code stringlengths 114 1.05M | path stringlengths 3 312 | quality_prob float64 0.5 0.99 | learning_prob float64 0.2 1 | filename stringlengths 3 168 | kind stringclasses 1
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|---|---|---|---|---|---|
"""Placeholder docstring"""
from __future__ import absolute_import
import inspect
import sys
from typing import Callable
import six
from sagemaker_containers import _mapping
def matching_args(fn, dictionary): # type: (Callable, _mapping.Mapping) -> dict
"""Given a function fn and a dict dictionary, returns th... | /sagemaker_containers-2.8.6.post0.tar.gz/sagemaker_containers-2.8.6.post0/src/sagemaker_containers/_functions.py | 0.742608 | 0.408749 | _functions.py | pypi |
"""Placeholder docstring"""
from __future__ import absolute_import
import warnings
import flask
from six.moves import http_client
from sagemaker_containers import _content_types, _env, _logging, _mapping
env = _env.ServingEnv()
def default_healthcheck_fn(): # type: () -> Response
"""Ping is default health-ch... | /sagemaker_containers-2.8.6.post0.tar.gz/sagemaker_containers-2.8.6.post0/src/sagemaker_containers/_worker.py | 0.839603 | 0.287668 | _worker.py | pypi |
"""This module contains utility functions used to generate recordio-protobuf format."""
import struct
import sys
import numpy as np
from scipy.sparse import issparse
from sagemaker_containers.record_pb2 import Record
def _resolve_type(dtype):
"""Returns the type string corresponding to the numpy.dtype
Args:... | /sagemaker_containers-2.8.6.post0.tar.gz/sagemaker_containers-2.8.6.post0/src/sagemaker_containers/_recordio.py | 0.799442 | 0.675577 | _recordio.py | pypi |
"""Placeholder docstring"""
from __future__ import absolute_import
import importlib
import os
import shlex
import subprocess # pylint: disable=unused-import
import sys
import textwrap
import warnings
import six
from sagemaker_containers import _env, _errors, _files, _logging, _process
logger = _logging.get_logger(... | /sagemaker_containers-2.8.6.post0.tar.gz/sagemaker_containers-2.8.6.post0/src/sagemaker_containers/_modules.py | 0.639849 | 0.159479 | _modules.py | pypi |
"""Placeholder docstring"""
from __future__ import absolute_import
import json
import textwrap
import traceback
from six.moves import http_client
from sagemaker_containers import _content_types, _encoders, _env, _errors, _functions, _worker
def default_model_fn(model_dir):
"""Function responsible to load the m... | /sagemaker_containers-2.8.6.post0.tar.gz/sagemaker_containers-2.8.6.post0/src/sagemaker_containers/_transformer.py | 0.862482 | 0.393909 | _transformer.py | pypi |
"""Placeholder docstring"""
from __future__ import absolute_import
import collections
import itertools
import json
import six
SplitResultSpec = collections.namedtuple("SplitResultSpec", "included excluded")
def to_env_vars(mapping): # type: (dict) -> dict
"""Transform a dictionary in a dictionary of env vars.... | /sagemaker_containers-2.8.6.post0.tar.gz/sagemaker_containers-2.8.6.post0/src/sagemaker_containers/_mapping.py | 0.88631 | 0.255791 | _mapping.py | pypi |
"""Placeholder docstring"""
from __future__ import absolute_import
import contextlib
import json
import os
import shutil
import tarfile
import tempfile
import boto3
from six.moves.urllib import parse
from sagemaker_containers import _env, _params
def write_success_file(): # type: () -> None
"""Create a file '... | /sagemaker_containers-2.8.6.post0.tar.gz/sagemaker_containers-2.8.6.post0/src/sagemaker_containers/_files.py | 0.711631 | 0.232114 | _files.py | pypi |
import logging
import pandas as pd
import scipy.stats as sp
from sagemaker_data_insights import PEARSON, ALLOWED_CROSS_COL_INSIGHTS
from sagemaker_data_insights import FEATURE_TYPES, FEATURE_DATA
from sagemaker_data_insights import FeatureType as ft
from sagemaker_data_insights.model_utils import _encode_features
de... | /sagemaker_data_insights-0.4.0-py3-none-any.whl/sagemaker_data_insights/cross_column_stats.py | 0.835013 | 0.703244 | cross_column_stats.py | pypi |
import logging
import pandas as pd
import numpy as np
import scipy
import sagemaker_data_insights.const as cs
from sagemaker_data_insights.const import TaskType as tt
from sagemaker_data_insights.histogram_functions import (
_verify_y,
calc_robust_histogram,
robust_histogram_num_outliers,
_unique_witho... | /sagemaker_data_insights-0.4.0-py3-none-any.whl/sagemaker_data_insights/analyze_target.py | 0.829803 | 0.537345 | analyze_target.py | pypi |
import numpy as np
import pandas as pd
from sagemaker_data_insights.const import TaskType as tt
def calc_robust_histogram( # noqa: C901
x: np.ndarray,
y: np.ndarray = None,
task=None,
num_bins=20,
stds=5,
robust_std_percentile=5,
robust_histogram_eps=1e-10,
):
"""
Calculates rob... | /sagemaker_data_insights-0.4.0-py3-none-any.whl/sagemaker_data_insights/histogram_functions.py | 0.831725 | 0.78156 | histogram_functions.py | pypi |
import pandas as pd
import numpy as np
import re
import scipy
import logging
import sagemaker_data_insights.const as cs
from sagemaker_data_insights.const import FeatureType as ft
from sagemaker_data_insights.const import TaskType as tt
from sagemaker_data_insights.insights import Insights
from sagemaker_data_insights... | /sagemaker_data_insights-0.4.0-py3-none-any.whl/sagemaker_data_insights/analyze_feature.py | 0.841207 | 0.53443 | analyze_feature.py | pypi |
import pandas as pd
import numpy as np
from sagemaker_data_insights.const import FeatureType as ft
from sagemaker_data_insights.histogram_functions import _unique_without_whitespaces
from sagemaker_data_insights.utils.feature_transform import get_feature_transform
def _calc_stats_pandas_series(
x: pd.Series,
... | /sagemaker_data_insights-0.4.0-py3-none-any.whl/sagemaker_data_insights/calc_stats_pandas_series.py | 0.649245 | 0.631537 | calc_stats_pandas_series.py | pypi |
from typing import List, Dict
from collections import Counter
from difflib import SequenceMatcher
import logging
import numpy as np
from sklearn.cluster import AgglomerativeClustering
def find_duplicate_categories(
strs: List[str],
max_categories: int = 100,
max_str_length=50,
correction_threshold=0.... | /sagemaker_data_insights-0.4.0-py3-none-any.whl/sagemaker_data_insights/categorical_utils.py | 0.962944 | 0.664731 | categorical_utils.py | pypi |
from typing import List
import numpy as np
from .tokens import Tokens
from .expression import Expression, ExpressionSet, ExpressionSetType
from .parse import Parse
def analyze_text_patterns(
strs: List[str],
min_coverage: int = 0.8,
sampling_iterations: int = 10,
sampling_size: int = 30,
max_toke... | /sagemaker_data_insights-0.4.0-py3-none-any.whl/sagemaker_data_insights/patterns/analyze_patterns.py | 0.943439 | 0.796253 | analyze_patterns.py | pypi |
from typing import Any
class AverageAccumulator:
"""An aggregator class to maintain an average."""
def __init__(self):
self.sum = 0.0
self.n = 0
def value(self) -> float:
"""Returns the average based on currently obtained data. If there is no data, returns 0.
Returns:
... | /sagemaker_data_insights-0.4.0-py3-none-any.whl/sagemaker_data_insights/patterns/utils.py | 0.909652 | 0.617455 | utils.py | pypi |
import re
from typing import List, Type, Tuple, Optional
LengthSpecifier = Tuple[int, int]
class Token:
"""A class to store tokens for pattern recognition..
This class represents a single token and contains information to generate
the equivalent regular expression for this token. The reason we have thi... | /sagemaker_data_insights-0.4.0-py3-none-any.whl/sagemaker_data_insights/patterns/tokens.py | 0.838481 | 0.574156 | tokens.py | pypi |
import logging
import pandas as pd
from sagemaker_data_insights.column_data_insights.utils import _get_transformed_col_data
from sagemaker_data_insights.const import FeatureType as ft
from sagemaker_data_insights.analyze_feature import get_feature_type, missing_ratio, valid_ratio
from .constants import ColumnDataInsig... | /sagemaker_data_insights-0.4.0-py3-none-any.whl/sagemaker_data_insights/column_data_insights/column_insights_data.py | 0.794664 | 0.338473 | column_insights_data.py | pypi |
import pandas as pd
import numpy as np
from sagemaker_data_insights.const import FeatureType as ft
from sagemaker_data_insights.analyze_feature import (
get_feature_transform_and_transformed_x,
get_valid_transformed_data,
_numpy_conversion,
)
from sagemaker_data_insights.calc_stats_pandas_series import _ca... | /sagemaker_data_insights-0.4.0-py3-none-any.whl/sagemaker_data_insights/column_data_insights/utils.py | 0.717111 | 0.716739 | utils.py | pypi |
import logging
import numpy as np
from sagemaker_data_insights.const import FeatureType as ft
def get_feature_type(metrics: dict, allowed_types: list = None, prefer_categorical=False) -> tuple:
"""
Feature type analyzer
Parameters
----------
metrics : dict
must include all the following k... | /sagemaker_data_insights-0.4.0-py3-none-any.whl/sagemaker_data_insights/profilers/type_inference.py | 0.792223 | 0.635477 | type_inference.py | pypi |
import pandas as pd
import numpy as np
from sagemaker_data_insights.const import FeatureType as ft
def _numpy_conversion(x: pd.Series, y: pd.Series = None) -> tuple:
"""
Converts original pandas column data to numpy and excludes null value.
Parameters
----------
x : pandas.Series
raw col... | /sagemaker_data_insights-0.4.0-py3-none-any.whl/sagemaker_data_insights/utils/column_utils.py | 0.784113 | 0.580233 | column_utils.py | pypi |
import logging
import scipy
import pandas as pd
import numpy as np
from sagemaker_data_insights.const import INSIGHTS, TaskType as tt
from sagemaker_data_insights.analyzers.insights.utils import get_label_encoder
from sagemaker_data_insights.analyzers.insights.model_insights import regression_insights, classification_... | /sagemaker_data_insights-0.4.0-py3-none-any.whl/sagemaker_data_insights/analyzers/target_column_analyzer.py | 0.858955 | 0.592814 | target_column_analyzer.py | pypi |
import numpy as np
import pandas as pd
from sagemaker_data_insights.const import INSIGHTS, FeatureType as ft
from sagemaker_data_insights.utils.column_utils import valid_ratio
from sagemaker_data_insights.insights import Insights
from sagemaker_data_insights.text_utils import CharacterStatistics, token_importance
from ... | /sagemaker_data_insights-0.4.0-py3-none-any.whl/sagemaker_data_insights/analyzers/text_analyzer.py | 0.576542 | 0.548613 | text_analyzer.py | pypi |
import pandas as pd
import scipy
from sagemaker_data_insights.insights import Insights
from sagemaker_data_insights.const import INSIGHTS, FeatureType as ft
from sagemaker_data_insights.utils.column_utils import valid_ratio, get_valid_transformed_data
def analyze_numeric_feature(x_transformed: pd.Series, metrics: dic... | /sagemaker_data_insights-0.4.0-py3-none-any.whl/sagemaker_data_insights/analyzers/numeric_analyzer.py | 0.655777 | 0.490968 | numeric_analyzer.py | pypi |
import pandas as pd
import logging
import sagemaker_data_insights.const as cs
from sagemaker_data_insights.const import FeatureType as ft
from sagemaker_data_insights.utils.column_utils import missing_ratio
from sagemaker_data_insights.analyzers.binary_analyzer import analyze_binary_feature
from sagemaker_data_insight... | /sagemaker_data_insights-0.4.0-py3-none-any.whl/sagemaker_data_insights/analyzers/feature_analyzer.py | 0.863593 | 0.635965 | feature_analyzer.py | pypi |
import numpy as np
from sagemaker_data_insights.utils.column_utils import valid_ratio
from sagemaker_data_insights.const import INSIGHTS, FeatureType as ft
from sagemaker_data_insights.histogram_functions import calc_frequent_elements, calc_robust_histogram
def analyze_datetime_feature(
feature_transform, x_trans... | /sagemaker_data_insights-0.4.0-py3-none-any.whl/sagemaker_data_insights/analyzers/datetime_analyzer.py | 0.668015 | 0.509581 | datetime_analyzer.py | pypi |
from enum import Enum
import numpy as np
import pandas as pd
from sagemaker_data_insights.insights import Insights
from sagemaker_data_insights.const import TaskType as tt
def regression_insights(outliers_ratio, skew, kurtosis, labels, label_counts, metrics):
insights = []
if outliers_ratio > 0:
if a... | /sagemaker_data_insights-0.4.0-py3-none-any.whl/sagemaker_data_insights/analyzers/insights/model_insights.py | 0.585457 | 0.338569 | model_insights.py | pypi |
import numpy as np
def _encode_numpy(values, uniques=None, encode=False, check_unknown=True):
# only used in _encode below, see docstring there for details
if uniques is None:
if encode:
uniques, encoded = np.unique(values, return_inverse=True)
return uniques, encoded
e... | /sagemaker_data_insights-0.4.0-py3-none-any.whl/sagemaker_data_insights/analyzers/insights/sklearn_utils.py | 0.793186 | 0.693479 | sklearn_utils.py | pypi |
import numpy as np
import pandas as pd
import warnings
from sagemaker_data_insights.const import TaskType as tt
from sagemaker_sklearn_extension.impute import RobustImputer
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.preprocessing import LabelEncoder
from sagemaker_data_insights.analyzers.ins... | /sagemaker_data_insights-0.4.0-py3-none-any.whl/sagemaker_data_insights/analyzers/insights/utils.py | 0.856962 | 0.719876 | utils.py | pypi |
from pyspark.sql import DataFrame
from sagemaker_data_insights.const import DeequFeatureType as ft, INSIGHTS
from sagemaker_data_insights.analyzers.spark_engine.numeric_analyzer import analyze_numeric_feature
from sagemaker_data_insights.analyzers.spark_engine.string_analyzer import analyze_string_feature
from sagemake... | /sagemaker_data_insights-0.4.0-py3-none-any.whl/sagemaker_data_insights/analyzers/spark_engine/feature_analyzer.py | 0.744749 | 0.534309 | feature_analyzer.py | pypi |
import logging
import ipywidgets as widgets
import pandas as pd
class ToggleWidget(widgets.VBox):
"""
Toggle display between the datawrangler widget and the pandas default display
"""
def __init__(
self, df, dw_widget_vbox, pandas_default_vbox, displaying_datawrangler=True
):
sup... | /sagemaker_datawrangler-0.4.3-py3-none-any.whl/sagemaker_datawrangler/config/toggle_widget.py | 0.494873 | 0.195671 | toggle_widget.py | pypi |
import json
import traceback
from collections import Counter
from enum import Enum
from typing import List
from .logging import ERROR
from .platform import APP_CONTEXT
class EventStatus(Enum):
"""
API event status options for OE logging
"""
START = "start"
FAILED = "failed"
SUCCESS = "succes... | /sagemaker_datawrangler-0.4.3-py3-none-any.whl/sagemaker_datawrangler/logging/metrics.py | 0.612541 | 0.215186 | metrics.py | pypi |
import json
import os
from sagemaker_datawrangler._version import version_info
LL_INTERNAL_METADATA_FILE = "/opt/.sagemakerinternal/internal-metadata.json"
KGW_APP_METADATA_FILE = "/opt/ml/metadata/resource-metadata.json"
PROD = "prod"
DEVO = "devo"
def _get_studio_metadata():
"""Read Studio metadata file from... | /sagemaker_datawrangler-0.4.3-py3-none-any.whl/sagemaker_datawrangler/logging/platform.py | 0.557604 | 0.241579 | platform.py | pypi |
import logging
import traceback
from copy import deepcopy
import pandas as pd
from joblib import Parallel, delayed
from sagemaker_datawrangler.logging.logging import get_metrics_logger
from sagemaker_datawrangler.logging.metrics import (
MetricsEventType,
create_structured_error_log,
)
from .data_quality_ins... | /sagemaker_datawrangler-0.4.3-py3-none-any.whl/sagemaker_datawrangler/insights/column_insights.py | 0.442155 | 0.327346 | column_insights.py | pypi |
from typing import List
import pandas as pd
from sagemaker_datawrangler.transformers.utils import get_rare_categories
from .feature_column_insights_schema import FEATURE_COLUMN_INSIGHTS_INFO
from .insights_constants import (
Insights,
InsightsInfo,
InsightsSeverity,
InsightsThresholds,
)
from .target... | /sagemaker_datawrangler-0.4.3-py3-none-any.whl/sagemaker_datawrangler/insights/data_quality_insights.py | 0.425128 | 0.211559 | data_quality_insights.py | pypi |
import logging
import pandas as pd
from sagemaker_datawrangler.insights.data_quality_insights import ColumnInsight, Warning
from sagemaker_datawrangler.logging.logging import get_metrics_logger
from sagemaker_datawrangler.logging.metrics import (
MetricsEventType,
create_structured_error_log,
)
metrics_logge... | /sagemaker_datawrangler-0.4.3-py3-none-any.whl/sagemaker_datawrangler/insights/target_column_insights.py | 0.496582 | 0.261987 | target_column_insights.py | pypi |
from sagemaker_datawrangler.transformers.constants import OPERATORS, TRANSFORMER_NAMES
from .insights_constants import Insights, InsightsInfo, InsightsSeverity
# Insights related to target column
TARGET_COLUMN_INSIGHTS_INFO = {
Insights.SKEWED_TARGET: {
"name": "Skewness in target",
"description":... | /sagemaker_datawrangler-0.4.3-py3-none-any.whl/sagemaker_datawrangler/insights/target_column_insights_schema.py | 0.621885 | 0.610134 | target_column_insights_schema.py | pypi |
"""Contains the SageMaker Experiment class."""
from smexperiments import _base_types, api_types, trial, _utils, trial_component
import time
class Experiment(_base_types.Record):
"""
An Amazon SageMaker experiment, which is a collection of related trials.
New experiments are created by calling :meth:`~sme... | /sagemaker_experiments-0.1.45-py3-none-any.whl/smexperiments/experiment.py | 0.936742 | 0.336086 | experiment.py | pypi |
"""Placeholder docstring"""
from smexperiments import _boto_functions, _utils
class ApiObject(object):
"""
A Python class representation of a boto API object. Converts boto dicts of 'UpperCamelCase' names
to dicts into/from a Python object with standard python members. Clients invoke to_boto on an instan... | /sagemaker_experiments-0.1.45-py3-none-any.whl/smexperiments/_base_types.py | 0.795777 | 0.483648 | _base_types.py | pypi |
"""Metrics module"""
import datetime
import json
import logging
import os
import time
import dateutil.tz
METRICS_DIR = os.environ.get("SAGEMAKER_METRICS_DIRECTORY", ".")
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class SageMakerFileMetricsWriter(object):
"""Writes metric data... | /sagemaker_experiments-0.1.45-py3-none-any.whl/smexperiments/metrics.py | 0.859958 | 0.178848 | metrics.py | pypi |
"""Contains API objects for SageMaker experiments."""
import numbers
from smexperiments import _base_types
class ExperimentSummary(_base_types.ApiObject):
"""Summary model of an experiment.
Attributes:
experiment_arn (str): ARN of the experiment.
experiment_name (str): Name of the experiment... | /sagemaker_experiments-0.1.45-py3-none-any.whl/smexperiments/api_types.py | 0.914037 | 0.410756 | api_types.py | pypi |
"""Contains the TrialComponent class."""
from smexperiments import _base_types, api_types, trial
import time
class TrialComponent(_base_types.Record):
"""This class represents a SageMaker trial component object.
A trial component is a stage in a trial.
Trial components are created automatically within t... | /sagemaker_experiments-0.1.45-py3-none-any.whl/smexperiments/trial_component.py | 0.924202 | 0.441492 | trial_component.py | pypi |
"""Contains the Trial class."""
from smexperiments import api_types, _base_types, trial_component, _utils, tracker
import time
class Trial(_base_types.Record):
"""
An execution of a data-science workflow with an experiment.
Consists of a list of trial component objects, which document individual activit... | /sagemaker_experiments-0.1.45-py3-none-any.whl/smexperiments/trial.py | 0.947247 | 0.363647 | trial.py | pypi |
"""Placeholder docstring"""
import re
def to_camel_case(snake_case):
"""Convert a snake case string to camel case.
Args:
snake_case (str): String to convert to camel case.
Returns:
str: String converted to camel case.
"""
return "".join([x.title() for x in snake_case.split("_")])... | /sagemaker_experiments-0.1.45-py3-none-any.whl/smexperiments/_boto_functions.py | 0.856317 | 0.551393 | _boto_functions.py | pypi |
"""Simplify Search Expression by provide a simplified DSL"""
from smexperiments._base_types import ApiObject
from enum import Enum, unique
@unique
class Operator(Enum):
"""Search operators"""
EQUALS = "Equals"
NOT_EQUALS = "NotEquals"
GREATER_THAN = "GreaterThan"
GREATER_THAN_OR_EQUAL = "GreaterT... | /sagemaker_experiments-0.1.45-py3-none-any.whl/smexperiments/search_expression.py | 0.926195 | 0.371735 | search_expression.py | pypi |
import os
import random
from datetime import datetime
import boto3
import botocore
import logging
from importlib import import_module
def sagemaker_client():
"""Instantiates a SageMaker client.
Returns:
SageMaker.Client
"""
if os.environ.get("SAGEMAKER_ENDPOINT", "").strip():
return ... | /sagemaker_experiments-0.1.45-py3-none-any.whl/smexperiments/_utils.py | 0.64713 | 0.154695 | _utils.py | pypi |
import enum
import json
import os
import time
from smexperiments import trial_component
TRAINING_JOB_ARN_ENV = "TRAINING_JOB_ARN"
PROCESSING_JOB_CONFIG_PATH = "/opt/ml/config/processingjobconfig.json"
class EnvironmentType(enum.Enum):
"""SageMaker jobs which data can be pulled from the environment."""
Sage... | /sagemaker_experiments-0.1.45-py3-none-any.whl/smexperiments/_environment.py | 0.650245 | 0.21498 | _environment.py | pypi |
import string
from typing import List
from pyspark.sql import DataFrame
from feature_store_pyspark.wrapper import SageMakerFeatureStoreJavaWrapper
class FeatureStoreManager(SageMakerFeatureStoreJavaWrapper):
"""A central manager for fature store data reporitory.
``ingest_data`` can be used to do batch data... | /sagemaker_feature_store_pyspark_3.0-1.1.2.tar.gz/sagemaker_feature_store_pyspark_3.0-1.1.2/src/feature_store_pyspark/FeatureStoreManager.py | 0.889235 | 0.561876 | FeatureStoreManager.py | pypi |
import string
from typing import List
from pyspark.sql import DataFrame
from feature_store_pyspark.wrapper import SageMakerFeatureStoreJavaWrapper
class FeatureStoreManager(SageMakerFeatureStoreJavaWrapper):
"""A central manager for fature store data reporitory.
``ingest_data`` can be used to do batch data... | /sagemaker_feature_store_pyspark_3.1-1.1.2.tar.gz/sagemaker_feature_store_pyspark_3.1-1.1.2/src/feature_store_pyspark/FeatureStoreManager.py | 0.889235 | 0.561876 | FeatureStoreManager.py | pypi |
import string
from typing import List
from pyspark.sql import DataFrame
from feature_store_pyspark.wrapper import SageMakerFeatureStoreJavaWrapper
class FeatureStoreManager(SageMakerFeatureStoreJavaWrapper):
"""A central manager for fature store data reporitory.
``ingest_data`` can be used to do batch data... | /sagemaker_feature_store_pyspark_3.2-1.1.2.tar.gz/sagemaker_feature_store_pyspark_3.2-1.1.2/src/feature_store_pyspark/FeatureStoreManager.py | 0.889235 | 0.561876 | FeatureStoreManager.py | pypi |
import string
from typing import List
from pyspark.sql import DataFrame
from feature_store_pyspark.wrapper import SageMakerFeatureStoreJavaWrapper
class FeatureStoreManager(SageMakerFeatureStoreJavaWrapper):
"""A central manager for fature store data reporitory.
``ingest_data`` can be used to do batch data... | /sagemaker_feature_store_pyspark_3.3-1.1.2.tar.gz/sagemaker_feature_store_pyspark_3.3-1.1.2/src/feature_store_pyspark/FeatureStoreManager.py | 0.889235 | 0.561876 | FeatureStoreManager.py | pypi |
import string
from typing import List
from pyspark.sql import DataFrame
from feature_store_pyspark.wrapper import SageMakerFeatureStoreJavaWrapper
class FeatureStoreManager(SageMakerFeatureStoreJavaWrapper):
"""A central manager for fature store data reporitory.
``ingest_data`` can be used to do batch data... | /sagemaker_feature_store_pyspark-1.1.2.tar.gz/sagemaker_feature_store_pyspark-1.1.2/src/feature_store_pyspark/FeatureStoreManager.py | 0.889235 | 0.561876 | FeatureStoreManager.py | pypi |
import importlib.util
import json
import logging
import os
from pathlib import Path
from typing import Optional
from huggingface_hub import HfApi
from huggingface_hub.file_download import cached_download, hf_hub_url
from transformers import pipeline
from transformers.file_utils import is_tf_available, is_torch_availab... | /sagemaker_huggingface_inference_toolkit-2.2.0-py3-none-any.whl/sagemaker_huggingface_inference_toolkit/transformers_utils.py | 0.762159 | 0.22946 | transformers_utils.py | pypi |
import base64
import csv
import datetime
import json
from io import BytesIO, StringIO
import numpy as np
from sagemaker_inference import errors
from sagemaker_inference.decoder import _npy_to_numpy
from sagemaker_inference.encoder import _array_to_npy
from mms.service import PredictionException
from PIL import Image
... | /sagemaker_huggingface_inference_toolkit-2.2.0-py3-none-any.whl/sagemaker_huggingface_inference_toolkit/decoder_encoder.py | 0.789518 | 0.252021 | decoder_encoder.py | pypi |
"""This module contains functionality for converting array-like objects
to various types of objects and files."""
from __future__ import absolute_import
import json
import numpy as np
from six import BytesIO, StringIO
from sagemaker_inference import content_types, errors
def _array_to_json(array_like):
"""Conv... | /sagemaker_inference-1.10.0.tar.gz/sagemaker_inference-1.10.0/src/sagemaker_inference/encoder.py | 0.924858 | 0.603319 | encoder.py | pypi |
"""This module contains custom exceptions."""
from __future__ import absolute_import
import textwrap
class UnsupportedFormatError(Exception):
"""Exception used to indicate that an unsupported content type was provided."""
def __init__(self, content_type, **kwargs):
self._message = textwrap.dedent(
... | /sagemaker_inference-1.10.0.tar.gz/sagemaker_inference-1.10.0/src/sagemaker_inference/errors.py | 0.898003 | 0.193052 | errors.py | pypi |
from __future__ import absolute_import
import re
CONTENT_TYPE_REGEX = re.compile("^[Cc]ontent-?[Tt]ype")
def read_file(path, mode="r"):
"""Read data from a file.
Args:
path (str): path to the file.
mode (str): mode which the file will be open.
Returns:
(str): contents of the fi... | /sagemaker_inference-1.10.0.tar.gz/sagemaker_inference-1.10.0/src/sagemaker_inference/utils.py | 0.844665 | 0.359926 | utils.py | pypi |
import textwrap
from sagemaker_inference import decoder, encoder, errors, utils
class DefaultInferenceHandler(object):
"""Bare-bones implementation of default inference functions."""
def default_model_fn(self, model_dir, context=None):
"""Function responsible for loading the model.
Args:
... | /sagemaker_inference-1.10.0.tar.gz/sagemaker_inference-1.10.0/src/sagemaker_inference/default_inference_handler.py | 0.884881 | 0.33035 | default_inference_handler.py | pypi |
"""This module contains functionality for converting various types of
files and objects to NumPy arrays."""
from __future__ import absolute_import
import json
import numpy as np
import scipy.sparse
from six import BytesIO, StringIO
from sagemaker_inference import content_types, errors
def _json_to_numpy(string_lik... | /sagemaker_inference-1.10.0.tar.gz/sagemaker_inference-1.10.0/src/sagemaker_inference/decoder.py | 0.927552 | 0.606469 | decoder.py | pypi |
"""This module contains functionality for the default handler service."""
from __future__ import absolute_import
import os
from sagemaker_inference.transformer import Transformer
PYTHON_PATH_ENV = "PYTHONPATH"
class DefaultHandlerService(object):
"""Default handler service that is executed by the model server.... | /sagemaker_inference-1.10.0.tar.gz/sagemaker_inference-1.10.0/src/sagemaker_inference/default_handler_service.py | 0.884713 | 0.296374 | default_handler_service.py | pypi |
from __future__ import absolute_import
import os
import mxnet as mx
from sagemaker_inference import (
content_types,
decoder,
default_inference_handler,
encoder,
errors,
)
from sagemaker_mxnet_serving_container.utils import (
get_default_context,
parse_accept,
read_data_shapes,
)
PRE... | /sagemaker_mxnet_inference-1.5.5.tar.gz/sagemaker_mxnet_inference-1.5.5/src/sagemaker_mxnet_serving_container/default_inference_handler.py | 0.775009 | 0.276376 | default_inference_handler.py | pypi |
from abc import ABCMeta, abstractmethod
from pyspark import keyword_only
from pyspark.ml.util import Identifiable
from pyspark.ml.wrapper import JavaEstimator
from sagemaker_pyspark import SageMakerJavaWrapper, RandomNamePolicyFactory, SageMakerClients, \
IAMRoleFromConfig, S3AutoCreatePath, Option
_sagemaker_s... | /sagemaker_pyspark-1.4.5.tar.gz/sagemaker_pyspark-1.4.5/src/sagemaker_pyspark/SageMakerEstimator.py | 0.914624 | 0.412116 | SageMakerEstimator.py | pypi |
from pyspark import keyword_only
from pyspark.ml.util import Identifiable
from pyspark.ml.wrapper import JavaModel
from sagemaker_pyspark import (SageMakerJavaWrapper, Option, EndpointCreationPolicy,
RandomNamePolicy, SageMakerClients)
class SageMakerModel(SageMakerJavaWrapper, JavaMo... | /sagemaker_pyspark-1.4.5.tar.gz/sagemaker_pyspark-1.4.5/src/sagemaker_pyspark/SageMakerModel.py | 0.927573 | 0.343755 | SageMakerModel.py | pypi |
from abc import ABCMeta
from sagemaker_pyspark import SageMakerJavaWrapper, Option
class RequestRowSerializer(SageMakerJavaWrapper):
__metaclass__ = ABCMeta
def setSchema(self, schema):
"""
Sets the rowSchema for this RequestRowSerializer.
Args:
schema (StructType): the ... | /sagemaker_pyspark-1.4.5.tar.gz/sagemaker_pyspark-1.4.5/src/sagemaker_pyspark/transformation/serializers/serializers.py | 0.906712 | 0.163445 | serializers.py | pypi |
from pyspark.ml.param import Params, Param, TypeConverters
from pyspark.ml.util import Identifiable
from sagemaker_pyspark import (SageMakerEstimatorBase, S3AutoCreatePath, Option, IAMRoleFromConfig,
EndpointCreationPolicy, SageMakerClients, RandomNamePolicyFactory)
from sagemaker_pyspa... | /sagemaker_pyspark-1.4.5.tar.gz/sagemaker_pyspark-1.4.5/src/sagemaker_pyspark/algorithms/PCASageMakerEstimator.py | 0.893176 | 0.610308 | PCASageMakerEstimator.py | pypi |
from pyspark.ml.param import Params, Param, TypeConverters
from pyspark.ml.util import Identifiable
from sagemaker_pyspark import (SageMakerEstimatorBase, S3AutoCreatePath, Option, IAMRoleFromConfig,
EndpointCreationPolicy, SageMakerClients, RandomNamePolicyFactory)
from sagemaker_pysp... | /sagemaker_pyspark-1.4.5.tar.gz/sagemaker_pyspark-1.4.5/src/sagemaker_pyspark/algorithms/LDASageMakerEstimator.py | 0.911906 | 0.573738 | LDASageMakerEstimator.py | pypi |
import numbers
from pyspark.ml.param import Params, Param, TypeConverters
from pyspark.ml.util import Identifiable
from sagemaker_pyspark import (SageMakerEstimatorBase, S3AutoCreatePath, Option, IAMRoleFromConfig,
EndpointCreationPolicy, SageMakerClients, RandomNamePolicyFactory)
from... | /sagemaker_pyspark-1.4.5.tar.gz/sagemaker_pyspark-1.4.5/src/sagemaker_pyspark/algorithms/KMeansSageMakerEstimator.py | 0.866345 | 0.557002 | KMeansSageMakerEstimator.py | pypi |
from pyspark.ml.param import Params, Param, TypeConverters
from pyspark.ml.util import Identifiable
from sagemaker_pyspark import (SageMakerEstimatorBase, S3AutoCreatePath, Option, IAMRoleFromConfig,
EndpointCreationPolicy, SageMakerClients, RandomNamePolicyFactory)
from sagemaker_pyspa... | /sagemaker_pyspark-1.4.5.tar.gz/sagemaker_pyspark-1.4.5/src/sagemaker_pyspark/algorithms/XGBoostSageMakerEstimator.py | 0.899055 | 0.64777 | XGBoostSageMakerEstimator.py | pypi |
import numbers
from pyspark.ml.param import Params, Param, TypeConverters
from pyspark.ml.util import Identifiable
from sagemaker_pyspark import (SageMakerEstimatorBase, S3AutoCreatePath, Option, IAMRoleFromConfig,
EndpointCreationPolicy, SageMakerClients, RandomNamePolicyFactory)
from ... | /sagemaker_pyspark-1.4.5.tar.gz/sagemaker_pyspark-1.4.5/src/sagemaker_pyspark/algorithms/LinearLearnerSageMakerEstimator.py | 0.80456 | 0.497986 | LinearLearnerSageMakerEstimator.py | pypi |
from pyspark.ml.param import Params, Param, TypeConverters
from pyspark.ml.util import Identifiable
from sagemaker_pyspark import (SageMakerEstimatorBase, S3AutoCreatePath, Option, IAMRoleFromConfig,
EndpointCreationPolicy, SageMakerClients, RandomNamePolicyFactory)
from sagemaker_pyspa... | /sagemaker_pyspark-1.4.5.tar.gz/sagemaker_pyspark-1.4.5/src/sagemaker_pyspark/algorithms/FactorizationMachinesSageMakerEstimator.py | 0.775817 | 0.563678 | FactorizationMachinesSageMakerEstimator.py | pypi |
from __future__ import absolute_import
import os
import torch
from sagemaker_inference import (
content_types,
decoder,
default_inference_handler,
encoder,
errors,
utils,
)
INFERENCE_ACCELERATOR_PRESENT_ENV = "SAGEMAKER_INFERENCE_ACCELERATOR_PRESENT"
DEFAULT_MODEL_FILENAME = "model.pt"
clas... | /sagemaker_pytorch_inference-2.0.17.tar.gz/sagemaker_pytorch_inference-2.0.17/src/sagemaker_pytorch_serving_container/default_pytorch_inference_handler.py | 0.853211 | 0.231245 | default_pytorch_inference_handler.py | pypi |
from __future__ import absolute_import
from sagemaker_pytorch_serving_container import ts_parameters
import os
import logging
logger = logging.getLogger()
DEFAULT_TS_BATCH_SIZE = 1
DEFAULT_TS_MAX_BATCH_DELAY = 100
DEFAULT_TS_MIN_WORKERS = 1
DEFAULT_TS_MAX_WORKERS = 1
DEFAULT_TS_RESPONSE_TIMEOUT = 60
class TorchSe... | /sagemaker_pytorch_inference-2.0.17.tar.gz/sagemaker_pytorch_inference-2.0.17/src/sagemaker_pytorch_serving_container/ts_environment.py | 0.818338 | 0.28077 | ts_environment.py | pypi |
"""This module contains functionality to configure and start Torchserve."""
from __future__ import absolute_import
import os
import signal
import subprocess
import pkg_resources
import psutil
import logging
from retrying import retry
import sagemaker_pytorch_serving_container
from sagemaker_pytorch_serving_container... | /sagemaker_pytorch_inference-2.0.17.tar.gz/sagemaker_pytorch_inference-2.0.17/src/sagemaker_pytorch_serving_container/torchserve.py | 0.649023 | 0.198316 | torchserve.py | pypi |
from __future__ import absolute_import
import os
import logging
from retrying import retry
import six
import socket
import sys
from sagemaker_training import entry_point, environment, errors, runner
MASTER_PORT = '7777'
LAUNCH_SMDATAPARALLEL_ENV_NAME = 'sagemaker_distributed_dataparallel_enabled'
LAUNCH_MPI_ENV_NAME =... | /sagemaker_pytorch_training-2.8.0.tar.gz/sagemaker_pytorch_training-2.8.0/src/sagemaker_pytorch_container/training.py | 0.621885 | 0.246273 | training.py | pypi |
from typing import Any, List, Union
from sagemaker_rightline.model import Rule, ValidationResult
class Equals(Rule):
"""Check if two lists are equal."""
def __init__(self, negative: bool = False) -> None:
"""Check if two lists are equal.
:param negative: whether the rule should be inverted,... | /sagemaker_rightline-0.3.6-py3-none-any.whl/sagemaker_rightline/rules.py | 0.957507 | 0.620277 | rules.py | pypi |
import logging
import re
from abc import ABC, abstractmethod
from copy import copy
from dataclasses import dataclass
from operator import attrgetter
from typing import Any, Iterable, List, Optional, Union
import pandas as pd
from sagemaker.workflow.pipeline import Pipeline
@dataclass
class ValidationResult:
"""V... | /sagemaker_rightline-0.3.6-py3-none-any.whl/sagemaker_rightline/model.py | 0.859929 | 0.456228 | model.py | pypi |
SageMaker Scikit-Learn Extension
================================
.. image:: https://img.shields.io/badge/License-Apache%202.0-blue.svg
:target: https://opensource.org/licenses/Apache-2.0
:alt: License
.. image:: https://img.shields.io/pypi/v/sagemaker-scikit-learn-extension.svg
:target: https://pypi.python.... | /sagemaker-scikit-learn-extension-2.5.0.tar.gz/sagemaker-scikit-learn-extension-2.5.0/README.rst | 0.937719 | 0.651805 | README.rst | pypi |
import torch
from torch import nn
import numpy as np
class LambdaLogSoftmax(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, *args, **kwargs):
return nn.functional.log_softmax(dim=self.dim, *args, **kwargs)
class GBN(torch.nn.Module):
"""
... | /sagemaker-scikit-learn-extension-2.5.0.tar.gz/sagemaker-scikit-learn-extension-2.5.0/src/sagemaker_sklearn_extension/contrib/taei/nn_utils.py | 0.937318 | 0.453262 | nn_utils.py | pypi |
import numpy as np
class StarOversampler:
"""
Implementation of the oversampler proposed in [1] using the `star` topology. The implementation is based on the
implementation of https://github.com/analyticalmindsltd/smote_variants
Parameters
----------
proportion: float (default = 1)
pr... | /sagemaker-scikit-learn-extension-2.5.0.tar.gz/sagemaker-scikit-learn-extension-2.5.0/src/sagemaker_sklearn_extension/contrib/taei/star_oversampler.py | 0.915474 | 0.901227 | star_oversampler.py | pypi |
from abc import abstractmethod
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import TensorDataset
from torch.optim.lr_scheduler import MultiplicativeLR
from .nn_utils import GBN, LambdaLogSoftmax, weight_init, EmbeddingGenerator
class BaseModel(nn.Module):
"""
Base class for all... | /sagemaker-scikit-learn-extension-2.5.0.tar.gz/sagemaker-scikit-learn-extension-2.5.0/src/sagemaker_sklearn_extension/contrib/taei/models.py | 0.961061 | 0.490419 | models.py | pypi |
import os
from math import ceil
import numpy as np
import pandas as pd
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.utils.validation import check_array, check_is_fitted
from tsfresh import extract_features
from tsfresh.feature_extraction import ComprehensiveFCParameters
from tsfresh.feature_ex... | /sagemaker-scikit-learn-extension-2.5.0.tar.gz/sagemaker-scikit-learn-extension-2.5.0/src/sagemaker_sklearn_extension/feature_extraction/sequences.py | 0.841663 | 0.439447 | sequences.py | pypi |
import numpy as np
import scipy.sparse as sp
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.feature_extraction.text import VectorizerMixin, TfidfVectorizer
from sklearn.utils.validation import check_array, check_is_fitted
class MultiColumnTfidfVectorizer(BaseEstimator, VectorizerMixin, Transf... | /sagemaker-scikit-learn-extension-2.5.0.tar.gz/sagemaker-scikit-learn-extension-2.5.0/src/sagemaker_sklearn_extension/feature_extraction/text.py | 0.937619 | 0.615406 | text.py | pypi |
import json
import os
from abc import ABC, abstractmethod
from sys import getsizeof
import mlio
from mlio.integ.numpy import as_numpy
import numpy as np
import psutil
def _convert_bytes_to_megabytes(b):
"""Converts bytes to megabytes"""
return b / 1000 ** 2
def _convert_megabytes_to_bytes(mb):
"""Conv... | /sagemaker-scikit-learn-extension-2.5.0.tar.gz/sagemaker-scikit-learn-extension-2.5.0/src/sagemaker_sklearn_extension/externals/read_data.py | 0.771672 | 0.520374 | read_data.py | pypi |
import numpy as np
from scipy.sparse import isspmatrix
from sklearn.base import BaseEstimator
from sklearn.base import TransformerMixin
class AutoMLTransformer(BaseEstimator, TransformerMixin):
"""Utility class encapsulating feature and target transformation functionality used in AutoML pipelines.
Paramete... | /sagemaker-scikit-learn-extension-2.5.0.tar.gz/sagemaker-scikit-learn-extension-2.5.0/src/sagemaker_sklearn_extension/externals/automl_transformer.py | 0.948656 | 0.775137 | automl_transformer.py | pypi |
from itertools import combinations
import numpy as np
from scipy.sparse import issparse
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.preprocessing import StandardScaler
from sklearn.utils import check_array
from sklearn.utils import check_random_state
from sklearn.utils.validation import che... | /sagemaker-scikit-learn-extension-2.5.0.tar.gz/sagemaker-scikit-learn-extension-2.5.0/src/sagemaker_sklearn_extension/preprocessing/data.py | 0.939004 | 0.687007 | data.py | pypi |
import numpy as np
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.utils.validation import check_array, check_is_fitted
from sklearn.preprocessing import QuantileTransformer, quantile_transform
def log_transform(x):
"""Apply a log-like transformation.
The transformation is log(x + 1) ... | /sagemaker-scikit-learn-extension-2.5.0.tar.gz/sagemaker-scikit-learn-extension-2.5.0/src/sagemaker_sklearn_extension/preprocessing/base.py | 0.960593 | 0.761006 | base.py | pypi |
import numpy as np
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.impute import MissingIndicator, SimpleImputer
from sklearn.utils.validation import check_array, check_is_fitted
def is_finite_numeric(arr):
"""Helper function to check if values in an array can be converted to finite numeri... | /sagemaker-scikit-learn-extension-2.5.0.tar.gz/sagemaker-scikit-learn-extension-2.5.0/src/sagemaker_sklearn_extension/impute/base.py | 0.893675 | 0.697763 | base.py | pypi |
import json
import logging
import os
from typing import Any
logger = logging.getLogger(__name__)
STDOUT_LEVEL = logging.INFO
class JSONFormatter(logging.Formatter):
def format(self, record: logging.LogRecord) -> str:
"""
Create a structured log message
CloudWatch does not separate log s... | /sagemaker_shim-0.1.1-py3-none-any.whl/sagemaker_shim/logging.py | 0.601008 | 0.223377 | logging.py | pypi |
import logging
import re
import zipfile
from os.path import commonpath
from pathlib import Path
from sagemaker_shim.exceptions import ZipExtractionError
from sagemaker_shim.vendor.werkzeug.security import safe_join
logger = logging.getLogger(__name__)
def _filter_members(members: list[zipfile.ZipInfo]) -> list[dict... | /sagemaker_shim-0.1.1-py3-none-any.whl/sagemaker_shim/utils.py | 0.405213 | 0.208884 | utils.py | pypi |
from typing import Union
import requests
import socket
from ssl import get_server_certificate, SSLError
from sagemaker_studio_analytics_extension.utils.string_utils import *
from sagemaker_studio_analytics_extension.utils.constants import (
VerifyCertificateArgument,
)
def check_host_and_port(host, port):
... | /sagemaker-studio-analytics-extension-0.0.19.tar.gz/sagemaker-studio-analytics-extension-0.0.19/src/sagemaker_studio_analytics_extension/utils/resource_check.py | 0.659734 | 0.192388 | resource_check.py | pypi |
=====================================
SageMaker TensorFlow Training Toolkit
=====================================
The SageMaker TensorFlow Training Toolkit is an open source library for making the
TensorFlow framework run on `Amazon SageMaker <https://aws.amazon.com/documentation/sagemaker/>`__.
This repository also ... | /sagemaker_tensorflow_training-20.4.1.tar.gz/sagemaker_tensorflow_training-20.4.1/README.rst | 0.932576 | 0.769037 | README.rst | pypi |
from __future__ import absolute_import
import json
import logging
import multiprocessing
import os
import subprocess
import time
from sagemaker_training import entry_point, environment, mapping, runner
import tensorflow as tf
from sagemaker_tensorflow_container import s3_utils
logger = logging.getLogger(__name__)
... | /sagemaker_tensorflow_training-20.4.1.tar.gz/sagemaker_tensorflow_training-20.4.1/src/sagemaker_tensorflow_container/training.py | 0.676727 | 0.221624 | training.py | pypi |
from __future__ import absolute_import
import errno
import json
import os
import tensorflow as tf
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_spec
from tensorflow.pyth... | /sagemaker_tensorflow-2.13.0.1.19.0-cp310-cp310-manylinux1_x86_64.whl/sagemaker_tensorflow/pipemode.py | 0.830181 | 0.236483 | pipemode.py | pypi |
# SageMaker Training Toolkit
[](https://pypi.python.org/pypi/sagemaker-training) [![Supported Python Versions](https://img.shields.io/pyp... | /sagemaker_training-4.7.0.tar.gz/sagemaker_training-4.7.0/README.md | 0.859487 | 0.960435 | README.md | pypi |
"""This module contains utility functions used to generate recordio-protobuf format."""
import struct
import sys
import numpy as np
from scipy.sparse import issparse
from sagemaker_training.record_pb2 import Record
def _resolve_type(dtype):
"""Return the type string corresponding to the numpy.dtype.
Args:
... | /sagemaker_training-4.7.0.tar.gz/sagemaker_training-4.7.0/src/sagemaker_training/recordio.py | 0.822937 | 0.687138 | recordio.py | pypi |
from __future__ import absolute_import
import os
import socket
import sys
from retrying import retry
from sagemaker_training import _entry_point_type, environment, files, modules, runner
def run(
uri,
user_entry_point,
args,
env_vars=None,
wait=True,
capture_error=False,
runner_type=run... | /sagemaker_training-4.7.0.tar.gz/sagemaker_training-4.7.0/src/sagemaker_training/entry_point.py | 0.51879 | 0.155591 | entry_point.py | pypi |
"""This module contains functionality related to distributed training using
PT-XLA (PyTorch - Accelerated Linear Algebra)."""
from __future__ import absolute_import
import os
from sagemaker_training import (
_entry_point_type,
environment,
errors,
logging_config,
process,
)
logger = logging_conf... | /sagemaker_training-4.7.0.tar.gz/sagemaker_training-4.7.0/src/sagemaker_training/pytorch_xla.py | 0.875121 | 0.330498 | pytorch_xla.py | pypi |
from __future__ import absolute_import
import contextlib
import json
import os
import shutil
import tarfile
import tempfile
import boto3
from six.moves.urllib import parse
from sagemaker_training import environment, logging_config, params
logger = logging_config.get_logger()
def write_success_file(): # type: () ... | /sagemaker_training-4.7.0.tar.gz/sagemaker_training-4.7.0/src/sagemaker_training/files.py | 0.683525 | 0.185892 | files.py | pypi |
from __future__ import absolute_import
import collections
import collections.abc
import itertools
import json
import six
SplitResultSpec = collections.namedtuple("SplitResultSpec", "included excluded")
def to_env_vars(mapping): # type: (dict) -> dict
"""Transform a dictionary in a dictionary of env vars.
... | /sagemaker_training-4.7.0.tar.gz/sagemaker_training-4.7.0/src/sagemaker_training/mapping.py | 0.905279 | 0.221793 | mapping.py | pypi |
"""This module contains utilities to encode and decode different content types."""
from __future__ import absolute_import
import csv
import io
import json
import numpy as np
from scipy.sparse import issparse
from six import BytesIO, StringIO
from sagemaker_training import content_types, errors
from sagemaker_trainin... | /sagemaker_training-4.7.0.tar.gz/sagemaker_training-4.7.0/src/sagemaker_training/encoders.py | 0.899784 | 0.589746 | encoders.py | pypi |
"""This module contains custom exceptions."""
from __future__ import absolute_import
import textwrap
import six
class ClientError(Exception):
"""Error class used to separate framework and user errors."""
class SMTrainingCompilerConfigurationError(Exception):
"""Error class used to separate configuration e... | /sagemaker_training-4.7.0.tar.gz/sagemaker_training-4.7.0/src/sagemaker_training/errors.py | 0.862453 | 0.154058 | errors.py | pypi |
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