हैं। इस ट्यूटोरियल में, हम केवल सरलता के लिए स्कीमा को एक पूर्वनिर्धारित फाइल सिस्टम पथ पर कॉपी करेंगे।

import shutil

_schema_filename = 'schema.pbtxt'
SCHEMA_PATH = 'schema'

os.makedirs(SCHEMA_PATH, exist_ok=True)
_generated_path = os.path.join(schema_artifacts[0].uri, _schema_filename)

# Copy the 'schema.pbtxt' file from the artifact uri to a predefined path.
shutil.copy(_generated_path, SCHEMA_PATH)

'schema/schema.pbtxt'

स्कीमा फ़ाइल का उपयोग करता है प्रोटोकॉल बफ़र पाठ स्वरूप और का एक उदाहरण TensorFlow मेटाडाटा स्कीमा आद्य ।

print(f'Schema at {SCHEMA_PATH}-----')
!cat {SCHEMA_PATH}/*

Schema at schema-----
feature {
  name: "body_mass_g"
  type: FLOAT
  presence {
    min_fraction: 1.0
    min_count: 1
  }
  shape {
    dim {
      size: 1
    }
  }
}
feature {
  name: "culmen_depth_mm"
  type: FLOAT
  presence {
    min_fraction: 1.0
    min_count: 1
  }
  shape {
    dim {
      size: 1
    }
  }
}
feature {
  name: "culmen_length_mm"
  type: FLOAT
  presence {
    min_fraction: 1.0
    min_count: 1
  }
  shape {
    dim {
      size: 1
    }
  }
}
feature {
  name: "flipper_length_mm"
  type: FLOAT
  presence {
    min_fraction: 1.0
    min_count: 1
  }
  shape {
    dim {
      size: 1
    }
  }
}
feature {
  name: "species"
  type: INT
  presence {
    min_fraction: 1.0
    min_count: 1
  }
  shape {
    dim {
      size: 1
    }
  }
}

आपको आवश्यकतानुसार स्कीमा परिभाषा की समीक्षा करना और संभावित रूप से संपादित करना सुनिश्चित करना चाहिए। इस ट्यूटोरियल में, हम केवल जेनरेट किए गए स्कीमा को अपरिवर्तित उपयोग करेंगे।

इनपुट उदाहरणों की पुष्टि करें और एक एमएल मॉडल को प्रशिक्षित करें

हम पाइपलाइन है कि हम में बनाया करने के लिए वापस जाना होगा सरल TFX पाइपलाइन ट्यूटोरियल , एक एमएल मॉडल को प्रशिक्षित करने और मॉडल प्रशिक्षण कोड लिखने के लिए उत्पन्न स्कीमा उपयोग करने के लिए।

हम यह भी एक जोड़ देगा ExampleValidator घटक है जो स्कीमा के संबंध में भेजे डेटासेट में विसंगतियों और लापता मूल्यों के लिए दिखेगा।

मॉडल प्रशिक्षण कोड लिखें

हम के रूप में हम में किया था मॉडल कोड लिखने की ज़रूरत सरल TFX पाइपलाइन ट्यूटोरियल ।

मॉडल स्वयं पिछले ट्यूटोरियल की तरह ही है, लेकिन इस बार हम मैन्युअल रूप से सुविधाओं को निर्दिष्ट करने के बजाय पिछली पाइपलाइन से उत्पन्न स्कीमा का उपयोग करेंगे। अधिकांश कोड नहीं बदला गया था। अंतर केवल इतना है कि हमें इस फ़ाइल में नाम और प्रकार की विशेषताओं को निर्दिष्ट करने की आवश्यकता नहीं है। इसके बजाय, हम उन्हें स्कीमा फ़ाइल से पढ़ने।

_trainer_module_file = 'penguin_trainer.py'

%%writefile {_trainer_module_file}

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

from tfx import v1 as tfx
from tfx_bsl.public import tfxio
from tensorflow_metadata.proto.v0 import schema_pb2

# We don't need to specify _FEATURE_KEYS and _FEATURE_SPEC any more.
# Those information can be read from the given schema file.

_LABEL_KEY = 'species'

_TRAIN_BATCH_SIZE = 20
_EVAL_BATCH_SIZE = 10

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

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

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


def _build_keras_model(schema: schema_pb2.Schema) -> tf.keras.Model:
  """Creates a DNN Keras model for classifying penguin data.

  Returns:
    A Keras Model.
  """
  # The model below is built with Functional API, please refer to
  # https://www.tensorflow.org/guide/keras/overview for all API options.

  # ++ Changed code: Uses all features in the schema except the label.
  feature_keys = [f.name for f in schema.feature if f.name != _LABEL_KEY]
  inputs = [keras.layers.Input(shape=(1,), name=f) for f in feature_keys]
  # ++ End of the changed code.

  d = keras.layers.concatenate(inputs)
  for _ in range(2):
    d = keras.layers.Dense(8, activation='relu')(d)
  outputs = keras.layers.Dense(3)(d)

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

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


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

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

  # ++ Changed code: Reads in schema file passed to the Trainer component.
  schema = tfx.utils.parse_pbtxt_file(fn_args.schema_path, schema_pb2.Schema())
  # ++ End of the changed code.

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

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

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

Writing penguin_trainer.py

अब आपने मॉडल प्रशिक्षण के लिए TFX पाइपलाइन बनाने की तैयारी के सभी चरण पूरे कर लिए हैं।

एक पाइपलाइन परिभाषा लिखें

हम दो नए घटकों, जोड़ देगा Importer और ExampleValidator । आयातक एक बाहरी फ़ाइल को TFX पाइपलाइन में लाता है। इस मामले में, यह एक फ़ाइल है जिसमें स्कीमा परिभाषा है। उदाहरण वैलिडेटर इनपुट डेटा की जांच करेगा और पुष्टि करेगा कि सभी इनपुट डेटा हमारे द्वारा प्रदान किए गए डेटा स्कीमा के अनुरूप हैं या नहीं।

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

  # Computes statistics over data for visualization and example validation.
  statistics_gen = tfx.components.StatisticsGen(
      examples=example_gen.outputs['examples'])

  # NEW: Import the schema.
  schema_importer = tfx.dsl.Importer(
      source_uri=schema_path,
      artifact_type=tfx.types.standard_artifacts.Schema).with_id(
          'schema_importer')

  # NEW: Performs anomaly detection based on statistics and data schema.
  example_validator = tfx.components.ExampleValidator(
      statistics=statistics_gen.outputs['statistics'],
      schema=schema_importer.outputs['result'])

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

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

  components = [
      example_gen,

      # NEW: Following three components were added to the pipeline.
      statistics_gen,
      schema_importer,
      example_validator,

      trainer,
      pusher,
  ]

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

पाइपलाइन चलाएं

tfx.orchestration.LocalDagRunner().run(
  _create_pipeline(
      pipeline_name=PIPELINE_NAME,
      pipeline_root=PIPELINE_ROOT,
      data_root=DATA_ROOT,
      schema_path=SCHEMA_PATH,
      module_file=_trainer_module_file,
      serving_model_dir=SERVING_MODEL_DIR,
      metadata_path=METADATA_PATH))

INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Excluding no splits because exclude_splits is not set.
INFO:absl:Generating ephemeral wheel package for '/tmpfs/src/temp/docs/tutorials/tfx/penguin_trainer.py' (including modules: ['penguin_trainer']).
INFO:absl:User module package has hash fingerprint version 000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmp/tmp50dqc5bp/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmp6_kn7s87', '--dist-dir', '/tmp/tmpwt7plki0']
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
  setuptools.SetuptoolsDeprecationWarning,
listing git files failed - pretending there aren't any
INFO:absl:Successfully built user code wheel distribution at 'pipelines/penguin-tfdv/_wheels/tfx_user_code_Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2-py3-none-any.whl'; target user module is 'penguin_trainer'.
INFO:absl:Full user module path is 'penguin_trainer@pipelines/penguin-tfdv/_wheels/tfx_user_code_Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2-py3-none-any.whl'
INFO:absl:Using deployment config:
 executor_specs {
  key: "CsvExampleGen"
  value {
    beam_executable_spec {
      python_executor_spec {
        class_path: "tfx.components.example_gen.csv_example_gen.executor.Executor"
      }
    }
  }
}
executor_specs {
  key: "ExampleValidator"
  value {
    python_class_executable_spec {
      class_path: "tfx.components.example_validator.executor.Executor"
    }
  }
}
executor_specs {
  key: "Pusher"
  value {
    python_class_executable_spec {
      class_path: "tfx.components.pusher.executor.Executor"
    }
  }
}
executor_specs {
  key: "StatisticsGen"
  value {
    beam_executable_spec {
      python_executor_spec {
        class_path: "tfx.components.statistics_gen.executor.Executor"
      }
    }
  }
}
executor_specs {
  key: "Trainer"
  value {
    python_class_executable_spec {
      class_path: "tfx.components.trainer.executor.GenericExecutor"
    }
  }
}
custom_driver_specs {
  key: "CsvExampleGen"
  value {
    python_class_executable_spec {
      class_path: "tfx.components.example_gen.driver.FileBasedDriver"
    }
  }
}
metadata_connection_config {
  sqlite {
    filename_uri: "metadata/penguin-tfdv/metadata.db"
    connection_mode: READWRITE_OPENCREATE
  }
}

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

INFO:absl:Component CsvExampleGen is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.components.example_gen.csv_example_gen.component.CsvExampleGen"
  }
  id: "CsvExampleGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-tfdv"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-12-05T11:10:11.667239"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-tfdv.CsvExampleGen"
      }
    }
  }
}
outputs {
  outputs {
    key: "examples"
    value {
      artifact_spec {
        type {
          name: "Examples"
          properties {
            key: "span"
            value: INT
          }
          properties {
            key: "split_names"
            value: STRING
          }
          properties {
            key: "version"
            value: INT
          }
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "input_base"
    value {
      field_value {
        string_value: "/tmp/tfx-datan3p7t1d2"
      }
    }
  }
  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"
downstream_nodes: "Trainer"
execution_options {
  caching_options {
  }
}

INFO:absl:MetadataStore with DB connection initialized
I1205 11:10:11.685647  4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 11:10:11.692644  4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 11:10:11.699625  4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 11:10:11.708110  4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:select span and version = (0, None)
INFO:absl:latest span and version = (0, None)
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 1
I1205 11:10:11.722760  4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=1, input_dict={}, output_dict=defaultdict(<class 'list'>, {'examples': [Artifact(artifact: uri: "pipelines/penguin-tfdv/CsvExampleGen/examples/1"
custom_properties {
  key: "input_fingerprint"
  value {
    string_value: "split:single_split,num_files:1,total_bytes:25648,xor_checksum:1638702606,sum_checksum:1638702606"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfdv:2021-12-05T11:10:11.667239:CsvExampleGen:examples:0"
  }
}
custom_properties {
  key: "span"
  value {
    int_value: 0
  }
}
, artifact_type: name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
)]}), exec_properties={'input_base': '/tmp/tfx-datan3p7t1d2', 'input_config': '{\n  "splits": [\n    {\n      "name": "single_split",\n      "pattern": "*"\n    }\n  ]\n}', 'output_data_format': 6, '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, 'span': 0, 'version': None, 'input_fingerprint': 'split:single_split,num_files:1,total_bytes:25648,xor_checksum:1638702606,sum_checksum:1638702606'}, execution_output_uri='pipelines/penguin-tfdv/CsvExampleGen/.system/executor_execution/1/executor_output.pb', stateful_working_dir='pipelines/penguin-tfdv/CsvExampleGen/.system/stateful_working_dir/2021-12-05T11:10:11.667239', tmp_dir='pipelines/penguin-tfdv/CsvExampleGen/.system/executor_execution/1/.temp/', pipeline_node=node_info {
  type {
    name: "tfx.components.example_gen.csv_example_gen.component.CsvExampleGen"
  }
  id: "CsvExampleGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-tfdv"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-12-05T11:10:11.667239"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-tfdv.CsvExampleGen"
      }
    }
  }
}
outputs {
  outputs {
    key: "examples"
    value {
      artifact_spec {
        type {
          name: "Examples"
          properties {
            key: "span"
            value: INT
          }
          properties {
            key: "split_names"
            value: STRING
          }
          properties {
            key: "version"
            value: INT
          }
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "input_base"
    value {
      field_value {
        string_value: "/tmp/tfx-datan3p7t1d2"
      }
    }
  }
  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"
downstream_nodes: "Trainer"
execution_options {
  caching_options {
  }
}
, pipeline_info=id: "penguin-tfdv"
, pipeline_run_id='2021-12-05T11:10:11.667239')
INFO:absl:Generating examples.
INFO:absl:Processing input csv data /tmp/tfx-datan3p7t1d2/* to TFExample.
running bdist_wheel
running build
running build_py
creating build
creating build/lib
copying penguin_trainer.py -> build/lib
installing to /tmp/tmp6_kn7s87
running install
running install_lib
copying build/lib/penguin_trainer.py -> /tmp/tmp6_kn7s87
running install_egg_info
running egg_info
creating tfx_user_code_Trainer.egg-info
writing tfx_user_code_Trainer.egg-info/PKG-INFO
writing dependency_links to tfx_user_code_Trainer.egg-info/dependency_links.txt
writing top-level names to tfx_user_code_Trainer.egg-info/top_level.txt
writing manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'
reading manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'
writing manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'
Copying tfx_user_code_Trainer.egg-info to /tmp/tmp6_kn7s87/tfx_user_code_Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2-py3.7.egg-info
running install_scripts
creating /tmp/tmp6_kn7s87/tfx_user_code_Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2.dist-info/WHEEL
creating '/tmp/tmpwt7plki0/tfx_user_code_Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2-py3-none-any.whl' and adding '/tmp/tmp6_kn7s87' to it
adding 'penguin_trainer.py'
adding 'tfx_user_code_Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2.dist-info/METADATA'
adding 'tfx_user_code_Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2.dist-info/WHEEL'
adding 'tfx_user_code_Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2.dist-info/top_level.txt'
adding 'tfx_user_code_Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2.dist-info/RECORD'
removing /tmp/tmp6_kn7s87
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
INFO:absl:Examples generated.
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 1 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'examples': [Artifact(artifact: uri: "pipelines/penguin-tfdv/CsvExampleGen/examples/1"
custom_properties {
  key: "input_fingerprint"
  value {
    string_value: "split:single_split,num_files:1,total_bytes:25648,xor_checksum:1638702606,sum_checksum:1638702606"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfdv:2021-12-05T11:10:11.667239:CsvExampleGen:examples:0"
  }
}
custom_properties {
  key: "span"
  value {
    int_value: 0
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.4.0"
  }
}
, artifact_type: name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
)]}) for execution 1
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component CsvExampleGen is finished.
INFO:absl:Component schema_importer is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.dsl.components.common.importer.Importer"
  }
  id: "schema_importer"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-tfdv"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-12-05T11:10:11.667239"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-tfdv.schema_importer"
      }
    }
  }
}
outputs {
  outputs {
    key: "result"
    value {
      artifact_spec {
        type {
          name: "Schema"
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "artifact_uri"
    value {
      field_value {
        string_value: "schema"
      }
    }
  }
  parameters {
    key: "reimport"
    value {
      field_value {
        int_value: 0
      }
    }
  }
}
downstream_nodes: "ExampleValidator"
downstream_nodes: "Trainer"
execution_options {
  caching_options {
  }
}

INFO:absl:Running as an importer node.
INFO:absl:MetadataStore with DB connection initialized
I1205 11:10:12.796727  4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Processing source uri: schema, properties: {}, custom_properties: {}
INFO:absl:Component schema_importer is finished.
I1205 11:10:12.806819  4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Component StatisticsGen is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.components.statistics_gen.component.StatisticsGen"
  }
  id: "StatisticsGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-tfdv"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-12-05T11:10:11.667239"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-tfdv.StatisticsGen"
      }
    }
  }
}
inputs {
  inputs {
    key: "examples"
    value {
      channels {
        producer_node_query {
          id: "CsvExampleGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "penguin-tfdv"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2021-12-05T11:10:11.667239"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "penguin-tfdv.CsvExampleGen"
            }
          }
        }
        artifact_query {
          type {
            name: "Examples"
          }
        }
        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
          }
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "exclude_splits"
    value {
      field_value {
        string_value: "[]"
      }
    }
  }
}
upstream_nodes: "CsvExampleGen"
downstream_nodes: "ExampleValidator"
execution_options {
  caching_options {
  }
}

INFO:absl:MetadataStore with DB connection initialized
I1205 11:10:12.827589  4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 3
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=3, input_dict={'examples': [Artifact(artifact: id: 1
type_id: 15
uri: "pipelines/penguin-tfdv/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:1638702606,sum_checksum:1638702606"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfdv:2021-12-05T11:10:11.667239: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: "1.4.0"
  }
}
state: LIVE
create_time_since_epoch: 1638702612780
last_update_time_since_epoch: 1638702612780
, artifact_type: id: 15
name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
)]}, output_dict=defaultdict(<class 'list'>, {'statistics': [Artifact(artifact: uri: "pipelines/penguin-tfdv/StatisticsGen/statistics/3"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfdv:2021-12-05T11:10:11.667239:StatisticsGen:statistics:0"
  }
}
, artifact_type: name: "ExampleStatistics"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
)]}), exec_properties={'exclude_splits': '[]'}, execution_output_uri='pipelines/penguin-tfdv/StatisticsGen/.system/executor_execution/3/executor_output.pb', stateful_working_dir='pipelines/penguin-tfdv/StatisticsGen/.system/stateful_working_dir/2021-12-05T11:10:11.667239', tmp_dir='pipelines/penguin-tfdv/StatisticsGen/.system/executor_execution/3/.temp/', pipeline_node=node_info {
  type {
    name: "tfx.components.statistics_gen.component.StatisticsGen"
  }
  id: "StatisticsGen"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-tfdv"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-12-05T11:10:11.667239"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-tfdv.StatisticsGen"
      }
    }
  }
}
inputs {
  inputs {
    key: "examples"
    value {
      channels {
        producer_node_query {
          id: "CsvExampleGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "penguin-tfdv"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2021-12-05T11:10:11.667239"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "penguin-tfdv.CsvExampleGen"
            }
          }
        }
        artifact_query {
          type {
            name: "Examples"
          }
        }
        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
          }
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "exclude_splits"
    value {
      field_value {
        string_value: "[]"
      }
    }
  }
}
upstream_nodes: "CsvExampleGen"
downstream_nodes: "ExampleValidator"
execution_options {
  caching_options {
  }
}
, pipeline_info=id: "penguin-tfdv"
, pipeline_run_id='2021-12-05T11:10:11.667239')
INFO:absl:Generating statistics for split train.
INFO:absl:Statistics for split train written to pipelines/penguin-tfdv/StatisticsGen/statistics/3/Split-train.
INFO:absl:Generating statistics for split eval.
INFO:absl:Statistics for split eval written to pipelines/penguin-tfdv/StatisticsGen/statistics/3/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 3 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'statistics': [Artifact(artifact: uri: "pipelines/penguin-tfdv/StatisticsGen/statistics/3"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfdv:2021-12-05T11:10:11.667239:StatisticsGen:statistics:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.4.0"
  }
}
, artifact_type: name: "ExampleStatistics"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
)]}) for execution 3
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component StatisticsGen is finished.
INFO:absl:Component Trainer is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.components.trainer.component.Trainer"
  }
  id: "Trainer"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-tfdv"
      }
    }
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  contexts {
    type {
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    name {
      field_value {
        string_value: "2021-12-05T11:10:11.667239"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-tfdv.Trainer"
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}
inputs {
  inputs {
    key: "examples"
    value {
      channels {
        producer_node_query {
          id: "CsvExampleGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "penguin-tfdv"
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        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2021-12-05T11:10:11.667239"
            }
          }
        }
        context_queries {
          type {
            name: "node"
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          name {
            field_value {
              string_value: "penguin-tfdv.CsvExampleGen"
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        }
        artifact_query {
          type {
            name: "Examples"
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        output_key: "examples"
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      min_count: 1
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  }
  inputs {
    key: "schema"
    value {
      channels {
        producer_node_query {
          id: "schema_importer"
        }
        context_queries {
          type {
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          name {
            field_value {
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        context_queries {
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          name {
            field_value {
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        artifact_query {
          type {
            name: "Schema"
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        output_key: "result"
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}
outputs {
  outputs {
    key: "model"
    value {
      artifact_spec {
        type {
          name: "Model"
        }
      }
    }
  }
  outputs {
    key: "model_run"
    value {
      artifact_spec {
        type {
          name: "ModelRun"
        }
      }
    }
  }
}
parameters {
  parameters {
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    value {
      field_value {
        string_value: "null"
      }
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  parameters {
    key: "eval_args"
    value {
      field_value {
        string_value: "{\n  \"num_steps\": 5\n}"
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  }
  parameters {
    key: "module_path"
    value {
      field_value {
        string_value: "penguin_trainer@pipelines/penguin-tfdv/_wheels/tfx_user_code_Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2-py3-none-any.whl"
      }
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  }
  parameters {
    key: "train_args"
    value {
      field_value {
        string_value: "{\n  \"num_steps\": 100\n}"
      }
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  }
}
upstream_nodes: "CsvExampleGen"
upstream_nodes: "schema_importer"
downstream_nodes: "Pusher"
execution_options {
  caching_options {
  }
}

INFO:absl:MetadataStore with DB connection initialized
I1205 11:10:15.426606  4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 4
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=4, input_dict={'examples': [Artifact(artifact: id: 1
type_id: 15
uri: "pipelines/penguin-tfdv/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:1638702606,sum_checksum:1638702606"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfdv:2021-12-05T11:10:11.667239: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: "1.4.0"
  }
}
state: LIVE
create_time_since_epoch: 1638702612780
last_update_time_since_epoch: 1638702612780
, artifact_type: id: 15
name: "Examples"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
properties {
  key: "version"
  value: INT
}
)], 'schema': [Artifact(artifact: id: 2
type_id: 17
uri: "schema"
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.4.0"
  }
}
state: LIVE
create_time_since_epoch: 1638702612810
last_update_time_since_epoch: 1638702612810
, artifact_type: id: 17
name: "Schema"
)]}, output_dict=defaultdict(<class 'list'>, {'model_run': [Artifact(artifact: uri: "pipelines/penguin-tfdv/Trainer/model_run/4"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfdv:2021-12-05T11:10:11.667239:Trainer:model_run:0"
  }
}
, artifact_type: name: "ModelRun"
)], 'model': [Artifact(artifact: uri: "pipelines/penguin-tfdv/Trainer/model/4"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfdv:2021-12-05T11:10:11.667239:Trainer:model:0"
  }
}
, artifact_type: name: "Model"
)]}), exec_properties={'eval_args': '{\n  "num_steps": 5\n}', 'module_path': 'penguin_trainer@pipelines/penguin-tfdv/_wheels/tfx_user_code_Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2-py3-none-any.whl', 'custom_config': 'null', 'train_args': '{\n  "num_steps": 100\n}'}, execution_output_uri='pipelines/penguin-tfdv/Trainer/.system/executor_execution/4/executor_output.pb', stateful_working_dir='pipelines/penguin-tfdv/Trainer/.system/stateful_working_dir/2021-12-05T11:10:11.667239', tmp_dir='pipelines/penguin-tfdv/Trainer/.system/executor_execution/4/.temp/', pipeline_node=node_info {
  type {
    name: "tfx.components.trainer.component.Trainer"
  }
  id: "Trainer"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-tfdv"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-12-05T11:10:11.667239"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-tfdv.Trainer"
      }
    }
  }
}
inputs {
  inputs {
    key: "examples"
    value {
      channels {
        producer_node_query {
          id: "CsvExampleGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "penguin-tfdv"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2021-12-05T11:10:11.667239"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "penguin-tfdv.CsvExampleGen"
            }
          }
        }
        artifact_query {
          type {
            name: "Examples"
          }
        }
        output_key: "examples"
      }
      min_count: 1
    }
  }
  inputs {
    key: "schema"
    value {
      channels {
        producer_node_query {
          id: "schema_importer"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "penguin-tfdv"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2021-12-05T11:10:11.667239"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "penguin-tfdv.schema_importer"
            }
          }
        }
        artifact_query {
          type {
            name: "Schema"
          }
        }
        output_key: "result"
      }
    }
  }
}
outputs {
  outputs {
    key: "model"
    value {
      artifact_spec {
        type {
          name: "Model"
        }
      }
    }
  }
  outputs {
    key: "model_run"
    value {
      artifact_spec {
        type {
          name: "ModelRun"
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "custom_config"
    value {
      field_value {
        string_value: "null"
      }
    }
  }
  parameters {
    key: "eval_args"
    value {
      field_value {
        string_value: "{\n  \"num_steps\": 5\n}"
      }
    }
  }
  parameters {
    key: "module_path"
    value {
      field_value {
        string_value: "penguin_trainer@pipelines/penguin-tfdv/_wheels/tfx_user_code_Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2-py3-none-any.whl"
      }
    }
  }
  parameters {
    key: "train_args"
    value {
      field_value {
        string_value: "{\n  \"num_steps\": 100\n}"
      }
    }
  }
}
upstream_nodes: "CsvExampleGen"
upstream_nodes: "schema_importer"
downstream_nodes: "Pusher"
execution_options {
  caching_options {
  }
}
, pipeline_info=id: "penguin-tfdv"
, pipeline_run_id='2021-12-05T11:10:11.667239')
INFO:absl:Train on the 'train' split when train_args.splits is not set.
INFO:absl:Evaluate on the 'eval' split when eval_args.splits is not set.
INFO:absl:udf_utils.get_fn {'eval_args': '{\n  "num_steps": 5\n}', 'module_path': 'penguin_trainer@pipelines/penguin-tfdv/_wheels/tfx_user_code_Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2-py3-none-any.whl', 'custom_config': 'null', 'train_args': '{\n  "num_steps": 100\n}'} 'run_fn'
INFO:absl:Installing 'pipelines/penguin-tfdv/_wheels/tfx_user_code_Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2-py3-none-any.whl' to a temporary directory.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmp/tmpbb1l9_v7', 'pipelines/penguin-tfdv/_wheels/tfx_user_code_Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2-py3-none-any.whl']
Processing ./pipelines/penguin-tfdv/_wheels/tfx_user_code_Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2-py3-none-any.whl
INFO:absl:Successfully installed 'pipelines/penguin-tfdv/_wheels/tfx_user_code_Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2-py3-none-any.whl'.
INFO:absl:Training model.
INFO:absl:Feature body_mass_g has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_depth_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
  size: 1
}
. Setting to DenseTensor.
Installing collected packages: tfx-user-code-Trainer
Successfully installed tfx-user-code-Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2
INFO:absl:Feature body_mass_g has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_depth_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature body_mass_g has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_depth_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature body_mass_g has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_depth_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
  size: 1
}
. Setting to DenseTensor.
INFO:absl:Model: "model"
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Layer (type)                    Output Shape         Param #     Connected to                     
INFO:absl:==================================================================================================
INFO:absl:body_mass_g (InputLayer)        [(None, 1)]          0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:culmen_depth_mm (InputLayer)    [(None, 1)]          0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:culmen_length_mm (InputLayer)   [(None, 1)]          0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:flipper_length_mm (InputLayer)  [(None, 1)]          0                                            
INFO:absl:__________________________________________________________________________________________________
INFO:absl:concatenate (Concatenate)       (None, 4)            0           body_mass_g[0][0]                
INFO:absl:                                                                 culmen_depth_mm[0][0]            
INFO:absl:                                                                 culmen_length_mm[0][0]           
INFO:absl:                                                                 flipper_length_mm[0][0]          
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense (Dense)                   (None, 8)            40          concatenate[0][0]                
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_1 (Dense)                 (None, 8)            72          dense[0][0]                      
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_2 (Dense)                 (None, 3)            27          dense_1[0][0]                    
INFO:absl:==================================================================================================
INFO:absl:Total params: 139
INFO:absl:Trainable params: 139
INFO:absl:Non-trainable params: 0
INFO:absl:__________________________________________________________________________________________________
100/100 [==============================] - 1s 3ms/step - loss: 0.5752 - sparse_categorical_accuracy: 0.8165 - val_loss: 0.2294 - val_sparse_categorical_accuracy: 0.9400
2021-12-05 11:10:20.208161: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
INFO:tensorflow:Assets written to: pipelines/penguin-tfdv/Trainer/model/4/Format-Serving/assets
INFO:tensorflow:Assets written to: pipelines/penguin-tfdv/Trainer/model/4/Format-Serving/assets
INFO:absl:Training complete. Model written to pipelines/penguin-tfdv/Trainer/model/4/Format-Serving. ModelRun written to pipelines/penguin-tfdv/Trainer/model_run/4
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 4 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'model_run': [Artifact(artifact: uri: "pipelines/penguin-tfdv/Trainer/model_run/4"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfdv:2021-12-05T11:10:11.667239:Trainer:model_run:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.4.0"
  }
}
, artifact_type: name: "ModelRun"
)], 'model': [Artifact(artifact: uri: "pipelines/penguin-tfdv/Trainer/model/4"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfdv:2021-12-05T11:10:11.667239:Trainer:model:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.4.0"
  }
}
, artifact_type: name: "Model"
)]}) for execution 4
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component Trainer is finished.
I1205 11:10:20.766410  4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 11:10:20.770478  4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Component ExampleValidator is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.components.example_validator.component.ExampleValidator"
  }
  id: "ExampleValidator"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-tfdv"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-12-05T11:10:11.667239"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-tfdv.ExampleValidator"
      }
    }
  }
}
inputs {
  inputs {
    key: "schema"
    value {
      channels {
        producer_node_query {
          id: "schema_importer"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "penguin-tfdv"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2021-12-05T11:10:11.667239"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "penguin-tfdv.schema_importer"
            }
          }
        }
        artifact_query {
          type {
            name: "Schema"
          }
        }
        output_key: "result"
      }
      min_count: 1
    }
  }
  inputs {
    key: "statistics"
    value {
      channels {
        producer_node_query {
          id: "StatisticsGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "penguin-tfdv"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2021-12-05T11:10:11.667239"
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          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "penguin-tfdv.StatisticsGen"
            }
          }
        }
        artifact_query {
          type {
            name: "ExampleStatistics"
          }
        }
        output_key: "statistics"
      }
      min_count: 1
    }
  }
}
outputs {
  outputs {
    key: "anomalies"
    value {
      artifact_spec {
        type {
          name: "ExampleAnomalies"
          properties {
            key: "span"
            value: INT
          }
          properties {
            key: "split_names"
            value: STRING
          }
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "exclude_splits"
    value {
      field_value {
        string_value: "[]"
      }
    }
  }
}
upstream_nodes: "StatisticsGen"
upstream_nodes: "schema_importer"
execution_options {
  caching_options {
  }
}

INFO:absl:MetadataStore with DB connection initialized
I1205 11:10:20.793696  4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 5
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=5, input_dict={'statistics': [Artifact(artifact: id: 3
type_id: 19
uri: "pipelines/penguin-tfdv/StatisticsGen/statistics/3"
properties {
  key: "split_names"
  value {
    string_value: "[\"train\", \"eval\"]"
  }
}
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfdv:2021-12-05T11:10:11.667239:StatisticsGen:statistics:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.4.0"
  }
}
state: LIVE
create_time_since_epoch: 1638702615406
last_update_time_since_epoch: 1638702615406
, artifact_type: id: 19
name: "ExampleStatistics"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
)], 'schema': [Artifact(artifact: id: 2
type_id: 17
uri: "schema"
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.4.0"
  }
}
state: LIVE
create_time_since_epoch: 1638702612810
last_update_time_since_epoch: 1638702612810
, artifact_type: id: 17
name: "Schema"
)]}, output_dict=defaultdict(<class 'list'>, {'anomalies': [Artifact(artifact: uri: "pipelines/penguin-tfdv/ExampleValidator/anomalies/5"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfdv:2021-12-05T11:10:11.667239:ExampleValidator:anomalies:0"
  }
}
, artifact_type: name: "ExampleAnomalies"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
)]}), exec_properties={'exclude_splits': '[]'}, execution_output_uri='pipelines/penguin-tfdv/ExampleValidator/.system/executor_execution/5/executor_output.pb', stateful_working_dir='pipelines/penguin-tfdv/ExampleValidator/.system/stateful_working_dir/2021-12-05T11:10:11.667239', tmp_dir='pipelines/penguin-tfdv/ExampleValidator/.system/executor_execution/5/.temp/', pipeline_node=node_info {
  type {
    name: "tfx.components.example_validator.component.ExampleValidator"
  }
  id: "ExampleValidator"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-tfdv"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-12-05T11:10:11.667239"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-tfdv.ExampleValidator"
      }
    }
  }
}
inputs {
  inputs {
    key: "schema"
    value {
      channels {
        producer_node_query {
          id: "schema_importer"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "penguin-tfdv"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2021-12-05T11:10:11.667239"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "penguin-tfdv.schema_importer"
            }
          }
        }
        artifact_query {
          type {
            name: "Schema"
          }
        }
        output_key: "result"
      }
      min_count: 1
    }
  }
  inputs {
    key: "statistics"
    value {
      channels {
        producer_node_query {
          id: "StatisticsGen"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "penguin-tfdv"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2021-12-05T11:10:11.667239"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "penguin-tfdv.StatisticsGen"
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        }
        artifact_query {
          type {
            name: "ExampleStatistics"
          }
        }
        output_key: "statistics"
      }
      min_count: 1
    }
  }
}
outputs {
  outputs {
    key: "anomalies"
    value {
      artifact_spec {
        type {
          name: "ExampleAnomalies"
          properties {
            key: "span"
            value: INT
          }
          properties {
            key: "split_names"
            value: STRING
          }
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "exclude_splits"
    value {
      field_value {
        string_value: "[]"
      }
    }
  }
}
upstream_nodes: "StatisticsGen"
upstream_nodes: "schema_importer"
execution_options {
  caching_options {
  }
}
, pipeline_info=id: "penguin-tfdv"
, pipeline_run_id='2021-12-05T11:10:11.667239')
INFO:absl:Validating schema against the computed statistics for split train.
INFO:absl:Validation complete for split train. Anomalies written to pipelines/penguin-tfdv/ExampleValidator/anomalies/5/Split-train.
INFO:absl:Validating schema against the computed statistics for split eval.
INFO:absl:Validation complete for split eval. Anomalies written to pipelines/penguin-tfdv/ExampleValidator/anomalies/5/Split-eval.
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'>, {'anomalies': [Artifact(artifact: uri: "pipelines/penguin-tfdv/ExampleValidator/anomalies/5"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfdv:2021-12-05T11:10:11.667239:ExampleValidator:anomalies:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.4.0"
  }
}
, artifact_type: name: "ExampleAnomalies"
properties {
  key: "span"
  value: INT
}
properties {
  key: "split_names"
  value: STRING
}
)]}) for execution 5
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component ExampleValidator is finished.
INFO:absl:Component Pusher is running.
INFO:absl:Running launcher for node_info {
  type {
    name: "tfx.components.pusher.component.Pusher"
  }
  id: "Pusher"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-tfdv"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-12-05T11:10:11.667239"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-tfdv.Pusher"
      }
    }
  }
}
inputs {
  inputs {
    key: "model"
    value {
      channels {
        producer_node_query {
          id: "Trainer"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "penguin-tfdv"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2021-12-05T11:10:11.667239"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "penguin-tfdv.Trainer"
            }
          }
        }
        artifact_query {
          type {
            name: "Model"
          }
        }
        output_key: "model"
      }
    }
  }
}
outputs {
  outputs {
    key: "pushed_model"
    value {
      artifact_spec {
        type {
          name: "PushedModel"
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "custom_config"
    value {
      field_value {
        string_value: "null"
      }
    }
  }
  parameters {
    key: "push_destination"
    value {
      field_value {
        string_value: "{\n  \"filesystem\": {\n    \"base_directory\": \"serving_model/penguin-tfdv\"\n  }\n}"
      }
    }
  }
}
upstream_nodes: "Trainer"
execution_options {
  caching_options {
  }
}

INFO:absl:MetadataStore with DB connection initialized
INFO:absl:MetadataStore with DB connection initialized
I1205 11:10:20.848567  4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Going to run a new execution 6
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=6, input_dict={'model': [Artifact(artifact: id: 5
type_id: 22
uri: "pipelines/penguin-tfdv/Trainer/model/4"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfdv:2021-12-05T11:10:11.667239:Trainer:model:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.4.0"
  }
}
state: LIVE
create_time_since_epoch: 1638702620774
last_update_time_since_epoch: 1638702620774
, artifact_type: id: 22
name: "Model"
)]}, output_dict=defaultdict(<class 'list'>, {'pushed_model': [Artifact(artifact: uri: "pipelines/penguin-tfdv/Pusher/pushed_model/6"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfdv:2021-12-05T11:10:11.667239:Pusher:pushed_model:0"
  }
}
, artifact_type: name: "PushedModel"
)]}), exec_properties={'push_destination': '{\n  "filesystem": {\n    "base_directory": "serving_model/penguin-tfdv"\n  }\n}', 'custom_config': 'null'}, execution_output_uri='pipelines/penguin-tfdv/Pusher/.system/executor_execution/6/executor_output.pb', stateful_working_dir='pipelines/penguin-tfdv/Pusher/.system/stateful_working_dir/2021-12-05T11:10:11.667239', tmp_dir='pipelines/penguin-tfdv/Pusher/.system/executor_execution/6/.temp/', pipeline_node=node_info {
  type {
    name: "tfx.components.pusher.component.Pusher"
  }
  id: "Pusher"
}
contexts {
  contexts {
    type {
      name: "pipeline"
    }
    name {
      field_value {
        string_value: "penguin-tfdv"
      }
    }
  }
  contexts {
    type {
      name: "pipeline_run"
    }
    name {
      field_value {
        string_value: "2021-12-05T11:10:11.667239"
      }
    }
  }
  contexts {
    type {
      name: "node"
    }
    name {
      field_value {
        string_value: "penguin-tfdv.Pusher"
      }
    }
  }
}
inputs {
  inputs {
    key: "model"
    value {
      channels {
        producer_node_query {
          id: "Trainer"
        }
        context_queries {
          type {
            name: "pipeline"
          }
          name {
            field_value {
              string_value: "penguin-tfdv"
            }
          }
        }
        context_queries {
          type {
            name: "pipeline_run"
          }
          name {
            field_value {
              string_value: "2021-12-05T11:10:11.667239"
            }
          }
        }
        context_queries {
          type {
            name: "node"
          }
          name {
            field_value {
              string_value: "penguin-tfdv.Trainer"
            }
          }
        }
        artifact_query {
          type {
            name: "Model"
          }
        }
        output_key: "model"
      }
    }
  }
}
outputs {
  outputs {
    key: "pushed_model"
    value {
      artifact_spec {
        type {
          name: "PushedModel"
        }
      }
    }
  }
}
parameters {
  parameters {
    key: "custom_config"
    value {
      field_value {
        string_value: "null"
      }
    }
  }
  parameters {
    key: "push_destination"
    value {
      field_value {
        string_value: "{\n  \"filesystem\": {\n    \"base_directory\": \"serving_model/penguin-tfdv\"\n  }\n}"
      }
    }
  }
}
upstream_nodes: "Trainer"
execution_options {
  caching_options {
  }
}
, pipeline_info=id: "penguin-tfdv"
, pipeline_run_id='2021-12-05T11:10:11.667239')
WARNING:absl:Pusher is going to push the model without validation. Consider using Evaluator or InfraValidator in your pipeline.
INFO:absl:Model version: 1638702620
INFO:absl:Model written to serving path serving_model/penguin-tfdv/1638702620.
INFO:absl:Model pushed to pipelines/penguin-tfdv/Pusher/pushed_model/6.
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'>, {'pushed_model': [Artifact(artifact: uri: "pipelines/penguin-tfdv/Pusher/pushed_model/6"
custom_properties {
  key: "name"
  value {
    string_value: "penguin-tfdv:2021-12-05T11:10:11.667239:Pusher:pushed_model:0"
  }
}
custom_properties {
  key: "tfx_version"
  value {
    string_value: "1.4.0"
  }
}
, artifact_type: name: "PushedModel"
)]}) for execution 6
INFO:absl:MetadataStore with DB connection initialized
I1205 11:10:20.879335  4006 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Component Pusher is finished.

आपको "जानकारी: एबीएसएल: घटक पुशर समाप्त हो गया है" देखना चाहिए। यदि पाइपलाइन सफलतापूर्वक समाप्त हो गई।

पाइपलाइन के आउटपुट की जांच करें

हमने पेंगुइन के लिए वर्गीकरण मॉडल को प्रशिक्षित किया है, और हमने उदाहरण वैलिडेटर घटक में इनपुट उदाहरणों को भी मान्य किया है। हम उदाहरण वैलिडेटर से आउटपुट का विश्लेषण कर सकते हैं जैसा कि हमने पिछली पाइपलाइन के साथ किया था।

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

with Metadata(metadata_connection_config) as metadata_handler:
  ev_output = get_latest_artifacts(metadata_handler, PIPELINE_NAME,
                                   'ExampleValidator')
  anomalies_artifacts = ev_output[standard_component_specs.ANOMALIES_KEY]

INFO:absl:MetadataStore with DB connection initialized

उदाहरण वैलिडेटर से उदाहरण विसंगतियों को भी देखा जा सकता है।

visualize_artifacts(anomalies_artifacts)

आपको उदाहरणों के प्रत्येक विभाजन के लिए "कोई विसंगति नहीं मिली" देखना चाहिए। चूंकि हमने उसी डेटा का उपयोग किया था जो इस पाइपलाइन में स्कीमा निर्माण के लिए उपयोग किया गया था, यहां कोई विसंगति की उम्मीद नहीं है। यदि आप इस पाइपलाइन को नए आने वाले डेटा के साथ बार-बार चलाते हैं, तो उदाहरण वैलिडेटर नए डेटा और मौजूदा स्कीमा के बीच किसी भी विसंगति को खोजने में सक्षम होना चाहिए।

यदि कोई विसंगति पाई जाती है, तो आप यह देखने के लिए अपने डेटा की समीक्षा कर सकते हैं कि क्या कोई उदाहरण आपकी धारणाओं का पालन नहीं करता है। स्टैटिस्टिक्सजेन जैसे अन्य घटकों के आउटपुट उपयोगी हो सकते हैं। हालांकि, कोई भी विसंगति जो पाई जाती है वह आगे पाइपलाइन निष्पादन को अवरुद्ध नहीं करेगी।

अगले कदम

आप के बारे में अधिक संसाधन प्राप्त कर सकते https://www.tensorflow.org/tfx/tutorials

कृपया देखें TFX पाइपलाइन को समझना TFX में विभिन्न अवधारणाओं के बारे में अधिक जानने के लिए।