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Merge branch 'master' into feat/fw-processor-normargs
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‎.github/ISSUE_TEMPLATE/bug_report.md

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name: Bug report
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about: File a report to help us reproduce and fix the problem
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title: ''
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labels: ''
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labels: 'bug'
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assignees: ''
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---

‎.github/ISSUE_TEMPLATE/feature_request.md

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name: Feature request
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about: Suggest new functionality for this library
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title: ''
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labels: ''
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labels: 'feature request'
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assignees: ''
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---

‎CHANGELOG.md

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# Changelog
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## v2.60.0 (2021-10-08)
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### Features
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* Add support for Hugging Face 4.10.2
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## v2.59.8 (2021-10-07)
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### Bug Fixes and Other Changes
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* fix feature store ingestion via data wrangler test
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## v2.59.7 (2021-10-04)
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### Bug Fixes and Other Changes
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* update feature request label
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* update bug template
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## v2.59.6 (2021-09-30)
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### Bug Fixes and Other Changes
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* ParamValidationError when scheduling a Clarify model monitor
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## v2.59.5 (2021-09-29)
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### Bug Fixes and Other Changes
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* support maps in step parameters
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## v2.59.4 (2021-09-27)
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### Bug Fixes and Other Changes
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* add checks for ExecutionRole in UserSettings, adds more unit tests
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* add pytorch 1.8.1 for huggingface
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## v2.59.3.post0 (2021-09-22)
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### Documentation Changes

‎VERSION

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2.59.4.dev0
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2.60.1.dev0

‎src/sagemaker/clarify.py

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@@ -228,7 +228,7 @@ def __init__(
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probability_threshold=None,
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label_headers=None,
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):
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"""Initializes a model output config to extract the predicted label.
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"""Initializes a model output config to extract the predicted label or predicted score(s).
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The following examples show different parameter configurations depending on the endpoint:
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* Regression Task: The model returns the score, e.g. 1.2. we don't need to specify
@@ -255,11 +255,11 @@ def __init__(
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'label_headers=['cat','dog','fish']' and infer the predicted label to be 'fish.'
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Args:
258-
label (str or int or list[int]): Optional index or JSONPath location in the model
259-
output for the prediction. In case, this is a predicted label of the same type as
260-
the label in the dataset no further arguments need to be specified.
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probability (str or int or list[int]): Optional index or JSONPath location in the model
262-
output for the predicted scores.
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label (str or int): Index or JSONPath location in the model output for the prediction.
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In case, this is a predicted label of the same type as the label in the dataset,
260+
no further arguments need to be specified.
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probability (str or int): Index or JSONPath location in the model output
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for the predicted score(s).
263263
probability_threshold (float): An optional value for binary prediction tasks in which
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the model returns a probability, to indicate the threshold to convert the
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prediction to a boolean value. Default is 0.5.

‎src/sagemaker/huggingface/estimator.py

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if "enable_sagemaker_metrics" not in kwargs:
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kwargs["enable_sagemaker_metrics"] = True
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kwargs["py_version"] = self.py_version
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super(HuggingFace, self).__init__(
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entry_point, source_dir, hyperparameters, image_uri=image_uri, **kwargs
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)

‎src/sagemaker/image_uri_config/huggingface.json

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"version_aliases": {
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"4.4": "4.4.2",
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"4.5": "4.5.0",
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"4.6": "4.6.1"
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"4.6": "4.6.1",
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"4.10": "4.10.2"
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},
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"versions": {
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"4.4.2": {
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"repository": "huggingface-tensorflow-training",
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"container_version": {"gpu":"cu110-ubuntu18.04"}
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}
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},
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"4.10.2": {
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"version_aliases": {
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"pytorch1.9": "pytorch1.9.0",
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"tensorflow2.5": "tensorflow2.5.1"
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},
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"pytorch1.9.0": {
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"py_versions": ["py38"],
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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": "huggingface-pytorch-training",
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"container_version": {"gpu": "cu111-ubuntu20.04"}
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},
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"tensorflow2.5.1": {
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"py_versions": ["py37"],
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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-south-1": "692866216735",
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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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"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": "huggingface-tensorflow-training",
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"container_version": {"gpu": "cu112-ubuntu18.04"}
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}
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}
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}
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},
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"inference": {
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"processors": ["gpu", "cpu"],
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"version_aliases": {
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"4.6": "4.6.1"
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"4.6": "4.6.1",
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"4.10": "4.10.2"
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},
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"versions": {
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"4.6.1": {
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"repository": "huggingface-tensorflow-inference",
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"container_version": {"gpu":"cu110-ubuntu18.04", "cpu":"ubuntu18.04" }
391463
}
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},
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"4.10.2": {
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"version_aliases": {
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"pytorch1.9": "pytorch1.9.0",
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"tensorflow2.5": "tensorflow2.5.1"
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},
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"pytorch1.9.0": {
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"py_versions": ["py38"],
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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": "huggingface-pytorch-inference",
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"container_version": {"gpu": "cu111-ubuntu20.04", "cpu": "ubuntu20.04" }
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},
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"tensorflow2.5.1": {
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"py_versions": ["py37"],
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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-south-1": "692866216735",
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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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"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": "huggingface-tensorflow-inference",
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"container_version": {"gpu": "cu112-ubuntu18.04", "cpu": "ubuntu18.04" }
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}
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}
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}
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}

‎src/sagemaker/model_monitor/clarify_model_monitoring.py

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features_attribute=data_config.features,
477477
)
478478
if model_predicted_label_config is not None:
479-
latest_baselining_job_config.inference_attribute = model_predicted_label_config.label
479+
latest_baselining_job_config.inference_attribute = (
480+
model_predicted_label_config.label
481+
if model_predicted_label_config.label is None
482+
else str(model_predicted_label_config.label)
483+
)
480484
latest_baselining_job_config.probability_attribute = (
481485
model_predicted_label_config.probability
486+
if model_predicted_label_config.probability is None
487+
else str(model_predicted_label_config.probability)
482488
)
483489
latest_baselining_job_config.probability_threshold_attribute = (
484490
model_predicted_label_config.probability_threshold
@@ -827,8 +833,9 @@ def suggest_baseline(
827833
specific explainability method. Currently, only SHAP is supported.
828834
model_config (:class:`~sagemaker.clarify.ModelConfig`): Config of the model and its
829835
endpoint to be created.
830-
model_scores: Index or JSONPath location in the model output for the predicted scores
831-
to be explained. This is not required if the model output is a single score.
836+
model_scores (int or str): Index or JSONPath location in the model output for the
837+
predicted scores to be explained. This is not required if the model output is
838+
a single score.
832839
wait (bool): Whether the call should wait until the job completes (default: False).
833840
logs (bool): Whether to show the logs produced by the job.
834841
Only meaningful when wait is True (default: False).
@@ -865,7 +872,7 @@ def suggest_baseline(
865872
headers=headers,
866873
),
867874
features_attribute=data_config.features,
868-
inference_attribute=model_scores,
875+
inference_attribute=model_scores if model_scores is None else str(model_scores),
869876
)
870877
self.latest_baselining_job_name = baselining_job_name
871878
self.latest_baselining_job = ClarifyBaseliningJob(

‎src/sagemaker/session.py

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user_profile_desc = self.sagemaker_client.describe_user_profile(
35553555
DomainId=domain_id, UserProfileName=user_profile_name
35563556
)
3557-
if user_profile_desc.get("UserSettings") is not None:
3557+
3558+
# First, try to find role in userSettings
3559+
if user_profile_desc.get("UserSettings", {}).get("ExecutionRole"):
35583560
return user_profile_desc["UserSettings"]["ExecutionRole"]
35593561

3562+
# If not found, fallback to the domain
35603563
domain_desc = self.sagemaker_client.describe_domain(DomainId=domain_id)
35613564
return domain_desc["DefaultUserSettings"]["ExecutionRole"]
35623565
except ClientError:

‎src/sagemaker/workflow/properties.py

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6767
for key, info in members.items():
6868
if Properties._shapes.get(info["shape"], {}).get("type") == "list":
6969
self.__dict__[key] = PropertiesList(f"{path}.{key}", info["shape"])
70+
elif Properties._shapes.get(info["shape"], {}).get("type") == "map":
71+
self.__dict__[key] = PropertiesMap(f"{path}.{key}", info["shape"])
7072
else:
7173
self.__dict__[key] = Properties(f"{path}.{key}", info["shape"])
7274

@@ -109,6 +111,38 @@ def __getitem__(self, item: Union[int, str]):
109111
return self._items.get(item)
110112

111113

114+
class PropertiesMap(Properties):
115+
"""PropertiesMap for use in workflow expressions."""
116+
117+
def __init__(self, path: str, shape_name: str = None):
118+
"""Create a PropertiesMap instance representing the given shape.
119+
120+
Args:
121+
path (str): The parent path of the PropertiesMap instance.
122+
shape_name (str): The botocore sagemaker service model shape name.
123+
"""
124+
super(PropertiesMap, self).__init__(path, shape_name)
125+
self.shape_name = shape_name
126+
self._items: Dict[Union[int, str], Properties] = dict()
127+
128+
def __getitem__(self, item: Union[int, str]):
129+
"""Populate the indexing item with a Property, for both lists and dictionaries.
130+
131+
Args:
132+
item (Union[int, str]): The index of the item in sequence.
133+
"""
134+
if item not in self._items.keys():
135+
shape = Properties._shapes.get(self.shape_name)
136+
member = shape["value"]["shape"]
137+
if isinstance(item, str):
138+
property_item = Properties(f"{self._path}['{item}']", member)
139+
else:
140+
property_item = Properties(f"{self._path}[{item}]", member)
141+
self._items[item] = property_item
142+
143+
return self._items.get(item)
144+
145+
112146
@attr.s
113147
class PropertyFile(Expression):
114148
"""Provides a property file struct.

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