id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
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166,112 | import functools
from typing import Callable, Dict, List
import absl
from flax import linen as nn
from flax.metrics import tensorboard
import jax
from jax import numpy as jnp
from jax.experimental import jax2tf
import numpy as np
import optax
import tensorflow as tf
import tensorflow_transform as tft
from tfx import v1... | Train the model based on given args. Args: fn_args: Holds args used to train the model as name/value pairs. |
166,113 | import os
from typing import List
import absl
import tensorflow_model_analysis as tfma
from tfx import v1 as tfx
The provided code snippet includes necessary dependencies for implementing the `_create_pipeline` function. Write a Python function `def _create_pipeline( pipeline_name: str, pipeline_root: str, ... | Implements the penguin pipeline with TFX. |
166,114 | from typing import List
import keras_tuner as kt
import tensorflow as tf
import tensorflow_decision_forests as tfdf
import tensorflow_transform as tft
from tfx import v1 as tfx
from tfx.examples.penguin import penguin_utils_base as base
from tfx_bsl.public import tfxio
def _get_hyperparameters() -> kt.HyperParameters:
... | Builds a Keras Tuner for the model. Args: fn_args: Holds args as name/value pairs. - working_dir: working dir for tuning. - train_files: List of file paths containing training tf.Example data. - eval_files: List of file paths containing eval tf.Example data. - train_steps: number of train steps. - eval_steps: number of... |
166,115 | from typing import List
import keras_tuner as kt
import tensorflow as tf
import tensorflow_decision_forests as tfdf
import tensorflow_transform as tft
from tfx import v1 as tfx
from tfx.examples.penguin import penguin_utils_base as base
from tfx_bsl.public import tfxio
def _get_hyperparameters() -> kt.HyperParameters:
... | Train the model based on given args. Args: fn_args: Holds args used to train the model as name/value pairs. |
166,116 | import datetime
import multiprocessing
import os
import socket
import sys
from typing import List, Optional
import absl
from absl import flags
import tensorflow_model_analysis as tfma
from tfx import v1 as tfx
from tfx.utils import proto_utils
def RangeConfigGenerator(input_date: tfx.dsl.components.Parameter[str],
... | Implements the penguin pipeline with TFX. Args: pipeline_name: name of the TFX pipeline being created. pipeline_root: root directory of the pipeline. data_root: directory containing the penguin data. module_file: path to files used in Trainer and Transform components. accuracy_threshold: minimum accuracy to push the mo... |
166,117 | import os
import sys
from typing import Dict, List, Optional, Union
from absl import flags
from absl import logging
import tensorflow_model_analysis as tfma
from tfx import v1 as tfx
_gcp_region = 'us-central1'
_vertex_job_spec = {
'project':
_project_id,
'worker_pool_specs': [{
'machine_spec': ... | Implements the penguin pipeline with TFX and Kubeflow Pipeline. Args: pipeline_name: name of the TFX pipeline being created. pipeline_root: root directory of the pipeline. Should be a valid GCS path. data_root: uri of the penguin data. module_file: uri of the module file used in Trainer, Transform and Tuner. ai_platfor... |
166,118 | import absl
import keras_tuner
import tensorflow as tf
from tensorflow import keras
import tensorflow_transform as tft
from tfx import v1 as tfx
from tfx.examples.penguin import penguin_utils_base as base
def _get_hyperparameters() -> keras_tuner.HyperParameters:
"""Returns hyperparameters for building Keras model.""... | Build the tuner using the KerasTuner API. Args: fn_args: Holds args as name/value pairs. - working_dir: working dir for tuning. - train_files: List of file paths containing training tf.Example data. - eval_files: List of file paths containing eval tf.Example data. - train_steps: number of train steps. - eval_steps: num... |
166,119 | import absl
import keras_tuner
import tensorflow as tf
from tensorflow import keras
import tensorflow_transform as tft
from tfx import v1 as tfx
from tfx.examples.penguin import penguin_utils_base as base
def _get_hyperparameters() -> keras_tuner.HyperParameters:
"""Returns hyperparameters for building Keras model.""... | Train the model based on given args. Args: fn_args: Holds args used to train the model as name/value pairs. |
166,120 | import os
from typing import Dict, List, Optional
import absl
import tensorflow_model_analysis as tfma
from tfx import v1 as tfx
The provided code snippet includes necessary dependencies for implementing the `_create_pipeline` function. Write a Python function `def _create_pipeline( pipeline_name: str, pipelin... | Implements the Penguin pipeline with TFX. |
166,121 | import os
import pickle
from typing import Tuple
import absl
import numpy as np
from sklearn.neural_network import MLPClassifier
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from tfx.components.trainer.fn_args_utils import DataAccessor
from tfx.components.trainer.fn_args_utils ... | Train the model based on given args. Args: fn_args: Holds args used to train the model as name/value pairs. |
166,122 | import os
from typing import List
import absl
import tensorflow_model_analysis as tfma
from tfx import v1 as tfx
The provided code snippet includes necessary dependencies for implementing the `_create_pipeline` function. Write a Python function `def _create_pipeline( pipeline_name: str, pipeline_root: str, ... | Implements the Penguin pipeline with TFX. |
166,123 | import copy
import os
import pickle
from typing import Dict, Iterable, List
import apache_beam as beam
import tensorflow as tf
import tensorflow_model_analysis as tfma
from tfx_bsl.tfxio import tensor_adapter
def _custom_model_loader_fn(model_path: str):
"""Returns a function that loads a scikit-learn model."""
ret... | Returns a single custom EvalSharedModel. |
166,124 | import copy
import os
import pickle
from typing import Dict, Iterable, List
import apache_beam as beam
import tensorflow as tf
import tensorflow_model_analysis as tfma
from tfx_bsl.tfxio import tensor_adapter
def _make_sklearn_predict_extractor(
eval_shared_model: tfma.EvalSharedModel,) -> tfma.extractors.Extractor... | Returns default extractors plus a custom prediction extractor. |
166,125 | import datetime
import os
from typing import List
import tensorflow_model_analysis as tfma
from tfx.components import CsvExampleGen
from tfx.components import Evaluator
from tfx.components import ExampleValidator
from tfx.components import Pusher
from tfx.components import SchemaGen
from tfx.components import Stat... | Implements the chicago taxi pipeline with TFX. |
166,126 | from typing import List
import absl
from keras.callbacks import LambdaCallback
import tensorflow as tf
import tensorflow_transform as tft
from tfx.components.trainer.executor import TrainerFnArgs
from tfx.components.trainer.fn_args_utils import DataAccessor
from tfx_bsl.tfxio import dataset_options
_NUMERICAL_FEATURES... | tf.transform's callback function for preprocessing inputs. Args: inputs: map from feature keys to raw not-yet-transformed features. Returns: Map from string feature key to transformed feature operations. |
166,127 | from typing import List
import absl
from keras.callbacks import LambdaCallback
import tensorflow as tf
import tensorflow_transform as tft
from tfx.components.trainer.executor import TrainerFnArgs
from tfx.components.trainer.fn_args_utils import DataAccessor
from tfx_bsl.tfxio import dataset_options
def export_serving_... | Train the model based on given args. Args: fn_args: Holds args used to train the model as name/value pairs. |
166,128 | import re
from IPython.display import display_html
import matplotlib.pyplot as plt
import networkx as nx
import pandas as pd
from ml_metadata.proto import metadata_store_pb2
The provided code snippet includes necessary dependencies for implementing the `_is_output_event` function. Write a Python function `def _is_outp... | Checks if event is an Output event. |
166,129 | import re
from IPython.display import display_html
import matplotlib.pyplot as plt
import networkx as nx
import pandas as pd
from ml_metadata.proto import metadata_store_pb2
The provided code snippet includes necessary dependencies for implementing the `_is_input_event` function. Write a Python function `def _is_input... | Checks if event is an Input event. |
166,130 | import re
from IPython.display import display_html
import matplotlib.pyplot as plt
import networkx as nx
import pandas as pd
from ml_metadata.proto import metadata_store_pb2
The provided code snippet includes necessary dependencies for implementing the `_get_value_str` function. Write a Python function `def _get_value... | Returns a string representation of a `metadata_store_pb2.Value` object. |
166,131 | import os
from typing import Dict, List
from tfx.components import Evaluator
from tfx.components import ExampleValidator
from tfx.components import ModelValidator
from tfx.components import Pusher
from tfx.components import SchemaGen
from tfx.components import StatisticsGen
from tfx.components import Trainer
from tfx.c... | Implements the chicago taxi pipeline with TFX and Kubeflow Pipelines. |
166,132 | from typing import List
import tensorflow as tf
from tensorflow import estimator as tf_estimator
import tensorflow_model_analysis as tfma
import tensorflow_transform as tft
from tensorflow_transform.tf_metadata import schema_utils
from tfx.components.trainer.fn_args_utils import DataAccessor
from tfx_bsl.tfxio import d... | tf.transform's callback function for preprocessing inputs. Args: inputs: map from feature keys to raw not-yet-transformed features. Returns: Map from string feature key to transformed feature operations. |
166,133 | from typing import List
import tensorflow as tf
from tensorflow import estimator as tf_estimator
import tensorflow_model_analysis as tfma
import tensorflow_transform as tft
from tensorflow_transform.tf_metadata import schema_utils
from tfx.components.trainer.fn_args_utils import DataAccessor
from tfx_bsl.tfxio import d... | Build the estimator using the high level API. Args: trainer_fn_args: Holds args used to train the model as name/value pairs. schema: Holds the schema of the training examples. Returns: A dict of the following: - estimator: The estimator that will be used for training and eval. - train_spec: Spec for training. - eval_sp... |
166,134 | import tensorflow as tf
import tensorflow_transform as tft
from tfx.components.trainer.fn_args_utils import FnArgs
from tfx.examples.mnist import mnist_utils_native_keras_base as base
The provided code snippet includes necessary dependencies for implementing the `preprocessing_fn` function. Write a Python function `de... | tf.transform's callback function for preprocessing inputs. Args: inputs: map from feature keys to raw not-yet-transformed features. Returns: Map from string feature key to transformed feature operations. |
166,135 | import tensorflow as tf
import tensorflow_transform as tft
from tfx.components.trainer.fn_args_utils import FnArgs
from tfx.examples.mnist import mnist_utils_native_keras_base as base
def _get_serve_tf_examples_fn(model, tf_transform_output):
"""Returns a function that parses a serialized tf.Example."""
model.tft_l... | Train the model based on given args. Args: fn_args: Holds args used to train the model as name/value pairs. |
166,136 | import os
import tensorflow as tf
import tensorflow_transform as tft
from tfx import v1 as tfx
from tfx.components.trainer.rewriting import converters
from tfx.components.trainer.rewriting import rewriter
from tfx.components.trainer.rewriting import rewriter_factory
from tfx.examples.mnist import mnist_utils_native_ker... | tf.transform's callback function for preprocessing inputs. Args: inputs: map from feature keys to raw not-yet-transformed features. Returns: Map from string feature key to transformed feature operations. |
166,137 | import os
import tensorflow as tf
import tensorflow_transform as tft
from tfx import v1 as tfx
from tfx.components.trainer.rewriting import converters
from tfx.components.trainer.rewriting import rewriter
from tfx.components.trainer.rewriting import rewriter_factory
from tfx.examples.mnist import mnist_utils_native_ker... | Train the model based on given args. Args: fn_args: Holds args used to train the model as name/value pairs. |
166,138 | from typing import List
import absl
import tensorflow as tf
import tensorflow_transform as tft
from tfx.components.trainer.fn_args_utils import DataAccessor
from tfx_bsl.tfxio import dataset_options
IMAGE_KEY = 'image_floats'
LABEL_KEY = 'image_class'
def transformed_name(key):
return key + '_xf'
The provided code s... | tf.transform's callback function for preprocessing inputs. Args: inputs: map from feature keys to raw not-yet-transformed features. Returns: Map from string feature key to transformed feature operations. |
166,139 | import os
from typing import List
import absl
import tensorflow_model_analysis as tfma
from tfx.components import Evaluator
from tfx.components import ExampleValidator
from tfx.components import ImportExampleGen
from tfx.components import Pusher
from tfx.components import SchemaGen
from tfx.components import Statistics... | Implements the handwritten digit classification example using TFX. |
166,140 | import itertools
from typing import Dict, List, Optional, Union
from struct2tensor import calculate
from struct2tensor import calculate_options
from struct2tensor import path
from struct2tensor import prensor_util
from struct2tensor.expression_impl import proto as proto_expr
import tensorflow as tf
from tfx_bsl.public ... | Parses a batch of ELWC records into RaggedTensors using struct2tensor. Args: records: A dictionary with a single item. The value of this single item is the serialized ELWC input. context_features: List of context-level features. example_features: List of example-level features. size_feature_name: A string, the name of ... |
166,141 | import tensorflow as tf
import tensorflow_ranking as tfr
import tensorflow_transform as tft
from tfx.examples.ranking import features
from tfx.examples.ranking import struct2tensor_parsing_utils
from tfx_bsl.public import tfxio
The provided code snippet includes necessary dependencies for implementing the `make_decode... | Creates a data decoder that that decodes ELWC records to tensors. A DataView (see "TfGraphDataViewProvider" component in the pipeline) will refer to this decoder. And any components that consumes the data with the DataView applied will use this decoder. Returns: A ELWC decoder. |
166,142 | import tensorflow as tf
import tensorflow_ranking as tfr
import tensorflow_transform as tft
from tfx.examples.ranking import features
from tfx.examples.ranking import struct2tensor_parsing_utils
from tfx_bsl.public import tfxio
The provided code snippet includes necessary dependencies for implementing the `preprocessi... | Transform preprocessing_fn. |
166,143 | import tensorflow as tf
import tensorflow_ranking as tfr
import tensorflow_transform as tft
from tfx.examples.ranking import features
from tfx.examples.ranking import struct2tensor_parsing_utils
from tfx_bsl.public import tfxio
def _input_fn(file_patterns,
data_accessor,
batch_size) -> tf.da... | TFX trainer entry point. |
166,144 | import os
from typing import List
import absl
import tensorflow_model_analysis as tfma
from tfx.components import Evaluator
from tfx.components import ImportExampleGen
from tfx.components import Pusher
from tfx.components import SchemaGen
from tfx.components import StatisticsGen
from tfx.components import Trainer
from ... | Creates pipeline. |
166,145 | import os
from typing import List
import absl
import tensorflow_model_analysis as tfma
from tfx.components import CsvExampleGen
from tfx.components import Evaluator
from tfx.components import ExampleValidator
from tfx.components import Pusher
from tfx.components import SchemaGen
from tfx.components import StatisticsGen... | Implements the Bert classication on Cola dataset pipline with TFX. |
166,146 | from typing import List
import tensorflow as tf
import tensorflow_data_validation as tfdv
import tensorflow_hub as hub
import tensorflow_transform as tft
from tfx import v1 as tfx
from tfx.components.transform import stats_options_util
from tfx.examples.bert.utils.bert_models import build_and_compile_bert_classifier
fr... | tf.transform's callback function for preprocessing inputs. Args: inputs: map from feature keys to raw not-yet-transformed features. Returns: Map from string feature key to transformed feature Tensors. |
166,147 | from typing import List
import tensorflow as tf
import tensorflow_data_validation as tfdv
import tensorflow_hub as hub
import tensorflow_transform as tft
from tfx import v1 as tfx
from tfx.components.transform import stats_options_util
from tfx.examples.bert.utils.bert_models import build_and_compile_bert_classifier
fr... | Update transform stats. This function is called by the Transform component before it computes pre-transform or post-transform statistics. It takes as input a stats_type, which indicates whether this call is intended for pre-transform or post-transform statistics. It also takes as argument the StatsOptions that are to b... |
166,148 | from typing import List
import tensorflow as tf
import tensorflow_data_validation as tfdv
import tensorflow_hub as hub
import tensorflow_transform as tft
from tfx import v1 as tfx
from tfx.components.transform import stats_options_util
from tfx.examples.bert.utils.bert_models import build_and_compile_bert_classifier
fr... | Train the model based on given args. Args: fn_args: Holds args used to train the model as name/value pairs. |
166,149 | import os
from typing import List
import absl
import tensorflow_model_analysis as tfma
from tfx.components import CsvExampleGen
from tfx.components import Evaluator
from tfx.components import ExampleValidator
from tfx.components import Pusher
from tfx.components import SchemaGen
from tfx.components import StatisticsGen... | Implements the Bert classication on mrpc dataset pipline with TFX. |
166,150 | from typing import List
import tensorflow as tf
import tensorflow_data_validation as tfdv
import tensorflow_hub as hub
import tensorflow_transform as tft
from tfx import v1 as tfx
from tfx.components.transform import stats_options_util
from tfx.examples.bert.utils.bert_models import build_and_compile_bert_classifier
fr... | tf.transform's callback function for preprocessing inputs. Args: inputs: map from feature keys to raw not-yet-transformed features. Returns: Map from string feature key to transformed feature Tensors. |
166,151 | from typing import List
import tensorflow as tf
import tensorflow_data_validation as tfdv
import tensorflow_hub as hub
import tensorflow_transform as tft
from tfx import v1 as tfx
from tfx.components.transform import stats_options_util
from tfx.examples.bert.utils.bert_models import build_and_compile_bert_classifier
fr... | Update transform stats. This function is called by the Transform component before it computes pre-transform or post-transform statistics. It takes as input a stats_type, which indicates whether this call is intended for pre-transform or post-transform statistics. It also takes as argument the StatsOptions that are to b... |
166,152 | from typing import List
import tensorflow as tf
import tensorflow_data_validation as tfdv
import tensorflow_hub as hub
import tensorflow_transform as tft
from tfx import v1 as tfx
from tfx.components.transform import stats_options_util
from tfx.examples.bert.utils.bert_models import build_and_compile_bert_classifier
fr... | Train the model based on given args. Args: fn_args: Holds args used to train the model as name/value pairs. |
166,153 | import os
import pandas as pd
import tensorflow_datasets as tfds
The provided code snippet includes necessary dependencies for implementing the `fetch_data` function. Write a Python function `def fetch_data()` to solve the following problem:
This downloads the full dataset to $(pwd)/data/imdb.csv.
Here is the functio... | This downloads the full dataset to $(pwd)/data/imdb.csv. |
166,154 | import os
from typing import List
import absl
import tensorflow_model_analysis as tfma
from tfx.components import CsvExampleGen
from tfx.components import Evaluator
from tfx.components import ExampleValidator
from tfx.components import Pusher
from tfx.components import SchemaGen
from tfx.components import StatisticsGen... | Implements the imdb sentiment analysis pipline with TFX. |
166,155 | from typing import List
import absl
import tensorflow as tf
from tensorflow import keras
import tensorflow_transform as tft
from tfx.components.trainer.fn_args_utils import DataAccessor
from tfx.components.trainer.fn_args_utils import FnArgs
from tfx_bsl.tfxio import dataset_options
_FEATURE_KEY = 'text'
_LABEL_KEY = '... | tf.transform's callback function for preprocessing inputs. Args: inputs: map from feature keys to raw not-yet-transformed features. Returns: Map from string feature key to transformed feature operations. |
166,156 | from typing import List
import absl
import tensorflow as tf
from tensorflow import keras
import tensorflow_transform as tft
from tfx.components.trainer.fn_args_utils import DataAccessor
from tfx.components.trainer.fn_args_utils import FnArgs
from tfx_bsl.tfxio import dataset_options
_EVAL_BATCH_SIZE = 5
_TRAIN_BATCH_SI... | Train the model based on given args. Args: fn_args: Holds args used to train the model as name/value pairs. |
166,157 | import os
import absl
from tfx.components import CsvExampleGen
from tfx.components import StatisticsGen
from tfx.examples.custom_components.hello_world.hello_component import component
from tfx.orchestration import metadata
from tfx.orchestration import pipeline
from tfx.orchestration.beam.beam_dag_runner import BeamDa... | Implements the chicago taxi pipeline with TFX. |
166,158 | import os
import absl
from tfx.components import Evaluator
from tfx.components import ExampleValidator
from tfx.components import ModelValidator
from tfx.components import Pusher
from tfx.components import SchemaGen
from tfx.components import StatisticsGen
from tfx.components import Trainer
from tfx.components import T... | Implements the chicago taxi pipeline with TFX. |
166,159 | import datetime
from typing import Any, Dict, Iterable, Tuple
import apache_beam as beam
import prestodb
import tensorflow as tf
from tfx.components.example_gen import base_example_gen_executor
from tfx.examples.custom_components.presto_example_gen.proto import presto_config_pb2
from tfx.proto import example_gen_pb2
fr... | Read from Presto and transform to TF examples. Args: pipeline: beam pipeline. exec_properties: A dict of execution properties. split_pattern: Split.pattern in Input config, a Presto sql string. Returns: PCollection of TF examples. |
166,160 | from tfx.dsl.component.experimental import container_component
from tfx.dsl.component.experimental import placeholders
from tfx.types import standard_artifacts
downloader_component = container_component.create_container_component(
name='DownloadFromHttp',
outputs={
'data': standard_artifacts.ExternalArt... | Creates tasks for the download_grep_print pipeline. |
166,161 | from typing import List
import tensorflow as tf
from tensorflow import estimator as tf_estimator
import tensorflow_model_analysis as tfma
import tensorflow_transform as tft
from tensorflow_transform.tf_metadata import schema_utils
from tfx.components.trainer.fn_args_utils import DataAccessor
from tfx_bsl.tfxio import d... | tf.transform's callback function for preprocessing inputs. Args: inputs: map from feature keys to raw not-yet-transformed features. Returns: Map from string feature key to transformed feature operations. |
166,162 | from typing import List
import tensorflow as tf
from tensorflow import estimator as tf_estimator
import tensorflow_model_analysis as tfma
import tensorflow_transform as tft
from tensorflow_transform.tf_metadata import schema_utils
from tfx.components.trainer.fn_args_utils import DataAccessor
from tfx_bsl.tfxio import d... | Build the estimator using the high level API. Args: trainer_fn_args: Holds args used to train the model as name/value pairs. schema: Holds the schema of the training examples. Returns: A dict of the following: - estimator: The estimator that will be used for training and eval. - train_spec: Spec for training. - eval_sp... |
166,163 | import datetime
import os
from tfx.components import CsvExampleGen
from tfx.components import Evaluator
from tfx.components import ExampleValidator
from tfx.components import ModelValidator
from tfx.components import Pusher
from tfx.components import SchemaGen
from tfx.components import StatisticsGen
from tfx.component... | Implements the chicago taxi pipeline with TFX. |
166,164 | import datetime
import os
from tfx.components import CsvExampleGen
from tfx.components import Evaluator
from tfx.components import ExampleValidator
from tfx.components import ModelValidator
from tfx.components import Pusher
from tfx.components import SchemaGen
from tfx.components import StatisticsGen
from tfx.component... | Implements the chicago taxi pipeline with TFX. |
166,165 | import os
from typing import List
import absl
import flatbuffers
import tensorflow as tf
import tensorflow_transform as tft
from tfx.components.trainer.fn_args_utils import DataAccessor
from tfx.components.trainer.fn_args_utils import FnArgs
from tfx.components.trainer.rewriting import converters
from tfx.components.tr... | tf.transform's callback function for preprocessing inputs. Args: inputs: map from feature keys to raw not-yet-transformed features. Returns: Map from string feature key to transformed feature operations. |
166,166 | import os
from typing import List
import absl
import flatbuffers
import tensorflow as tf
import tensorflow_transform as tft
from tfx.components.trainer.fn_args_utils import DataAccessor
from tfx.components.trainer.fn_args_utils import FnArgs
from tfx.components.trainer.rewriting import converters
from tfx.components.tr... | Train the model based on given args. Args: fn_args: Holds args used to train the model as name/value pairs. Raises: ValueError: if invalid inputs. |
166,167 | import os
from typing import List
import absl
import tensorflow_model_analysis as tfma
from tfx.components import Evaluator
from tfx.components import ExampleValidator
from tfx.components import ImportExampleGen
from tfx.components import Pusher
from tfx.components import SchemaGen
from tfx.components import Statistics... | Implements the CIFAR10 image classification pipeline using TFX. |
166,168 | import os
def make_extra_packages_docker_image():
# Packages needed for tfx docker image.
return [
# TODO(b/304892416): Migrate from KFP SDK v1 to v2.
'kfp>=1.8.14,<2',
'kfp-pipeline-spec>=0.1.10,<0.2',
'mmh>=2.2,<3',
'python-snappy>=0.5,<0.6',
# Required for tfx/examples/pengui... | null |
166,169 | import os
def make_extra_packages_test():
"""Prepare extra packages needed for running unit tests."""
# Note: It is okay to pin packages to exact versions in this list to minimize
# conflicts.
return make_extra_packages_airflow() + make_extra_packages_kfp() + [
'pytest>=5,<7',
]
def make_extra_packages_... | null |
166,170 | import collections
import shutil
import tempfile
import time
from absl import logging
import apache_beam as beam
from apache_beam.utils import shared
import tensorflow as tf
import tensorflow_transform as tft
from tensorflow_transform import graph_tools
from tensorflow_transform import impl_helper
import tensorflow_tra... | Regenerate intermediate outputs required for the benchmark. |
166,171 | import collections
import shutil
import tempfile
import time
from absl import logging
import apache_beam as beam
from apache_beam.utils import shared
import tensorflow as tf
import tensorflow_transform as tft
from tensorflow_transform import graph_tools
from tensorflow_transform import impl_helper
import tensorflow_tra... | Returns a (batch_size, iterator for batched records) tuple for the dataset. Args: dataset: BenchmarkDataset object. force_tf_compat_v1: If False then Transform will use its native TF2 version, if True then Transform will use its TF1 version. max_num_examples: Maximum number of examples to read from the dataset. Returns... |
166,172 | import importlib
from google.protobuf import text_format
from tensorflow_metadata.proto.v0 import schema_pb2
The provided code snippet includes necessary dependencies for implementing the `get_dataset` function. Write a Python function `def get_dataset(name, base_dir=None)` to solve the following problem:
Imports the ... | Imports the given dataset and returns an instance of it. |
166,173 | import itertools
import math
import os
import shutil
import tempfile
from typing import Optional
from absl import logging
import apache_beam as beam
import tensorflow_transform as tft
from tfx import components
from tfx.benchmarks import benchmark_dataset
from tfx.components.example_gen.csv_example_gen import executor ... | null |
166,174 | import itertools
import math
import os
import shutil
import tempfile
from typing import Optional
from absl import logging
import apache_beam as beam
import tensorflow_transform as tft
from tfx import components
from tfx.benchmarks import benchmark_dataset
from tfx.components.example_gen.csv_example_gen import executor ... | null |
166,175 | from typing import TypeVar
from absl import flags
from tfx.orchestration.portable import data_types
from tfx.orchestration.python_execution_binary import python_execution_binary_utils as flag_utils
_LEGACY_EXECUTION_INVOCATION = flags.DEFINE_string(
'tfx_execution_info_b64',
None,
'url safe base64 encoded t... | null |
166,176 | from typing import Optional, Union
from absl import logging
from tfx.dsl.io import fileio
from tfx.orchestration import metadata
from tfx.orchestration.portable import data_types
from tfx.orchestration.portable import python_driver_operator
from tfx.proto.orchestration import driver_output_pb2
from tfx.proto.orchestrat... | Run Python executable. |
166,177 | import base64
from typing import Union
from tfx.orchestration import metadata
from tfx.orchestration.portable import data_types
from tfx.proto.orchestration import executable_spec_pb2
from tfx.proto.orchestration import execution_invocation_pb2
from tfx.proto.orchestration import metadata_pb2
from tfx.utils import impo... | Import the class path from Python or Beam executor spec. |
166,178 | import base64
from typing import Union
from tfx.orchestration import metadata
from tfx.orchestration.portable import data_types
from tfx.proto.orchestration import executable_spec_pb2
from tfx.proto.orchestration import execution_invocation_pb2
from tfx.proto.orchestration import metadata_pb2
from tfx.utils import impo... | De-serializes an MLMD connection config from base64 flag. |
166,179 | import base64
from typing import Union
from tfx.orchestration import metadata
from tfx.orchestration.portable import data_types
from tfx.proto.orchestration import executable_spec_pb2
from tfx.proto.orchestration import execution_invocation_pb2
from tfx.proto.orchestration import metadata_pb2
from tfx.utils import impo... | De-serializes an executable spec from base64 flag. |
166,180 | import base64
from typing import Union
from tfx.orchestration import metadata
from tfx.orchestration.portable import data_types
from tfx.proto.orchestration import executable_spec_pb2
from tfx.proto.orchestration import execution_invocation_pb2
from tfx.proto.orchestration import metadata_pb2
from tfx.utils import impo... | Serializes an MLMD connection config into a base64 flag of its wrapper. |
166,181 | import base64
from typing import Union
from tfx.orchestration import metadata
from tfx.orchestration.portable import data_types
from tfx.proto.orchestration import executable_spec_pb2
from tfx.proto.orchestration import execution_invocation_pb2
from tfx.proto.orchestration import metadata_pb2
from tfx.utils import impo... | Serializes an executable spec into a base64 flag. |
166,182 | import base64
from typing import Union
from tfx.orchestration import metadata
from tfx.orchestration.portable import data_types
from tfx.proto.orchestration import executable_spec_pb2
from tfx.proto.orchestration import execution_invocation_pb2
from tfx.proto.orchestration import metadata_pb2
from tfx.utils import impo... | Serializes the ExecutionInfo class from a base64 flag. |
166,183 | import itertools
from typing import Any, Dict, List, Optional, Tuple, cast
from absl import logging
from kfp.pipeline_spec import pipeline_spec_pb2 as pipeline_pb2
from tfx import components
from tfx.components.evaluator import constants
from tfx.dsl.compiler import compiler_utils as tfx_compiler_utils
from tfx.dsl.com... | Resolves placeholders in the command line of a container. Args: container_spec: Container structure to resolve exec_properties: The map of component's execution properties Returns: Resolved command line. Raises: TypeError: On unsupported type of command-line arguments, or when the resolved argument is not a string. |
166,184 | import itertools
import json
import os
import re
from typing import Any, Dict, List, Mapping, Optional, Type, Union
from kfp.pipeline_spec import pipeline_spec_pb2 as pipeline_pb2
from tfx import types
from tfx.dsl.io import fileio
from tfx.orchestration import data_types
from tfx.orchestration.kubeflow.v2 import param... | Converts RuntimeParameters to mapping from names to proto messages. |
166,185 | import itertools
import json
import os
import re
from typing import Any, Dict, List, Mapping, Optional, Type, Union
from kfp.pipeline_spec import pipeline_spec_pb2 as pipeline_pb2
from tfx import types
from tfx.dsl.io import fileio
from tfx.orchestration import data_types
from tfx.orchestration.kubeflow.v2 import param... | Extracts the artifact type info into ComponentInputsSpec.ParameterSpec. |
166,186 | import itertools
import json
import os
import re
from typing import Any, Dict, List, Mapping, Optional, Type, Union
from kfp.pipeline_spec import pipeline_spec_pb2 as pipeline_pb2
from tfx import types
from tfx.dsl.io import fileio
from tfx.orchestration import data_types
from tfx.orchestration.kubeflow.v2 import param... | Builds artifact type spec for an input channel. |
166,187 | import itertools
import json
import os
import re
from typing import Any, Dict, List, Mapping, Optional, Type, Union
from kfp.pipeline_spec import pipeline_spec_pb2 as pipeline_pb2
from tfx import types
from tfx.dsl.io import fileio
from tfx.orchestration import data_types
from tfx.orchestration.kubeflow.v2 import param... | Builds parameter type spec for an output channel. |
166,188 | import itertools
import json
import os
import re
from typing import Any, Dict, List, Mapping, Optional, Type, Union
from kfp.pipeline_spec import pipeline_spec_pb2 as pipeline_pb2
from tfx import types
from tfx.dsl.io import fileio
from tfx.orchestration import data_types
from tfx.orchestration.kubeflow.v2 import param... | Builds artifact type spec for an output channel. |
166,189 | import itertools
import json
import os
import re
from typing import Any, Dict, List, Mapping, Optional, Type, Union
from kfp.pipeline_spec import pipeline_spec_pb2 as pipeline_pb2
from tfx import types
from tfx.dsl.io import fileio
from tfx.orchestration import data_types
from tfx.orchestration.kubeflow.v2 import param... | Packs artifact properties and custom properties into a Struct proto. |
166,190 | import itertools
import json
import os
import re
from typing import Any, Dict, List, Mapping, Optional, Type, Union
from kfp.pipeline_spec import pipeline_spec_pb2 as pipeline_pb2
from tfx import types
from tfx.dsl.io import fileio
from tfx.orchestration import data_types
from tfx.orchestration.kubeflow.v2 import param... | Gets the schema title from the artifact python class. |
166,191 | import itertools
import json
import os
import re
from typing import Any, Dict, List, Mapping, Optional, Type, Union
from kfp.pipeline_spec import pipeline_spec_pb2 as pipeline_pb2
from tfx import types
from tfx.dsl.io import fileio
from tfx.orchestration import data_types
from tfx.orchestration.kubeflow.v2 import param... | Encodes a Predicate into a CEL string expression. The CEL specification is at: https://github.com/google/cel-spec/blob/master/doc/langdef.md Args: expression: A PlaceholderExpression proto descrbing a Predicate. Returns: A CEL expression in string format. |
166,192 | import argparse
import os
from typing import List, Tuple
from absl import app
from absl import logging
from absl.flags import argparse_flags
from kfp.pipeline_spec import pipeline_spec_pb2
from tfx.components.evaluator import executor as evaluator_executor
from tfx.dsl.components.base import base_beam_executor
from tfx... | Selects a particular executor and run it based on name. Args: args: --executor_class_path: The import path of the executor class. --json_serialized_invocation_args: Full JSON-serialized parameters for this execution. beam_args: Optional parameter that maps to the optional_pipeline_args parameter in the pipeline, which ... |
166,193 | import argparse
import os
from typing import List, Tuple
from absl import app
from absl import logging
from absl.flags import argparse_flags
from kfp.pipeline_spec import pipeline_spec_pb2
from tfx.components.evaluator import executor as evaluator_executor
from tfx.dsl.components.base import base_beam_executor
from tfx... | Parses command line arguments. Args: argv: Unparsed arguments for run_executor.py. Known argument names include --executor_class_path: Python class of executor in format of <module>.<class>. --json_serialized_invocation_args: Full JSON-serialized parameters for this execution. The remaining part of the arguments will b... |
166,194 | import random
import re
import string
import typing
from typing import Any, Dict, List, Mapping, Optional, Union
from absl import logging
from kfp.pipeline_spec import pipeline_spec_pb2 as pipeline_pb2
from tfx.dsl.components.base import base_node
from tfx.dsl.placeholder import placeholder
from tfx.orchestration impor... | Checks the user-provided pipeline name. |
166,195 | import random
import re
import string
import typing
from typing import Any, Dict, List, Mapping, Optional, Union
from absl import logging
from kfp.pipeline_spec import pipeline_spec_pb2 as pipeline_pb2
from tfx.dsl.components.base import base_node
from tfx.dsl.placeholder import placeholder
from tfx.orchestration impor... | null |
166,196 | import random
import re
import string
import typing
from typing import Any, Dict, List, Mapping, Optional, Union
from absl import logging
from kfp.pipeline_spec import pipeline_spec_pb2 as pipeline_pb2
from tfx.dsl.components.base import base_node
from tfx.dsl.placeholder import placeholder
from tfx.orchestration impor... | Gets component image path given component_id. |
166,197 | import random
import re
import string
import typing
from typing import Any, Dict, List, Mapping, Optional, Union
from absl import logging
from kfp.pipeline_spec import pipeline_spec_pb2 as pipeline_pb2
from tfx.dsl.components.base import base_node
from tfx.dsl.placeholder import placeholder
from tfx.orchestration impor... | null |
166,198 | import argparse
import os
from typing import List
from absl import app
from absl import logging
from absl.flags import argparse_flags
from kfp.pipeline_spec import pipeline_spec_pb2
from tfx.components.example_gen import driver
from tfx.components.example_gen import input_processor
from tfx.components.example_gen impor... | Runs the driver, writing its output as a ExecutorOutput proto. The main goal of this driver is to calculate the span and fingerprint of input data, allowing for the executor invocation to be skipped if the ExampleGen component has been previously run on the same data with the same configuration. This span and fingerpri... |
166,199 | import argparse
import os
from typing import List
from absl import app
from absl import logging
from absl.flags import argparse_flags
from kfp.pipeline_spec import pipeline_spec_pb2
from tfx.components.example_gen import driver
from tfx.components.example_gen import input_processor
from tfx.components.example_gen impor... | Command lines flag parsing. |
166,200 | import datetime
import json
import os
from typing import Any, Dict, List, Optional, Union, MutableMapping
from absl import logging
from kfp.pipeline_spec import pipeline_spec_pb2
from tfx import version
from tfx.dsl.components.base import base_component
from tfx.dsl.components.base import base_node
from tfx.dsl.io impo... | Gets the current timestamp. |
166,201 | from typing import Any, Dict, List, Optional
from tfx.dsl.component.experimental import component_utils
from tfx.dsl.component.experimental import placeholders
from tfx.dsl.components.base import base_component
from tfx.dsl.components.base import executor_spec
from tfx.orchestration.kubeflow.v2.components.experimental ... | Creates a pipeline step that launches a AIP training job. The generated TFX component will have a component spec specified dynamically, through inputs/outputs/parameters in the following format: - inputs: A mapping from input name to the upstream channel connected. The artifact type of the channel will be automatically... |
166,202 | import datetime
import time
from absl import logging
from google.cloud.aiplatform import pipeline_jobs
from google.cloud.aiplatform_v1.types import pipeline_state
_PIPELINE_COMPLETE_STATES = frozenset([
pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED,
pipeline_state.PipelineState.PIPELINE_STATE_FAILED,
... | Checks the status of the job. NOTE: aiplatform.init() should be already called. Args: job_id: The relative ID of the pipeline job. timeout: Timeout duration for the job execution. polling_interval_secs: Interval to check the job status. Raises: RuntimeError: On (1) unexpected response from service; or (2) on unexpected... |
166,203 | from tfx.dsl.components.base import base_node
from tfx.orchestration.kubeflow.decorators import FinalStatusStr
class FinalStatusStr(str):
"""FinalStatusStr: is the type for parameter receiving PipelineTaskFinalStatus.
Vertex AI backend passes in jsonlized string of
kfp.pipeline_spec.pipeline_spec_pb2.PipelineTa... | Replaces TFX placeholders in execution properties with KFP placeholders. |
166,204 | from tfx.dsl.components.base import base_node
from tfx.orchestration.kubeflow.decorators import FinalStatusStr
The provided code snippet includes necessary dependencies for implementing the `fix_brackets` function. Write a Python function `def fix_brackets(placeholder: str) -> str` to solve the following problem:
Fix ... | Fix the imbalanced brackets in placeholder. When ptype is not null, regex matching might grab a placeholder with } missing. This function fix the missing bracket. Args: placeholder: string placeholder of RuntimeParameter Returns: Placeholder with re-balanced brackets. Raises: RuntimeError: if left brackets are less tha... |
166,205 | from typing import Dict, List, Set
from absl import logging
from kfp import dsl
from kubernetes import client as k8s_client
from tfx.dsl.components.base import base_node as tfx_base_node
from tfx.orchestration import data_types
from tfx.orchestration import pipeline as tfx_pipeline
from tfx.orchestration.kubeflow.proto... | Encode a runtime parameter into a placeholder for value substitution. |
166,206 | from typing import Dict, List, Set
from absl import logging
from kfp import dsl
from kubernetes import client as k8s_client
from tfx.dsl.components.base import base_node as tfx_base_node
from tfx.orchestration import data_types
from tfx.orchestration import pipeline as tfx_pipeline
from tfx.orchestration.kubeflow.proto... | Replaces the RuntimeParameter placeholders with kfp.dsl.PipelineParam. |
166,207 | import types
from typing import Any, Callable
from tfx.dsl.component.experimental.decorators import component
def component(func: types.FunctionType, /) -> BaseFunctionalComponentFactory:
...
def component(
*,
component_annotation: Optional[
type[system_executions.SystemExecution]
] = None,
... | Creates an exit handler from a typehint-annotated Python function. This decorator creates an exit handler wrapping the component typehint annotation - typehint annotations specified for the arguments and return value for a Python function. Exit handler is to annotate the component for post actions of a pipeline, only s... |
166,208 | import collections
import copy
import os
from typing import Any, Callable, Dict, List, Optional, Type, cast, MutableMapping
from absl import logging
from kfp import compiler
from kfp import dsl
from kfp import gcp
from kubernetes import client as k8s_client
from tfx import version
from tfx.dsl.compiler import compiler ... | Mounts all key-value pairs found in the named Kubernetes Secret. All key-value pairs in the Secret are mounted as environment variables. Args: secret_name: The name of the Secret resource. Returns: An OpFunc for mounting the Secret. |
166,209 | import collections
import copy
import os
from typing import Any, Callable, Dict, List, Optional, Type, cast, MutableMapping
from absl import logging
from kfp import compiler
from kfp import dsl
from kfp import gcp
from kubernetes import client as k8s_client
from tfx import version
from tfx.dsl.compiler import compiler ... | Returns a default list of pipeline operator functions. Args: use_gcp_sa: If true, mount a GCP service account secret to each pod, with the name _KUBEFLOW_GCP_SECRET_NAME. Returns: A list of functions with type OpFunc. |
166,210 | import collections
import copy
import os
from typing import Any, Callable, Dict, List, Optional, Type, cast, MutableMapping
from absl import logging
from kfp import compiler
from kfp import dsl
from kfp import gcp
from kubernetes import client as k8s_client
from tfx import version
from tfx.dsl.compiler import compiler ... | Returns the default metadata connection config for Kubeflow. Returns: A config proto that will be serialized as JSON and passed to the running container so the TFX component driver is able to communicate with MLMD in a Kubeflow cluster. |
166,211 | import collections
import copy
import os
from typing import Any, Callable, Dict, List, Optional, Type, cast, MutableMapping
from absl import logging
from kfp import compiler
from kfp import dsl
from kfp import gcp
from kubernetes import client as k8s_client
from tfx import version
from tfx.dsl.compiler import compiler ... | Returns the default pod label dict for Kubeflow. |
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