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‎CHANGELOG.md‎

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# Changelog
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## v2.66.0 (2021-10-25)
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### Features
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* Add image_uris.retrieve() support for AutoGluon
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### Documentation Changes
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* fix documentation for input types in estimator.fit
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* Add JsonGet v2 deprecation
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## v2.65.0 (2021-10-21)
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### Features
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* modify RLEstimator to use newly generated Ray image (1.6.0)
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* network isolation mode for xgboost
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* update clarify imageURI for PDT
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### Bug Fixes and Other Changes
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* retry downstream_trials test
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* Add retries to pipeline execution
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## v2.64.0 (2021-10-20)
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### Deprecations and Removals
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* warn for deprecation - Lambda model-predictor
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### Features
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* Add support for TF 2.5
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* Add a pre-push git hook
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### Bug Fixes and Other Changes
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* add s3_analysis_config_output_path field in DataConfig constructor
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* make marketplace jobnames random
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## v2.63.2 (2021-10-18)
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### Bug Fixes and Other Changes

‎VERSION‎

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2.63.3.dev0
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2.66.1.dev0

‎doc/v2.rst‎

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The class ``sagemaker.session.ModelContainer`` has been deprecated, as it is not needed for creating inference pipelines.
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``sagemaker.workflow.condition_step.JsonGet``
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---------------------------------------------
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The class ``sagemaker.workflow.condition_step.JsonGet`` has been deprecated.
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Please use :class:`sagemaker.workflow.functions.JsonGet` instead.
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Parameter and Class Name Changes
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================================
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‎src/sagemaker/clarify.py‎

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self,
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s3_data_input_path,
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s3_output_path,
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s3_analysis_config_output_path=None,
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label=None,
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headers=None,
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features=None,
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Args:
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s3_data_input_path (str): Dataset S3 prefix/object URI.
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s3_output_path (str): S3 prefix to store the output.
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s3_analysis_config_output_path (str): S3 prefix to store the analysis_config output
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If this field is None, then the s3_output_path will be used
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to store the analysis_config output
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label (str): Target attribute of the model required by bias metrics (optional for SHAP)
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Specified as column name or index for CSV dataset, or as JSONPath for JSONLines.
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headers (list[str]): A list of column names in the input dataset.
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)
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self.s3_data_input_path = s3_data_input_path
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self.s3_output_path = s3_output_path
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self.s3_analysis_config_output_path = s3_analysis_config_output_path
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self.s3_data_distribution_type = s3_data_distribution_type
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self.s3_compression_type = s3_compression_type
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self.label = label
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json.dump(analysis_config, f)
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s3_analysis_config_file = _upload_analysis_config(
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analysis_config_file,
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data_config.s3_output_path,
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data_config.s3_analysis_config_output_path or data_config.s3_output_path,
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self.sagemaker_session,
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kms_key,
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)

‎src/sagemaker/estimator.py‎

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model using the Amazon SageMaker hosting services.
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Args:
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inputs (str or dict or sagemaker.inputs.TrainingInput): Information
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about the training data. This can be one of three types:
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inputs (str or dict or sagemaker.inputs.TrainingInput or
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sagemaker.inputs.FileSystemInput): Information about the training data.
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This can be one of four types:
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* (str) the S3 location where training data is saved, or a file:// path in
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local mode.
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* (dict[str, str] or dict[str, sagemaker.inputs.TrainingInput]) If using multiple
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channels for training data, you can specify a dict mapping channel names to
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strings or :func:`~sagemaker.inputs.TrainingInput` objects.
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* (dict[str, str] or dict[str, sagemaker.inputs.TrainingInput] or
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dict[str, sagemaker.inputs.FileSystemInput]) If using multiple channels for
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training data, you can specify a dict mapping channel names to strings or
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:func:`~sagemaker.inputs.TrainingInput` objects or
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:func:`~sagemaker.inputs.FileSystemInput` objects.
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* (sagemaker.inputs.TrainingInput) - channel configuration for S3 data sources
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that can provide additional information as well as the path to the training
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dataset.
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See :func:`sagemaker.inputs.TrainingInput` for full details.
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* (sagemaker.session.FileSystemInput) - channel configuration for
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* (sagemaker.inputs.FileSystemInput) - channel configuration for
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a file system data source that can provide additional information as well as
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the path to the training dataset.
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‎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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"tensorflow": ["2.3", "2.3.1", "2.3.2", "2.4", "2.4.1"],
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"tensorflow": ["2.3", "2.3.1", "2.3.2", "2.4", "2.4.1", "2.4.3", "2.5", "2.5.0", "2.5.1"],
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"pytorch": ["1.6", "1.6.0", "1.7", "1.7.1", "1.8", "1.8.0", "1.8.1", "1.9", "1.9.0"],
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}
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SMDISTRIBUTED_SUPPORTED_STRATEGIES = ["dataparallel", "modelparallel"]
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if "py3" not in py_version:
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err_msg += (
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f"Provided py_version {py_version} is not supported by smdataparallel.\n"
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"Please specify py_version=py3"
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"Please specify py_version>=py3"
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)
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if err_msg:

‎src/sagemaker/image_uri_config/autogluon.json‎

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{
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"training": {
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"processors": ["cpu", "gpu"],
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"versions": {
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"0.3.1": {
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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-southeast-1": "763104351884",
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"ap-southeast-2": "763104351884",
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"ca-central-1": "763104351884",
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"eu-central-1": "763104351884",
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"eu-north-1": "763104351884",
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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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"eu-south-1": "692866216735",
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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-west-1": "442386744353",
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"us-iso-east-1": "886529160074",
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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": "autogluon-training",
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"py_versions": ["py37"]
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}
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}
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},
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"inference": {
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"processors": ["cpu"],
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"versions": {
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"0.3.1": {
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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-southeast-1": "763104351884",
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"ap-southeast-2": "763104351884",
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"ca-central-1": "763104351884",
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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-north-1": "763104351884",
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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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"eu-south-1": "692866216735",
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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-west-1": "442386744353",
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"us-iso-east-1": "886529160074",
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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": "autogluon-inference",
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"py_versions": ["py37"]
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}
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}
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}
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}

‎src/sagemaker/image_uri_config/clarify.json‎

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"us-east-1": "205585389593",
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"us-east-2": "211330385671",
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"us-west-1": "740489534195",
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"us-west-2": "306415355426"
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"us-west-2": "306415355426",
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"us-gov-west-1": "598674086554"
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},
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"repository": "sagemaker-clarify-processing"
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}

‎src/sagemaker/image_uri_config/ray-pytorch.json‎

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},
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"repository": "sagemaker-rl-ray-container",
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"tag_prefix": "ray-0.8.5-torch"
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},
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"1.6.0": {
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"py_versions": ["py36"],
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"registries": {
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"ap-northeast-1": "462105765813",
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"ap-northeast-2": "462105765813",
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"ap-south-1": "462105765813",
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"ap-southeast-1": "462105765813",
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"ap-southeast-2": "462105765813",
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"ca-central-1": "462105765813",
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"eu-central-1": "462105765813",
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"eu-west-1": "462105765813",
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"eu-west-2": "462105765813",
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"us-east-1": "462105765813",
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"us-east-2": "462105765813",
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"us-west-1": "462105765813",
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"us-west-2": "462105765813"
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},
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"repository": "sagemaker-rl-ray-container",
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"tag_prefix": "ray-1.6.0-torch"
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}
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}

‎src/sagemaker/image_uri_config/ray-tensorflow.json‎

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},
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"repository": "sagemaker-rl-ray-container",
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"tag_prefix": "ray-0.8.5-tf"
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},
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"1.6.0": {
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"py_versions": ["py37"],
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"registries": {
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"ap-northeast-1": "462105765813",
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"ap-northeast-2": "462105765813",
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"ap-south-1": "462105765813",
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"ap-southeast-1": "462105765813",
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"ap-southeast-2": "462105765813",
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"ca-central-1": "462105765813",
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"eu-central-1": "462105765813",
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"eu-west-1": "462105765813",
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"eu-west-2": "462105765813",
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"us-east-1": "462105765813",
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"us-east-2": "462105765813",
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"us-west-1": "462105765813",
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"us-west-2": "462105765813"
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},
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"repository": "sagemaker-rl-ray-container",
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"tag_prefix": "ray-1.6.0-tf"
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}
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}
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}

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