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Merge branch 'master' into fix/sagemaker-session-region-not-being-used
2 parents 18d388c + 0b4faf9 commit e8ad3b3

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‎doc/api/prep_data/feature_store.rst

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@@ -60,6 +60,7 @@ Feature Definition
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:members:
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:show-inheritance:
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Inputs
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******
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@@ -181,9 +182,13 @@ Feature Processor Data Source
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:members:
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:show-inheritance:
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.. autoclass:: sagemaker.feature_store.feature_processor.PySparkDataSource
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:members:
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:show-inheritance:
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Feature Processor Scheduler
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***************************
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Feature Processor Scheduler and Triggers
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****************************************
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.. automethod:: sagemaker.feature_store.feature_processor.to_pipeline
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@@ -196,3 +201,12 @@ Feature Processor Scheduler
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.. automethod:: sagemaker.feature_store.feature_processor.describe
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.. automethod:: sagemaker.feature_store.feature_processor.list_pipelines
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.. automethod:: sagemaker.feature_store.feature_processor.put_trigger
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.. automethod:: sagemaker.feature_store.feature_processor.enable_trigger
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.. automethod:: sagemaker.feature_store.feature_processor.disable_trigger
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.. automethod:: sagemaker.feature_store.feature_processor.delete_trigger
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‎doc/api/training/automlv2.rst

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AutoMLV2
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--------
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.. automodule:: sagemaker.automl.automlv2
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:members:
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:undoc-members:
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:show-inheritance:

‎doc/api/training/index.rst

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@@ -8,6 +8,7 @@ Training APIs
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algorithm
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analytics
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automl
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automlv2
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debugger
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estimators
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tuner

‎src/sagemaker/__init__.py

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from sagemaker.automl.automl import AutoML, AutoMLJob, AutoMLInput # noqa: F401
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from sagemaker.automl.candidate_estimator import CandidateEstimator, CandidateStep # noqa: F401
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from sagemaker.automl.automlv2 import ( # noqa: F401
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AutoMLV2,
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AutoMLJobV2,
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LocalAutoMLDataChannel,
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AutoMLDataChannel,
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AutoMLTimeSeriesForecastingConfig,
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AutoMLImageClassificationConfig,
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AutoMLTabularConfig,
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AutoMLTextClassificationConfig,
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AutoMLTextGenerationConfig,
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)
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from sagemaker.debugger import ProfilerConfig, Profiler # noqa: F401
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‎src/sagemaker/automl/automlv2.py

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‎src/sagemaker/config/__init__.py

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MONITORING_SCHEDULE,
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MONITORING_SCHEDULE_INTER_CONTAINER_ENCRYPTION_PATH,
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AUTO_ML_ROLE_ARN_PATH,
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AUTO_ML_V2_ROLE_ARN_PATH,
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AUTO_ML_OUTPUT_CONFIG_PATH,
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AUTO_ML_V2_OUTPUT_CONFIG_PATH,
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AUTO_ML_JOB_CONFIG_PATH,
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AUTO_ML_JOB,
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AUTO_ML_JOB_V2,
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COMPILATION_JOB_ROLE_ARN_PATH,
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COMPILATION_JOB_OUTPUT_CONFIG_PATH,
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COMPILATION_JOB_VPC_CONFIG_PATH,
@@ -111,9 +114,13 @@
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FEATURE_GROUP_OFFLINE_STORE_KMS_KEY_ID_PATH,
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FEATURE_GROUP_ONLINE_STORE_KMS_KEY_ID_PATH,
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AUTO_ML_KMS_KEY_ID_PATH,
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AUTO_ML_V2_KMS_KEY_ID_PATH,
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AUTO_ML_VPC_CONFIG_PATH,
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AUTO_ML_V2_VPC_CONFIG_PATH,
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AUTO_ML_VOLUME_KMS_KEY_ID_PATH,
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AUTO_ML_V2_VOLUME_KMS_KEY_ID_PATH,
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AUTO_ML_INTER_CONTAINER_ENCRYPTION_PATH,
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AUTO_ML_V2_INTER_CONTAINER_ENCRYPTION_PATH,
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ENDPOINT_CONFIG_DATA_CAPTURE_KMS_KEY_ID_PATH,
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SESSION_DEFAULT_S3_BUCKET_PATH,
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SESSION_DEFAULT_S3_OBJECT_KEY_PREFIX_PATH,

‎src/sagemaker/config/config_schema.py

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ENDPOINT = "Endpoint"
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INFERENCE_COMPONENT = "InferenceComponent"
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AUTO_ML_JOB = "AutoMLJob"
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AUTO_ML_JOB_V2 = "AutoMLJobV2"
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COMPILATION_JOB = "CompilationJob"
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CUSTOM_PARAMETERS = "CustomParameters"
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PIPELINE = "Pipeline"
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FEATURE_GROUP_ONLINE_STORE_CONFIG_PATH, SECURITY_CONFIG, KMS_KEY_ID
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)
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AUTO_ML_OUTPUT_CONFIG_PATH = _simple_path(SAGEMAKER, AUTO_ML_JOB, OUTPUT_DATA_CONFIG)
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AUTO_ML_V2_OUTPUT_CONFIG_PATH = _simple_path(SAGEMAKER, AUTO_ML_JOB_V2, OUTPUT_DATA_CONFIG)
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AUTO_ML_KMS_KEY_ID_PATH = _simple_path(SAGEMAKER, AUTO_ML_JOB, OUTPUT_DATA_CONFIG, KMS_KEY_ID)
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AUTO_ML_V2_KMS_KEY_ID_PATH = _simple_path(SAGEMAKER, AUTO_ML_JOB_V2, OUTPUT_DATA_CONFIG, KMS_KEY_ID)
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AUTO_ML_VOLUME_KMS_KEY_ID_PATH = _simple_path(
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SAGEMAKER, AUTO_ML_JOB, AUTO_ML_JOB_CONFIG, SECURITY_CONFIG, VOLUME_KMS_KEY_ID
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)
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AUTO_ML_V2_VOLUME_KMS_KEY_ID_PATH = _simple_path(
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SAGEMAKER, AUTO_ML_JOB_V2, SECURITY_CONFIG, VOLUME_KMS_KEY_ID
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)
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AUTO_ML_ROLE_ARN_PATH = _simple_path(SAGEMAKER, AUTO_ML_JOB, ROLE_ARN)
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AUTO_ML_V2_ROLE_ARN_PATH = _simple_path(SAGEMAKER, AUTO_ML_JOB_V2, ROLE_ARN)
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AUTO_ML_VPC_CONFIG_PATH = _simple_path(
191198
SAGEMAKER, AUTO_ML_JOB, AUTO_ML_JOB_CONFIG, SECURITY_CONFIG, VPC_CONFIG
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)
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AUTO_ML_V2_VPC_CONFIG_PATH = _simple_path(SAGEMAKER, AUTO_ML_JOB_V2, SECURITY_CONFIG, VPC_CONFIG)
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AUTO_ML_JOB_CONFIG_PATH = _simple_path(SAGEMAKER, AUTO_ML_JOB, AUTO_ML_JOB_CONFIG)
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MONITORING_JOB_DEFINITION_PREFIX = _simple_path(
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SAGEMAKER,
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SECURITY_CONFIG,
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ENABLE_INTER_CONTAINER_TRAFFIC_ENCRYPTION,
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)
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AUTO_ML_V2_INTER_CONTAINER_ENCRYPTION_PATH = _simple_path(
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SAGEMAKER,
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AUTO_ML_JOB_V2,
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SECURITY_CONFIG,
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ENABLE_INTER_CONTAINER_TRAFFIC_ENCRYPTION,
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)
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PROCESSING_JOB_ENVIRONMENT_PATH = _simple_path(SAGEMAKER, PROCESSING_JOB, ENVIRONMENT)
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PROCESSING_JOB_INTER_CONTAINER_ENCRYPTION_PATH = _simple_path(
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SAGEMAKER, PROCESSING_JOB, NETWORK_CONFIG, ENABLE_INTER_CONTAINER_TRAFFIC_ENCRYPTION
@@ -947,6 +961,30 @@ def _simple_path(*args: str):
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TAGS: {"$ref": "#/definitions/tags"},
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},
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},
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# Auto ML V2
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# https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateAutoMLJobV2.html
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AUTO_ML_JOB_V2: {
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TYPE: OBJECT,
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ADDITIONAL_PROPERTIES: False,
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PROPERTIES: {
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SECURITY_CONFIG: {
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TYPE: OBJECT,
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ADDITIONAL_PROPERTIES: False,
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PROPERTIES: {
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ENABLE_INTER_CONTAINER_TRAFFIC_ENCRYPTION: {TYPE: "boolean"},
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VOLUME_KMS_KEY_ID: {"$ref": "#/definitions/kmsKeyId"},
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VPC_CONFIG: {"$ref": "#/definitions/vpcConfig"},
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},
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},
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OUTPUT_DATA_CONFIG: {
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TYPE: OBJECT,
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ADDITIONAL_PROPERTIES: False,
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PROPERTIES: {KMS_KEY_ID: {"$ref": "#/definitions/kmsKeyId"}},
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},
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ROLE_ARN: {"$ref": "#/definitions/roleArn"},
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TAGS: {"$ref": "#/definitions/tags"},
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},
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},
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# Transform Job
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# https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTransformJob.html
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TRANSFORM_JOB: {

‎src/sagemaker/fw_utils.py

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"local_gpu",
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)
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SM_DATAPARALLEL_SUPPORTED_FRAMEWORK_VERSIONS = {
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# tf 2.12 should not be supported: smdataparallel excludes support for tf 2.12.
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# tf 2.12 should not be supported: smdataparallel excludes support for tf>=2.12.
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"tensorflow": [
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"2.3",
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"2.3.1",

‎src/sagemaker/image_uri_config/tensorflow.json

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"2.10": "2.10.1",
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"2.11": "2.11.1",
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"2.12": "2.12.1",
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"2.13": "2.13.0"
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"2.13": "2.13.0",
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"2.14": "2.14.1"
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},
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"versions": {
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"1.10.0": {
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"ca-west-1": "204538143572"
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},
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"repository": "tensorflow-inference"
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},
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"2.14.1": {
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"registries": {
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"af-south-1": "626614931356",
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"ap-east-1": "871362719292",
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"ap-northeast-1": "763104351884",
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"ap-northeast-2": "763104351884",
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"ap-northeast-3": "364406365360",
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"ap-south-1": "763104351884",
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"ap-south-2": "772153158452",
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"ap-southeast-1": "763104351884",
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"ap-southeast-2": "763104351884",
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"ap-southeast-3": "907027046896",
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"ap-southeast-4": "457447274322",
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"ca-central-1": "763104351884",
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"ca-west-1": "204538143572",
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"cn-north-1": "727897471807",
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"cn-northwest-1": "727897471807",
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"eu-central-1": "763104351884",
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"eu-central-2": "380420809688",
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"eu-north-1": "763104351884",
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"eu-south-1": "692866216735",
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"eu-south-2": "503227376785",
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"eu-west-1": "763104351884",
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"eu-west-2": "763104351884",
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"eu-west-3": "763104351884",
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"il-central-1": "780543022126",
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"me-central-1": "914824155844",
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"me-south-1": "217643126080",
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"sa-east-1": "763104351884",
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"us-east-1": "763104351884",
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"us-east-2": "763104351884",
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"us-gov-east-1": "446045086412",
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"us-gov-west-1": "442386744353",
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"us-iso-east-1": "886529160074",
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"us-isob-east-1": "094389454867",
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"us-west-1": "763104351884",
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"us-west-2": "763104351884"
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},
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"repository": "tensorflow-inference"
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}
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}
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},
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"2.10": "2.10.1",
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"2.11": "2.11.0",
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"2.12": "2.12.0",
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"2.13": "2.13.0"
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"2.13": "2.13.0",
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"2.14": "2.14.1"
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},
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"versions": {
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"1.10.0": {
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"ca-west-1": "204538143572"
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},
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"repository": "tensorflow-training"
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},
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"2.14.1": {
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"py_versions": [
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"py310"
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],
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"registries": {
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"af-south-1": "626614931356",
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"ap-east-1": "871362719292",
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"ap-northeast-1": "763104351884",
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"ap-northeast-2": "763104351884",
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"ap-northeast-3": "364406365360",
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"ap-south-1": "763104351884",
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"ap-south-2": "772153158452",
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"ap-southeast-1": "763104351884",
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"ap-southeast-2": "763104351884",
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"ap-southeast-3": "907027046896",
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"ap-southeast-4": "457447274322",
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"ca-central-1": "763104351884",
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"ca-west-1": "204538143572",
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"cn-north-1": "727897471807",
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"cn-northwest-1": "727897471807",
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"eu-central-1": "763104351884",
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"eu-central-2": "380420809688",
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"eu-north-1": "763104351884",
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"eu-south-1": "692866216735",
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"eu-south-2": "503227376785",
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"eu-west-1": "763104351884",
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"eu-west-2": "763104351884",
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"eu-west-3": "763104351884",
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"il-central-1": "780543022126",
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"me-central-1": "914824155844",
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"me-south-1": "217643126080",
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"sa-east-1": "763104351884",
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"us-east-1": "763104351884",
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"us-east-2": "763104351884",
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"us-gov-east-1": "446045086412",
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"us-gov-west-1": "442386744353",
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"us-iso-east-1": "886529160074",
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"us-isob-east-1": "094389454867",
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"us-west-1": "763104351884",
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"us-west-2": "763104351884"
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},
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"repository": "tensorflow-training"
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}
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}
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}
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}
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}

‎src/sagemaker/remote_function/client.py

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encrypt_inter_container_traffic (bool): A flag that specifies whether traffic between
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training containers is encrypted for the training job. Defaults to ``False``.
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enable_network_isolation (bool): A flag that specifies whether container will run in
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network isolation mode. Defaults to ``False``. Network isolation mode restricts the
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container access to outside networks (such as the Internet). The container does not
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make any inbound or outbound network calls. Also known as Internet-free mode.
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spark_config (SparkConfig): Configurations to the Spark application that runs on
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Spark image. If ``spark_config`` is specified, a SageMaker Spark image uri
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will be used for training. Note that ``image_uri`` can not be specified at the

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