在 TensorFlow.org 上檢視
在 Google Colab 中執行
在 GitHub 上檢視原始碼
下載筆記本在這個以筆記本為基礎的教學課程中，我們將建立並執行 TFX 管線，以驗證輸入資料並建立 ML 模型。本筆記本是以我們在簡易 TFX 管線教學課程中建置的 TFX 管線為基礎。如果您尚未閱讀該教學課程，您應該先閱讀它，再繼續進行本筆記本。
在任何資料科學或 ML 專案中，首要任務是理解和清理資料，包括
- 瞭解每個特徵的資料類型、分佈和其他資訊 (例如，平均值或唯一值的數量)
- 產生描述資料的初步綱要
- 根據給定的綱要識別資料中的異常和遺失值
在本教學課程中，我們將建立兩個 TFX 管線。
首先，我們將建立一個管線來分析資料集，並產生給定資料集的初步綱要。此管線將包含兩個新元件：StatisticsGen 和 SchemaGen。
一旦我們有了適當的資料綱要，我們將根據先前教學課程中的管線建立一個管線來訓練 ML 分類模型。在此管線中，我們將使用第一個管線中的綱要和一個新元件 ExampleValidator 來驗證輸入資料。
這三個新元件 StatisticsGen、SchemaGen 和 ExampleValidator 是用於資料分析和驗證的 TFX 元件，它們是使用 TensorFlow Data Validation 程式庫實作的。
請參閱瞭解 TFX 管線，以深入瞭解 TFX 中的各種概念。
設定
我們首先需要安裝 TFX Python 套件並下載我們將用於模型的資料集。
升級 Pip
為了避免在本機執行時升級系統中的 Pip，請檢查以確保我們在 Colab 中執行。本機系統當然可以單獨升級。
try:
import colab
!pip install --upgrade pip
except:
pass
安裝 TFX
pip install -U tfx
您是否重新啟動執行階段？
如果您使用的是 Google Colab，第一次執行上述儲存格時，您必須按一下上方的「RESTART RUNTIME」按鈕或使用「執行階段 > 重新啟動執行階段...」選單來重新啟動執行階段。這是因為 Colab 載入套件的方式。
檢查 TensorFlow 和 TFX 版本。
import tensorflow as tf
print('TensorFlow version: {}'.format(tf.__version__))
from tfx import v1 as tfx
print('TFX version: {}'.format(tfx.__version__))
2024-05-08 09:36:04.670322: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered 2024-05-08 09:36:04.670389: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2024-05-08 09:36:04.671916: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered TensorFlow version: 2.15.1 TFX version: 1.15.0
設定變數
有些變數用於定義管線。您可以根據需要自訂這些變數。預設情況下，管線的所有輸出都將在目前目錄下產生。
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.
準備範例資料
我們將下載範例資料集以用於我們的 TFX 管線。我們使用的資料集是 Palmer Penguins 資料集，該資料集也用於其他 TFX 範例中。
此資料集包含四個數值特徵
- culmen_length_mm
- culmen_depth_mm
- flipper_length_mm
- body_mass_g
所有特徵都已標準化為範圍 [0,1]。我們將建構一個分類模型，以預測企鵝的 species。
由於 TFX ExampleGen 元件從目錄讀取輸入，因此我們需要建立一個目錄並將資料集複製到其中。
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)
('/tmpfs/tmp/tfx-dataj_6ovg52/data.csv',
<http.client.HTTPMessage at 0x7ff39cac30a0>)
快速查看 CSV 檔案。
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
您應該能夠看到五個特徵欄。species 是 0、1 或 2 其中之一，所有其他特徵的值應介於 0 和 1 之間。我們將建立一個 TFX 管線來分析此資料集。
產生初步綱要
TFX 管線是使用 Python API 定義的。我們將建立一個管線，以從輸入範例自動產生綱要。此綱要可以由人審閱並根據需要進行調整。綱要定稿後，可用於後續任務中的訓練和範例驗證。
除了 簡易 TFX 管線教學課程中使用的 CsvExampleGen 之外，我們還將使用 StatisticsGen 和 SchemaGen
- StatisticsGen 計算資料集的統計資料。
- SchemaGen 檢查統計資料並建立初始資料綱要。
請參閱每個元件的指南或 TFX 元件教學課程，以深入瞭解這些元件。
撰寫管線定義
我們定義一個函數來建立 TFX 管線。Pipeline 物件代表一個 TFX 管線，可以使用 TFX 支援的其中一個管線協調系統來執行。
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)
執行管線
我們將像先前的教學課程一樣使用 LocalDagRunner。
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 {
database_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: "2024-05-08T09:36:10.555564"
}
}
}
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
}
base_type: DATASET
}
}
}
}
}
parameters {
parameters {
key: "input_base"
value {
field_value {
string_value: "/tmpfs/tmp/tfx-dataj_6ovg52"
}
}
}
parameters {
key: "input_config"
value {
field_value {
string_value: "{\n \"splits\": [\n {\n \"name\": \"single_split\",\n \"pattern\": \"*\"\n }\n ]\n}"
}
}
}
parameters {
key: "output_config"
value {
field_value {
string_value: "{\n \"split_config\": {\n \"splits\": [\n {\n \"hash_buckets\": 2,\n \"name\": \"train\"\n },\n {\n \"hash_buckets\": 1,\n \"name\": \"eval\"\n }\n ]\n }\n}"
}
}
}
parameters {
key: "output_data_format"
value {
field_value {
int_value: 6
}
}
}
parameters {
key: "output_file_format"
value {
field_value {
int_value: 5
}
}
}
}
downstream_nodes: "StatisticsGen"
execution_options {
caching_options {
}
}
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:[CsvExampleGen] Resolved inputs: ({},)
INFO:absl:select span and version = (0, None)
INFO:absl:latest span and version = (0, None)
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 1
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=1, input_dict={}, output_dict=defaultdict(<class 'list'>, {'examples': [Artifact(artifact: uri: "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:1715160970,sum_checksum:1715160970"
}
}
custom_properties {
key: "span"
value {
int_value: 0
}
}
, artifact_type: name: "Examples"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
properties {
key: "version"
value: INT
}
base_type: DATASET
)]}), exec_properties={'output_data_format': 6, 'output_file_format': 5, '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': '/tmpfs/tmp/tfx-dataj_6ovg52', 'span': 0, 'version': None, 'input_fingerprint': 'split:single_split,num_files:1,total_bytes:25648,xor_checksum:1715160970,sum_checksum:1715160970'}, 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/d65151e8-a6c7-4b12-8076-f56938dd89f4', 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: "2024-05-08T09:36:10.555564"
}
}
}
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
}
base_type: DATASET
}
}
}
}
}
parameters {
parameters {
key: "input_base"
value {
field_value {
string_value: "/tmpfs/tmp/tfx-dataj_6ovg52"
}
}
}
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='2024-05-08T09:36:10.555564', top_level_pipeline_run_id=None, frontend_url=None)
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 /tmpfs/tmp/tfx-dataj_6ovg52/* to TFExample.
WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.
INFO:absl:Examples generated.
INFO:absl:Value type <class 'NoneType'> of key version in exec_properties is not supported, going to drop it
INFO:absl:Value type <class 'list'> of key _beam_pipeline_args in exec_properties is not supported, going to drop it
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 1 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Deleted stateful_working_dir pipelines/penguin-tfdv-schema/CsvExampleGen/.system/stateful_working_dir/d65151e8-a6c7-4b12-8076-f56938dd89f4
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:1715160970,sum_checksum:1715160970"
}
}
custom_properties {
key: "span"
value {
int_value: 0
}
}
, artifact_type: name: "Examples"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
properties {
key: "version"
value: INT
}
base_type: DATASET
)]}) for execution 1
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component CsvExampleGen is finished.
INFO:absl:Component StatisticsGen is running.
INFO:absl:Running launcher for node_info {
type {
name: "tfx.components.statistics_gen.component.StatisticsGen"
base_type: PROCESS
}
id: "StatisticsGen"
}
contexts {
contexts {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfdv-schema"
}
}
}
contexts {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2024-05-08T09:36:10.555564"
}
}
}
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: "2024-05-08T09:36:10.555564"
}
}
}
context_queries {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfdv-schema.CsvExampleGen"
}
}
}
artifact_query {
type {
name: "Examples"
base_type: DATASET
}
}
output_key: "examples"
}
min_count: 1
}
}
}
outputs {
outputs {
key: "statistics"
value {
artifact_spec {
type {
name: "ExampleStatistics"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
base_type: STATISTICS
}
}
}
}
}
parameters {
parameters {
key: "exclude_splits"
value {
field_value {
string_value: "[]"
}
}
}
}
upstream_nodes: "CsvExampleGen"
downstream_nodes: "SchemaGen"
execution_options {
caching_options {
}
}
INFO:absl:MetadataStore with DB connection initialized
WARNING:absl:ArtifactQuery.property_predicate is not supported.
INFO:absl:[StatisticsGen] Resolved inputs: ({'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:1715160970,sum_checksum:1715160970"
}
}
custom_properties {
key: "is_external"
value {
int_value: 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: "1.15.0"
}
}
state: LIVE
type: "Examples"
create_time_since_epoch: 1715160971690
last_update_time_since_epoch: 1715160971690
, artifact_type: id: 15
name: "Examples"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
properties {
key: "version"
value: INT
}
base_type: DATASET
)]},)
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:1715160970,sum_checksum:1715160970"
}
}
custom_properties {
key: "is_external"
value {
int_value: 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: "1.15.0"
}
}
state: LIVE
type: "Examples"
create_time_since_epoch: 1715160971690
last_update_time_since_epoch: 1715160971690
, artifact_type: id: 15
name: "Examples"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
properties {
key: "version"
value: INT
}
base_type: DATASET
)]}, output_dict=defaultdict(<class 'list'>, {'statistics': [Artifact(artifact: uri: "pipelines/penguin-tfdv-schema/StatisticsGen/statistics/2"
, artifact_type: name: "ExampleStatistics"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
base_type: STATISTICS
)]}), exec_properties={'exclude_splits': '[]'}, execution_output_uri='pipelines/penguin-tfdv-schema/StatisticsGen/.system/executor_execution/2/executor_output.pb', stateful_working_dir='pipelines/penguin-tfdv-schema/StatisticsGen/.system/stateful_working_dir/3dc1ed50-c155-41f6-8457-b71a0b0ebe51', tmp_dir='pipelines/penguin-tfdv-schema/StatisticsGen/.system/executor_execution/2/.temp/', pipeline_node=node_info {
type {
name: "tfx.components.statistics_gen.component.StatisticsGen"
base_type: PROCESS
}
id: "StatisticsGen"
}
contexts {
contexts {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfdv-schema"
}
}
}
contexts {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2024-05-08T09:36:10.555564"
}
}
}
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: "2024-05-08T09:36:10.555564"
}
}
}
context_queries {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfdv-schema.CsvExampleGen"
}
}
}
artifact_query {
type {
name: "Examples"
base_type: DATASET
}
}
output_key: "examples"
}
min_count: 1
}
}
}
outputs {
outputs {
key: "statistics"
value {
artifact_spec {
type {
name: "ExampleStatistics"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
base_type: STATISTICS
}
}
}
}
}
parameters {
parameters {
key: "exclude_splits"
value {
field_value {
string_value: "[]"
}
}
}
}
upstream_nodes: "CsvExampleGen"
downstream_nodes: "SchemaGen"
execution_options {
caching_options {
}
}
, pipeline_info=id: "penguin-tfdv-schema"
, pipeline_run_id='2024-05-08T09:36:10.555564', top_level_pipeline_run_id=None, frontend_url=None)
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.
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 2 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Deleted stateful_working_dir pipelines/penguin-tfdv-schema/StatisticsGen/.system/stateful_working_dir/3dc1ed50-c155-41f6-8457-b71a0b0ebe51
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'statistics': [Artifact(artifact: uri: "pipelines/penguin-tfdv-schema/StatisticsGen/statistics/2"
, artifact_type: name: "ExampleStatistics"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
base_type: STATISTICS
)]}) for execution 2
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component StatisticsGen is finished.
INFO:absl:Component SchemaGen is running.
INFO:absl:Running launcher for node_info {
type {
name: "tfx.components.schema_gen.component.SchemaGen"
base_type: PROCESS
}
id: "SchemaGen"
}
contexts {
contexts {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfdv-schema"
}
}
}
contexts {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2024-05-08T09:36:10.555564"
}
}
}
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: "2024-05-08T09:36:10.555564"
}
}
}
context_queries {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfdv-schema.StatisticsGen"
}
}
}
artifact_query {
type {
name: "ExampleStatistics"
base_type: STATISTICS
}
}
output_key: "statistics"
}
min_count: 1
}
}
}
outputs {
outputs {
key: "schema"
value {
artifact_spec {
type {
name: "Schema"
}
}
}
}
}
parameters {
parameters {
key: "exclude_splits"
value {
field_value {
string_value: "[]"
}
}
}
parameters {
key: "infer_feature_shape"
value {
field_value {
int_value: 1
}
}
}
}
upstream_nodes: "StatisticsGen"
execution_options {
caching_options {
}
}
INFO:absl:MetadataStore with DB connection initialized
WARNING:absl:ArtifactQuery.property_predicate is not supported.
INFO:absl:[SchemaGen] Resolved inputs: ({'statistics': [Artifact(artifact: id: 2
type_id: 17
uri: "pipelines/penguin-tfdv-schema/StatisticsGen/statistics/2"
properties {
key: "span"
value {
int_value: 0
}
}
properties {
key: "split_names"
value {
string_value: "[\"train\", \"eval\"]"
}
}
custom_properties {
key: "is_external"
value {
int_value: 0
}
}
custom_properties {
key: "stats_dashboard_link"
value {
string_value: ""
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.15.0"
}
}
state: LIVE
type: "ExampleStatistics"
create_time_since_epoch: 1715160975131
last_update_time_since_epoch: 1715160975131
, artifact_type: id: 17
name: "ExampleStatistics"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
base_type: STATISTICS
)]},)
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: "span"
value {
int_value: 0
}
}
properties {
key: "split_names"
value {
string_value: "[\"train\", \"eval\"]"
}
}
custom_properties {
key: "is_external"
value {
int_value: 0
}
}
custom_properties {
key: "stats_dashboard_link"
value {
string_value: ""
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.15.0"
}
}
state: LIVE
type: "ExampleStatistics"
create_time_since_epoch: 1715160975131
last_update_time_since_epoch: 1715160975131
, artifact_type: id: 17
name: "ExampleStatistics"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
base_type: STATISTICS
)]}, output_dict=defaultdict(<class 'list'>, {'schema': [Artifact(artifact: uri: "pipelines/penguin-tfdv-schema/SchemaGen/schema/3"
, artifact_type: name: "Schema"
)]}), exec_properties={'infer_feature_shape': 1, 'exclude_splits': '[]'}, 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/9cb63ad1-17a3-4aaa-a3d3-5059f958bf6f', tmp_dir='pipelines/penguin-tfdv-schema/SchemaGen/.system/executor_execution/3/.temp/', pipeline_node=node_info {
type {
name: "tfx.components.schema_gen.component.SchemaGen"
base_type: PROCESS
}
id: "SchemaGen"
}
contexts {
contexts {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfdv-schema"
}
}
}
contexts {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2024-05-08T09:36:10.555564"
}
}
}
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: "2024-05-08T09:36:10.555564"
}
}
}
context_queries {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfdv-schema.StatisticsGen"
}
}
}
artifact_query {
type {
name: "ExampleStatistics"
base_type: STATISTICS
}
}
output_key: "statistics"
}
min_count: 1
}
}
}
outputs {
outputs {
key: "schema"
value {
artifact_spec {
type {
name: "Schema"
}
}
}
}
}
parameters {
parameters {
key: "exclude_splits"
value {
field_value {
string_value: "[]"
}
}
}
parameters {
key: "infer_feature_shape"
value {
field_value {
int_value: 1
}
}
}
}
upstream_nodes: "StatisticsGen"
execution_options {
caching_options {
}
}
, pipeline_info=id: "penguin-tfdv-schema"
, pipeline_run_id='2024-05-08T09:36:10.555564', top_level_pipeline_run_id=None, frontend_url=None)
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:Deleted stateful_working_dir pipelines/penguin-tfdv-schema/SchemaGen/.system/stateful_working_dir/9cb63ad1-17a3-4aaa-a3d3-5059f958bf6f
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'schema': [Artifact(artifact: uri: "pipelines/penguin-tfdv-schema/SchemaGen/schema/3"
, artifact_type: name: "Schema"
)]}) for execution 3
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component SchemaGen is finished.
如果管線成功完成，您應該會看到「INFO:absl:Component SchemaGen is finished.」。
我們將檢查管線的輸出，以瞭解我們的資料集。
檢閱管線的輸出
如先前的教學課程中所述，TFX 管線產生兩種輸出：artifacts 和 metadata DB (MLMD)，其中包含 artifacts 和管線執行的中繼資料。我們在上面的儲存格中定義了這些輸出的位置。預設情況下，artifacts 儲存在 pipelines 目錄下，中繼資料儲存為 metadata 目錄下的 sqlite 資料庫。
您可以使用 MLMD API 以程式設計方式找到這些輸出。首先，我們將定義一些公用程式函數來搜尋剛產生的輸出 artifacts。
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_output_artifacts(metadata, latest_execution.id)
# 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()
現在我們可以檢查管線執行的輸出。
# 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
現在是檢查每個元件輸出的時候了。如上所述，Tensorflow Data Validation (TFDV) 用於 StatisticsGen 和 SchemaGen 中，TFDV 也提供這些元件輸出的視覺化。
在本教學課程中，我們將使用 TFX 中的視覺化輔助方法，這些方法在內部使用 TFDV 來顯示視覺化。
檢查 StatisticsGen 的輸出
# docs-infra: no-execute
visualize_artifacts(stats_artifacts)
您可以看到輸入資料的各種統計資料。這些統計資料提供給 SchemaGen，以自動建構初始資料綱要。
檢查 SchemaGen 的輸出
visualize_artifacts(schema_artifacts)
此綱要是從 StatisticsGen 的輸出自動推斷的。您應該能夠看到 4 個 FLOAT 特徵和 1 個 INT 特徵。
匯出綱要以供日後使用
我們需要審閱和完善產生的綱要。審閱後的綱要需要持久化，以便在後續用於 ML 模型訓練的管線中使用。換句話說，您可能希望將綱要檔案新增至您的版本控制系統以用於實際使用案例。在本教學課程中，為了簡單起見，我們只會將綱要複製到預定義的檔案系統路徑。
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'
綱要檔案使用 Protocol Buffer 文字格式和 TensorFlow Metadata Schema proto 的一個實例。
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
}
}
}
您應務必審閱並可能根據需要編輯綱要定義。在本教學課程中，我們將僅使用產生的綱要，不做任何變更。
驗證輸入範例並訓練 ML 模型
我們將回到在 簡易 TFX 管線教學課程中建立的管線，以訓練 ML 模型並使用產生的綱要來撰寫模型訓練程式碼。
我們還將新增一個 ExampleValidator 元件，該元件將根據綱要尋找傳入資料集中的異常和遺失值。
撰寫模型訓練程式碼
我們需要像在 簡易 TFX 管線教學課程中一樣撰寫模型程式碼。
模型本身與先前的教學課程相同，但這次我們將使用從先前管線產生的綱要，而不是手動指定特徵。大多數程式碼都沒有變更。唯一的區別是我們不需要在此檔案中指定特徵的名稱和類型。相反，我們從綱要檔案中讀取它們。
_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://tensorflow.dev.org.tw/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
現在您已完成建構用於模型訓練的 TFX 管線的所有準備步驟。
撰寫管線定義
我們將新增兩個新元件：Importer 和 ExampleValidator。Importer 將外部檔案帶入 TFX 管線。在本例中，它是一個包含綱要定義的檔案。ExampleValidator 將檢查輸入資料，並驗證所有輸入資料是否符合我們提供的資料綱要。
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)
執行管線
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', '/tmpfs/tmp/tmpw96a2pj7/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmpfs/tmp/tmp42iap5mu', '--dist-dir', '/tmpfs/tmp/tmpx8p04zcg']
/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated.
!!
********************************************************************************
Please avoid running ``setup.py`` directly.
Instead, use pypa/build, pypa/installer or other
standards-based tools.
See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.
********************************************************************************
!!
self.initialize_options()
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 {
database_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: "2024-05-08T09:36:15.816321"
}
}
}
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
}
base_type: DATASET
}
}
}
}
}
parameters {
parameters {
key: "input_base"
value {
field_value {
string_value: "/tmpfs/tmp/tfx-dataj_6ovg52"
}
}
}
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
INFO:absl:[CsvExampleGen] Resolved inputs: ({},)
running bdist_wheel
running build
running build_py
creating build
creating build/lib
copying penguin_trainer.py -> build/lib
installing to /tmpfs/tmp/tmp42iap5mu
running install
running install_lib
copying build/lib/penguin_trainer.py -> /tmpfs/tmp/tmp42iap5mu
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 /tmpfs/tmp/tmp42iap5mu/tfx_user_code_Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2-py3.9.egg-info
running install_scripts
creating /tmpfs/tmp/tmp42iap5mu/tfx_user_code_Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2.dist-info/WHEEL
creating '/tmpfs/tmp/tmpx8p04zcg/tfx_user_code_Trainer-0.0+000876a22093ec764e3751d5a3ed939f1b107d1d6ade133f954ea2a767b8dfb2-py3-none-any.whl' and adding '/tmpfs/tmp/tmp42iap5mu' 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 /tmpfs/tmp/tmp42iap5mu
INFO:absl:select span and version = (0, None)
INFO:absl:latest span and version = (0, None)
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 1
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=1, input_dict={}, output_dict=defaultdict(<class 'list'>, {'examples': [Artifact(artifact: uri: "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:1715160970,sum_checksum:1715160970"
}
}
custom_properties {
key: "span"
value {
int_value: 0
}
}
, artifact_type: name: "Examples"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
properties {
key: "version"
value: INT
}
base_type: DATASET
)]}), exec_properties={'output_file_format': 5, 'input_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}', 'input_base': '/tmpfs/tmp/tfx-dataj_6ovg52', 'span': 0, 'version': None, 'input_fingerprint': 'split:single_split,num_files:1,total_bytes:25648,xor_checksum:1715160970,sum_checksum:1715160970'}, 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/0858d568-1a97-401a-afc6-a9932ff9a1e3', 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: "2024-05-08T09:36:15.816321"
}
}
}
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
}
base_type: DATASET
}
}
}
}
}
parameters {
parameters {
key: "input_base"
value {
field_value {
string_value: "/tmpfs/tmp/tfx-dataj_6ovg52"
}
}
}
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='2024-05-08T09:36:15.816321', top_level_pipeline_run_id=None, frontend_url=None)
INFO:absl:Generating examples.
INFO:absl:Processing input csv data /tmpfs/tmp/tfx-dataj_6ovg52/* to TFExample.
INFO:absl:Examples generated.
INFO:absl:Value type <class 'NoneType'> of key version in exec_properties is not supported, going to drop it
INFO:absl:Value type <class 'list'> of key _beam_pipeline_args in exec_properties is not supported, going to drop it
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 1 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Deleted stateful_working_dir pipelines/penguin-tfdv/CsvExampleGen/.system/stateful_working_dir/0858d568-1a97-401a-afc6-a9932ff9a1e3
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:1715160970,sum_checksum:1715160970"
}
}
custom_properties {
key: "span"
value {
int_value: 0
}
}
, artifact_type: name: "Examples"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
properties {
key: "version"
value: INT
}
base_type: DATASET
)]}) 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: "2024-05-08T09:36:15.816321"
}
}
}
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: "output_key"
value {
field_value {
string_value: "result"
}
}
}
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
INFO:absl:Processing source uri: schema, properties: {}, custom_properties: {}
INFO:absl:Component schema_importer is finished.
INFO:absl:Component StatisticsGen is running.
INFO:absl:Running launcher for node_info {
type {
name: "tfx.components.statistics_gen.component.StatisticsGen"
base_type: PROCESS
}
id: "StatisticsGen"
}
contexts {
contexts {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfdv"
}
}
}
contexts {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2024-05-08T09:36:15.816321"
}
}
}
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: "2024-05-08T09:36:15.816321"
}
}
}
context_queries {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfdv.CsvExampleGen"
}
}
}
artifact_query {
type {
name: "Examples"
base_type: DATASET
}
}
output_key: "examples"
}
min_count: 1
}
}
}
outputs {
outputs {
key: "statistics"
value {
artifact_spec {
type {
name: "ExampleStatistics"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
base_type: STATISTICS
}
}
}
}
}
parameters {
parameters {
key: "exclude_splits"
value {
field_value {
string_value: "[]"
}
}
}
}
upstream_nodes: "CsvExampleGen"
downstream_nodes: "ExampleValidator"
execution_options {
caching_options {
}
}
INFO:absl:MetadataStore with DB connection initialized
WARNING:absl:ArtifactQuery.property_predicate is not supported.
INFO:absl:[StatisticsGen] Resolved inputs: ({'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:1715160970,sum_checksum:1715160970"
}
}
custom_properties {
key: "is_external"
value {
int_value: 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: "1.15.0"
}
}
state: LIVE
type: "Examples"
create_time_since_epoch: 1715160976759
last_update_time_since_epoch: 1715160976759
, artifact_type: id: 15
name: "Examples"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
properties {
key: "version"
value: INT
}
base_type: DATASET
)]},)
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:1715160970,sum_checksum:1715160970"
}
}
custom_properties {
key: "is_external"
value {
int_value: 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: "1.15.0"
}
}
state: LIVE
type: "Examples"
create_time_since_epoch: 1715160976759
last_update_time_since_epoch: 1715160976759
, artifact_type: id: 15
name: "Examples"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
properties {
key: "version"
value: INT
}
base_type: DATASET
)]}, output_dict=defaultdict(<class 'list'>, {'statistics': [Artifact(artifact: uri: "pipelines/penguin-tfdv/StatisticsGen/statistics/3"
, artifact_type: name: "ExampleStatistics"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
base_type: STATISTICS
)]}), exec_properties={'exclude_splits': '[]'}, execution_output_uri='pipelines/penguin-tfdv/StatisticsGen/.system/executor_execution/3/executor_output.pb', stateful_working_dir='pipelines/penguin-tfdv/StatisticsGen/.system/stateful_working_dir/110d150d-d7b8-4a54-9e4b-de96d2f275fe', tmp_dir='pipelines/penguin-tfdv/StatisticsGen/.system/executor_execution/3/.temp/', pipeline_node=node_info {
type {
name: "tfx.components.statistics_gen.component.StatisticsGen"
base_type: PROCESS
}
id: "StatisticsGen"
}
contexts {
contexts {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfdv"
}
}
}
contexts {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2024-05-08T09:36:15.816321"
}
}
}
contexts {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfdv.StatisticsGen"
}
}
}
}
inputs {
inputs {
key: "examples"
value {
channels {
producer_node_query {
id: "CsvExampleGen"
}
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artifact_query {
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outputs {
outputs {
key: "statistics"
value {
artifact_spec {
type {
name: "ExampleStatistics"
properties {
key: "span"
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properties {
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execution_options {
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, pipeline_info=id: "penguin-tfdv"
, pipeline_run_id='2024-05-08T09:36:15.816321', top_level_pipeline_run_id=None, frontend_url=None)
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.
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 3 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Deleted stateful_working_dir pipelines/penguin-tfdv/StatisticsGen/.system/stateful_working_dir/110d150d-d7b8-4a54-9e4b-de96d2f275fe
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'statistics': [Artifact(artifact: uri: "pipelines/penguin-tfdv/StatisticsGen/statistics/3"
, artifact_type: name: "ExampleStatistics"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
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base_type: STATISTICS
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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"
base_type: TRAIN
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id: "Trainer"
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INFO:absl:MetadataStore with DB connection initialized
WARNING:absl:ArtifactQuery.property_predicate is not supported.
WARNING:absl:ArtifactQuery.property_predicate is not supported.
INFO:absl:[Trainer] Resolved inputs: ({'schema': [Artifact(artifact: id: 2
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INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 4
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execution_options {
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, pipeline_info=id: "penguin-tfdv"
, pipeline_run_id='2024-05-08T09:36:15.816321', top_level_pipeline_run_id=None, frontend_url=None)
INFO:absl:Train on the 'train' split when train_args.splits is not set.
INFO:absl:Evaluate on the 'eval' split when eval_args.splits is not set.
INFO:absl:udf_utils.get_fn {'custom_config': 'null', 'train_args': '{\n "num_steps": 100\n}', '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'} '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', '/tmpfs/tmp/tmp8javf_ly', '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
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tfx_bsl/tfxio/tf_example_record.py:343: parse_example_dataset (from tensorflow.python.data.experimental.ops.parsing_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.map(tf.io.parse_example(...))` instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tfx_bsl/tfxio/tf_example_record.py:343: parse_example_dataset (from tensorflow.python.data.experimental.ops.parsing_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.map(tf.io.parse_example(...))` instead.
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 (InputLaye [(None, 1)] 0 []
INFO:absl: r)
INFO:absl:
INFO:absl: culmen_length_mm (InputLay [(None, 1)] 0 []
INFO:absl: er)
INFO:absl:
INFO:absl: flipper_length_mm (InputLa [(None, 1)] 0 []
INFO:absl: yer)
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:==================================================================================================
INFO:absl:Total params: 139 (556.00 Byte)
INFO:absl:Trainable params: 139 (556.00 Byte)
INFO:absl:Non-trainable params: 0 (0.00 Byte)
INFO:absl:__________________________________________________________________________________________________
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1715160986.539652 29188 device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.
100/100 [==============================] - 2s 5ms/step - loss: 0.5183 - sparse_categorical_accuracy: 0.8225 - val_loss: 0.1883 - val_sparse_categorical_accuracy: 0.9000
INFO:absl:Function `_wrapped_model` contains input name(s) resource with unsupported characters which will be renamed to model_dense_2_biasadd_readvariableop_resource in the SavedModel.
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:Writing fingerprint to pipelines/penguin-tfdv/Trainer/model/4/Format-Serving/fingerprint.pb
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:Deleted stateful_working_dir pipelines/penguin-tfdv/Trainer/.system/stateful_working_dir/d3fcfa17-7c4e-4e63-a48c-deb3cc064ab5
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'model': [Artifact(artifact: uri: "pipelines/penguin-tfdv/Trainer/model/4"
, artifact_type: name: "Model"
base_type: MODEL
)], 'model_run': [Artifact(artifact: uri: "pipelines/penguin-tfdv/Trainer/model_run/4"
, artifact_type: name: "ModelRun"
)]}) for execution 4
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component Trainer is finished.
INFO:absl:Component ExampleValidator is running.
INFO:absl:Running launcher for node_info {
type {
name: "tfx.components.example_validator.component.ExampleValidator"
}
id: "ExampleValidator"
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name {
field_value {
string_value: "penguin-tfdv"
}
}
}
context_queries {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2024-05-08T09:36:15.816321"
}
}
}
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: "2024-05-08T09:36:15.816321"
}
}
}
context_queries {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfdv.StatisticsGen"
}
}
}
artifact_query {
type {
name: "ExampleStatistics"
base_type: STATISTICS
}
}
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
WARNING:absl:ArtifactQuery.property_predicate is not supported.
WARNING:absl:ArtifactQuery.property_predicate is not supported.
INFO:absl:[ExampleValidator] Resolved inputs: ({'schema': [Artifact(artifact: id: 2
type_id: 17
uri: "schema"
custom_properties {
key: "is_external"
value {
int_value: 1
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.15.0"
}
}
state: LIVE
type: "Schema"
create_time_since_epoch: 1715160976782
last_update_time_since_epoch: 1715160976782
, artifact_type: id: 17
name: "Schema"
)], 'statistics': [Artifact(artifact: id: 3
type_id: 19
uri: "pipelines/penguin-tfdv/StatisticsGen/statistics/3"
properties {
key: "span"
value {
int_value: 0
}
}
properties {
key: "split_names"
value {
string_value: "[\"train\", \"eval\"]"
}
}
custom_properties {
key: "is_external"
value {
int_value: 0
}
}
custom_properties {
key: "stats_dashboard_link"
value {
string_value: ""
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.15.0"
}
}
state: LIVE
type: "ExampleStatistics"
create_time_since_epoch: 1715160979570
last_update_time_since_epoch: 1715160979570
, artifact_type: id: 19
name: "ExampleStatistics"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
base_type: STATISTICS
)]},)
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={'schema': [Artifact(artifact: id: 2
type_id: 17
uri: "schema"
custom_properties {
key: "is_external"
value {
int_value: 1
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.15.0"
}
}
state: LIVE
type: "Schema"
create_time_since_epoch: 1715160976782
last_update_time_since_epoch: 1715160976782
, artifact_type: id: 17
name: "Schema"
)], 'statistics': [Artifact(artifact: id: 3
type_id: 19
uri: "pipelines/penguin-tfdv/StatisticsGen/statistics/3"
properties {
key: "span"
value {
int_value: 0
}
}
properties {
key: "split_names"
value {
string_value: "[\"train\", \"eval\"]"
}
}
custom_properties {
key: "is_external"
value {
int_value: 0
}
}
custom_properties {
key: "stats_dashboard_link"
value {
string_value: ""
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.15.0"
}
}
state: LIVE
type: "ExampleStatistics"
create_time_since_epoch: 1715160979570
last_update_time_since_epoch: 1715160979570
, artifact_type: id: 19
name: "ExampleStatistics"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
base_type: STATISTICS
)]}, output_dict=defaultdict(<class 'list'>, {'anomalies': [Artifact(artifact: uri: "pipelines/penguin-tfdv/ExampleValidator/anomalies/5"
, 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/37093a55-ba7a-42c7-a4fc-388b5f69b7d8', 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: "2024-05-08T09:36:15.816321"
}
}
}
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: "2024-05-08T09:36:15.816321"
}
}
}
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: "2024-05-08T09:36:15.816321"
}
}
}
context_queries {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfdv.StatisticsGen"
}
}
}
artifact_query {
type {
name: "ExampleStatistics"
base_type: STATISTICS
}
}
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='2024-05-08T09:36:15.816321', top_level_pipeline_run_id=None, frontend_url=None)
INFO:absl:Validating schema against the computed statistics for split train.
INFO:absl:Anomalies alerts created 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:Anomalies alerts created 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:Deleted stateful_working_dir pipelines/penguin-tfdv/ExampleValidator/.system/stateful_working_dir/37093a55-ba7a-42c7-a4fc-388b5f69b7d8
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'anomalies': [Artifact(artifact: uri: "pipelines/penguin-tfdv/ExampleValidator/anomalies/5"
, 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"
base_type: DEPLOY
}
id: "Pusher"
}
contexts {
contexts {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfdv"
}
}
}
contexts {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2024-05-08T09:36:15.816321"
}
}
}
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: "2024-05-08T09:36:15.816321"
}
}
}
context_queries {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfdv.Trainer"
}
}
}
artifact_query {
type {
name: "Model"
base_type: MODEL
}
}
output_key: "model"
}
}
}
}
outputs {
outputs {
key: "pushed_model"
value {
artifact_spec {
type {
name: "PushedModel"
base_type: MODEL
}
}
}
}
}
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
WARNING:absl:ArtifactQuery.property_predicate is not supported.
INFO:absl:[Pusher] Resolved inputs: ({'model': [Artifact(artifact: id: 4
type_id: 21
uri: "pipelines/penguin-tfdv/Trainer/model/4"
custom_properties {
key: "is_external"
value {
int_value: 0
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.15.0"
}
}
state: LIVE
type: "Model"
create_time_since_epoch: 1715160988205
last_update_time_since_epoch: 1715160988205
, artifact_type: id: 21
name: "Model"
base_type: MODEL
)]},)
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 6
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=6, input_dict={'model': [Artifact(artifact: id: 4
type_id: 21
uri: "pipelines/penguin-tfdv/Trainer/model/4"
custom_properties {
key: "is_external"
value {
int_value: 0
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.15.0"
}
}
state: LIVE
type: "Model"
create_time_since_epoch: 1715160988205
last_update_time_since_epoch: 1715160988205
, artifact_type: id: 21
name: "Model"
base_type: MODEL
)]}, output_dict=defaultdict(<class 'list'>, {'pushed_model': [Artifact(artifact: uri: "pipelines/penguin-tfdv/Pusher/pushed_model/6"
, artifact_type: name: "PushedModel"
base_type: MODEL
)]}), exec_properties={'custom_config': 'null', 'push_destination': '{\n "filesystem": {\n "base_directory": "serving_model/penguin-tfdv"\n }\n}'}, 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/5146eed9-4c4d-4bef-b849-ce87e44956ad', tmp_dir='pipelines/penguin-tfdv/Pusher/.system/executor_execution/6/.temp/', pipeline_node=node_info {
type {
name: "tfx.components.pusher.component.Pusher"
base_type: DEPLOY
}
id: "Pusher"
}
contexts {
contexts {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfdv"
}
}
}
contexts {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2024-05-08T09:36:15.816321"
}
}
}
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: "2024-05-08T09:36:15.816321"
}
}
}
context_queries {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfdv.Trainer"
}
}
}
artifact_query {
type {
name: "Model"
base_type: MODEL
}
}
output_key: "model"
}
}
}
}
outputs {
outputs {
key: "pushed_model"
value {
artifact_spec {
type {
name: "PushedModel"
base_type: MODEL
}
}
}
}
}
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='2024-05-08T09:36:15.816321', top_level_pipeline_run_id=None, frontend_url=None)
WARNING:absl:Pusher is going to push the model without validation. Consider using Evaluator or InfraValidator in your pipeline.
INFO:absl:Model version: 1715160988
INFO:absl:Model written to serving path serving_model/penguin-tfdv/1715160988.
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:Deleted stateful_working_dir pipelines/penguin-tfdv/Pusher/.system/stateful_working_dir/5146eed9-4c4d-4bef-b849-ce87e44956ad
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'pushed_model': [Artifact(artifact: uri: "pipelines/penguin-tfdv/Pusher/pushed_model/6"
, artifact_type: name: "PushedModel"
base_type: MODEL
)]}) for execution 6
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Component Pusher is finished.
如果管線成功完成，您應該會看到「INFO:absl:Component Pusher is finished.」。
檢查管線的輸出
我們已經訓練了企鵝的分類模型，並且我們也在 ExampleValidator 元件中驗證了輸入範例。我們可以像處理先前的管線一樣分析 ExampleValidator 的輸出。
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
ExampleValidator 中的 ExampleAnomalies 也可以視覺化。
visualize_artifacts(anomalies_artifacts)
您應該在每個範例分割中看到「No anomalies found」。由於我們使用了與此管線中綱要產生相同的資料，因此此處預期不會出現異常。如果您使用新的傳入資料重複執行此管線，ExampleValidator 應該能夠找到新資料與現有綱要之間的任何差異。
如果發現任何異常，您可以檢查您的資料以查看是否有任何範例不符合您的假設。來自 StatisticsGen 等其他元件的輸出可能很有用。但是，發現的任何異常都不會阻止進一步的管線執行。
後續步驟
您可以在 https://tensorflow.dev.org.tw/tfx/tutorials 找到更多資源
請參閱瞭解 TFX 管線，以深入瞭解 TFX 中的各種概念。