id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
166,512 | import os
from absl import logging
from tfx import v1 as tfx
from tfx.experimental.templates.penguin.pipeline import configs
from tfx.experimental.templates.penguin.pipeline import pipeline
PIPELINE_ROOT = os.path.join(OUTPUT_DIR, 'tfx_pipeline_output',
configs.PIPELINE_NAME)
SERVING_MODEL_... | Define a kubeflow pipeline. |
166,513 | import os
from absl import logging
from tfx import v1 as tfx
from tfx.experimental.templates.penguin.pipeline import configs
from tfx.experimental.templates.penguin.pipeline import pipeline
PIPELINE_ROOT = os.path.join(OUTPUT_DIR, 'tfx_pipeline_output',
configs.PIPELINE_NAME)
METADATA_PATH ... | Define a pipeline. |
166,514 | import tensorflow_transform as tft
from tfx.experimental.templates.penguin.models import features
The provided code snippet includes necessary dependencies for implementing the `preprocessing_fn` function. Write a Python function `def preprocessing_fn(inputs)` to solve the following problem:
tf.transform's callback fu... | 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. |
166,515 | from typing import List
from absl import logging
import tensorflow as tf
from tensorflow import keras
import tensorflow_transform as tft
from tensorflow_transform.tf_metadata import schema_utils
from tfx import v1 as tfx
from tfx.experimental.templates.penguin.models import constants
from tfx.experimental.templates.pen... | Train the model based on given args. Args: fn_args: Holds args used to train the model as name/value pairs. |
166,516 | import os
from absl import logging
from tfx import v1 as tfx
from tfx.experimental.templates.taxi.pipeline import configs
from tfx.experimental.templates.taxi.pipeline import pipeline
PIPELINE_ROOT = os.path.join(OUTPUT_DIR, 'tfx_pipeline_output',
configs.PIPELINE_NAME)
SERVING_MODEL_DIR = ... | Define a kubeflow pipeline. |
166,517 | import os
from absl import logging
from tfx import v1 as tfx
from tfx.experimental.templates.taxi.pipeline import configs
from tfx.experimental.templates.taxi.pipeline import pipeline
PIPELINE_ROOT = os.path.join(OUTPUT_DIR, 'tfx_pipeline_output',
configs.PIPELINE_NAME)
METADATA_PATH = os.p... | Define a local pipeline. |
166,518 | import os
from absl import logging
from tfx.experimental.templates.taxi.pipeline import configs
from tfx.experimental.templates.taxi.pipeline import pipeline
from tfx.orchestration.kubeflow.v2 import kubeflow_v2_dag_runner
from tfx.proto import trainer_pb2
_PIPELINE_ROOT = os.path.join(_OUTPUT_DIR, 'tfx_pipeline_output... | Define a pipeline to be executed using Kubeflow V2 runner. |
166,519 | from absl import logging
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 import v1 as tfx
from tfx.experimental.templates.taxi.models import features
fr... | Small utility returning a record reader that can read gzip'ed files. |
166,520 | from absl import logging
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 import v1 as tfx
from tfx.experimental.templates.taxi.models import features
fr... | Train the model based on given args. Args: fn_args: Holds args used to train the model as name/value pairs. |
166,521 | import tensorflow as tf
import tensorflow_transform as tft
from tfx.experimental.templates.taxi.models import features
def _fill_in_missing(x):
"""Replace missing values in a SparseTensor.
Fills in missing values of `x` with '' or 0, and converts to a dense tensor.
Args:
x: A `SparseTensor` of rank 2. Its 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. |
166,522 | from absl import logging
import tensorflow as tf
import tensorflow_transform as tft
from tfx.experimental.templates.taxi.models import features
from tfx.experimental.templates.taxi.models.keras_model import constants
from tfx_bsl.public import tfxio
def _get_tf_examples_serving_signature(model, tf_transform_output):
... | Train the model based on given args. Args: fn_args: Holds args used to train the model as name/value pairs. |
166,523 | from typing import List
The provided code snippet includes necessary dependencies for implementing the `vocabulary_name` function. Write a Python function `def vocabulary_name(key: str) -> str` to solve the following problem:
Generate the name of the vocabulary feature from original name.
Here is the function:
def v... | Generate the name of the vocabulary feature from original name. |
166,524 | import collections
from typing import Dict, List, Mapping, Set
import tensorflow as tf
from tfx.dsl.io import fileio
from tfx.experimental.distributed_inference.graphdef_experiments.subgraph_partitioning import execution_spec
def _get_graph_def(filepath: str) -> tf.compat.v1.GraphDef:
graph_def = tf.compat.v1.GraphDe... | Gets the `GraphDef` protos from files. Args: graph_name_to_filepath: A mapping from graph names to filepaths. Each filepath points to a `GraphDef` proto in binary. Returns: A mapping from graph names to `GraphDef` protos. |
166,525 | import collections
from typing import Dict, List, Mapping, Set
import tensorflow as tf
from tfx.dsl.io import fileio
from tfx.experimental.distributed_inference.graphdef_experiments.subgraph_partitioning import execution_spec
def _partition_one_graph(
graph_def: tf.compat.v1.GraphDef,
output_names: List[str]) -... | Partitions all the graphs. For each graph, the partitioning algorithm takes in the graph's `GraphDef` proto and output names, partitions the graph, and returns a list of ExecutionSpecs. Later, the beam_pipeline library can take in the ExecutionSpecs and execute the partitioned subgraphs. Args: graph_name_to_graph_def: ... |
166,526 | import tensorflow as tf
tf.compat.v1.disable_eager_execution()
def create_session(graph):
return tf.compat.v1.Session(
graph=graph,
config=tf.compat.v1.ConfigProto(inter_op_parallelism_threads=8))
graph_a = tf.Graph()
with graph_a.as_default():
table_a = tf.random.uniform(shape=[N, NDIMS], seed=10)
i... | Mimics a remote op by numpy_function. |
166,527 | import tensorflow as tf
tf.compat.v1.disable_eager_execution()
def create_session(graph):
return tf.compat.v1.Session(
graph=graph,
config=tf.compat.v1.ConfigProto(inter_op_parallelism_threads=8))
graph_b = tf.Graph()
with graph_b.as_default():
ids_b2 = tf.compat.v1.placeholder(dtype=tf.int32, name='id... | Mimics another remote op. |
166,528 | import tensorflow as tf
tf.compat.v1.disable_eager_execution()
graph_a = tf.Graph()
with graph_a.as_default():
table_a = tf.random.uniform(shape=[N, NDIMS], seed=10)
ids_a = tf.compat.v1.placeholder(dtype=tf.int32, name='ids_a')
result_a = tf.nn.embedding_lookup(table_a, ids_a)
graph_b = tf.Graph()
with graph_b.... | null |
166,529 | import copy
from typing import Any, Dict, Iterator, List, Mapping
import apache_beam as beam
import tensorflow as tf
from tfx.experimental.distributed_inference.graphdef_experiments.subgraph_partitioning import execution_spec
class _SubgraphLayerDoFn(beam.DoFn):
"""DoFn that executes one subgraph layer."""
def proc... | A PTransform that executes a graph. Each graph has a list of ExecutionSpecs, in which the order of the list represents the order of execution. An ExecutionSpec can either represent a subgraph layer or a remote op in a remote op layer. When executing a subgraph layer, we can load and execute the subgraph with a beam Par... |
166,530 | from absl import app
from absl import flags
import tensorflow_docs.api_generator as api_generator
from tensorflow_docs.api_generator import generate_lib
from tfx import v1
from tfx import version
from tfx.utils import doc_controls
from google.protobuf.reflection import GeneratedProtocolMessageType
The provided code sn... | Removes "test" and "example" modules. These are not part of the public api. Args: path: A tuple of name parts forming the attribute-lookup path to this object. For `tf.keras.layers.Dense` path is: ("tf","keras","layers","Dense") parent: The parent object. children: A list of (name, value) pairs. The attributes of the p... |
166,531 | from absl import app
from absl import flags
import tensorflow_docs.api_generator as api_generator
from tensorflow_docs.api_generator import generate_lib
from tfx import v1
from tfx import version
from tfx.utils import doc_controls
from google.protobuf.reflection import GeneratedProtocolMessageType
The provided code sn... | Remove all the proto inherited methods. Args: path: A tuple of name parts forming the attribute-lookup path to this object. For `tf.keras.layers.Dense` path is: ("tf","keras","layers","Dense") parent: The parent object. children: A list of (name, value) pairs. The attributes of the parent. Returns: A filtered list of c... |
166,532 | from typing import Iterable, Dict
import click
from tfx.tools.cli import labels
from tfx.tools.cli.cli_context import Context
from tfx.tools.cli.cli_context import pass_context
from tfx.tools.cli.handler import handler_factory
def delete_run(ctx: Context, engine: str, run_id: str, endpoint: str,
iap_cli... | null |
166,533 | from typing import Iterable, Dict
import click
from tfx.tools.cli import labels
from tfx.tools.cli.cli_context import Context
from tfx.tools.cli.cli_context import pass_context
from tfx.tools.cli.handler import handler_factory
def _parse_runtime_parameters(
runtime_parameters: Iterable[str]) -> Dict[str, str]:
""... | Command definition to create a pipeline run. |
166,534 | from typing import Iterable, Dict
import click
from tfx.tools.cli import labels
from tfx.tools.cli.cli_context import Context
from tfx.tools.cli.cli_context import pass_context
from tfx.tools.cli.handler import handler_factory
def terminate_run(ctx: Context, engine: str, run_id: str, endpoint: str,
ia... | Command definition to stop a run. |
166,535 | from typing import Iterable, Dict
import click
from tfx.tools.cli import labels
from tfx.tools.cli.cli_context import Context
from tfx.tools.cli.cli_context import pass_context
from tfx.tools.cli.handler import handler_factory
def terminate_run(ctx: Context, engine: str, run_id: str, endpoint: str,
ia... | Command definition to list all runs of a pipeline. |
166,536 | from typing import Iterable, Dict
import click
from tfx.tools.cli import labels
from tfx.tools.cli.cli_context import Context
from tfx.tools.cli.cli_context import pass_context
from tfx.tools.cli.handler import handler_factory
def terminate_run(ctx: Context, engine: str, run_id: str, endpoint: str,
ia... | Command definition to stop a run. |
166,537 | from typing import Iterable, Dict
import click
from tfx.tools.cli import labels
from tfx.tools.cli.cli_context import Context
from tfx.tools.cli.cli_context import pass_context
from tfx.tools.cli.handler import handler_factory
def terminate_run(ctx: Context, engine: str, run_id: str, endpoint: str,
ia... | Command definition to delete a run. |
166,538 | import click
from tfx.tools.cli import labels
from tfx.tools.cli.cli_context import Context
from tfx.tools.cli.cli_context import pass_context
from tfx.tools.cli.handler import template_handler
def template_group() -> None:
pass | null |
166,539 | import click
from tfx.tools.cli import labels
from tfx.tools.cli.cli_context import Context
from tfx.tools.cli.cli_context import pass_context
from tfx.tools.cli.handler import template_handler
def list_templates() -> None:
click.echo('Available templates:')
for model in template_handler.list_template():
click... | null |
166,540 | import click
from tfx.tools.cli import labels
from tfx.tools.cli.cli_context import Context
from tfx.tools.cli.cli_context import pass_context
from tfx.tools.cli.handler import template_handler
class Context:
"""Context shared between all command groups.
Attributes :
flags_dict: A dictionary containing the fl... | Command definition to copy template to specified directory. |
166,541 | import sys
from typing import Optional
import click
from tfx.tools.cli import labels
from tfx.tools.cli.cli_context import Context
from tfx.tools.cli.cli_context import pass_context
from tfx.tools.cli.handler import handler_factory
def create_pipeline(ctx: Context, engine: str, pipeline_path: str,
p... | null |
166,542 | import sys
from typing import Optional
import click
from tfx.tools.cli import labels
from tfx.tools.cli.cli_context import Context
from tfx.tools.cli.cli_context import pass_context
from tfx.tools.cli.handler import handler_factory
def _check_deprecated_image_build_flags(build_target_image=None,
... | Command definition to create a pipeline. |
166,543 | import sys
from typing import Optional
import click
from tfx.tools.cli import labels
from tfx.tools.cli.cli_context import Context
from tfx.tools.cli.cli_context import pass_context
from tfx.tools.cli.handler import handler_factory
def _check_deprecated_image_build_flags(build_target_image=None,
... | Command definition to update a pipeline. |
166,544 | import sys
from typing import Optional
import click
from tfx.tools.cli import labels
from tfx.tools.cli.cli_context import Context
from tfx.tools.cli.cli_context import pass_context
from tfx.tools.cli.handler import handler_factory
def create_pipeline(ctx: Context, engine: str, pipeline_path: str,
p... | Command definition to delete a pipeline. |
166,545 | import sys
from typing import Optional
import click
from tfx.tools.cli import labels
from tfx.tools.cli.cli_context import Context
from tfx.tools.cli.cli_context import pass_context
from tfx.tools.cli.handler import handler_factory
def create_pipeline(ctx: Context, engine: str, pipeline_path: str,
p... | Command definition to list pipelines. |
166,546 | import sys
from typing import Optional
import click
from tfx.tools.cli import labels
from tfx.tools.cli.cli_context import Context
from tfx.tools.cli.cli_context import pass_context
from tfx.tools.cli.handler import handler_factory
def _check_deprecated_image_build_flags(build_target_image=None,
... | Command definition to compile a pipeline. |
166,547 | import sys
from typing import Optional
import click
from tfx.tools.cli import labels
from tfx.tools.cli.cli_context import Context
from tfx.tools.cli.cli_context import pass_context
from tfx.tools.cli.handler import handler_factory
def create_pipeline(ctx: Context, engine: str, pipeline_path: str,
p... | Command definition to infer latest schema. |
166,548 | import click
from tfx.tools.cli.commands.pipeline import pipeline_group
from tfx.tools.cli.commands.run import run_group
from tfx.tools.cli.commands.template import template_group
def cli_group():
click.echo('CLI') | null |
166,549 | import functools
import os
import sys
import time
from typing import Any, Dict, Optional
import click
import kfp
from tfx.orchestration.kubeflow import kubeflow_dag_runner
from tfx.tools.cli import labels
from tfx.tools.cli.container_builder import builder
from tfx.tools.cli.handler import base_handler
from tfx.tools.c... | null |
166,550 | import os
import tempfile
import typing
from typing import Any, Callable, MutableMapping, Optional, Type
from tfx.orchestration import pipeline as tfx_pipeline
from tfx.orchestration import tfx_runner
from tfx.orchestration.kubeflow import kubeflow_dag_runner
from tfx.tools.cli.handler import dag_runner_patcher
def _g... | null |
166,551 |
def get_source_path(path: str) -> str:
return path | null |
166,552 | from typing import Any, Dict, List, Mapping, MutableMapping, MutableSequence, Sequence, TypeVar
import tfx.types
from tfx.utils import pure_typing_utils
from typing_extensions import ( # pylint: disable=g-multiple-import
TypeGuard, # New in python 3.10
)
is_compatible = pure_typing_utils.is_compatible
_TArtifact ... | Checks value is Sequence[T] where T is subclass of Artifact. |
166,553 | from typing import Any, Dict, List, Mapping, MutableMapping, MutableSequence, Sequence, TypeVar
import tfx.types
from tfx.utils import pure_typing_utils
from typing_extensions import ( # pylint: disable=g-multiple-import
TypeGuard, # New in python 3.10
)
is_compatible = pure_typing_utils.is_compatible
def is_art... | null |
166,554 | from typing import Any, Dict, List, Mapping, MutableMapping, MutableSequence, Sequence, TypeVar
import tfx.types
from tfx.utils import pure_typing_utils
from typing_extensions import ( # pylint: disable=g-multiple-import
TypeGuard, # New in python 3.10
)
ArtifactMultiMap = MultiMap[str, tfx.types.Artifact]
is_com... | Checks value is Sequence[Mapping[str, Sequence[Artifact]]] type. |
166,555 | import copy
import logging
import os
from typing import Any, Dict, Optional
from tfx.dsl.io import fileio
The provided code snippet includes necessary dependencies for implementing the `get_logger` function. Write a Python function `def get_logger(config)` to solve the following problem:
Create and configure a TFX-spe... | Create and configure a TFX-specific logger. Args: config: LoggingConfig class used to configure logger Returns: A logger that outputs to log_dir/log_file_name. Raises: RuntimeError: if log dir exists as a file. |
166,556 | import os
import re
import tempfile
from typing import List, TypeVar, Iterable
from tfx.dsl.io import fileio
from google.protobuf import json_format
from google.protobuf import text_format
from google.protobuf.message import Message
The provided code snippet includes necessary dependencies for implementing the `write_... | Writes a serialized tfrecord to file. |
166,557 | import os
import re
import tempfile
from typing import List, TypeVar, Iterable
from tfx.dsl.io import fileio
from google.protobuf import json_format
from google.protobuf import text_format
from google.protobuf.message import Message
ProtoMessage = TypeVar('ProtoMessage', bound=Message)
The provided code snippet includ... | Parses a protobuf message from a JSON file and return itself. |
166,558 | import os
import re
import tempfile
from typing import List, TypeVar, Iterable
from tfx.dsl.io import fileio
from google.protobuf import json_format
from google.protobuf import text_format
from google.protobuf.message import Message
The provided code snippet includes necessary dependencies for implementing the `load_c... | Parse the first line of a csv file as column names. |
166,559 | import os
import re
import tempfile
from typing import List, TypeVar, Iterable
from tfx.dsl.io import fileio
from google.protobuf import json_format
from google.protobuf import text_format
from google.protobuf.message import Message
The provided code snippet includes necessary dependencies for implementing the `read_s... | Reads a string from a file. |
166,560 | import os
import re
import tempfile
from typing import List, TypeVar, Iterable
from tfx.dsl.io import fileio
from google.protobuf import json_format
from google.protobuf import text_format
from google.protobuf.message import Message
The provided code snippet includes necessary dependencies for implementing the `read_b... | Reads bytes from a file. |
166,561 | import functools
import time
from typing import Type
from absl import logging
The provided code snippet includes necessary dependencies for implementing the `retry` function. Write a Python function `def retry(max_retries: int = 3, delay_seconds: int = 1, expected_exception: Type[Exception] = Excep... | Function decorator to retry a function automatically. Example: from tfx.utils import retry @retry.retry() def some_fragile_func(): ... If `ignore_eventual_failure` is False, the last expected exception caught will raised from this function. If `ignore_eventual_failure` is True, no exception will raised and will return ... |
166,562 | import functools
import inspect
import warnings
def _should_warn(func_or_class, warn_once=True):
"""Check whether to warn or not with side effect."""
if id(func_or_class) in _PRINTED_WARNING:
return False
if warn_once:
_PRINTED_WARNING.add(id(func_or_class))
return True
def _validate_callable(func):
i... | Decorator marking function or method as deprecated. Note: this function does not currently support deprecation of classes. To perform such deprecation, decorate its constructor instead. Args: date: String date at which function will be removed, or None. instructions: Instructions on updating use of deprecated code. war... |
166,563 | import functools
import inspect
import warnings
def _should_warn(func_or_class, warn_once=True):
"""Check whether to warn or not with side effect."""
if id(func_or_class) in _PRINTED_WARNING:
return False
if warn_once:
_PRINTED_WARNING.add(id(func_or_class))
return True
def _validate_callable(func):
i... | Deprecates a symbol in favor of a renamed function or class. Args: deprecated_name: Fully qualified name of deprecated symbol. name: New symbol name. func_or_class: Non-deprecated function or class, to be used as alias. warn_once: Whether only one warning should be emitted for multiple calls to deprecated symbol. Retur... |
166,564 |
def do_not_doc_in_subclasses(obj):
return obj | null |
166,565 |
def do_not_doc_inheritable(obj):
return obj | null |
166,566 |
def do_not_generate_docs(obj):
return obj | null |
166,567 | EXTRA_DOCS = dict()
The provided code snippet includes necessary dependencies for implementing the `documented` function. Write a Python function `def documented(obj, doc)` to solve the following problem:
Adds a docstring to typealias by overriding the `__doc__` attribute. Note: Overriding `__doc__` is only possible a... | Adds a docstring to typealias by overriding the `__doc__` attribute. Note: Overriding `__doc__` is only possible after python 3.7. Args: obj: Typealias object that needs to be documented. doc: Docstring of the typealias. It should follow the standard pystyle docstring rules. Returns: Documented variables. |
166,568 | import subprocess
import docker
The provided code snippet includes necessary dependencies for implementing the `delete_image` function. Write a Python function `def delete_image(name: str, remote: bool = True)` to solve the following problem:
Delete container image in local and remote registry.
Here is the function:
... | Delete container image in local and remote registry. |
166,569 |
def generate_monitoring_metrics(
unused_test_stats_split,
unused_baseline_stats_split,
unused_split_pair,
unused_span,
unused_artifact,
) -> None:
return | null |
166,570 | import os
from typing import Optional, Tuple
The provided code snippet includes necessary dependencies for implementing the `make_model_path` function. Write a Python function `def make_model_path(model_base_path: str, model_name: str, version: int) -> str` to solve the following problem:
Make a TF... | Make a TFS-flavored model path. Args: model_base_path: A base path containing the directory of model_name. model_name: A name of the model. version: An integer version of the model. Returns: `{model_base_path}/{model_name}/{version}`. |
166,571 | import os
from typing import Optional, Tuple
def parse_model_path(
model_path: str,
expected_model_name: Optional[str] = None) -> Tuple[str, str, int]:
"""Parse model_path into parts of TFS flavor.
Args:
model_path: A TFS-flavored model path.
expected_model_name: Expected model_name as defined from ... | Parse model_base_path from the TFS-flavored model path. Args: model_path: A TFS-flavored model path. Raises: ValueError: If model path is invalid (not TFS-flavored). Returns: model_base_path as defined from the module docstring. |
166,572 | import os
import absl
from tfx.dsl.io import fileio
from tfx.types import artifact
from tfx.types import artifact_utils
from tfx.types import standard_artifacts
from tfx.utils import io_utils
from tfx.utils import path_constants
def eval_model_dir(output_uri: str, is_old_artifact: bool = False) -> str:
"""Returns dir... | Returns final path to exported model for evaluation purpose. |
166,573 | import os
import absl
from tfx.dsl.io import fileio
from tfx.types import artifact
from tfx.types import artifact_utils
from tfx.types import standard_artifacts
from tfx.utils import io_utils
from tfx.utils import path_constants
def eval_model_dir(output_uri: str, is_old_artifact: bool = False) -> str:
"""Returns dir... | Returns directly for exported model depending on model_type. |
166,574 | import os
import absl
from tfx.dsl.io import fileio
from tfx.types import artifact
from tfx.types import artifact_utils
from tfx.types import standard_artifacts
from tfx.utils import io_utils
from tfx.utils import path_constants
The provided code snippet includes necessary dependencies for implementing the `stamped_mo... | Returns path for the stamped model. |
166,575 | import os
import absl
from tfx.dsl.io import fileio
from tfx.types import artifact
from tfx.types import artifact_utils
from tfx.types import standard_artifacts
from tfx.utils import io_utils
from tfx.utils import path_constants
The provided code snippet includes necessary dependencies for implementing the `warmup_fil... | Returns SavedModel Warmup file path. See https://www.tensorflow.org/tfx/serving/saved_model_warmup. This is a lexical operation, and does not guarantee the path is valid. Args: saved_model_path: A POSIX path to the TensorFlow SavedModel. Returns: A POSIX path to the SavedModel Warmup file. |
166,576 | import contextlib
import functools
import re
import sys
import threading
from typing import Dict, List, Any, Callable
from absl import logging
from googleapiclient import http
from tfx import version
def make_labels_dict() -> Dict[str, str]:
"""Get all registered and system generated labels as a dict.
Returns:
... | Make Beam arguments for common labels used in TFX pipelines. Returns: New Beam pipeline args with labels. |
166,577 | import contextlib
import functools
import re
import sys
import threading
from typing import Dict, List, Any, Callable
from absl import logging
from googleapiclient import http
from tfx import version
def noop_telemetry(
event_metric: Any
) -> Callable[[Callable[..., Any]], Callable[..., Any]]:
del event_metric
... | null |
166,578 | import os
import shutil
import subprocess
import sys
import tempfile
from typing import List
import absl
from tfx import dependencies
from tfx import version
from tfx.dsl.io import fileio
from tfx.utils import io_utils
def build_ephemeral_package() -> str:
"""Repackage current installation of TFX into a tfx_ephemeral... | Make beam arguments for TFX python dependencies, if latter was not set. When TFX executors are used with non-local beam runners (Dataflow, Flink, etc) the remote runner needs to have access to TFX executors. This function acts as a helper to provide TFX source package to Beam if user does not provide that through Beam ... |
166,579 | from tfx.utils import io_utils
from tensorflow_metadata.proto.v0 import anomalies_pb2
The provided code snippet includes necessary dependencies for implementing the `write_anomalies` function. Write a Python function `def write_anomalies( filepath: str, anomalies: anomalies_pb2.Anomalies, ) -> None` to solve t... | Writes Anomalies to a binary proto file. |
166,580 | from typing import Dict, Iterable, List, Union
from tfx import types
from tfx.types import channel_utils
from tfx.utils import deprecation_utils
class Channel(types.Channel):
pass
None, 'tfx.utils.channel.as_channel has been renamed to '
'tfx.types.channel_utils.as_channel as of TFX 0.14.0.')
def as_channel(... | null |
166,581 | from typing import Dict, Iterable, List, Union
from tfx import types
from tfx.types import channel_utils
from tfx.utils import deprecation_utils
class Channel(types.Channel):
pass
None, 'tfx.utils.channel.as_channel has been renamed to '
'tfx.types.channel_utils.as_channel as of TFX 0.14.0.')
def unwrap_chan... | null |
166,582 | import abc
from typing import Any
The provided code snippet includes necessary dependencies for implementing the `abstract_property` function. Write a Python function `def abstract_property() -> Any` to solve the following problem:
Returns an abstract property for use in an ABC abstract class.
Here is the function:
... | Returns an abstract property for use in an ABC abstract class. |
166,583 | import datetime
import enum
import os
import re
import time
from typing import Callable, Dict, List, Optional
from absl import logging
from kubernetes import client as k8s_client
from kubernetes import config as k8s_config
KFP_POD_NAME = 'KFP_POD_NAME'
KFP_NAMESPACE = 'KFP_NAMESPACE'
def is_inside_cluster() -> bool:
... | Whether current running environment is inside the KFP runtime. |
166,584 | import datetime
import enum
import os
import re
import time
from typing import Callable, Dict, List, Optional
from absl import logging
from kubernetes import client as k8s_client
from kubernetes import config as k8s_config
KFP_NAMESPACE = 'KFP_NAMESPACE'
The provided code snippet includes necessary dependencies for im... | Get kubernetes namespace for the KFP. Raises: RuntimeError: If KFP pod cannot be determined from the environment, i.e. this program is not running inside the KFP. Returns: The namespace of the KFP app, to which the pod this program is running on belongs. |
166,585 | import datetime
import enum
import os
import re
import time
from typing import Callable, Dict, List, Optional
from absl import logging
from kubernetes import client as k8s_client
from kubernetes import config as k8s_config
KFP_POD_NAME = 'KFP_POD_NAME'
KFP_NAMESPACE = 'KFP_NAMESPACE'
The provided code snippet includes... | Get manifest of the KFP pod in which this program is running. Args: client: A kubernetes CoreV1Api client. Raises: RuntimeError: If KFP pod cannot be determined from the environment, i.e. this program is not running inside the KFP. Returns: The manifest of the pod this program is running on. |
166,586 | from typing import Any, Dict, Iterator, TypeVar, Optional
from google.protobuf import any_pb2
from google.protobuf import descriptor_pb2
from google.protobuf import descriptor as descriptor_lib
from google.protobuf import descriptor_pool
from google.protobuf import json_format
from google.protobuf import message
from g... | Simple JSON Formatter wrapper for consistent formatting. |
166,587 | from typing import Any, Dict, Iterator, TypeVar, Optional
from google.protobuf import any_pb2
from google.protobuf import descriptor_pb2
from google.protobuf import descriptor as descriptor_lib
from google.protobuf import descriptor_pool
from google.protobuf import json_format
from google.protobuf import message
from g... | Simple JSON Parser wrapper for consistent parsing. |
166,588 | import abc
import functools
import inspect
import sys
from typing import Any, Callable, Generic, Optional, TypeVar, Union, get_args, get_origin
from tfx.utils import pure_typing_utils
The provided code snippet includes necessary dependencies for implementing the `_is_subclass` function. Write a Python function `def _i... | issubclass that supports Union and Optional correctly. |
166,589 | import re
from absl import logging
from tfx import version
_REGULAR_NIGHTLY_VERSION_PATTERN = re.compile(r'\d+\.\d+\.\d+(\.dev\d{8}){0,1}')
_RC_VERSION_PATTERN = re.compile(r'\d+\.\d+\.\d+\-rc\d+')
The provided code snippet includes necessary dependencies for implementing the `get_image_version` function. Write a Pyth... | Gets the version for image tag based on SDK version. Args: version_str: The SDK version. Returns: Version string representing the image version should be used. For offcially released version of TFX SDK, we'll align the SDK and the image versions; For 'dev' or customized versions we'll use the latest image version. |
166,590 | import pathway.internals as pw
from pathway.internals import ColumnReference, Table
from pathway.xpacks.llm.llms import prompt_chat_single_qa
from pathway.xpacks.llm.prompts import prompt_qa
def _query_chat_with_k_documents(chat: pw.UDF, k: int, t: pw.Table) -> pw.Table:
limited_documents = t.select(
pw.thi... | Function for querying LLM chat while providing increasing number of documents until an answer is found. Documents are taken from `documents` argument. Initially first `n_starting_documents` documents are embedded in the query. If the LLM chat fails to find an answer, the number of documents is multiplied by `factor` an... |
166,591 | import re
import pathway as pw
def prompt_citing_qa(query: str, docs: list[pw.Json]):
context_pieces = []
for i, doc in enumerate(docs, 1):
context_pieces.append(f"# Source {i}")
context_pieces.append(doc["text"]) # type: ignore
context_pieces.append("")
context_str = "\n".join(co... | null |
166,592 | import re
import pathway as pw
def prompt_short_qa(query: str, docs: list[pw.Json]):
context_pieces = []
for i, doc in enumerate(docs, 1):
context_pieces.append(doc["text"])
context_pieces.append("") # type: ignore
context_str = "\n".join(context_pieces) # type: ignore
prompt = (
... | null |
166,593 | import re
import pathway as pw
def prompt_summarize(text_list: list[str]):
text = "\n".join(text_list)
prompt = f"""Given a list of documents, summarize them in few sentences \
while preserving important points and entities.
Documents: {text}
Summary:"""
return prompt | null |
166,594 | import re
import pathway as pw
def prompt_query_rewrite_hyde(query: str) -> str:
prompt = f"""Write 4 responses to answer the given question with hypothetical data.
Try to include as many key details as possible.
Question: `{query}`.
Responses:"""
return prompt | null |
166,595 | import re
import pathway as pw
def prompt_query_rewrite(query: str, *additional_args: str) -> str:
prompt = f"""Given a question that will be used to retrieve similar documents for RAG application.
Rewrite question to be better usable in retrieval search.
Use important entities, words that may be related t... | null |
166,596 | import re
import pathway as pw
def parse_cited_response(response_text, docs):
cited_docs = [
int(cite[1:-1]) - 1
for cite in set(re.findall("\[\d+\]", response_text)) # noqa: W605
]
start_index = response_text.find("*") + 1
end_index = response_text.find("*", start_index)
citation... | null |
166,597 | import unicodedata
import pathway as pw
The provided code snippet includes necessary dependencies for implementing the `null_splitter` function. Write a Python function `def null_splitter(txt: str) -> list[tuple[str, dict]]` to solve the following problem:
A splitter which returns its argument as one long text ith nul... | A splitter which returns its argument as one long text ith null metadata. Args: txt: text to be split Returns: list of pairs: chunk text and metadata. The null splitter always return a list of length one containing the full text and empty metadata. |
166,598 | import unicodedata
import pathway as pw
The provided code snippet includes necessary dependencies for implementing the `_normalize_unicode` function. Write a Python function `def _normalize_unicode(text: str)` to solve the following problem:
Get rid of ligatures
Here is the function:
def _normalize_unicode(text: str... | Get rid of ligatures |
166,599 | import asyncio
import functools
import json
import logging
import threading
from collections.abc import Callable, Coroutine
from typing import TYPE_CHECKING
import jmespath
import numpy as np
import requests
import pathway as pw
import pathway.xpacks.llm.parsers
import pathway.xpacks.llm.splitters
from pathway.stdlib.m... | null |
166,600 | import asyncio
import functools
import json
import logging
import threading
from collections.abc import Callable, Coroutine
from typing import TYPE_CHECKING
import jmespath
import numpy as np
import requests
import pathway as pw
import pathway.xpacks.llm.parsers
import pathway.xpacks.llm.splitters
from pathway.stdlib.m... | null |
166,601 | import asyncio
import openai as openai_mod
import pathway as pw
from pathway.internals import udfs
async def _safe_aclose(self):
try:
await self.aclose()
except RuntimeError:
pass
The provided code snippet includes necessary dependencies for implementing the `_mokeypatch_openai_async` function.... | Be more permissive on errors happening in httpx loop closing. Without this patch, many runtime errors appear while the server is running in a thread. The errors can be ignored, but look scary. |
166,602 | import os
import subprocess
import sys
import uuid
from typing import NoReturn
import click
import pathway as pw
def cli() -> None:
pass | null |
166,603 | import os
import subprocess
import sys
import uuid
from typing import NoReturn
import click
import pathway as pw
def spawn_program(threads, processes, first_port, program, arguments, env_base):
processes_str = plural(processes, "process", "processes")
workers_str = plural(processes * threads, "total worker", "t... | null |
166,604 | import os
import subprocess
import sys
import uuid
from typing import NoReturn
import click
import pathway as pw
def spawn_program(threads, processes, first_port, program, arguments, env_base):
def replay(
threads,
processes,
first_port,
record_path,
mode,
continue_after_replay,
program,
... | null |
166,605 | from __future__ import annotations
from collections.abc import Callable
import pathway.internals as pw
def classifier_accuracy(predicted_labels, exact_labels):
pw.universes.promise_is_subset_of(predicted_labels, exact_labels)
comparative_results = predicted_labels.select(
predicted_label=predicted_labe... | null |
166,606 | from __future__ import annotations
from collections.abc import Callable
import pathway.internals as pw
The provided code snippet includes necessary dependencies for implementing the `_predict_asof_now` function. Write a Python function `def _predict_asof_now( prediction_function: Callable[..., pw.Table], *quer... | A helper function used to predict answers to queries without updating them in the future. It passes a query and its forgetting counterpart through the prediction function. Parameters: prediction_function: A function that is called with transformed column reference `query`. queries: References to a column/columns with q... |
166,607 | from __future__ import annotations
import math
from collections.abc import Callable
from enum import IntEnum, auto
from typing import Any
import pathway.internals as pw
from pathway.internals.helpers import StableSet
def _tokenize(obj: Any) -> Any:
return str(obj).split() | null |
166,608 | from __future__ import annotations
import math
from collections.abc import Callable
from enum import IntEnum, auto
from typing import Any
import pathway.internals as pw
from pathway.internals.helpers import StableSet
def _letters(obj: Any) -> Any:
return [c.lower() for c in str(obj) if c.isalnum()] | null |
166,609 | from __future__ import annotations
import math
from collections.abc import Callable
from enum import IntEnum, auto
from typing import Any
import pathway.internals as pw
from pathway.internals.helpers import StableSet
def _discrete_weight(cnt: float) -> float:
if cnt == 0:
return 0.0
else:
retur... | null |
166,610 | from __future__ import annotations
import math
from collections.abc import Callable
from enum import IntEnum, auto
from typing import Any
import pathway.internals as pw
from pathway.internals.helpers import StableSet
def _discrete_logweight(cnt: float) -> float:
if cnt == 0:
return 0.0
else:
re... | null |
166,611 | from __future__ import annotations
import math
from collections.abc import Callable
from enum import IntEnum, auto
from typing import Any
import pathway.internals as pw
from pathway.internals.helpers import StableSet
def _none(cnt: float) -> float:
return cnt | null |
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