py_version doc fixes

src/sagemaker/fw_utils.py

@@ -10,37 +10,37 @@
 # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
 # ANY KIND, either express or implied. See the License for the specific
 # language governing permissions and limitations under the License.
-"""Utility methods used by framework classes"""
+"""Utility methods used by framework classes."""
 from __future__ import absolute_import
 
 import json
 import logging
 import os
 import re
-import time
 import shutil
 import tempfile
+import time
 from collections import namedtuple
-from typing import List, Optional, Union, Dict
+from typing import Dict, List, Optional, Union
+
 from packaging import version
 
 import sagemaker.image_uris
+import sagemaker.utils
+from sagemaker.deprecations import deprecation_warn_base, renamed_kwargs, renamed_warning
 from sagemaker.instance_group import InstanceGroup
 from sagemaker.s3_utils import s3_path_join
 from sagemaker.session_settings import SessionSettings
-import sagemaker.utils
 from sagemaker.workflow import is_pipeline_variable
-
-from sagemaker.deprecations import renamed_warning, renamed_kwargs
 from sagemaker.workflow.entities import PipelineVariable
-from sagemaker.deprecations import deprecation_warn_base
 
 logger = logging.getLogger(__name__)
 
 _TAR_SOURCE_FILENAME = "source.tar.gz"
 
 UploadedCode = namedtuple("UploadedCode", ["s3_prefix", "script_name"])
 """sagemaker.fw_utils.UploadedCode: An object containing the S3 prefix and script name.
+
 This is for the source code used for the entry point with an ``Estimator``. It can be
 instantiated with positional or keyword arguments.
 """
@@ -211,7 +211,7 @@ def validate_source_code_input_against_pipeline_variables(
     git_config: Optional[Dict[str, str]] = None,
     enable_network_isolation: Union[bool, PipelineVariable] = False,
 ):
-    """Validate source code input against pipeline variables
+    """Validate source code input against pipeline variables.
 
     Args:
         entry_point (str or PipelineVariable): The path to the local Python source file that
@@ -481,7 +481,7 @@ def tar_and_upload_dir(
 
 
 def _list_files_to_compress(script, directory):
-    """Placeholder docstring"""
+    """Placeholder docstring."""
     if directory is None:
         return [script]
 
@@ -585,7 +585,6 @@ def model_code_key_prefix(code_location_key_prefix, model_name, image):
     The location returned is a potential concatenation of 2 parts
         1. code_location_key_prefix if it exists
         2. model_name or a name derived from the image
-
     Args:
         code_location_key_prefix (str): the s3 key prefix from code_location
         model_name (str): the name of the model
@@ -620,8 +619,6 @@ def warn_if_parameter_server_with_multi_gpu(training_instance_type, distribution
                         "enabled": True
                     }
                 }
-
-
     """
     if training_instance_type == "local" or distribution is None:
         return
@@ -646,7 +643,7 @@ def warn_if_parameter_server_with_multi_gpu(training_instance_type, distribution
 def profiler_config_deprecation_warning(
     profiler_config, image_uri, framework_name, framework_version
 ):
-    """Put out a deprecation message for if framework profiling is specified TF >= 2.12 and PT >= 2.0"""
+    """Deprecation message if framework profiling is specified TF >= 2.12 and PT >= 2.0."""
     if profiler_config is None or profiler_config.framework_profile_params is None:
         return
 
@@ -692,6 +689,7 @@ def validate_smdistributed(
         framework_name (str): A string representing the name of framework selected.
         framework_version (str): A string representing the framework version selected.
         py_version (str): A string representing the python version selected.
+        Ex: `py38, py39, py310, py311`
         distribution (dict): A dictionary with information to enable distributed training.
             (Defaults to None if distributed training is not enabled.) For example:
 
@@ -763,7 +761,8 @@ def _validate_smdataparallel_args(
         instance_type (str): A string representing the type of training instance selected. Ex: `ml.p3.16xlarge`
         framework_name (str): A string representing the name of framework selected. Ex: `tensorflow`
         framework_version (str): A string representing the framework version selected. Ex: `2.3.1`
-        py_version (str): A string representing the python version selected. Ex: `py3`
+        py_version (str): A string representing the python version selected.
+        Ex: `py38, py39, py310, py311`
         distribution (dict): A dictionary with information to enable distributed training.
             (Defaults to None if distributed training is not enabled.) Ex:
 
@@ -847,6 +846,7 @@ def validate_distribution(
         framework_name (str): A string representing the name of framework selected.
         framework_version (str): A string representing the framework version selected.
         py_version (str): A string representing the python version selected.
+        Ex: `py38, py39, py310, py311`
         image_uri (str): A string representing a Docker image URI.
         kwargs(dict): Additional kwargs passed to this function
 
@@ -953,7 +953,7 @@ def validate_distribution(
 
 
 def validate_distribution_for_instance_type(instance_type, distribution):
-    """Check if the provided distribution strategy is supported for the instance_type
+    """Check if the provided distribution strategy is supported for the instance_type.
 
     Args:
         instance_type (str): A string representing the type of training instance selected.
@@ -1010,6 +1010,7 @@ def validate_torch_distributed_distribution(
                 }
         framework_version (str): A string representing the framework version selected.
         py_version (str): A string representing the python version selected.
+        Ex: `py38, py39, py310, py311`
         image_uri (str): A string representing a Docker image URI.
         entry_point (str or PipelineVariable): The absolute or relative path to the local Python
             source file that should be executed as the entry point to
@@ -1072,7 +1073,7 @@ def validate_torch_distributed_distribution(
 
 
 def _is_gpu_instance(instance_type):
-    """Returns bool indicating whether instance_type supports GPU
+    """Returns bool indicating whether instance_type supports GPU.
 
     Args:
         instance_type (str): Name of the instance_type to check against.
@@ -1091,7 +1092,7 @@ def _is_gpu_instance(instance_type):
 
 
 def _is_trainium_instance(instance_type):
-    """Returns bool indicating whether instance_type is a Trainium instance
+    """Returns bool indicating whether instance_type is a Trainium instance.
 
     Args:
         instance_type (str): Name of the instance_type to check against.
@@ -1107,7 +1108,7 @@ def _is_trainium_instance(instance_type):
 
 
 def python_deprecation_warning(framework, latest_supported_version):
-    """Placeholder docstring"""
+    """Placeholder docstring."""
     return PYTHON_2_DEPRECATION_WARNING.format(
         framework=framework, latest_supported_version=latest_supported_version
     )
@@ -1121,7 +1122,6 @@ def _region_supports_debugger(region_name):
 
     Returns:
         bool: Whether or not the region supports Amazon SageMaker Debugger.
-
     """
     return region_name.lower() not in DEBUGGER_UNSUPPORTED_REGIONS
 
@@ -1134,7 +1134,6 @@ def _region_supports_profiler(region_name):
 
     Returns:
         bool: Whether or not the region supports Amazon SageMaker Debugger profiling feature.
-
     """
     return region_name.lower() not in PROFILER_UNSUPPORTED_REGIONS
 
@@ -1162,7 +1161,8 @@ def validate_version_or_image_args(framework_version, py_version, image_uri):
 
     Args:
         framework_version (str): The version of the framework.
-        py_version (str): The version of Python.
+        py_version (str): A string representing the python version selected.
+        Ex: `py38, py39, py310, py311`
         image_uri (str): The URI of the image.
 
     Raises:
@@ -1194,9 +1194,8 @@ def create_image_uri(
         instance_type (str): SageMaker instance type. Used to determine device
             type (cpu/gpu/family-specific optimized).
         framework_version (str): The version of the framework.
-        py_version (str): Optional. Python version. If specified, should be one
-            of 'py2' or 'py3'. If not specified, image uri will not include a
-            python component.
+        py_version (str): Optional. Python version Ex: `py38, py39, py310, py311`.
+            If not specified, image uri will not include a python component.
         account (str): AWS account that contains the image. (default:
             '520713654638')
         accelerator_type (str): SageMaker Elastic Inference accelerator type.

src/sagemaker/huggingface/estimator.py

@@ -15,17 +15,13 @@
 
 import logging
 import re
-from typing import Optional, Union, Dict
+from typing import Dict, Optional, Union
 
-from sagemaker.estimator import Framework, EstimatorBase
-from sagemaker.fw_utils import (
-    framework_name_from_image,
-    validate_distribution,
-)
+from sagemaker.estimator import EstimatorBase, Framework
+from sagemaker.fw_utils import framework_name_from_image, validate_distribution
 from sagemaker.huggingface.model import HuggingFaceModel
-from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
-
 from sagemaker.huggingface.training_compiler.config import TrainingCompilerConfig
+from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
 from sagemaker.workflow.entities import PipelineVariable
 
 logger = logging.getLogger("sagemaker")
@@ -66,7 +62,7 @@ def __init__(
         Args:
             py_version (str): Python version you want to use for executing your model training
                 code. Defaults to ``None``. Required unless ``image_uri`` is provided.  If
-                using PyTorch, the current supported version is ``py36``. If using TensorFlow,
+                using PyTorch, the current supported version is ``py39``. If using TensorFlow,
                 the current supported version is ``py37``.
             entry_point (str or PipelineVariable): Path (absolute or relative) to the Python source
                 file which should be executed as the entry point to training.

src/sagemaker/processing.py

@@ -18,51 +18,51 @@
 """
 from __future__ import absolute_import
 
+import logging
 import os
 import pathlib
-import logging
+import re
+from copy import copy
 from textwrap import dedent
 from typing import Dict, List, Optional, Union
-from copy import copy
-import re
 
 import attr
-
 from six.moves.urllib.parse import urlparse
 from six.moves.urllib.request import url2pathname
+
 from sagemaker import s3
+from sagemaker.apiutils._base_types import ApiObject
 from sagemaker.config import (
+    PROCESSING_JOB_ENABLE_NETWORK_ISOLATION_PATH,
+    PROCESSING_JOB_ENVIRONMENT_PATH,
+    PROCESSING_JOB_INTER_CONTAINER_ENCRYPTION_PATH,
     PROCESSING_JOB_KMS_KEY_ID_PATH,
+    PROCESSING_JOB_ROLE_ARN_PATH,
     PROCESSING_JOB_SECURITY_GROUP_IDS_PATH,
     PROCESSING_JOB_SUBNETS_PATH,
-    PROCESSING_JOB_ENABLE_NETWORK_ISOLATION_PATH,
     PROCESSING_JOB_VOLUME_KMS_KEY_ID_PATH,
-    PROCESSING_JOB_ROLE_ARN_PATH,
-    PROCESSING_JOB_INTER_CONTAINER_ENCRYPTION_PATH,
-    PROCESSING_JOB_ENVIRONMENT_PATH,
 )
+from sagemaker.dataset_definition.inputs import DatasetDefinition, S3Input
 from sagemaker.job import _Job
 from sagemaker.local import LocalSession
 from sagemaker.network import NetworkConfig
+from sagemaker.s3 import S3Uploader
+from sagemaker.session import Session
 from sagemaker.utils import (
+    Tags,
     base_name_from_image,
+    check_and_get_run_experiment_config,
+    format_tags,
     get_config_value,
     name_from_base,
-    check_and_get_run_experiment_config,
-    resolve_value_from_config,
     resolve_class_attribute_from_config,
-    Tags,
-    format_tags,
+    resolve_value_from_config,
 )
-from sagemaker.session import Session
 from sagemaker.workflow import is_pipeline_variable
+from sagemaker.workflow.entities import PipelineVariable
+from sagemaker.workflow.execution_variables import ExecutionVariables
 from sagemaker.workflow.functions import Join
 from sagemaker.workflow.pipeline_context import runnable_by_pipeline
-from sagemaker.workflow.execution_variables import ExecutionVariables
-from sagemaker.workflow.entities import PipelineVariable
-from sagemaker.dataset_definition.inputs import S3Input, DatasetDefinition
-from sagemaker.apiutils._base_types import ApiObject
-from sagemaker.s3 import S3Uploader
 
 logger = logging.getLogger(__name__)
 
@@ -1465,7 +1465,7 @@ def __init__(
             instance_type (str or PipelineVariable): The type of EC2 instance to use for
                 processing, for example, 'ml.c4.xlarge'.
             py_version (str): Python version you want to use for executing your
-                model training code. One of 'py2' or 'py3'. Defaults to 'py3'. Value
+                model training code. Ex `py38, py39, py310, py311`. Value
                 is ignored when ``image_uri`` is provided.
             image_uri (str or PipelineVariable): The URI of the Docker image to use for the
                 processing jobs (default: None).