TensorFlow.org এ দেখুন
Google Colab-এ চালান
GitHub-এ উৎস দেখুন
নোটবুক ডাউনলোড করুনএই নোটবুক-ভিত্তিক টিউটোরিয়ালে, আমরা একটি TFX পাইপলাইন তৈরি এবং চালাব যা একটি সাধারণ শ্রেণিবিন্যাস মডেল তৈরি করে এবং একাধিক রান জুড়ে এর কার্যক্ষমতা বিশ্লেষণ করে। এই নোটবুক TFX পাইপলাইন আমরা সালে নির্মিত উপর ভিত্তি করে তৈরি সরল TFX পাইপলাইন টিউটোরিয়াল । আপনি যদি এখনও সেই টিউটোরিয়ালটি না পড়ে থাকেন তবে এই নোটবুকটি নিয়ে এগিয়ে যাওয়ার আগে আপনার এটি পড়া উচিত।
আপনি যখন আপনার মডেলকে পরিবর্তন করবেন বা এটিকে একটি নতুন ডেটাসেট দিয়ে প্রশিক্ষিত করবেন, তখন আপনার মডেলটি উন্নত হয়েছে বা খারাপ হয়েছে কিনা তা পরীক্ষা করতে হবে। শুধু সঠিকতার মতো শীর্ষ-স্তরের মেট্রিক পরীক্ষা করা যথেষ্ট নাও হতে পারে। প্রতিটি প্রশিক্ষিত মডেল উৎপাদনে ঠেলে দেওয়ার আগে মূল্যায়ন করা উচিত।
আমরা একটি যোগ হবে Evaluator পূর্ববর্তী টিউটোরিয়ালে নির্মিত পাইপলাইন অংশ। মূল্যায়নকারী উপাদান আপনার মডেলগুলির জন্য গভীর বিশ্লেষণ করে এবং একটি বেসলাইনের সাথে নতুন মডেলের তুলনা করে তা নির্ধারণ করে যে তারা "যথেষ্ট ভাল"। এটি ব্যবহার বাস্তবায়িত হয় TensorFlow মডেল বিশ্লেষণ গ্রন্থাগার।
দয়া করে দেখুন TFX পাইপলাইন বুঝুন TFX বিভিন্ন ধারণা সম্পর্কে আরো জানতে।
সেট আপ করুন
সেট আপ প্রক্রিয়াটি আগের টিউটোরিয়ালের মতোই।
আমাদের প্রথমে TFX পাইথন প্যাকেজ ইনস্টল করতে হবে এবং ডেটাসেটটি ডাউনলোড করতে হবে যা আমরা আমাদের মডেলের জন্য ব্যবহার করব।
পিপ আপগ্রেড করুন
স্থানীয়ভাবে চালানোর সময় একটি সিস্টেমে পিপ আপগ্রেড করা এড়াতে, আমরা Colab-এ চালাচ্ছি কিনা তা নিশ্চিত করুন। স্থানীয় সিস্টেম অবশ্যই আলাদাভাবে আপগ্রেড করা যেতে পারে।
try:
import colab
!pip install --upgrade pip
except:
pass
TFX ইনস্টল করুন
pip install -U tfx
আপনি কি রানটাইম রিস্টার্ট করেছেন?
আপনি যদি Google Colab ব্যবহার করেন, প্রথমবার যখন আপনি উপরের সেলটি চালান, তাহলে আপনাকে অবশ্যই উপরে "রিস্টার্ট RUNTIME" বোতামে ক্লিক করে বা "রানটাইম > রানটাইম রিস্টার্ট..." মেনু ব্যবহার করে রানটাইম রিস্টার্ট করতে হবে। Colab যেভাবে প্যাকেজগুলি লোড করে তার কারণেই এটি হয়েছে৷
TensorFlow এবং TFX সংস্করণ পরীক্ষা করুন।
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
ভেরিয়েবল সেট আপ করুন
একটি পাইপলাইন সংজ্ঞায়িত করতে ব্যবহৃত কিছু ভেরিয়েবল আছে। আপনি আপনার ইচ্ছা মত এই ভেরিয়েবল কাস্টমাইজ করতে পারেন. ডিফল্টরূপে পাইপলাইন থেকে সমস্ত আউটপুট বর্তমান ডিরেক্টরির অধীনে তৈরি করা হবে।
import os
PIPELINE_NAME = "penguin-tfma"
# Output directory to store artifacts generated from the pipeline.
PIPELINE_ROOT = os.path.join('pipelines', PIPELINE_NAME)
# Path to a SQLite DB file to use as an MLMD storage.
METADATA_PATH = os.path.join('metadata', PIPELINE_NAME, 'metadata.db')
# Output directory where created models from the pipeline will be exported.
SERVING_MODEL_DIR = os.path.join('serving_model', PIPELINE_NAME)
from absl import logging
logging.set_verbosity(logging.INFO) # Set default logging level.
উদাহরণ ডেটা প্রস্তুত করুন
আমরা একই ব্যবহার করবে পামার পেঙ্গুইনদের ডেটা সেটটি ।
এই ডেটাসেটে চারটি সংখ্যাসূচক বৈশিষ্ট্য রয়েছে যা ইতিমধ্যেই পরিসীমা [0,1] থাকার জন্য স্বাভাবিক করা হয়েছে। আমরা একটি শ্রেণীবিভাগ মডেল যা অনুমান গড়ে তুলবে species পেঙ্গুইনদের করুন।
যেহেতু TFX ExampleGen একটি ডিরেক্টরি থেকে ইনপুট পড়ে, আমাদের একটি ডিরেক্টরি তৈরি করতে হবে এবং এতে ডেটাসেট কপি করতে হবে।
import urllib.request
import tempfile
DATA_ROOT = tempfile.mkdtemp(prefix='tfx-data') # Create a temporary directory.
_data_url = 'https://raw.githubusercontent.com/tensorflow/tfx/master/tfx/examples/penguin/data/labelled/penguins_processed.csv'
_data_filepath = os.path.join(DATA_ROOT, "data.csv")
urllib.request.urlretrieve(_data_url, _data_filepath)
('/tmp/tfx-datal5lxy_yw/data.csv', <http.client.HTTPMessage at 0x7fa18a9da150>)
একটি পাইপলাইন তৈরি করুন
আমরা একটি যোগ হবে Evaluator পাইপলাইন আমরা তৈরি করতে কম্পোনেন্ট সরল TFX পাইপলাইন টিউটোরিয়াল ।
একটি Evaluator উপাদান একটি থেকে ইনপুট ডেটার প্রয়োজন ExampleGen উপাদান এবং একটি থেকে একটি মডেল Trainer উপাদান এবং tfma.EvalConfig অবজেক্ট। আমরা ঐচ্ছিকভাবে একটি বেসলাইন মডেল সরবরাহ করতে পারি যা নতুন প্রশিক্ষিত মডেলের সাথে মেট্রিক্স তুলনা করতে ব্যবহার করা যেতে পারে।
একটি evaluator আউটপুট নিদর্শন, দুই ধরণের সৃষ্টি ModelEvaluation এবং ModelBlessing । মডেল ইভালুয়েশনে বিস্তারিত মূল্যায়নের ফলাফল রয়েছে যা TFMA লাইব্রেরির সাহায্যে আরও তদন্ত এবং কল্পনা করা যেতে পারে। মডেলব্লেসিং-এ একটি বুলিয়ান ফলাফল রয়েছে যে মডেলটি প্রদত্ত মানদণ্ডে উত্তীর্ণ হয়েছে কিনা এবং একটি সংকেত হিসাবে পুশারের মতো পরবর্তী উপাদানগুলিতে ব্যবহার করা যেতে পারে।
মডেল প্রশিক্ষণ কোড লিখুন
আমরা হিসাবে একই মডেল কোড ব্যবহার করবে সরল TFX পাইপলাইন টিউটোরিয়াল ।
_trainer_module_file = 'penguin_trainer.py'
%%writefile {_trainer_module_file}
# Copied from https://www.tensorflow.org/tfx/tutorials/tfx/penguin_simple
from typing import List
from absl import logging
import tensorflow as tf
from tensorflow import keras
from tensorflow_transform.tf_metadata import schema_utils
from tfx.components.trainer.executor import TrainerFnArgs
from tfx.components.trainer.fn_args_utils import DataAccessor
from tfx_bsl.tfxio import dataset_options
from tensorflow_metadata.proto.v0 import schema_pb2
_FEATURE_KEYS = [
'culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm', 'body_mass_g'
]
_LABEL_KEY = 'species'
_TRAIN_BATCH_SIZE = 20
_EVAL_BATCH_SIZE = 10
# Since we're not generating or creating a schema, we will instead create
# a feature spec. Since there are a fairly small number of features this is
# manageable for this dataset.
_FEATURE_SPEC = {
**{
feature: tf.io.FixedLenFeature(shape=[1], dtype=tf.float32)
for feature in _FEATURE_KEYS
},
_LABEL_KEY: tf.io.FixedLenFeature(shape=[1], dtype=tf.int64)
}
def _input_fn(file_pattern: List[str],
data_accessor: DataAccessor,
schema: schema_pb2.Schema,
batch_size: int = 200) -> tf.data.Dataset:
"""Generates features and label for training.
Args:
file_pattern: List of paths or patterns of input tfrecord files.
data_accessor: DataAccessor for converting input to RecordBatch.
schema: schema of the input data.
batch_size: representing the number of consecutive elements of returned
dataset to combine in a single batch
Returns:
A dataset that contains (features, indices) tuple where features is a
dictionary of Tensors, and indices is a single Tensor of label indices.
"""
return data_accessor.tf_dataset_factory(
file_pattern,
dataset_options.TensorFlowDatasetOptions(
batch_size=batch_size, label_key=_LABEL_KEY),
schema=schema).repeat()
def _build_keras_model() -> tf.keras.Model:
"""Creates a DNN Keras model for classifying penguin data.
Returns:
A Keras Model.
"""
# The model below is built with Functional API, please refer to
# https://www.tensorflow.org/guide/keras/overview for all API options.
inputs = [keras.layers.Input(shape=(1,), name=f) for f in _FEATURE_KEYS]
d = keras.layers.concatenate(inputs)
for _ in range(2):
d = keras.layers.Dense(8, activation='relu')(d)
outputs = keras.layers.Dense(3)(d)
model = keras.Model(inputs=inputs, outputs=outputs)
model.compile(
optimizer=keras.optimizers.Adam(1e-2),
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=[keras.metrics.SparseCategoricalAccuracy()])
model.summary(print_fn=logging.info)
return model
# TFX Trainer will call this function.
def run_fn(fn_args: TrainerFnArgs):
"""Train the model based on given args.
Args:
fn_args: Holds args used to train the model as name/value pairs.
"""
# This schema is usually either an output of SchemaGen or a manually-curated
# version provided by pipeline author. A schema can also derived from TFT
# graph if a Transform component is used. In the case when either is missing,
# `schema_from_feature_spec` could be used to generate schema from very simple
# feature_spec, but the schema returned would be very primitive.
schema = schema_utils.schema_from_feature_spec(_FEATURE_SPEC)
train_dataset = _input_fn(
fn_args.train_files,
fn_args.data_accessor,
schema,
batch_size=_TRAIN_BATCH_SIZE)
eval_dataset = _input_fn(
fn_args.eval_files,
fn_args.data_accessor,
schema,
batch_size=_EVAL_BATCH_SIZE)
model = _build_keras_model()
model.fit(
train_dataset,
steps_per_epoch=fn_args.train_steps,
validation_data=eval_dataset,
validation_steps=fn_args.eval_steps)
# The result of the training should be saved in `fn_args.serving_model_dir`
# directory.
model.save(fn_args.serving_model_dir, save_format='tf')
Writing penguin_trainer.py
পাইপলাইনের সংজ্ঞা লিখ
আমরা একটি TFX পাইপলাইন তৈরি করার জন্য একটি ফাংশন সংজ্ঞায়িত করব। Evaluator উপাদান আমরা উপরে উল্লিখিত ছাড়াও, আমরা আরো এক নোড বলা যোগ হবে Resolver । একটি নতুন মডেল আগের মডেলের চেয়ে ভালো হচ্ছে তা পরীক্ষা করতে, আমাদের এটিকে পূর্ববর্তী প্রকাশিত মডেলের সাথে তুলনা করতে হবে, যাকে বেসলাইন বলা হয়। এমএল মেটাডেটা (MLMD) পাইপলাইন সমস্ত পূর্ববর্তী নিদর্শন ট্র্যাক করে এবং Resolver সর্বশেষ সুখী মডেল কি জানতে পারেন - একটি কৌশল বর্গ নামের বস্তু ব্যবহার MLMD থেকে - একটি মডেল সফলভাবে Evaluator পাস LatestBlessedModelStrategy ।
import tensorflow_model_analysis as tfma
def _create_pipeline(pipeline_name: str, pipeline_root: str, data_root: str,
module_file: str, serving_model_dir: str,
metadata_path: str) -> tfx.dsl.Pipeline:
"""Creates a three component penguin pipeline with TFX."""
# Brings data into the pipeline.
example_gen = tfx.components.CsvExampleGen(input_base=data_root)
# Uses user-provided Python function that trains a model.
trainer = tfx.components.Trainer(
module_file=module_file,
examples=example_gen.outputs['examples'],
train_args=tfx.proto.TrainArgs(num_steps=100),
eval_args=tfx.proto.EvalArgs(num_steps=5))
# NEW: Get the latest blessed model for Evaluator.
model_resolver = tfx.dsl.Resolver(
strategy_class=tfx.dsl.experimental.LatestBlessedModelStrategy,
model=tfx.dsl.Channel(type=tfx.types.standard_artifacts.Model),
model_blessing=tfx.dsl.Channel(
type=tfx.types.standard_artifacts.ModelBlessing)).with_id(
'latest_blessed_model_resolver')
# NEW: Uses TFMA to compute evaluation statistics over features of a model and
# perform quality validation of a candidate model (compared to a baseline).
eval_config = tfma.EvalConfig(
model_specs=[tfma.ModelSpec(label_key='species')],
slicing_specs=[
# An empty slice spec means the overall slice, i.e. the whole dataset.
tfma.SlicingSpec(),
# Calculate metrics for each penguin species.
tfma.SlicingSpec(feature_keys=['species']),
],
metrics_specs=[
tfma.MetricsSpec(per_slice_thresholds={
'sparse_categorical_accuracy':
tfma.PerSliceMetricThresholds(thresholds=[
tfma.PerSliceMetricThreshold(
slicing_specs=[tfma.SlicingSpec()],
threshold=tfma.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
lower_bound={'value': 0.6}),
# Change threshold will be ignored if there is no
# baseline model resolved from MLMD (first run).
change_threshold=tfma.GenericChangeThreshold(
direction=tfma.MetricDirection.HIGHER_IS_BETTER,
absolute={'value': -1e-10}))
)]),
})],
)
evaluator = tfx.components.Evaluator(
examples=example_gen.outputs['examples'],
model=trainer.outputs['model'],
baseline_model=model_resolver.outputs['model'],
eval_config=eval_config)
# Checks whether the model passed the validation steps and pushes the model
# to a file destination if check passed.
pusher = tfx.components.Pusher(
model=trainer.outputs['model'],
model_blessing=evaluator.outputs['blessing'], # Pass an evaluation result.
push_destination=tfx.proto.PushDestination(
filesystem=tfx.proto.PushDestination.Filesystem(
base_directory=serving_model_dir)))
components = [
example_gen,
trainer,
# Following two components were added to the pipeline.
model_resolver,
evaluator,
pusher,
]
return tfx.dsl.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
metadata_connection_config=tfx.orchestration.metadata
.sqlite_metadata_connection_config(metadata_path),
components=components)
আমরা Evaluator মাধ্যমে নিম্নলিখিত তথ্য সরবরাহ করতে হবে eval_config :
- কনফিগার করার জন্য অতিরিক্ত মেট্রিক (যদি মডেলে সংজ্ঞায়িত করা থেকে বেশি মেট্রিক চান)।
- কনফিগার করার জন্য স্লাইস
- বৈধতা অন্তর্ভুক্ত করা হবে কিনা তা যাচাই করার জন্য মডেল বৈধতা থ্রেশহোল্ড
কারণ SparseCategoricalAccuracy ইতিমধ্যে অন্তর্ভুক্ত করা হয় model.compile() কল, এটা বিশ্লেষণ স্বয়ংক্রিয়ভাবে অন্তর্ভুক্ত করা হবে। তাই আমরা এখানে কোনো অতিরিক্ত মেট্রিক যোগ করি না। SparseCategoricalAccuracy সিদ্ধান্ত নিতে হবে মডেল যথেষ্ট, অত্যধিক ব্যবহার করা হবে।
আমরা সমগ্র ডেটাসেট এবং প্রতিটি পেঙ্গুইন প্রজাতির জন্য মেট্রিক্স গণনা করি। SlicingSpec নির্দিষ্ট করে আমরা কিভাবে ঘোষিত মেট্রিক্স সমষ্টি।
দুটি থ্রেশহোল্ড রয়েছে যা একটি নতুন মডেলের পাস করা উচিত, একটি হল 0.6 এর পরম থ্রেশহোল্ড এবং অন্যটি একটি আপেক্ষিক থ্রেশহোল্ড যা এটি বেসলাইন মডেলের চেয়ে বেশি হওয়া উচিত। আপনি যখন প্রথমবারের মত পাইপলাইন রান change_threshold উপেক্ষা করা হবে এবং শুধুমাত্র value_threshold চেক করা হবে। আপনি পাইপলাইন একাধিকবার চালানো, Resolver পূর্ববর্তী রান থেকে একটি মডেল পাবেন এবং এটি তুলনা জন্য একটি বেসলাইন মডেল হিসেবে ব্যবহার করা হবে।
দেখুন Evaluator উপাদান নির্দেশিকা আরও তথ্যের জন্য।
পাইপলাইন চালান
আমরা ব্যবহার করবে LocalDagRunner পূর্ববর্তী টিউটোরিয়ালে হিসাবে।
tfx.orchestration.LocalDagRunner().run(
_create_pipeline(
pipeline_name=PIPELINE_NAME,
pipeline_root=PIPELINE_ROOT,
data_root=DATA_ROOT,
module_file=_trainer_module_file,
serving_model_dir=SERVING_MODEL_DIR,
metadata_path=METADATA_PATH))
INFO:absl:Generating ephemeral wheel package for '/tmpfs/src/temp/docs/tutorials/tfx/penguin_trainer.py' (including modules: ['penguin_trainer']).
INFO:absl:User module package has hash fingerprint version 1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703.
INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmp/tmpr3anh67s/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmp/tmp6s2sw4dj', '--dist-dir', '/tmp/tmp6jr76e54']
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
setuptools.SetuptoolsDeprecationWarning,
listing git files failed - pretending there aren't any
INFO:absl:Successfully built user code wheel distribution at 'pipelines/penguin-tfma/_wheels/tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703-py3-none-any.whl'; target user module is 'penguin_trainer'.
INFO:absl:Full user module path is 'penguin_trainer@pipelines/penguin-tfma/_wheels/tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703-py3-none-any.whl'
INFO:absl:Using deployment config:
executor_specs {
key: "CsvExampleGen"
value {
beam_executable_spec {
python_executor_spec {
class_path: "tfx.components.example_gen.csv_example_gen.executor.Executor"
}
}
}
}
executor_specs {
key: "Evaluator"
value {
beam_executable_spec {
python_executor_spec {
class_path: "tfx.components.evaluator.executor.Executor"
}
}
}
}
executor_specs {
key: "Pusher"
value {
python_class_executable_spec {
class_path: "tfx.components.pusher.executor.Executor"
}
}
}
executor_specs {
key: "Trainer"
value {
python_class_executable_spec {
class_path: "tfx.components.trainer.executor.GenericExecutor"
}
}
}
custom_driver_specs {
key: "CsvExampleGen"
value {
python_class_executable_spec {
class_path: "tfx.components.example_gen.driver.FileBasedDriver"
}
}
}
metadata_connection_config {
sqlite {
filename_uri: "metadata/penguin-tfma/metadata.db"
connection_mode: READWRITE_OPENCREATE
}
}
INFO:absl:Using connection config:
sqlite {
filename_uri: "metadata/penguin-tfma/metadata.db"
connection_mode: READWRITE_OPENCREATE
}
INFO:absl:Component CsvExampleGen is running.
INFO:absl:Running launcher for node_info {
type {
name: "tfx.components.example_gen.csv_example_gen.component.CsvExampleGen"
}
id: "CsvExampleGen"
}
contexts {
contexts {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfma"
}
}
}
contexts {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T10:34:23.517028"
}
}
}
contexts {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfma.CsvExampleGen"
}
}
}
}
outputs {
outputs {
key: "examples"
value {
artifact_spec {
type {
name: "Examples"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
properties {
key: "version"
value: INT
}
}
}
}
}
}
parameters {
parameters {
key: "input_base"
value {
field_value {
string_value: "/tmp/tfx-datal5lxy_yw"
}
}
}
parameters {
key: "input_config"
value {
field_value {
string_value: "{\n \"splits\": [\n {\n \"name\": \"single_split\",\n \"pattern\": \"*\"\n }\n ]\n}"
}
}
}
parameters {
key: "output_config"
value {
field_value {
string_value: "{\n \"split_config\": {\n \"splits\": [\n {\n \"hash_buckets\": 2,\n \"name\": \"train\"\n },\n {\n \"hash_buckets\": 1,\n \"name\": \"eval\"\n }\n ]\n }\n}"
}
}
}
parameters {
key: "output_data_format"
value {
field_value {
int_value: 6
}
}
}
parameters {
key: "output_file_format"
value {
field_value {
int_value: 5
}
}
}
}
downstream_nodes: "Evaluator"
downstream_nodes: "Trainer"
execution_options {
caching_options {
}
}
INFO:absl:MetadataStore with DB connection initialized
running bdist_wheel
running build
running build_py
creating build
creating build/lib
copying penguin_trainer.py -> build/lib
installing to /tmp/tmp6s2sw4dj
running install
running install_lib
copying build/lib/penguin_trainer.py -> /tmp/tmp6s2sw4dj
running install_egg_info
running egg_info
creating tfx_user_code_Trainer.egg-info
writing tfx_user_code_Trainer.egg-info/PKG-INFO
writing dependency_links to tfx_user_code_Trainer.egg-info/dependency_links.txt
writing top-level names to tfx_user_code_Trainer.egg-info/top_level.txt
writing manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'
reading manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'
writing manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'
Copying tfx_user_code_Trainer.egg-info to /tmp/tmp6s2sw4dj/tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703-py3.7.egg-info
running install_scripts
creating /tmp/tmp6s2sw4dj/tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703.dist-info/WHEEL
creating '/tmp/tmp6jr76e54/tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703-py3-none-any.whl' and adding '/tmp/tmp6s2sw4dj' to it
adding 'penguin_trainer.py'
adding 'tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703.dist-info/METADATA'
adding 'tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703.dist-info/WHEEL'
adding 'tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703.dist-info/top_level.txt'
adding 'tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703.dist-info/RECORD'
removing /tmp/tmp6s2sw4dj
WARNING: Logging before InitGoogleLogging() is written to STDERR
I1205 10:34:23.723806 28099 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 10:34:23.730262 28099 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 10:34:23.736788 28099 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 10:34:23.744907 28099 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:select span and version = (0, None)
INFO:absl:latest span and version = (0, None)
INFO:absl:MetadataStore with DB connection initialized
I1205 10:34:23.758380 28099 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Going to run a new execution 1
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=1, input_dict={}, output_dict=defaultdict(<class 'list'>, {'examples': [Artifact(artifact: uri: "pipelines/penguin-tfma/CsvExampleGen/examples/1"
custom_properties {
key: "input_fingerprint"
value {
string_value: "split:single_split,num_files:1,total_bytes:25648,xor_checksum:1638700463,sum_checksum:1638700463"
}
}
custom_properties {
key: "name"
value {
string_value: "penguin-tfma:2021-12-05T10:34:23.517028: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={'output_file_format': 5, 'output_config': '{\n "split_config": {\n "splits": [\n {\n "hash_buckets": 2,\n "name": "train"\n },\n {\n "hash_buckets": 1,\n "name": "eval"\n }\n ]\n }\n}', 'input_config': '{\n "splits": [\n {\n "name": "single_split",\n "pattern": "*"\n }\n ]\n}', 'output_data_format': 6, 'input_base': '/tmp/tfx-datal5lxy_yw', 'span': 0, 'version': None, 'input_fingerprint': 'split:single_split,num_files:1,total_bytes:25648,xor_checksum:1638700463,sum_checksum:1638700463'}, execution_output_uri='pipelines/penguin-tfma/CsvExampleGen/.system/executor_execution/1/executor_output.pb', stateful_working_dir='pipelines/penguin-tfma/CsvExampleGen/.system/stateful_working_dir/2021-12-05T10:34:23.517028', tmp_dir='pipelines/penguin-tfma/CsvExampleGen/.system/executor_execution/1/.temp/', pipeline_node=node_info {
type {
name: "tfx.components.example_gen.csv_example_gen.component.CsvExampleGen"
}
id: "CsvExampleGen"
}
contexts {
contexts {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfma"
}
}
}
contexts {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T10:34:23.517028"
}
}
}
contexts {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfma.CsvExampleGen"
}
}
}
}
outputs {
outputs {
key: "examples"
value {
artifact_spec {
type {
name: "Examples"
properties {
key: "span"
value: INT
}
properties {
key: "split_names"
value: STRING
}
properties {
key: "version"
value: INT
}
}
}
}
}
}
parameters {
parameters {
key: "input_base"
value {
field_value {
string_value: "/tmp/tfx-datal5lxy_yw"
}
}
}
parameters {
key: "input_config"
value {
field_value {
string_value: "{\n \"splits\": [\n {\n \"name\": \"single_split\",\n \"pattern\": \"*\"\n }\n ]\n}"
}
}
}
parameters {
key: "output_config"
value {
field_value {
string_value: "{\n \"split_config\": {\n \"splits\": [\n {\n \"hash_buckets\": 2,\n \"name\": \"train\"\n },\n {\n \"hash_buckets\": 1,\n \"name\": \"eval\"\n }\n ]\n }\n}"
}
}
}
parameters {
key: "output_data_format"
value {
field_value {
int_value: 6
}
}
}
parameters {
key: "output_file_format"
value {
field_value {
int_value: 5
}
}
}
}
downstream_nodes: "Evaluator"
downstream_nodes: "Trainer"
execution_options {
caching_options {
}
}
, pipeline_info=id: "penguin-tfma"
, pipeline_run_id='2021-12-05T10:34:23.517028')
INFO:absl:Generating examples.
WARNING:apache_beam.runners.interactive.interactive_environment:Dependencies required for Interactive Beam PCollection visualization are not available, please use: `pip install apache-beam[interactive]` to install necessary dependencies to enable all data visualization features.
INFO:absl:Processing input csv data /tmp/tfx-datal5lxy_yw/* to TFExample.
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.
INFO:absl:Examples generated.
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 1 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'examples': [Artifact(artifact: uri: "pipelines/penguin-tfma/CsvExampleGen/examples/1"
custom_properties {
key: "input_fingerprint"
value {
string_value: "split:single_split,num_files:1,total_bytes:25648,xor_checksum:1638700463,sum_checksum:1638700463"
}
}
custom_properties {
key: "name"
value {
string_value: "penguin-tfma:2021-12-05T10:34:23.517028: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 latest_blessed_model_resolver is running.
INFO:absl:Running launcher for node_info {
type {
name: "tfx.dsl.components.common.resolver.Resolver"
}
id: "latest_blessed_model_resolver"
}
contexts {
contexts {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfma"
}
}
}
contexts {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T10:34:23.517028"
}
}
}
contexts {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfma.latest_blessed_model_resolver"
}
}
}
}
inputs {
inputs {
key: "model"
value {
channels {
context_queries {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfma"
}
}
}
artifact_query {
type {
name: "Model"
}
}
}
}
}
inputs {
key: "model_blessing"
value {
channels {
context_queries {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfma"
}
}
}
artifact_query {
type {
name: "ModelBlessing"
}
}
}
}
}
resolver_config {
resolver_steps {
class_path: "tfx.dsl.input_resolution.strategies.latest_blessed_model_strategy.LatestBlessedModelStrategy"
config_json: "{}"
input_keys: "model"
input_keys: "model_blessing"
}
}
}
downstream_nodes: "Evaluator"
execution_options {
caching_options {
}
}
INFO:absl:Running as an resolver node.
INFO:absl:MetadataStore with DB connection initialized
WARNING:absl:Artifact type Model is not found in MLMD.
WARNING:absl:Artifact type ModelBlessing is not found in MLMD.
I1205 10:34:24.899447 28099 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Component latest_blessed_model_resolver is finished.
INFO:absl:Component Trainer is running.
INFO:absl:Running launcher for node_info {
type {
name: "tfx.components.trainer.component.Trainer"
}
id: "Trainer"
}
contexts {
contexts {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfma"
}
}
}
contexts {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T10:34:23.517028"
}
}
}
contexts {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfma.Trainer"
}
}
}
}
inputs {
inputs {
key: "examples"
value {
channels {
producer_node_query {
id: "CsvExampleGen"
}
context_queries {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfma"
}
}
}
context_queries {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T10:34:23.517028"
}
}
}
context_queries {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfma.CsvExampleGen"
}
}
}
artifact_query {
type {
name: "Examples"
}
}
output_key: "examples"
}
min_count: 1
}
}
}
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-tfma/_wheels/tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703-py3-none-any.whl"
}
}
}
parameters {
key: "train_args"
value {
field_value {
string_value: "{\n \"num_steps\": 100\n}"
}
}
}
}
upstream_nodes: "CsvExampleGen"
downstream_nodes: "Evaluator"
downstream_nodes: "Pusher"
execution_options {
caching_options {
}
}
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:MetadataStore with DB connection initialized
I1205 10:34:24.924589 28099 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Going to run a new execution 3
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=3, input_dict={'examples': [Artifact(artifact: id: 1
type_id: 15
uri: "pipelines/penguin-tfma/CsvExampleGen/examples/1"
properties {
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:1638700463,sum_checksum:1638700463"
}
}
custom_properties {
key: "name"
value {
string_value: "penguin-tfma:2021-12-05T10:34:23.517028:CsvExampleGen:examples:0"
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INFO:absl:Evaluate on the 'eval' split when eval_args.splits is not set.
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INFO:absl:Installing 'pipelines/penguin-tfma/_wheels/tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703-py3-none-any.whl' to a temporary directory.
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Processing ./pipelines/penguin-tfma/_wheels/tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703-py3-none-any.whl
INFO:absl:Successfully installed 'pipelines/penguin-tfma/_wheels/tfx_user_code_Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703-py3-none-any.whl'.
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INFO:absl:Feature culmen_depth_mm has a shape dim {
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. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
size: 1
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. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
size: 1
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. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
size: 1
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. Setting to DenseTensor.
Installing collected packages: tfx-user-code-Trainer
Successfully installed tfx-user-code-Trainer-0.0+1e19049dced0ccb21e0af60dae1c6e0ef09b63d1ff0e370d7f699920c2735703
INFO:absl:Feature body_mass_g has a shape dim {
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INFO:absl:Feature culmen_depth_mm has a shape dim {
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. Setting to DenseTensor.
INFO:absl:Feature culmen_length_mm has a shape dim {
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. Setting to DenseTensor.
INFO:absl:Feature flipper_length_mm has a shape dim {
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. Setting to DenseTensor.
INFO:absl:Feature species has a shape dim {
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. Setting to DenseTensor.
INFO:absl:Feature culmen_depth_mm has a shape dim {
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INFO:absl:Model: "model"
INFO:absl:__________________________________________________________________________________________________
INFO:absl:Layer (type) Output Shape Param # Connected to
INFO:absl:==================================================================================================
INFO:absl:culmen_length_mm (InputLayer) [(None, 1)] 0
INFO:absl:__________________________________________________________________________________________________
INFO:absl:culmen_depth_mm (InputLayer) [(None, 1)] 0
INFO:absl:__________________________________________________________________________________________________
INFO:absl:flipper_length_mm (InputLayer) [(None, 1)] 0
INFO:absl:__________________________________________________________________________________________________
INFO:absl:body_mass_g (InputLayer) [(None, 1)] 0
INFO:absl:__________________________________________________________________________________________________
INFO:absl:concatenate (Concatenate) (None, 4) 0 culmen_length_mm[0][0]
INFO:absl: culmen_depth_mm[0][0]
INFO:absl: flipper_length_mm[0][0]
INFO:absl: body_mass_g[0][0]
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense (Dense) (None, 8) 40 concatenate[0][0]
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_1 (Dense) (None, 8) 72 dense[0][0]
INFO:absl:__________________________________________________________________________________________________
INFO:absl:dense_2 (Dense) (None, 3) 27 dense_1[0][0]
INFO:absl:==================================================================================================
INFO:absl:Total params: 139
INFO:absl:Trainable params: 139
INFO:absl:Non-trainable params: 0
INFO:absl:__________________________________________________________________________________________________
100/100 [==============================] - 1s 3ms/step - loss: 0.5273 - sparse_categorical_accuracy: 0.8175 - val_loss: 0.2412 - val_sparse_categorical_accuracy: 0.9600
2021-12-05 10:34:29.879208: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
INFO:tensorflow:Assets written to: pipelines/penguin-tfma/Trainer/model/3/Format-Serving/assets
INFO:tensorflow:Assets written to: pipelines/penguin-tfma/Trainer/model/3/Format-Serving/assets
INFO:absl:Training complete. Model written to pipelines/penguin-tfma/Trainer/model/3/Format-Serving. ModelRun written to pipelines/penguin-tfma/Trainer/model_run/3
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 3 succeeded.
INFO:absl:Cleaning up stateful execution info.
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INFO:absl:MetadataStore with DB connection initialized
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}
artifact_query {
type {
name: "Model"
}
}
output_key: "model"
}
}
}
}
outputs {
outputs {
key: "blessing"
value {
artifact_spec {
type {
name: "ModelBlessing"
}
}
}
}
outputs {
key: "evaluation"
value {
artifact_spec {
type {
name: "ModelEvaluation"
}
}
}
}
}
parameters {
parameters {
key: "eval_config"
value {
field_value {
string_value: "{\n \"metrics_specs\": [\n {\n \"per_slice_thresholds\": {\n \"sparse_categorical_accuracy\": {\n \"thresholds\": [\n {\n \"slicing_specs\": [\n {}\n ],\n \"threshold\": {\n \"change_threshold\": {\n \"absolute\": -1e-10,\n \"direction\": \"HIGHER_IS_BETTER\"\n },\n \"value_threshold\": {\n \"lower_bound\": 0.6\n }\n }\n }\n ]\n }\n }\n }\n ],\n \"model_specs\": [\n {\n \"label_key\": \"species\"\n }\n ],\n \"slicing_specs\": [\n {},\n {\n \"feature_keys\": [\n \"species\"\n ]\n }\n ]\n}"
}
}
}
parameters {
key: "example_splits"
value {
field_value {
string_value: "null"
}
}
}
parameters {
key: "fairness_indicator_thresholds"
value {
field_value {
string_value: "null"
}
}
}
}
upstream_nodes: "CsvExampleGen"
upstream_nodes: "Trainer"
upstream_nodes: "latest_blessed_model_resolver"
downstream_nodes: "Pusher"
execution_options {
caching_options {
}
}
, pipeline_info=id: "penguin-tfma"
, pipeline_run_id='2021-12-05T10:34:23.517028')
INFO:absl:udf_utils.get_fn {'example_splits': 'null', 'eval_config': '{\n "metrics_specs": [\n {\n "per_slice_thresholds": {\n "sparse_categorical_accuracy": {\n "thresholds": [\n {\n "slicing_specs": [\n {}\n ],\n "threshold": {\n "change_threshold": {\n "absolute": -1e-10,\n "direction": "HIGHER_IS_BETTER"\n },\n "value_threshold": {\n "lower_bound": 0.6\n }\n }\n }\n ]\n }\n }\n }\n ],\n "model_specs": [\n {\n "label_key": "species"\n }\n ],\n "slicing_specs": [\n {},\n {\n "feature_keys": [\n "species"\n ]\n }\n ]\n}', 'fairness_indicator_thresholds': 'null'} 'custom_eval_shared_model'
INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
label_key: "species"
}
slicing_specs {
}
slicing_specs {
feature_keys: "species"
}
metrics_specs {
per_slice_thresholds {
key: "sparse_categorical_accuracy"
value {
thresholds {
slicing_specs {
}
threshold {
value_threshold {
lower_bound {
value: 0.6
}
}
}
}
}
}
}
INFO:absl:Using pipelines/penguin-tfma/Trainer/model/3/Format-Serving as model.
INFO:absl:The 'example_splits' parameter is not set, using 'eval' split.
INFO:absl:Evaluating model.
INFO:absl:udf_utils.get_fn {'example_splits': 'null', 'eval_config': '{\n "metrics_specs": [\n {\n "per_slice_thresholds": {\n "sparse_categorical_accuracy": {\n "thresholds": [\n {\n "slicing_specs": [\n {}\n ],\n "threshold": {\n "change_threshold": {\n "absolute": -1e-10,\n "direction": "HIGHER_IS_BETTER"\n },\n "value_threshold": {\n "lower_bound": 0.6\n }\n }\n }\n ]\n }\n }\n }\n ],\n "model_specs": [\n {\n "label_key": "species"\n }\n ],\n "slicing_specs": [\n {},\n {\n "feature_keys": [\n "species"\n ]\n }\n ]\n}', 'fairness_indicator_thresholds': 'null'} 'custom_extractors'
INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
label_key: "species"
}
slicing_specs {
}
slicing_specs {
feature_keys: "species"
}
metrics_specs {
model_names: ""
per_slice_thresholds {
key: "sparse_categorical_accuracy"
value {
thresholds {
slicing_specs {
}
threshold {
value_threshold {
lower_bound {
value: 0.6
}
}
}
}
}
}
}
INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
label_key: "species"
}
slicing_specs {
}
slicing_specs {
feature_keys: "species"
}
metrics_specs {
model_names: ""
per_slice_thresholds {
key: "sparse_categorical_accuracy"
value {
thresholds {
slicing_specs {
}
threshold {
value_threshold {
lower_bound {
value: 0.6
}
}
}
}
}
}
}
INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=
model_specs {
label_key: "species"
}
slicing_specs {
}
slicing_specs {
feature_keys: "species"
}
metrics_specs {
model_names: ""
per_slice_thresholds {
key: "sparse_categorical_accuracy"
value {
thresholds {
slicing_specs {
}
threshold {
value_threshold {
lower_bound {
value: 0.6
}
}
}
}
}
}
}
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
INFO:absl:Evaluation complete. Results written to pipelines/penguin-tfma/Evaluator/evaluation/4.
INFO:absl:Checking validation results.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/writers/metrics_plots_and_validations_writer.py:114: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.
Instructions for updating:
Use eager execution and:
`tf.data.TFRecordDataset(path)`
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_model_analysis/writers/metrics_plots_and_validations_writer.py:114: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.
Instructions for updating:
Use eager execution and:
`tf.data.TFRecordDataset(path)`
INFO:absl:Blessing result True written to pipelines/penguin-tfma/Evaluator/blessing/4.
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 4 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'blessing': [Artifact(artifact: uri: "pipelines/penguin-tfma/Evaluator/blessing/4"
custom_properties {
key: "name"
value {
string_value: "penguin-tfma:2021-12-05T10:34:23.517028:Evaluator:blessing:0"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.4.0"
}
}
, artifact_type: name: "ModelBlessing"
)], 'evaluation': [Artifact(artifact: uri: "pipelines/penguin-tfma/Evaluator/evaluation/4"
custom_properties {
key: "name"
value {
string_value: "penguin-tfma:2021-12-05T10:34:23.517028:Evaluator:evaluation:0"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.4.0"
}
}
, artifact_type: name: "ModelEvaluation"
)]}) for execution 4
INFO:absl:MetadataStore with DB connection initialized
I1205 10:34:35.040588 28099 rdbms_metadata_access_object.cc:686] No property is defined for the Type
I1205 10:34:35.045548 28099 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Component Evaluator is finished.
INFO:absl:Component Pusher is running.
INFO:absl:Running launcher for node_info {
type {
name: "tfx.components.pusher.component.Pusher"
}
id: "Pusher"
}
contexts {
contexts {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfma"
}
}
}
contexts {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T10:34:23.517028"
}
}
}
contexts {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfma.Pusher"
}
}
}
}
inputs {
inputs {
key: "model"
value {
channels {
producer_node_query {
id: "Trainer"
}
context_queries {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfma"
}
}
}
context_queries {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T10:34:23.517028"
}
}
}
context_queries {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfma.Trainer"
}
}
}
artifact_query {
type {
name: "Model"
}
}
output_key: "model"
}
}
}
inputs {
key: "model_blessing"
value {
channels {
producer_node_query {
id: "Evaluator"
}
context_queries {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfma"
}
}
}
context_queries {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T10:34:23.517028"
}
}
}
context_queries {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfma.Evaluator"
}
}
}
artifact_query {
type {
name: "ModelBlessing"
}
}
output_key: "blessing"
}
}
}
}
outputs {
outputs {
key: "pushed_model"
value {
artifact_spec {
type {
name: "PushedModel"
}
}
}
}
}
parameters {
parameters {
key: "custom_config"
value {
field_value {
string_value: "null"
}
}
}
parameters {
key: "push_destination"
value {
field_value {
string_value: "{\n \"filesystem\": {\n \"base_directory\": \"serving_model/penguin-tfma\"\n }\n}"
}
}
}
}
upstream_nodes: "Evaluator"
upstream_nodes: "Trainer"
execution_options {
caching_options {
}
}
INFO:absl:MetadataStore with DB connection initialized
I1205 10:34:35.068168 28099 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Going to run a new execution 5
INFO:absl:Going to run a new execution: ExecutionInfo(execution_id=5, input_dict={'model': [Artifact(artifact: id: 3
type_id: 19
uri: "pipelines/penguin-tfma/Trainer/model/3"
custom_properties {
key: "name"
value {
string_value: "penguin-tfma:2021-12-05T10:34:23.517028:Trainer:model:0"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.4.0"
}
}
state: LIVE
create_time_since_epoch: 1638700470409
last_update_time_since_epoch: 1638700470409
, artifact_type: id: 19
name: "Model"
)], 'model_blessing': [Artifact(artifact: id: 4
type_id: 21
uri: "pipelines/penguin-tfma/Evaluator/blessing/4"
custom_properties {
key: "blessed"
value {
int_value: 1
}
}
custom_properties {
key: "current_model"
value {
string_value: "pipelines/penguin-tfma/Trainer/model/3"
}
}
custom_properties {
key: "current_model_id"
value {
int_value: 3
}
}
custom_properties {
key: "name"
value {
string_value: "penguin-tfma:2021-12-05T10:34:23.517028:Evaluator:blessing:0"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.4.0"
}
}
state: LIVE
create_time_since_epoch: 1638700475049
last_update_time_since_epoch: 1638700475049
, artifact_type: id: 21
name: "ModelBlessing"
)]}, output_dict=defaultdict(<class 'list'>, {'pushed_model': [Artifact(artifact: uri: "pipelines/penguin-tfma/Pusher/pushed_model/5"
custom_properties {
key: "name"
value {
string_value: "penguin-tfma:2021-12-05T10:34:23.517028:Pusher:pushed_model:0"
}
}
, artifact_type: name: "PushedModel"
)]}), exec_properties={'custom_config': 'null', 'push_destination': '{\n "filesystem": {\n "base_directory": "serving_model/penguin-tfma"\n }\n}'}, execution_output_uri='pipelines/penguin-tfma/Pusher/.system/executor_execution/5/executor_output.pb', stateful_working_dir='pipelines/penguin-tfma/Pusher/.system/stateful_working_dir/2021-12-05T10:34:23.517028', tmp_dir='pipelines/penguin-tfma/Pusher/.system/executor_execution/5/.temp/', pipeline_node=node_info {
type {
name: "tfx.components.pusher.component.Pusher"
}
id: "Pusher"
}
contexts {
contexts {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfma"
}
}
}
contexts {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T10:34:23.517028"
}
}
}
contexts {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfma.Pusher"
}
}
}
}
inputs {
inputs {
key: "model"
value {
channels {
producer_node_query {
id: "Trainer"
}
context_queries {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfma"
}
}
}
context_queries {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T10:34:23.517028"
}
}
}
context_queries {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfma.Trainer"
}
}
}
artifact_query {
type {
name: "Model"
}
}
output_key: "model"
}
}
}
inputs {
key: "model_blessing"
value {
channels {
producer_node_query {
id: "Evaluator"
}
context_queries {
type {
name: "pipeline"
}
name {
field_value {
string_value: "penguin-tfma"
}
}
}
context_queries {
type {
name: "pipeline_run"
}
name {
field_value {
string_value: "2021-12-05T10:34:23.517028"
}
}
}
context_queries {
type {
name: "node"
}
name {
field_value {
string_value: "penguin-tfma.Evaluator"
}
}
}
artifact_query {
type {
name: "ModelBlessing"
}
}
output_key: "blessing"
}
}
}
}
outputs {
outputs {
key: "pushed_model"
value {
artifact_spec {
type {
name: "PushedModel"
}
}
}
}
}
parameters {
parameters {
key: "custom_config"
value {
field_value {
string_value: "null"
}
}
}
parameters {
key: "push_destination"
value {
field_value {
string_value: "{\n \"filesystem\": {\n \"base_directory\": \"serving_model/penguin-tfma\"\n }\n}"
}
}
}
}
upstream_nodes: "Evaluator"
upstream_nodes: "Trainer"
execution_options {
caching_options {
}
}
, pipeline_info=id: "penguin-tfma"
, pipeline_run_id='2021-12-05T10:34:23.517028')
INFO:absl:Model version: 1638700475
INFO:absl:Model written to serving path serving_model/penguin-tfma/1638700475.
INFO:absl:Model pushed to pipelines/penguin-tfma/Pusher/pushed_model/5.
INFO:absl:Cleaning up stateless execution info.
INFO:absl:Execution 5 succeeded.
INFO:absl:Cleaning up stateful execution info.
INFO:absl:Publishing output artifacts defaultdict(<class 'list'>, {'pushed_model': [Artifact(artifact: uri: "pipelines/penguin-tfma/Pusher/pushed_model/5"
custom_properties {
key: "name"
value {
string_value: "penguin-tfma:2021-12-05T10:34:23.517028:Pusher:pushed_model:0"
}
}
custom_properties {
key: "tfx_version"
value {
string_value: "1.4.0"
}
}
, artifact_type: name: "PushedModel"
)]}) for execution 5
INFO:absl:MetadataStore with DB connection initialized
I1205 10:34:35.098553 28099 rdbms_metadata_access_object.cc:686] No property is defined for the Type
INFO:absl:Component Pusher is finished.
পাইপলাইন সম্পন্ন হলে, আপনি নিম্নলিখিত মত কিছু দেখতে সক্ষম হওয়া উচিত:
INFO:absl:Blessing result True written to pipelines/penguin-tfma/Evaluator/blessing/4.
অথবা আপনি ম্যানুয়ালি আউটপুট ডিরেক্টরিটিও পরীক্ষা করতে পারেন যেখানে উৎপন্ন শিল্পকর্মগুলি সংরক্ষণ করা হয়। আপনার পরিদর্শন করা যদি pipelines/penguin-tfma/Evaluator/blessing/ একটি ফাইল broswer সঙ্গে, আপনি একটি নাম সহ একটি ফাইল দেখতে পারেন BLESSED বা NOT_BLESSED মূল্যায়ন ফলাফলের অনুযায়ী।
তাহলে আশীর্বাদ ফল False , বিমানপোত করার মডেল ধাক্কা আপত্তি করবে serving_model_dir , কারণ মডেল যথেষ্ট উৎপাদনে ব্যবহার করা হয় না।
আপনি বিভিন্ন মূল্যায়ন কনফিগারেশনের সাথে আবার পাইপলাইন চালাতে পারেন। এমনকি যদি আপনি সঠিক একই কনফিগ এবং ডেটা সেটটি সঙ্গে পাইপলাইন চালানোর প্রশিক্ষণ মডেল সামান্য মডেল প্রশিক্ষণ যা হতে পারে অন্তর্বর্তী যদৃচ্ছতা কারণে বিভিন্ন হতে পারে NOT_BLESSED মডেল।
পাইপলাইনের আউটপুট পরীক্ষা করুন
আপনি মডেল ইভালুয়েশন আর্টিফ্যাক্টে মূল্যায়নের ফলাফল অনুসন্ধান এবং কল্পনা করতে TFMA ব্যবহার করতে পারেন।
আউটপুট শিল্পকর্ম থেকে বিশ্লেষণ ফলাফল পান
আপনি এই আউটপুটগুলি প্রোগ্রামেটিকভাবে সনাক্ত করতে MLMD API ব্যবহার করতে পারেন। প্রথমত, আমরা কিছু ইউটিলিটি ফাংশন সংজ্ঞায়িত করব আউটপুট আর্টিফ্যাক্টগুলি অনুসন্ধান করার জন্য যা এইমাত্র উত্পাদিত হয়েছে।
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])
আমরা এর সর্বশেষ সম্পাদন জানতে পারেন Evaluator উপাদান এবং এটি আউটপুট নিদর্শন পেতে।
# Non-public APIs, just for showcase.
from tfx.orchestration.metadata import Metadata
from tfx.types import standard_component_specs
metadata_connection_config = tfx.orchestration.metadata.sqlite_metadata_connection_config(
METADATA_PATH)
with Metadata(metadata_connection_config) as metadata_handler:
# Find output artifacts from MLMD.
evaluator_output = get_latest_artifacts(metadata_handler, PIPELINE_NAME,
'Evaluator')
eval_artifact = evaluator_output[standard_component_specs.EVALUATION_KEY][0]
INFO:absl:MetadataStore with DB connection initialized
Evaluator সবসময় এক মূল্যায়ন হস্তনির্মিত বস্তু ফেরৎ, এবং আমরা TensorFlow মডেল বিশ্লেষণ গ্রন্থাগার ব্যবহার করে এটি ঠাহর করতে পারেন। উদাহরণস্বরূপ, নিম্নলিখিত কোড প্রতিটি পেঙ্গুইন প্রজাতির জন্য নির্ভুলতা মেট্রিক্স রেন্ডার করবে।
import tensorflow_model_analysis as tfma
eval_result = tfma.load_eval_result(eval_artifact.uri)
tfma.view.render_slicing_metrics(eval_result, slicing_column='species')
SlicingMetricsViewer(config={'weightedExamplesColumn': 'example_count'}, data=[{'slice': 'species:0', 'metrics…
আপনি 'sparse_categorical_accuracy' চয়ন করেন তাহলে Show ড্রপ-ডাউন তালিকা, আপনি প্রজাতি প্রতি সঠিকতা মান দেখতে পারেন। আপনি আরও স্লাইস যোগ করতে এবং আপনার মডেলটি সমস্ত বিতরণের জন্য ভাল কিনা এবং কোনও সম্ভাব্য পক্ষপাত আছে কিনা তা পরীক্ষা করতে চাইতে পারেন।
পরবর্তী পদক্ষেপ
এ মডেল বিশ্লেষণ আরো জানুন TensorFlow মডেল বিশ্লেষণ গ্রন্থাগার টিউটোরিয়াল ।
আপনি আরও রিসোর্স জানতে পারেন https://www.tensorflow.org/tfx/tutorials
দয়া করে দেখুন TFX পাইপলাইন বুঝুন TFX বিভিন্ন ধারণা সম্পর্কে আরো জানতে।