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fix: Transformer tags pydantic validation and shape compatibility #6103

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1bc9bfd
feat: Add application/x-parquet support to clarify.ModelConfig
wasim-builds Jul 21, 2026
a24011d
ci: trigger readthedocs rebuild [skip ci]
wasim-builds Jul 25, 2026
2c6b973
fix(core): Transformer tags pydantic validation and shape compatibility
wasim-builds Jul 25, 2026
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16 changes: 10 additions & 6 deletions sagemaker-core/src/sagemaker/core/clarify/__init__.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -379,12 +379,13 @@
"image/jpeg",
"image/png",
"application/x-npy",
"application/x-parquet",
),
),
SchemaOptional("accept_type"): And(
str,
Use(str.lower),
lambda s: s in ("text/csv", "application/jsonlines", "application/json"),
lambda s: s in ("text/csv", "application/jsonlines", "application/json", "application/x-parquet"),
),
SchemaOptional("label"): Or(str, int),
SchemaOptional("probability"): Or(str, int),
Expand DownExpand Up@@ -1020,11 +1021,13 @@ def __init__(
Must be set with ``instance_count``, ``model_name``
accept_type (str): The model output format to be used for getting inferences with the
shadow endpoint. Valid values are ``"text/csv"`` for CSV,
``"application/jsonlines"`` for JSON Lines, and ``"application/json"`` for JSON.
``"application/jsonlines"`` for JSON Lines, ``"application/json"`` for JSON, and
``"application/x-parquet"`` for Parquet.
Default is the same as ``content_type``.
content_type (str): The model input format to be used for getting inferences with the
shadow endpoint. Valid values are ``"text/csv"`` for CSV,
``"application/jsonlines"`` for JSON Lines, and ``"application/json"`` for JSON.
``"application/jsonlines"`` for JSON Lines, ``"application/json"`` for JSON, and
``"application/x-parquet"`` for Parquet.
Default is the same as ``dataset_format``.
content_template (str): A template string to be used to construct the model input from
dataset instances. It is only used, and required, when ``model_content_type`` is
Expand DownExpand Up@@ -1160,10 +1163,10 @@ def __init__(
)
self.predictor_config["endpoint_name_prefix"] = endpoint_name_prefix
if accept_type is not None:
if accept_type not in ["text/csv", "application/jsonlines", "application/json"]:
if accept_type not in ["text/csv", "application/jsonlines", "application/json", "application/x-parquet"]:
raise ValueError(
f"Invalid accept_type {accept_type}."
f" Please choose text/csvor application/jsonlines."
f" Please choose text/csv, application/jsonlines, application/json, or application/x-parquet."
)
if time_series_model_config and accept_type == "text/csv":
raise ValueError(
Expand All@@ -1179,10 +1182,11 @@ def __init__(
"image/jpg",
"image/png",
"application/x-npy",
"application/x-parquet",
]:
raise ValueError(
f"Invalid content_type {content_type}."
f" Please choose text/csv or application/jsonlines."
f" Please choose a valid content_type."
)
if content_type == "application/jsonlines":
if content_template is None:
Expand Down
14 changes: 12 additions & 2 deletions sagemaker-core/src/sagemaker/core/transformer.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -409,8 +409,9 @@ def submit(request):
from sagemaker.core.utils.code_injection.codec import transform as transform_util

transformed = transform_util(serialized_request, "CreateTransformJobRequest")
# Remove tags from transformed dict as TransformJob resource doesn't accept it
# TransformJob does not accept 'tags'/'Tags', so we must pop them to avoid pydantic ValidationError
transformed.pop("tags", None)
transformed.pop("Tags", None)
self.latest_transform_job = TransformJob(**transformed)

if wait:
Expand DownExpand Up@@ -716,7 +717,16 @@ def _get_transform_request(
transform_request["Environment"] = env

if tags is not None:
transform_request["Tags"] = tags
from sagemaker.core.common_utils import format_tags
formatted_tags = format_tags(tags)

if isinstance(formatted_tags, list):
formatted_tags = [
{"Key": t.get("Key", t.get("key", "")), "Value": t.get("Value", t.get("value", ""))}
if isinstance(t, dict) else t
for t in formatted_tags
]
transform_request["Tags"] = formatted_tags

if data_processing is not None:
transform_request["DataProcessing"] = data_processing
Expand Down
Loading
, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all 
 blocks
(function() {
function addCopyButtons() {
document.querySelectorAll('pre code').forEach(function(codeBlock) {
if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;
codeBlock.parentElement.setAttribute('data-copy-added', 'true');
var btn = document.createElement('button');
btn.textContent = 'Copy';
btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';
btn.onmouseover = function() { this.style.opacity = '1'; };
btn.onmouseout = function() { this.style.opacity = '0.7'; };
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navigator.clipboard.writeText(codeBlock.textContent).then(function() {
btn.textContent = 'Copied!';
setTimeout(function() { btn.textContent = 'Copy'; }, 1500);
});
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codeBlock.parentElement.style.position = 'relative';
codeBlock.parentElement.appendChild(btn);
});
}
addCopyButtons();
// Re-run on dynamic content
var observer = new MutationObserver(addCopyButtons);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
fix: Transformer tags pydantic validation and shape compatibility by wasim-builds · Pull Request #6103 · aws/sagemaker-python-sdk · GitHub
Skip to content
fix: Transformer tags pydantic validation and shape compatibility#6103

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fix: Transformer tags pydantic validation and shape compatibility
#6103
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1bc9bfd

feat: Add application/x-parquet support to clarify.ModelConfig


wasim-builds Jul 21, 2026
a24011d

ci: trigger readthedocs rebuild [skip ci]


wasim-builds Jul 25, 2026
2c6b973

fix(core): Transformer tags pydantic validation and shape compatibility


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16 changes: 10 additions & 6 deletions
sagemaker-core/src/sagemaker/core/clarify/__init__.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -379,12 +379,13 @@
 "image/jpeg",
 "image/png",
 "application/x-npy",
 "application/x-parquet",
 ),
 ),
 SchemaOptional("accept_type"): And(
 str,
 Use(str.lower),
 lambda s: s in ("text/csv", "application/jsonlines", "application/json"),
 lambda s: s in ("text/csv", "application/jsonlines", "application/json", "application/x-parquet"),
 ),
 SchemaOptional("label"): Or(str, int),
 SchemaOptional("probability"): Or(str, int),
Expand DownExpand Up@@ -1020,11 +1021,13 @@ def __init__(
 Must be set with ``instance_count``, ``model_name``
 accept_type (str): The model output format to be used for getting inferences with the
 shadow endpoint. Valid values are ``"text/csv"`` for CSV,
 ``"application/jsonlines"`` for JSON Lines, and ``"application/json"`` for JSON.
 ``"application/jsonlines"`` for JSON Lines, ``"application/json"`` for JSON, and
 ``"application/x-parquet"`` for Parquet.
 Default is the same as ``content_type``.
 content_type (str): The model input format to be used for getting inferences with the
 shadow endpoint. Valid values are ``"text/csv"`` for CSV,
 ``"application/jsonlines"`` for JSON Lines, and ``"application/json"`` for JSON.
 ``"application/jsonlines"`` for JSON Lines, ``"application/json"`` for JSON, and
 ``"application/x-parquet"`` for Parquet.
 Default is the same as ``dataset_format``.
 content_template (str): A template string to be used to construct the model input from
 dataset instances. It is only used, and required, when ``model_content_type`` is
Expand DownExpand Up@@ -1160,10 +1163,10 @@ def __init__(
 )
 self.predictor_config["endpoint_name_prefix"] = endpoint_name_prefix
 if accept_type is not None:
 if accept_type not in ["text/csv", "application/jsonlines", "application/json"]:
 if accept_type not in ["text/csv", "application/jsonlines", "application/json", "application/x-parquet"]:
 raise ValueError(
 f"Invalid accept_type {accept_type}."
 f" Please choose text/csvor application/jsonlines."
 f" Please choose text/csv, application/jsonlines, application/json, or application/x-parquet."
 )
 if time_series_model_config and accept_type == "text/csv":
 raise ValueError(
Expand All@@ -1179,10 +1182,11 @@ def __init__(
 "image/jpg",
 "image/png",
 "application/x-npy",
 "application/x-parquet",
 ]:
 raise ValueError(
 f"Invalid content_type {content_type}."
 f" Please choose text/csv or application/jsonlines."
 f" Please choose a valid content_type."
 )
 if content_type == "application/jsonlines":
 if content_template is None:
Expand Down

14 changes: 12 additions & 2 deletions
sagemaker-core/src/sagemaker/core/transformer.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -409,8 +409,9 @@ def submit(request):
 from sagemaker.core.utils.code_injection.codec import transform as transform_util

 transformed = transform_util(serialized_request, "CreateTransformJobRequest")
 # Remove tags from transformed dict as TransformJob resource doesn't accept it
 # TransformJob does not accept 'tags'/'Tags', so we must pop them to avoid pydantic ValidationError
 transformed.pop("tags", None)
 transformed.pop("Tags", None)
 self.latest_transform_job = TransformJob(**transformed)

 if wait:
Expand DownExpand Up@@ -716,7 +717,16 @@ def _get_transform_request(
 transform_request["Environment"] = env

 if tags is not None:
 transform_request["Tags"] = tags
 from sagemaker.core.common_utils import format_tags
 formatted_tags = format_tags(tags)

 if isinstance(formatted_tags, list):
 formatted_tags = [
 {"Key": t.get("Key", t.get("key", "")), "Value": t.get("Value", t.get("value", ""))}
 if isinstance(t, dict) else t
 for t in formatted_tags
 ]
 transform_request["Tags"] = formatted_tags

 if data_processing is not None:
 transform_request["DataProcessing"] = data_processing
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Force GitHub README to respect dark mode
(function() {
var style = document.createElement('style');
style.textContent = '
.markdown-body {
color-scheme: dark light;
}
.markdown-body pre { background: #161b22 !important; }
.markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }
.markdown-body table th, .markdown-body table td { border-color: #30363d !important; }
.markdown-body img { background: #0d1117; }
.markdown-body blockquote { border-left-color: #8b949e; }
.markdown-body hr { border-color: #30363d; }
';
document.head.appendChild(style);
})();
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
fix: Transformer tags pydantic validation and shape compatibility by wasim-builds · Pull Request #6103 · aws/sagemaker-python-sdk · GitHub
Skip to content
fix: Transformer tags pydantic validation and shape compatibility#6103

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fix: Transformer tags pydantic validation and shape compatibility
#6103
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1bc9bfd

feat: Add application/x-parquet support to clarify.ModelConfig


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a24011d

ci: trigger readthedocs rebuild [skip ci]


wasim-builds Jul 25, 2026
2c6b973

fix(core): Transformer tags pydantic validation and shape compatibility


wasim-builds Jul 25, 2026

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16 changes: 10 additions & 6 deletions
sagemaker-core/src/sagemaker/core/clarify/__init__.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -379,12 +379,13 @@
 "image/jpeg",
 "image/png",
 "application/x-npy",
 "application/x-parquet",
 ),
 ),
 SchemaOptional("accept_type"): And(
 str,
 Use(str.lower),
 lambda s: s in ("text/csv", "application/jsonlines", "application/json"),
 lambda s: s in ("text/csv", "application/jsonlines", "application/json", "application/x-parquet"),
 ),
 SchemaOptional("label"): Or(str, int),
 SchemaOptional("probability"): Or(str, int),
Expand DownExpand Up@@ -1020,11 +1021,13 @@ def __init__(
 Must be set with ``instance_count``, ``model_name``
 accept_type (str): The model output format to be used for getting inferences with the
 shadow endpoint. Valid values are ``"text/csv"`` for CSV,
 ``"application/jsonlines"`` for JSON Lines, and ``"application/json"`` for JSON.
 ``"application/jsonlines"`` for JSON Lines, ``"application/json"`` for JSON, and
 ``"application/x-parquet"`` for Parquet.
 Default is the same as ``content_type``.
 content_type (str): The model input format to be used for getting inferences with the
 shadow endpoint. Valid values are ``"text/csv"`` for CSV,
 ``"application/jsonlines"`` for JSON Lines, and ``"application/json"`` for JSON.
 ``"application/jsonlines"`` for JSON Lines, ``"application/json"`` for JSON, and
 ``"application/x-parquet"`` for Parquet.
 Default is the same as ``dataset_format``.
 content_template (str): A template string to be used to construct the model input from
 dataset instances. It is only used, and required, when ``model_content_type`` is
Expand DownExpand Up@@ -1160,10 +1163,10 @@ def __init__(
 )
 self.predictor_config["endpoint_name_prefix"] = endpoint_name_prefix
 if accept_type is not None:
 if accept_type not in ["text/csv", "application/jsonlines", "application/json"]:
 if accept_type not in ["text/csv", "application/jsonlines", "application/json", "application/x-parquet"]:
 raise ValueError(
 f"Invalid accept_type {accept_type}."
 f" Please choose text/csvor application/jsonlines."
 f" Please choose text/csv, application/jsonlines, application/json, or application/x-parquet."
 )
 if time_series_model_config and accept_type == "text/csv":
 raise ValueError(
Expand All@@ -1179,10 +1182,11 @@ def __init__(
 "image/jpg",
 "image/png",
 "application/x-npy",
 "application/x-parquet",
 ]:
 raise ValueError(
 f"Invalid content_type {content_type}."
 f" Please choose text/csv or application/jsonlines."
 f" Please choose a valid content_type."
 )
 if content_type == "application/jsonlines":
 if content_template is None:
Expand Down

14 changes: 12 additions & 2 deletions
sagemaker-core/src/sagemaker/core/transformer.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -409,8 +409,9 @@ def submit(request):
 from sagemaker.core.utils.code_injection.codec import transform as transform_util

 transformed = transform_util(serialized_request, "CreateTransformJobRequest")
 # Remove tags from transformed dict as TransformJob resource doesn't accept it
 # TransformJob does not accept 'tags'/'Tags', so we must pop them to avoid pydantic ValidationError
 transformed.pop("tags", None)
 transformed.pop("Tags", None)
 self.latest_transform_job = TransformJob(**transformed)

 if wait:
Expand DownExpand Up@@ -716,7 +717,16 @@ def _get_transform_request(
 transform_request["Environment"] = env

 if tags is not None:
 transform_request["Tags"] = tags
 from sagemaker.core.common_utils import format_tags
 formatted_tags = format_tags(tags)

 if isinstance(formatted_tags, list):
 formatted_tags = [
 {"Key": t.get("Key", t.get("key", "")), "Value": t.get("Value", t.get("value", ""))}
 if isinstance(t, dict) else t
 for t in formatted_tags
 ]
 transform_request["Tags"] = formatted_tags

 if data_processing is not None:
 transform_request["DataProcessing"] = data_processing
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
(function() {
var ref = document.referrer;
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var q = url.searchParams.get('q') || url.searchParams.get('p');
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frag.appendChild(span);
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Array.from(node.childNodes).forEach(highlight);
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mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1 || node.nodeType === 3) highlight(node);
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
fix: Transformer tags pydantic validation and shape compatibility by wasim-builds · Pull Request #6103 · aws/sagemaker-python-sdk · GitHub
Skip to content
fix: Transformer tags pydantic validation and shape compatibility#6103

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fix: Transformer tags pydantic validation and shape compatibility
#6103
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1bc9bfd

feat: Add application/x-parquet support to clarify.ModelConfig


wasim-builds Jul 21, 2026
a24011d

ci: trigger readthedocs rebuild [skip ci]


wasim-builds Jul 25, 2026
2c6b973

fix(core): Transformer tags pydantic validation and shape compatibility


wasim-builds Jul 25, 2026

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16 changes: 10 additions & 6 deletions
sagemaker-core/src/sagemaker/core/clarify/__init__.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -379,12 +379,13 @@
 "image/jpeg",
 "image/png",
 "application/x-npy",
 "application/x-parquet",
 ),
 ),
 SchemaOptional("accept_type"): And(
 str,
 Use(str.lower),
 lambda s: s in ("text/csv", "application/jsonlines", "application/json"),
 lambda s: s in ("text/csv", "application/jsonlines", "application/json", "application/x-parquet"),
 ),
 SchemaOptional("label"): Or(str, int),
 SchemaOptional("probability"): Or(str, int),
Expand DownExpand Up@@ -1020,11 +1021,13 @@ def __init__(
 Must be set with ``instance_count``, ``model_name``
 accept_type (str): The model output format to be used for getting inferences with the
 shadow endpoint. Valid values are ``"text/csv"`` for CSV,
 ``"application/jsonlines"`` for JSON Lines, and ``"application/json"`` for JSON.
 ``"application/jsonlines"`` for JSON Lines, ``"application/json"`` for JSON, and
 ``"application/x-parquet"`` for Parquet.
 Default is the same as ``content_type``.
 content_type (str): The model input format to be used for getting inferences with the
 shadow endpoint. Valid values are ``"text/csv"`` for CSV,
 ``"application/jsonlines"`` for JSON Lines, and ``"application/json"`` for JSON.
 ``"application/jsonlines"`` for JSON Lines, ``"application/json"`` for JSON, and
 ``"application/x-parquet"`` for Parquet.
 Default is the same as ``dataset_format``.
 content_template (str): A template string to be used to construct the model input from
 dataset instances. It is only used, and required, when ``model_content_type`` is
Expand DownExpand Up@@ -1160,10 +1163,10 @@ def __init__(
 )
 self.predictor_config["endpoint_name_prefix"] = endpoint_name_prefix
 if accept_type is not None:
 if accept_type not in ["text/csv", "application/jsonlines", "application/json"]:
 if accept_type not in ["text/csv", "application/jsonlines", "application/json", "application/x-parquet"]:
 raise ValueError(
 f"Invalid accept_type {accept_type}."
 f" Please choose text/csvor application/jsonlines."
 f" Please choose text/csv, application/jsonlines, application/json, or application/x-parquet."
 )
 if time_series_model_config and accept_type == "text/csv":
 raise ValueError(
Expand All@@ -1179,10 +1182,11 @@ def __init__(
 "image/jpg",
 "image/png",
 "application/x-npy",
 "application/x-parquet",
 ]:
 raise ValueError(
 f"Invalid content_type {content_type}."
 f" Please choose text/csv or application/jsonlines."
 f" Please choose a valid content_type."
 )
 if content_type == "application/jsonlines":
 if content_template is None:
Expand Down

14 changes: 12 additions & 2 deletions
sagemaker-core/src/sagemaker/core/transformer.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -409,8 +409,9 @@ def submit(request):
 from sagemaker.core.utils.code_injection.codec import transform as transform_util

 transformed = transform_util(serialized_request, "CreateTransformJobRequest")
 # Remove tags from transformed dict as TransformJob resource doesn't accept it
 # TransformJob does not accept 'tags'/'Tags', so we must pop them to avoid pydantic ValidationError
 transformed.pop("tags", None)
 transformed.pop("Tags", None)
 self.latest_transform_job = TransformJob(**transformed)

 if wait:
Expand DownExpand Up@@ -716,7 +717,16 @@ def _get_transform_request(
 transform_request["Environment"] = env

 if tags is not None:
 transform_request["Tags"] = tags
 from sagemaker.core.common_utils import format_tags
 formatted_tags = format_tags(tags)

 if isinstance(formatted_tags, list):
 formatted_tags = [
 {"Key": t.get("Key", t.get("key", "")), "Value": t.get("Value", t.get("value", ""))}
 if isinstance(t, dict) else t
 for t in formatted_tags
 ]
 transform_request["Tags"] = formatted_tags

 if data_processing is not None:
 transform_request["DataProcessing"] = data_processing
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Strip utm_, fbclid, gclid, etc. from all links on page
(function() {
var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
function cleanUrl(url) {
try {
var u = new URL(url, window.location.origin);
var changed = false;
trackingParams.forEach(function(p) {
if (u.searchParams.has(p)) {
u.searchParams.delete(p);
changed = true;
}
});
return changed ? u.toString() : url;
} catch (e) {
return url;
}
}
function cleanLinks() {
document.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
cleanLinks();
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1) {
if (node.tagName === 'A') cleanLinks();
node.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
fix: Transformer tags pydantic validation and shape compatibility by wasim-builds · Pull Request #6103 · aws/sagemaker-python-sdk · GitHub
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feat: Add application/x-parquet support to clarify.ModelConfig


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a24011d

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fix(core): Transformer tags pydantic validation and shape compatibility


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16 changes: 10 additions & 6 deletions
sagemaker-core/src/sagemaker/core/clarify/__init__.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -379,12 +379,13 @@
 "image/jpeg",
 "image/png",
 "application/x-npy",
 "application/x-parquet",
 ),
 ),
 SchemaOptional("accept_type"): And(
 str,
 Use(str.lower),
 lambda s: s in ("text/csv", "application/jsonlines", "application/json"),
 lambda s: s in ("text/csv", "application/jsonlines", "application/json", "application/x-parquet"),
 ),
 SchemaOptional("label"): Or(str, int),
 SchemaOptional("probability"): Or(str, int),
Expand DownExpand Up@@ -1020,11 +1021,13 @@ def __init__(
 Must be set with ``instance_count``, ``model_name``
 accept_type (str): The model output format to be used for getting inferences with the
 shadow endpoint. Valid values are ``"text/csv"`` for CSV,
 ``"application/jsonlines"`` for JSON Lines, and ``"application/json"`` for JSON.
 ``"application/jsonlines"`` for JSON Lines, ``"application/json"`` for JSON, and
 ``"application/x-parquet"`` for Parquet.
 Default is the same as ``content_type``.
 content_type (str): The model input format to be used for getting inferences with the
 shadow endpoint. Valid values are ``"text/csv"`` for CSV,
 ``"application/jsonlines"`` for JSON Lines, and ``"application/json"`` for JSON.
 ``"application/jsonlines"`` for JSON Lines, ``"application/json"`` for JSON, and
 ``"application/x-parquet"`` for Parquet.
 Default is the same as ``dataset_format``.
 content_template (str): A template string to be used to construct the model input from
 dataset instances. It is only used, and required, when ``model_content_type`` is
Expand DownExpand Up@@ -1160,10 +1163,10 @@ def __init__(
 )
 self.predictor_config["endpoint_name_prefix"] = endpoint_name_prefix
 if accept_type is not None:
 if accept_type not in ["text/csv", "application/jsonlines", "application/json"]:
 if accept_type not in ["text/csv", "application/jsonlines", "application/json", "application/x-parquet"]:
 raise ValueError(
 f"Invalid accept_type {accept_type}."
 f" Please choose text/csvor application/jsonlines."
 f" Please choose text/csv, application/jsonlines, application/json, or application/x-parquet."
 )
 if time_series_model_config and accept_type == "text/csv":
 raise ValueError(
Expand All@@ -1179,10 +1182,11 @@ def __init__(
 "image/jpg",
 "image/png",
 "application/x-npy",
 "application/x-parquet",
 ]:
 raise ValueError(
 f"Invalid content_type {content_type}."
 f" Please choose text/csv or application/jsonlines."
 f" Please choose a valid content_type."
 )
 if content_type == "application/jsonlines":
 if content_template is None:
Expand Down

14 changes: 12 additions & 2 deletions
sagemaker-core/src/sagemaker/core/transformer.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -409,8 +409,9 @@ def submit(request):
 from sagemaker.core.utils.code_injection.codec import transform as transform_util

 transformed = transform_util(serialized_request, "CreateTransformJobRequest")
 # Remove tags from transformed dict as TransformJob resource doesn't accept it
 # TransformJob does not accept 'tags'/'Tags', so we must pop them to avoid pydantic ValidationError
 transformed.pop("tags", None)
 transformed.pop("Tags", None)
 self.latest_transform_job = TransformJob(**transformed)

 if wait:
Expand DownExpand Up@@ -716,7 +717,16 @@ def _get_transform_request(
 transform_request["Environment"] = env

 if tags is not None:
 transform_request["Tags"] = tags
 from sagemaker.core.common_utils import format_tags
 formatted_tags = format_tags(tags)

 if isinstance(formatted_tags, list):
 formatted_tags = [
 {"Key": t.get("Key", t.get("key", "")), "Value": t.get("Value", t.get("value", ""))}
 if isinstance(t, dict) else t
 for t in formatted_tags
 ]
 transform_request["Tags"] = formatted_tags

 if data_processing is not None:
 transform_request["DataProcessing"] = data_processing
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
if (btn && !btn.classList.contains('activated')) {
btn.click();
}
}
// Try immediately
tryTheater();
// Try after navigation (SPA)
var lastUrl = location.href;
setInterval(function() {
if (location.href !== lastUrl) {
lastUrl = location.href;
setTimeout(tryTheater, 500);
}
}, 1000);
// Also try on player load
var observer = new MutationObserver(tryTheater);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
fix: Transformer tags pydantic validation and shape compatibility by wasim-builds · Pull Request #6103 · aws/sagemaker-python-sdk · GitHub
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fix: Transformer tags pydantic validation and shape compatibility#6103

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fix: Transformer tags pydantic validation and shape compatibility
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feat: Add application/x-parquet support to clarify.ModelConfig


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a24011d

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fix(core): Transformer tags pydantic validation and shape compatibility


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16 changes: 10 additions & 6 deletions
sagemaker-core/src/sagemaker/core/clarify/__init__.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -379,12 +379,13 @@
 "image/jpeg",
 "image/png",
 "application/x-npy",
 "application/x-parquet",
 ),
 ),
 SchemaOptional("accept_type"): And(
 str,
 Use(str.lower),
 lambda s: s in ("text/csv", "application/jsonlines", "application/json"),
 lambda s: s in ("text/csv", "application/jsonlines", "application/json", "application/x-parquet"),
 ),
 SchemaOptional("label"): Or(str, int),
 SchemaOptional("probability"): Or(str, int),
Expand DownExpand Up@@ -1020,11 +1021,13 @@ def __init__(
 Must be set with ``instance_count``, ``model_name``
 accept_type (str): The model output format to be used for getting inferences with the
 shadow endpoint. Valid values are ``"text/csv"`` for CSV,
 ``"application/jsonlines"`` for JSON Lines, and ``"application/json"`` for JSON.
 ``"application/jsonlines"`` for JSON Lines, ``"application/json"`` for JSON, and
 ``"application/x-parquet"`` for Parquet.
 Default is the same as ``content_type``.
 content_type (str): The model input format to be used for getting inferences with the
 shadow endpoint. Valid values are ``"text/csv"`` for CSV,
 ``"application/jsonlines"`` for JSON Lines, and ``"application/json"`` for JSON.
 ``"application/jsonlines"`` for JSON Lines, ``"application/json"`` for JSON, and
 ``"application/x-parquet"`` for Parquet.
 Default is the same as ``dataset_format``.
 content_template (str): A template string to be used to construct the model input from
 dataset instances. It is only used, and required, when ``model_content_type`` is
Expand DownExpand Up@@ -1160,10 +1163,10 @@ def __init__(
 )
 self.predictor_config["endpoint_name_prefix"] = endpoint_name_prefix
 if accept_type is not None:
 if accept_type not in ["text/csv", "application/jsonlines", "application/json"]:
 if accept_type not in ["text/csv", "application/jsonlines", "application/json", "application/x-parquet"]:
 raise ValueError(
 f"Invalid accept_type {accept_type}."
 f" Please choose text/csvor application/jsonlines."
 f" Please choose text/csv, application/jsonlines, application/json, or application/x-parquet."
 )
 if time_series_model_config and accept_type == "text/csv":
 raise ValueError(
Expand All@@ -1179,10 +1182,11 @@ def __init__(
 "image/jpg",
 "image/png",
 "application/x-npy",
 "application/x-parquet",
 ]:
 raise ValueError(
 f"Invalid content_type {content_type}."
 f" Please choose text/csv or application/jsonlines."
 f" Please choose a valid content_type."
 )
 if content_type == "application/jsonlines":
 if content_template is None:
Expand Down

14 changes: 12 additions & 2 deletions
sagemaker-core/src/sagemaker/core/transformer.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -409,8 +409,9 @@ def submit(request):
 from sagemaker.core.utils.code_injection.codec import transform as transform_util

 transformed = transform_util(serialized_request, "CreateTransformJobRequest")
 # Remove tags from transformed dict as TransformJob resource doesn't accept it
 # TransformJob does not accept 'tags'/'Tags', so we must pop them to avoid pydantic ValidationError
 transformed.pop("tags", None)
 transformed.pop("Tags", None)
 self.latest_transform_job = TransformJob(**transformed)

 if wait:
Expand DownExpand Up@@ -716,7 +717,16 @@ def _get_transform_request(
 transform_request["Environment"] = env

 if tags is not None:
 transform_request["Tags"] = tags
 from sagemaker.core.common_utils import format_tags
 formatted_tags = format_tags(tags)

 if isinstance(formatted_tags, list):
 formatted_tags = [
 {"Key": t.get("Key", t.get("key", "")), "Value": t.get("Value", t.get("value", ""))}
 if isinstance(t, dict) else t
 for t in formatted_tags
 ]
 transform_request["Tags"] = formatted_tags

 if data_processing is not None:
 transform_request["DataProcessing"] = data_processing
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
(function() {
function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {
el.style.position = 'static';
el.style.top = 'auto';
el.style.zIndex = 'auto';
}
});
}
unstick();
var observer = new MutationObserver(unstick);
observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });
})();
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
fix: Transformer tags pydantic validation and shape compatibility by wasim-builds · Pull Request #6103 · aws/sagemaker-python-sdk · GitHub
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fix: Transformer tags pydantic validation and shape compatibility#6103

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fix: Transformer tags pydantic validation and shape compatibility
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feat: Add application/x-parquet support to clarify.ModelConfig


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a24011d

ci: trigger readthedocs rebuild [skip ci]


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2c6b973

fix(core): Transformer tags pydantic validation and shape compatibility


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16 changes: 10 additions & 6 deletions
sagemaker-core/src/sagemaker/core/clarify/__init__.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -379,12 +379,13 @@
 "image/jpeg",
 "image/png",
 "application/x-npy",
 "application/x-parquet",
 ),
 ),
 SchemaOptional("accept_type"): And(
 str,
 Use(str.lower),
 lambda s: s in ("text/csv", "application/jsonlines", "application/json"),
 lambda s: s in ("text/csv", "application/jsonlines", "application/json", "application/x-parquet"),
 ),
 SchemaOptional("label"): Or(str, int),
 SchemaOptional("probability"): Or(str, int),
Expand DownExpand Up@@ -1020,11 +1021,13 @@ def __init__(
 Must be set with ``instance_count``, ``model_name``
 accept_type (str): The model output format to be used for getting inferences with the
 shadow endpoint. Valid values are ``"text/csv"`` for CSV,
 ``"application/jsonlines"`` for JSON Lines, and ``"application/json"`` for JSON.
 ``"application/jsonlines"`` for JSON Lines, ``"application/json"`` for JSON, and
 ``"application/x-parquet"`` for Parquet.
 Default is the same as ``content_type``.
 content_type (str): The model input format to be used for getting inferences with the
 shadow endpoint. Valid values are ``"text/csv"`` for CSV,
 ``"application/jsonlines"`` for JSON Lines, and ``"application/json"`` for JSON.
 ``"application/jsonlines"`` for JSON Lines, ``"application/json"`` for JSON, and
 ``"application/x-parquet"`` for Parquet.
 Default is the same as ``dataset_format``.
 content_template (str): A template string to be used to construct the model input from
 dataset instances. It is only used, and required, when ``model_content_type`` is
Expand DownExpand Up@@ -1160,10 +1163,10 @@ def __init__(
 )
 self.predictor_config["endpoint_name_prefix"] = endpoint_name_prefix
 if accept_type is not None:
 if accept_type not in ["text/csv", "application/jsonlines", "application/json"]:
 if accept_type not in ["text/csv", "application/jsonlines", "application/json", "application/x-parquet"]:
 raise ValueError(
 f"Invalid accept_type {accept_type}."
 f" Please choose text/csvor application/jsonlines."
 f" Please choose text/csv, application/jsonlines, application/json, or application/x-parquet."
 )
 if time_series_model_config and accept_type == "text/csv":
 raise ValueError(
Expand All@@ -1179,10 +1182,11 @@ def __init__(
 "image/jpg",
 "image/png",
 "application/x-npy",
 "application/x-parquet",
 ]:
 raise ValueError(
 f"Invalid content_type {content_type}."
 f" Please choose text/csv or application/jsonlines."
 f" Please choose a valid content_type."
 )
 if content_type == "application/jsonlines":
 if content_template is None:
Expand Down

14 changes: 12 additions & 2 deletions
sagemaker-core/src/sagemaker/core/transformer.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -409,8 +409,9 @@ def submit(request):
 from sagemaker.core.utils.code_injection.codec import transform as transform_util

 transformed = transform_util(serialized_request, "CreateTransformJobRequest")
 # Remove tags from transformed dict as TransformJob resource doesn't accept it
 # TransformJob does not accept 'tags'/'Tags', so we must pop them to avoid pydantic ValidationError
 transformed.pop("tags", None)
 transformed.pop("Tags", None)
 self.latest_transform_job = TransformJob(**transformed)

 if wait:
Expand DownExpand Up@@ -716,7 +717,16 @@ def _get_transform_request(
 transform_request["Environment"] = env

 if tags is not None:
 transform_request["Tags"] = tags
 from sagemaker.core.common_utils import format_tags
 formatted_tags = format_tags(tags)

 if isinstance(formatted_tags, list):
 formatted_tags = [
 {"Key": t.get("Key", t.get("key", "")), "Value": t.get("Value", t.get("value", ""))}
 if isinstance(t, dict) else t
 for t in formatted_tags
 ]
 transform_request["Tags"] = formatted_tags

 if data_processing is not None:
 transform_request["DataProcessing"] = data_processing
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Universal Dark Mode - works on any site
(function() {
var enabled = true;
function applyDarkMode() {
if (!enabled) return;
// Create style element if it doesn't exist
var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
// Dark mode CSS - inverts colors but preserves images/video
style.textContent = '
/* Invert everything except media */
html {
filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
}
/* Restore images, videos, iframes, canvas */
img, video, iframe, canvas, svg, picture, [style*="background-image"] {
filter: invert(1) hue-rotate(180deg) !important;
}
/* Preserve specific elements that should not be inverted */
.no-dark-mode, .no-dark-mode *,
[data-theme="light"], [data-theme="light"],
.ace_editor, .ace_editor *,
.CodeMirror, .CodeMirror *,
.monaco-editor, .monaco-editor *,
.markdown-body pre, .markdown-body pre *,
.highlight, .highlight *,
pre code, pre code * {
filter: none !important;
}
/* Fix common UI elements */
.modal, .popup, .dropdown-menu, .tooltip, .popover {
filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
/* Scrollbars */
::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
// Toggle with Alt+Shift+D
document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
}
}
});
// Apply on load
applyDarkMode();
// Re-apply on dynamic content
var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
}
});
observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();
fix: Transformer tags pydantic validation and shape compatibility by wasim-builds · Pull Request #6103 · aws/sagemaker-python-sdk · GitHub
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fix: Transformer tags pydantic validation and shape compatibility#6103

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fix: Transformer tags pydantic validation and shape compatibility
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feat: Add application/x-parquet support to clarify.ModelConfig


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a24011d

ci: trigger readthedocs rebuild [skip ci]


wasim-builds Jul 25, 2026
2c6b973

fix(core): Transformer tags pydantic validation and shape compatibility


wasim-builds Jul 25, 2026

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16 changes: 10 additions & 6 deletions
sagemaker-core/src/sagemaker/core/clarify/__init__.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -379,12 +379,13 @@
 "image/jpeg",
 "image/png",
 "application/x-npy",
 "application/x-parquet",
 ),
 ),
 SchemaOptional("accept_type"): And(
 str,
 Use(str.lower),
 lambda s: s in ("text/csv", "application/jsonlines", "application/json"),
 lambda s: s in ("text/csv", "application/jsonlines", "application/json", "application/x-parquet"),
 ),
 SchemaOptional("label"): Or(str, int),
 SchemaOptional("probability"): Or(str, int),
Expand DownExpand Up@@ -1020,11 +1021,13 @@ def __init__(
 Must be set with ``instance_count``, ``model_name``
 accept_type (str): The model output format to be used for getting inferences with the
 shadow endpoint. Valid values are ``"text/csv"`` for CSV,
 ``"application/jsonlines"`` for JSON Lines, and ``"application/json"`` for JSON.
 ``"application/jsonlines"`` for JSON Lines, ``"application/json"`` for JSON, and
 ``"application/x-parquet"`` for Parquet.
 Default is the same as ``content_type``.
 content_type (str): The model input format to be used for getting inferences with the
 shadow endpoint. Valid values are ``"text/csv"`` for CSV,
 ``"application/jsonlines"`` for JSON Lines, and ``"application/json"`` for JSON.
 ``"application/jsonlines"`` for JSON Lines, ``"application/json"`` for JSON, and
 ``"application/x-parquet"`` for Parquet.
 Default is the same as ``dataset_format``.
 content_template (str): A template string to be used to construct the model input from
 dataset instances. It is only used, and required, when ``model_content_type`` is
Expand DownExpand Up@@ -1160,10 +1163,10 @@ def __init__(
 )
 self.predictor_config["endpoint_name_prefix"] = endpoint_name_prefix
 if accept_type is not None:
 if accept_type not in ["text/csv", "application/jsonlines", "application/json"]:
 if accept_type not in ["text/csv", "application/jsonlines", "application/json", "application/x-parquet"]:
 raise ValueError(
 f"Invalid accept_type {accept_type}."
 f" Please choose text/csvor application/jsonlines."
 f" Please choose text/csv, application/jsonlines, application/json, or application/x-parquet."
 )
 if time_series_model_config and accept_type == "text/csv":
 raise ValueError(
Expand All@@ -1179,10 +1182,11 @@ def __init__(
 "image/jpg",
 "image/png",
 "application/x-npy",
 "application/x-parquet",
 ]:
 raise ValueError(
 f"Invalid content_type {content_type}."
 f" Please choose text/csv or application/jsonlines."
 f" Please choose a valid content_type."
 )
 if content_type == "application/jsonlines":
 if content_template is None:
Expand Down

14 changes: 12 additions & 2 deletions
sagemaker-core/src/sagemaker/core/transformer.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -409,8 +409,9 @@ def submit(request):
 from sagemaker.core.utils.code_injection.codec import transform as transform_util

 transformed = transform_util(serialized_request, "CreateTransformJobRequest")
 # Remove tags from transformed dict as TransformJob resource doesn't accept it
 # TransformJob does not accept 'tags'/'Tags', so we must pop them to avoid pydantic ValidationError
 transformed.pop("tags", None)
 transformed.pop("Tags", None)
 self.latest_transform_job = TransformJob(**transformed)

 if wait:
Expand DownExpand Up@@ -716,7 +717,16 @@ def _get_transform_request(
 transform_request["Environment"] = env

 if tags is not None:
 transform_request["Tags"] = tags
 from sagemaker.core.common_utils import format_tags
 formatted_tags = format_tags(tags)

 if isinstance(formatted_tags, list):
 formatted_tags = [
 {"Key": t.get("Key", t.get("key", "")), "Value": t.get("Value", t.get("value", ""))}
 if isinstance(t, dict) else t
 for t in formatted_tags
 ]
 transform_request["Tags"] = formatted_tags

 if data_processing is not None:
 transform_request["DataProcessing"] = data_processing
Expand Down
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