feat(telemetry): Add tracking for dry_run, notifications, reuse#6159

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Merged
zhaoqizqwang merged 8 commits into from
Aug 5, 2026
Merged

feat(telemetry): Add tracking for dry_run, notifications, reuse #6159

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691db06
fix: usuability update from feedback
zhaoqizqwang Aug 4, 2026
5ef9216
update documentation
zhaoqizqwang Aug 4, 2026
d4e2042
docs: organize model-customization examples into serverless/serverful…
zhaoqizqwang Aug 4, 2026
1675758
add job notification integ test
zhaoqizqwang Aug 4, 2026
33d52af
feat(telemetry): Add tracking for dry_run, notifications, reuse_resou…
zhaoqizqwang Aug 5, 2026
02a8843
Merge remote-tracking branch 'upstream/master-nova-follow-ups' into s…
zhaoqizqwang Aug 5, 2026
4e0a944
update telemetry
zhaoqizqwang Aug 5, 2026
c637ebb
update telemetry
zhaoqizqwang Aug 5, 2026
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2 changes: 2 additions & 0 deletions sagemaker-serve/src/sagemaker/serve/bedrock_model_builder.py
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Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -258,7 +258,9 @@ def _is_nova_model_for_telemetry(self) -> bool:
func_name="BedrockModelBuilder.deploy",
telemetry_params=[
("model_package", TelemetryParamType.ATTR_EXISTS),
("_is_nova_model_for_telemetry", TelemetryParamType.ATTR_CALL),

@mujtaba1747mujtaba1747Aug 5, 2026

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how does this work? What if bedrock model builder doesn't call _is_nova_model_for_telemetry() and calls _is_nova_model instead?

@zhaoqizqwangzhaoqizqwangAug 5, 2026

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so every time BedrockModelBuilder.deploy() is called, the decorator runs _is_nova_model_for_telemetry before the actual deploy() body executes. The actual method called inside deploy doesn't matter

@mujtaba1747mujtaba1747Aug 5, 2026

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Thanks for explaining.

("imported_model_kms_key_id", TelemetryParamType.KWARG_EXISTS),
("reuse_resources", TelemetryParamType.KWARG_EXISTS),
],
)
def deploy(
Expand Down
4 changes: 4 additions & 0 deletions sagemaker-serve/src/sagemaker/serve/model_builder.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -3948,9 +3948,11 @@ def _reset_build_state(self):
func_name="model_builder.build",
telemetry_params=[
("mode", TelemetryParamType.ATTR_VALUE),
("_is_nova_model_for_telemetry", TelemetryParamType.ATTR_CALL),
("network", TelemetryParamType.ATTR_EXISTS),
("source_code", TelemetryParamType.ATTR_EXISTS),
("inference_spec", TelemetryParamType.ATTR_EXISTS),
("reuse_resources", TelemetryParamType.KWARG_EXISTS),
],
)
@runnable_by_pipeline
Expand DownExpand Up@@ -5326,9 +5328,11 @@ def _deploy_recommendation(
("mode", TelemetryParamType.ATTR_VALUE),
("instance_type", TelemetryParamType.ATTR_VALUE),
("_is_model_customization", TelemetryParamType.ATTR_CALL),
("_is_nova_model_for_telemetry", TelemetryParamType.ATTR_CALL),
("network", TelemetryParamType.ATTR_EXISTS),
("compute", TelemetryParamType.ATTR_EXISTS),
("update_endpoint", TelemetryParamType.KWARG_EXISTS),
("reuse_resources", TelemetryParamType.KWARG_EXISTS),
],
)
def deploy(
Expand Down
16 changes: 16 additions & 0 deletions sagemaker-train/src/sagemaker/train/base_trainer.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -41,6 +41,8 @@
from sagemaker.train.common_utils.notifications import enable_notifications, delete_notification_rule, list_notification_rules
from sagemaker.train.common_utils.validator import validate_hyperpod_compute
from sagemaker.train.common_utils.cloudwatch_metrics import fetch_and_plot_metrics, _get_smhp_log_group
from sagemaker.core.telemetry.telemetry_logging import _telemetry_emitter, TelemetryParamType
from sagemaker.core.telemetry.constants import Feature
from sagemaker.train.defaults import TrainDefaults
from sagemaker.train.model_trainer import ModelTrainer
from sagemaker.train.utils import _get_unique_name
Expand DownExpand Up@@ -365,6 +367,13 @@ def _apply_recipe_to_hyperparameters(

return final_hyperparameters

@_telemetry_emitter(
feature=Feature.MODEL_CUSTOMIZATION,
func_name="BaseTrainer.show_metrics",
telemetry_params=[
("compute", TelemetryParamType.ATTR_TYPE),
],
)
def show_metrics(
self,
metrics: Optional[List[str]] = None,
Expand DownExpand Up@@ -627,6 +636,13 @@ def list_notification_rules(
event_bus_arn=event_bus_arn,
)

@_telemetry_emitter(
feature=Feature.MODEL_CUSTOMIZATION,
func_name="BaseTrainer.stream_logs",
telemetry_params=[
("compute", TelemetryParamType.ATTR_TYPE),
],
)
def stream_logs(self, poll: int = 5, start_time: Optional[Any] = None, tail_lines: Optional[int] = None) -> None:
"""Stream CloudWatch logs in real-time (like ``kubectl logs -f``).

Expand Down
3 changes: 3 additions & 0 deletions sagemaker-train/src/sagemaker/train/common_utils/telemetry_params.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -9,8 +9,10 @@
("kms_key_id", TelemetryParamType.ATTR_EXISTS),
("mlflow_resource_arn", TelemetryParamType.ATTR_EXISTS),
("stopping_condition", TelemetryParamType.ATTR_EXISTS),
("notification_rule_arn", TelemetryParamType.ATTR_EXISTS),
("validation_dataset", TelemetryParamType.KWARG_EXISTS),
("wait", TelemetryParamType.KWARG_EXISTS),
("dry_run", TelemetryParamType.KWARG_EXISTS),
]

# Common params for all evaluators
Expand All@@ -20,4 +22,5 @@
("networking", TelemetryParamType.ATTR_EXISTS),
("kms_key_id", TelemetryParamType.ATTR_EXISTS),
("mlflow_resource_arn", TelemetryParamType.ATTR_EXISTS),
("dry_run", TelemetryParamType.KWARG_EXISTS),
]
9 changes: 9 additions & 0 deletions sagemaker-train/src/sagemaker/train/evaluate/base_evaluator.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -35,6 +35,8 @@
_is_nova_model,
)
from sagemaker.train.common_utils.cloudwatch_metrics import _get_smhp_log_group
from sagemaker.core.telemetry.telemetry_logging import _telemetry_emitter, TelemetryParamType
from sagemaker.core.telemetry.constants import Feature
from sagemaker.train.common_utils.log_streamer import (
LogStreamer,
_format_timestamp,
Expand DownExpand Up@@ -1096,6 +1098,13 @@ def evaluate(self, dry_run: bool = False) -> Any:
"""
raise NotImplementedError("Subclasses must implement evaluate method")

@_telemetry_emitter(
feature=Feature.MODEL_CUSTOMIZATION,
func_name="BaseEvaluator.stream_logs",
telemetry_params=[
("compute", TelemetryParamType.ATTR_TYPE),
],
)
def stream_logs(self, poll: int = 5, start_time=None) -> None:
"""Stream CloudWatch logs for the latest evaluation execution.

Expand Down
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n 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;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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feat(telemetry): Add tracking for dry_run, notifications, reuse#6159

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 Merged

zhaoqizqwang
merged 8 commits into

from

Aug 5, 2026
 Merged


feat(telemetry): Add tracking for dry_run, notifications, reuse
#6159
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691db06

fix: usuability update from feedback


zhaoqizqwang Aug 4, 2026
5ef9216

update documentation


zhaoqizqwang Aug 4, 2026
d4e2042

docs: organize model-customization examples into serverless/serverful…


zhaoqizqwang Aug 4, 2026
1675758

add job notification integ test


zhaoqizqwang Aug 4, 2026
33d52af

feat(telemetry): Add tracking for dry_run, notifications, reuse_resou…


zhaoqizqwang Aug 5, 2026
02a8843

Merge remote-tracking branch 'upstream/master-nova-follow-ups' into s…


zhaoqizqwang Aug 5, 2026
4e0a944

update telemetry


zhaoqizqwang Aug 5, 2026
c637ebb

update telemetry


zhaoqizqwang Aug 5, 2026

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2 changes: 2 additions & 0 deletions
sagemaker-serve/src/sagemaker/serve/bedrock_model_builder.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -258,7 +258,9 @@ def _is_nova_model_for_telemetry(self) -> bool:
 func_name="BedrockModelBuilder.deploy",
 telemetry_params=[
 ("model_package", TelemetryParamType.ATTR_EXISTS),
 ("_is_nova_model_for_telemetry", TelemetryParamType.ATTR_CALL),
@mujtaba1747mujtaba1747Aug 5, 2026

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how does this work? What if bedrock model builder doesn't call _is_nova_model_for_telemetry() and calls _is_nova_model instead?
@zhaoqizqwangzhaoqizqwangAug 5, 2026

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so every time BedrockModelBuilder.deploy() is called, the decorator runs _is_nova_model_for_telemetry before the actual deploy() body executes. The actual method called inside deploy doesn't matter
@mujtaba1747mujtaba1747Aug 5, 2026

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Thanks for explaining.
 ("imported_model_kms_key_id", TelemetryParamType.KWARG_EXISTS),
 ("reuse_resources", TelemetryParamType.KWARG_EXISTS),
 ],
 )
 def deploy(
Expand Down

4 changes: 4 additions & 0 deletions
sagemaker-serve/src/sagemaker/serve/model_builder.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -3948,9 +3948,11 @@ def _reset_build_state(self):
 func_name="model_builder.build",
 telemetry_params=[
 ("mode", TelemetryParamType.ATTR_VALUE),
 ("_is_nova_model_for_telemetry", TelemetryParamType.ATTR_CALL),
 ("network", TelemetryParamType.ATTR_EXISTS),
 ("source_code", TelemetryParamType.ATTR_EXISTS),
 ("inference_spec", TelemetryParamType.ATTR_EXISTS),
 ("reuse_resources", TelemetryParamType.KWARG_EXISTS),
 ],
 )
 @runnable_by_pipeline
Expand DownExpand Up@@ -5326,9 +5328,11 @@ def _deploy_recommendation(
 ("mode", TelemetryParamType.ATTR_VALUE),
 ("instance_type", TelemetryParamType.ATTR_VALUE),
 ("_is_model_customization", TelemetryParamType.ATTR_CALL),
 ("_is_nova_model_for_telemetry", TelemetryParamType.ATTR_CALL),
 ("network", TelemetryParamType.ATTR_EXISTS),
 ("compute", TelemetryParamType.ATTR_EXISTS),
 ("update_endpoint", TelemetryParamType.KWARG_EXISTS),
 ("reuse_resources", TelemetryParamType.KWARG_EXISTS),
 ],
 )
 def deploy(
Expand Down

16 changes: 16 additions & 0 deletions
sagemaker-train/src/sagemaker/train/base_trainer.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -41,6 +41,8 @@
from sagemaker.train.common_utils.notifications import enable_notifications, delete_notification_rule, list_notification_rules
from sagemaker.train.common_utils.validator import validate_hyperpod_compute
from sagemaker.train.common_utils.cloudwatch_metrics import fetch_and_plot_metrics, _get_smhp_log_group
from sagemaker.core.telemetry.telemetry_logging import _telemetry_emitter, TelemetryParamType
from sagemaker.core.telemetry.constants import Feature
from sagemaker.train.defaults import TrainDefaults
from sagemaker.train.model_trainer import ModelTrainer
from sagemaker.train.utils import _get_unique_name
Expand DownExpand Up@@ -365,6 +367,13 @@ def _apply_recipe_to_hyperparameters(

 return final_hyperparameters

 @_telemetry_emitter(
 feature=Feature.MODEL_CUSTOMIZATION,
 func_name="BaseTrainer.show_metrics",
 telemetry_params=[
 ("compute", TelemetryParamType.ATTR_TYPE),
 ],
 )
 def show_metrics(
 self,
 metrics: Optional[List[str]] = None,
Expand DownExpand Up@@ -627,6 +636,13 @@ def list_notification_rules(
 event_bus_arn=event_bus_arn,
 )

 @_telemetry_emitter(
 feature=Feature.MODEL_CUSTOMIZATION,
 func_name="BaseTrainer.stream_logs",
 telemetry_params=[
 ("compute", TelemetryParamType.ATTR_TYPE),
 ],
 )
 def stream_logs(self, poll: int = 5, start_time: Optional[Any] = None, tail_lines: Optional[int] = None) -> None:
 """Stream CloudWatch logs in real-time (like ``kubectl logs -f``).

Expand Down

3 changes: 3 additions & 0 deletions
sagemaker-train/src/sagemaker/train/common_utils/telemetry_params.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,8 +9,10 @@
 ("kms_key_id", TelemetryParamType.ATTR_EXISTS),
 ("mlflow_resource_arn", TelemetryParamType.ATTR_EXISTS),
 ("stopping_condition", TelemetryParamType.ATTR_EXISTS),
 ("notification_rule_arn", TelemetryParamType.ATTR_EXISTS),
 ("validation_dataset", TelemetryParamType.KWARG_EXISTS),
 ("wait", TelemetryParamType.KWARG_EXISTS),
 ("dry_run", TelemetryParamType.KWARG_EXISTS),
]

# Common params for all evaluators
Expand All@@ -20,4 +22,5 @@
 ("networking", TelemetryParamType.ATTR_EXISTS),
 ("kms_key_id", TelemetryParamType.ATTR_EXISTS),
 ("mlflow_resource_arn", TelemetryParamType.ATTR_EXISTS),
 ("dry_run", TelemetryParamType.KWARG_EXISTS),
]

9 changes: 9 additions & 0 deletions
sagemaker-train/src/sagemaker/train/evaluate/base_evaluator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -35,6 +35,8 @@
 _is_nova_model,
)
from sagemaker.train.common_utils.cloudwatch_metrics import _get_smhp_log_group
from sagemaker.core.telemetry.telemetry_logging import _telemetry_emitter, TelemetryParamType
from sagemaker.core.telemetry.constants import Feature
from sagemaker.train.common_utils.log_streamer import (
 LogStreamer,
 _format_timestamp,
Expand DownExpand Up@@ -1096,6 +1098,13 @@ def evaluate(self, dry_run: bool = False) -> Any:
 """
 raise NotImplementedError("Subclasses must implement evaluate method")

 @_telemetry_emitter(
 feature=Feature.MODEL_CUSTOMIZATION,
 func_name="BaseEvaluator.stream_logs",
 telemetry_params=[
 ("compute", TelemetryParamType.ATTR_TYPE),
 ],
 )
 def stream_logs(self, poll: int = 5, start_time=None) -> None:
 """Stream CloudWatch logs for the latest evaluation execution.

Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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feat(telemetry): Add tracking for dry_run, notifications, reuse#6159

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 Merged

zhaoqizqwang
merged 8 commits into

from

Aug 5, 2026
 Merged


feat(telemetry): Add tracking for dry_run, notifications, reuse
#6159
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691db06

fix: usuability update from feedback


zhaoqizqwang Aug 4, 2026
5ef9216

update documentation


zhaoqizqwang Aug 4, 2026
d4e2042

docs: organize model-customization examples into serverless/serverful…


zhaoqizqwang Aug 4, 2026
1675758

add job notification integ test


zhaoqizqwang Aug 4, 2026
33d52af

feat(telemetry): Add tracking for dry_run, notifications, reuse_resou…


zhaoqizqwang Aug 5, 2026
02a8843

Merge remote-tracking branch 'upstream/master-nova-follow-ups' into s…


zhaoqizqwang Aug 5, 2026
4e0a944

update telemetry


zhaoqizqwang Aug 5, 2026
c637ebb

update telemetry


zhaoqizqwang Aug 5, 2026

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2 changes: 2 additions & 0 deletions
sagemaker-serve/src/sagemaker/serve/bedrock_model_builder.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -258,7 +258,9 @@ def _is_nova_model_for_telemetry(self) -> bool:
 func_name="BedrockModelBuilder.deploy",
 telemetry_params=[
 ("model_package", TelemetryParamType.ATTR_EXISTS),
 ("_is_nova_model_for_telemetry", TelemetryParamType.ATTR_CALL),
@mujtaba1747mujtaba1747Aug 5, 2026

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how does this work? What if bedrock model builder doesn't call _is_nova_model_for_telemetry() and calls _is_nova_model instead?
@zhaoqizqwangzhaoqizqwangAug 5, 2026

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so every time BedrockModelBuilder.deploy() is called, the decorator runs _is_nova_model_for_telemetry before the actual deploy() body executes. The actual method called inside deploy doesn't matter
@mujtaba1747mujtaba1747Aug 5, 2026

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Thanks for explaining.
 ("imported_model_kms_key_id", TelemetryParamType.KWARG_EXISTS),
 ("reuse_resources", TelemetryParamType.KWARG_EXISTS),
 ],
 )
 def deploy(
Expand Down

4 changes: 4 additions & 0 deletions
sagemaker-serve/src/sagemaker/serve/model_builder.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -3948,9 +3948,11 @@ def _reset_build_state(self):
 func_name="model_builder.build",
 telemetry_params=[
 ("mode", TelemetryParamType.ATTR_VALUE),
 ("_is_nova_model_for_telemetry", TelemetryParamType.ATTR_CALL),
 ("network", TelemetryParamType.ATTR_EXISTS),
 ("source_code", TelemetryParamType.ATTR_EXISTS),
 ("inference_spec", TelemetryParamType.ATTR_EXISTS),
 ("reuse_resources", TelemetryParamType.KWARG_EXISTS),
 ],
 )
 @runnable_by_pipeline
Expand DownExpand Up@@ -5326,9 +5328,11 @@ def _deploy_recommendation(
 ("mode", TelemetryParamType.ATTR_VALUE),
 ("instance_type", TelemetryParamType.ATTR_VALUE),
 ("_is_model_customization", TelemetryParamType.ATTR_CALL),
 ("_is_nova_model_for_telemetry", TelemetryParamType.ATTR_CALL),
 ("network", TelemetryParamType.ATTR_EXISTS),
 ("compute", TelemetryParamType.ATTR_EXISTS),
 ("update_endpoint", TelemetryParamType.KWARG_EXISTS),
 ("reuse_resources", TelemetryParamType.KWARG_EXISTS),
 ],
 )
 def deploy(
Expand Down

16 changes: 16 additions & 0 deletions
sagemaker-train/src/sagemaker/train/base_trainer.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -41,6 +41,8 @@
from sagemaker.train.common_utils.notifications import enable_notifications, delete_notification_rule, list_notification_rules
from sagemaker.train.common_utils.validator import validate_hyperpod_compute
from sagemaker.train.common_utils.cloudwatch_metrics import fetch_and_plot_metrics, _get_smhp_log_group
from sagemaker.core.telemetry.telemetry_logging import _telemetry_emitter, TelemetryParamType
from sagemaker.core.telemetry.constants import Feature
from sagemaker.train.defaults import TrainDefaults
from sagemaker.train.model_trainer import ModelTrainer
from sagemaker.train.utils import _get_unique_name
Expand DownExpand Up@@ -365,6 +367,13 @@ def _apply_recipe_to_hyperparameters(

 return final_hyperparameters

 @_telemetry_emitter(
 feature=Feature.MODEL_CUSTOMIZATION,
 func_name="BaseTrainer.show_metrics",
 telemetry_params=[
 ("compute", TelemetryParamType.ATTR_TYPE),
 ],
 )
 def show_metrics(
 self,
 metrics: Optional[List[str]] = None,
Expand DownExpand Up@@ -627,6 +636,13 @@ def list_notification_rules(
 event_bus_arn=event_bus_arn,
 )

 @_telemetry_emitter(
 feature=Feature.MODEL_CUSTOMIZATION,
 func_name="BaseTrainer.stream_logs",
 telemetry_params=[
 ("compute", TelemetryParamType.ATTR_TYPE),
 ],
 )
 def stream_logs(self, poll: int = 5, start_time: Optional[Any] = None, tail_lines: Optional[int] = None) -> None:
 """Stream CloudWatch logs in real-time (like ``kubectl logs -f``).

Expand Down

3 changes: 3 additions & 0 deletions
sagemaker-train/src/sagemaker/train/common_utils/telemetry_params.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,8 +9,10 @@
 ("kms_key_id", TelemetryParamType.ATTR_EXISTS),
 ("mlflow_resource_arn", TelemetryParamType.ATTR_EXISTS),
 ("stopping_condition", TelemetryParamType.ATTR_EXISTS),
 ("notification_rule_arn", TelemetryParamType.ATTR_EXISTS),
 ("validation_dataset", TelemetryParamType.KWARG_EXISTS),
 ("wait", TelemetryParamType.KWARG_EXISTS),
 ("dry_run", TelemetryParamType.KWARG_EXISTS),
]

# Common params for all evaluators
Expand All@@ -20,4 +22,5 @@
 ("networking", TelemetryParamType.ATTR_EXISTS),
 ("kms_key_id", TelemetryParamType.ATTR_EXISTS),
 ("mlflow_resource_arn", TelemetryParamType.ATTR_EXISTS),
 ("dry_run", TelemetryParamType.KWARG_EXISTS),
]

9 changes: 9 additions & 0 deletions
sagemaker-train/src/sagemaker/train/evaluate/base_evaluator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -35,6 +35,8 @@
 _is_nova_model,
)
from sagemaker.train.common_utils.cloudwatch_metrics import _get_smhp_log_group
from sagemaker.core.telemetry.telemetry_logging import _telemetry_emitter, TelemetryParamType
from sagemaker.core.telemetry.constants import Feature
from sagemaker.train.common_utils.log_streamer import (
 LogStreamer,
 _format_timestamp,
Expand DownExpand Up@@ -1096,6 +1098,13 @@ def evaluate(self, dry_run: bool = False) -> Any:
 """
 raise NotImplementedError("Subclasses must implement evaluate method")

 @_telemetry_emitter(
 feature=Feature.MODEL_CUSTOMIZATION,
 func_name="BaseEvaluator.stream_logs",
 telemetry_params=[
 ("compute", TelemetryParamType.ATTR_TYPE),
 ],
 )
 def stream_logs(self, poll: int = 5, start_time=None) -> None:
 """Stream CloudWatch logs for the latest evaluation execution.

Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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zhaoqizqwang
merged 8 commits into

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Aug 5, 2026
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feat(telemetry): Add tracking for dry_run, notifications, reuse
#6159
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691db06

fix: usuability update from feedback


zhaoqizqwang Aug 4, 2026
5ef9216

update documentation


zhaoqizqwang Aug 4, 2026
d4e2042

docs: organize model-customization examples into serverless/serverful…


zhaoqizqwang Aug 4, 2026
1675758

add job notification integ test


zhaoqizqwang Aug 4, 2026
33d52af

feat(telemetry): Add tracking for dry_run, notifications, reuse_resou…


zhaoqizqwang Aug 5, 2026
02a8843

Merge remote-tracking branch 'upstream/master-nova-follow-ups' into s…


zhaoqizqwang Aug 5, 2026
4e0a944

update telemetry


zhaoqizqwang Aug 5, 2026
c637ebb

update telemetry


zhaoqizqwang Aug 5, 2026

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2 changes: 2 additions & 0 deletions
sagemaker-serve/src/sagemaker/serve/bedrock_model_builder.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -258,7 +258,9 @@ def _is_nova_model_for_telemetry(self) -> bool:
 func_name="BedrockModelBuilder.deploy",
 telemetry_params=[
 ("model_package", TelemetryParamType.ATTR_EXISTS),
 ("_is_nova_model_for_telemetry", TelemetryParamType.ATTR_CALL),
@mujtaba1747mujtaba1747Aug 5, 2026

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how does this work? What if bedrock model builder doesn't call _is_nova_model_for_telemetry() and calls _is_nova_model instead?
@zhaoqizqwangzhaoqizqwangAug 5, 2026

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so every time BedrockModelBuilder.deploy() is called, the decorator runs _is_nova_model_for_telemetry before the actual deploy() body executes. The actual method called inside deploy doesn't matter
@mujtaba1747mujtaba1747Aug 5, 2026

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Thanks for explaining.
 ("imported_model_kms_key_id", TelemetryParamType.KWARG_EXISTS),
 ("reuse_resources", TelemetryParamType.KWARG_EXISTS),
 ],
 )
 def deploy(
Expand Down

4 changes: 4 additions & 0 deletions
sagemaker-serve/src/sagemaker/serve/model_builder.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -3948,9 +3948,11 @@ def _reset_build_state(self):
 func_name="model_builder.build",
 telemetry_params=[
 ("mode", TelemetryParamType.ATTR_VALUE),
 ("_is_nova_model_for_telemetry", TelemetryParamType.ATTR_CALL),
 ("network", TelemetryParamType.ATTR_EXISTS),
 ("source_code", TelemetryParamType.ATTR_EXISTS),
 ("inference_spec", TelemetryParamType.ATTR_EXISTS),
 ("reuse_resources", TelemetryParamType.KWARG_EXISTS),
 ],
 )
 @runnable_by_pipeline
Expand DownExpand Up@@ -5326,9 +5328,11 @@ def _deploy_recommendation(
 ("mode", TelemetryParamType.ATTR_VALUE),
 ("instance_type", TelemetryParamType.ATTR_VALUE),
 ("_is_model_customization", TelemetryParamType.ATTR_CALL),
 ("_is_nova_model_for_telemetry", TelemetryParamType.ATTR_CALL),
 ("network", TelemetryParamType.ATTR_EXISTS),
 ("compute", TelemetryParamType.ATTR_EXISTS),
 ("update_endpoint", TelemetryParamType.KWARG_EXISTS),
 ("reuse_resources", TelemetryParamType.KWARG_EXISTS),
 ],
 )
 def deploy(
Expand Down

16 changes: 16 additions & 0 deletions
sagemaker-train/src/sagemaker/train/base_trainer.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -41,6 +41,8 @@
from sagemaker.train.common_utils.notifications import enable_notifications, delete_notification_rule, list_notification_rules
from sagemaker.train.common_utils.validator import validate_hyperpod_compute
from sagemaker.train.common_utils.cloudwatch_metrics import fetch_and_plot_metrics, _get_smhp_log_group
from sagemaker.core.telemetry.telemetry_logging import _telemetry_emitter, TelemetryParamType
from sagemaker.core.telemetry.constants import Feature
from sagemaker.train.defaults import TrainDefaults
from sagemaker.train.model_trainer import ModelTrainer
from sagemaker.train.utils import _get_unique_name
Expand DownExpand Up@@ -365,6 +367,13 @@ def _apply_recipe_to_hyperparameters(

 return final_hyperparameters

 @_telemetry_emitter(
 feature=Feature.MODEL_CUSTOMIZATION,
 func_name="BaseTrainer.show_metrics",
 telemetry_params=[
 ("compute", TelemetryParamType.ATTR_TYPE),
 ],
 )
 def show_metrics(
 self,
 metrics: Optional[List[str]] = None,
Expand DownExpand Up@@ -627,6 +636,13 @@ def list_notification_rules(
 event_bus_arn=event_bus_arn,
 )

 @_telemetry_emitter(
 feature=Feature.MODEL_CUSTOMIZATION,
 func_name="BaseTrainer.stream_logs",
 telemetry_params=[
 ("compute", TelemetryParamType.ATTR_TYPE),
 ],
 )
 def stream_logs(self, poll: int = 5, start_time: Optional[Any] = None, tail_lines: Optional[int] = None) -> None:
 """Stream CloudWatch logs in real-time (like ``kubectl logs -f``).

Expand Down

3 changes: 3 additions & 0 deletions
sagemaker-train/src/sagemaker/train/common_utils/telemetry_params.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,8 +9,10 @@
 ("kms_key_id", TelemetryParamType.ATTR_EXISTS),
 ("mlflow_resource_arn", TelemetryParamType.ATTR_EXISTS),
 ("stopping_condition", TelemetryParamType.ATTR_EXISTS),
 ("notification_rule_arn", TelemetryParamType.ATTR_EXISTS),
 ("validation_dataset", TelemetryParamType.KWARG_EXISTS),
 ("wait", TelemetryParamType.KWARG_EXISTS),
 ("dry_run", TelemetryParamType.KWARG_EXISTS),
]

# Common params for all evaluators
Expand All@@ -20,4 +22,5 @@
 ("networking", TelemetryParamType.ATTR_EXISTS),
 ("kms_key_id", TelemetryParamType.ATTR_EXISTS),
 ("mlflow_resource_arn", TelemetryParamType.ATTR_EXISTS),
 ("dry_run", TelemetryParamType.KWARG_EXISTS),
]

9 changes: 9 additions & 0 deletions
sagemaker-train/src/sagemaker/train/evaluate/base_evaluator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -35,6 +35,8 @@
 _is_nova_model,
)
from sagemaker.train.common_utils.cloudwatch_metrics import _get_smhp_log_group
from sagemaker.core.telemetry.telemetry_logging import _telemetry_emitter, TelemetryParamType
from sagemaker.core.telemetry.constants import Feature
from sagemaker.train.common_utils.log_streamer import (
 LogStreamer,
 _format_timestamp,
Expand DownExpand Up@@ -1096,6 +1098,13 @@ def evaluate(self, dry_run: bool = False) -> Any:
 """
 raise NotImplementedError("Subclasses must implement evaluate method")

 @_telemetry_emitter(
 feature=Feature.MODEL_CUSTOMIZATION,
 func_name="BaseEvaluator.stream_logs",
 telemetry_params=[
 ("compute", TelemetryParamType.ATTR_TYPE),
 ],
 )
 def stream_logs(self, poll: int = 5, start_time=None) -> None:
 """Stream CloudWatch logs for the latest evaluation execution.

Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
feat(telemetry): Add tracking for dry_run, notifications, reuse#6159

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 Merged

zhaoqizqwang
merged 8 commits into

from

Aug 5, 2026
 Merged


feat(telemetry): Add tracking for dry_run, notifications, reuse
#6159
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691db06

fix: usuability update from feedback


zhaoqizqwang Aug 4, 2026
5ef9216

update documentation


zhaoqizqwang Aug 4, 2026
d4e2042

docs: organize model-customization examples into serverless/serverful…


zhaoqizqwang Aug 4, 2026
1675758

add job notification integ test


zhaoqizqwang Aug 4, 2026
33d52af

feat(telemetry): Add tracking for dry_run, notifications, reuse_resou…


zhaoqizqwang Aug 5, 2026
02a8843

Merge remote-tracking branch 'upstream/master-nova-follow-ups' into s…


zhaoqizqwang Aug 5, 2026
4e0a944

update telemetry


zhaoqizqwang Aug 5, 2026
c637ebb

update telemetry


zhaoqizqwang Aug 5, 2026

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2 changes: 2 additions & 0 deletions
sagemaker-serve/src/sagemaker/serve/bedrock_model_builder.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -258,7 +258,9 @@ def _is_nova_model_for_telemetry(self) -> bool:
 func_name="BedrockModelBuilder.deploy",
 telemetry_params=[
 ("model_package", TelemetryParamType.ATTR_EXISTS),
 ("_is_nova_model_for_telemetry", TelemetryParamType.ATTR_CALL),
@mujtaba1747mujtaba1747Aug 5, 2026

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how does this work? What if bedrock model builder doesn't call _is_nova_model_for_telemetry() and calls _is_nova_model instead?
@zhaoqizqwangzhaoqizqwangAug 5, 2026

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so every time BedrockModelBuilder.deploy() is called, the decorator runs _is_nova_model_for_telemetry before the actual deploy() body executes. The actual method called inside deploy doesn't matter
@mujtaba1747mujtaba1747Aug 5, 2026

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Thanks for explaining.
 ("imported_model_kms_key_id", TelemetryParamType.KWARG_EXISTS),
 ("reuse_resources", TelemetryParamType.KWARG_EXISTS),
 ],
 )
 def deploy(
Expand Down

4 changes: 4 additions & 0 deletions
sagemaker-serve/src/sagemaker/serve/model_builder.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -3948,9 +3948,11 @@ def _reset_build_state(self):
 func_name="model_builder.build",
 telemetry_params=[
 ("mode", TelemetryParamType.ATTR_VALUE),
 ("_is_nova_model_for_telemetry", TelemetryParamType.ATTR_CALL),
 ("network", TelemetryParamType.ATTR_EXISTS),
 ("source_code", TelemetryParamType.ATTR_EXISTS),
 ("inference_spec", TelemetryParamType.ATTR_EXISTS),
 ("reuse_resources", TelemetryParamType.KWARG_EXISTS),
 ],
 )
 @runnable_by_pipeline
Expand DownExpand Up@@ -5326,9 +5328,11 @@ def _deploy_recommendation(
 ("mode", TelemetryParamType.ATTR_VALUE),
 ("instance_type", TelemetryParamType.ATTR_VALUE),
 ("_is_model_customization", TelemetryParamType.ATTR_CALL),
 ("_is_nova_model_for_telemetry", TelemetryParamType.ATTR_CALL),
 ("network", TelemetryParamType.ATTR_EXISTS),
 ("compute", TelemetryParamType.ATTR_EXISTS),
 ("update_endpoint", TelemetryParamType.KWARG_EXISTS),
 ("reuse_resources", TelemetryParamType.KWARG_EXISTS),
 ],
 )
 def deploy(
Expand Down

16 changes: 16 additions & 0 deletions
sagemaker-train/src/sagemaker/train/base_trainer.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -41,6 +41,8 @@
from sagemaker.train.common_utils.notifications import enable_notifications, delete_notification_rule, list_notification_rules
from sagemaker.train.common_utils.validator import validate_hyperpod_compute
from sagemaker.train.common_utils.cloudwatch_metrics import fetch_and_plot_metrics, _get_smhp_log_group
from sagemaker.core.telemetry.telemetry_logging import _telemetry_emitter, TelemetryParamType
from sagemaker.core.telemetry.constants import Feature
from sagemaker.train.defaults import TrainDefaults
from sagemaker.train.model_trainer import ModelTrainer
from sagemaker.train.utils import _get_unique_name
Expand DownExpand Up@@ -365,6 +367,13 @@ def _apply_recipe_to_hyperparameters(

 return final_hyperparameters

 @_telemetry_emitter(
 feature=Feature.MODEL_CUSTOMIZATION,
 func_name="BaseTrainer.show_metrics",
 telemetry_params=[
 ("compute", TelemetryParamType.ATTR_TYPE),
 ],
 )
 def show_metrics(
 self,
 metrics: Optional[List[str]] = None,
Expand DownExpand Up@@ -627,6 +636,13 @@ def list_notification_rules(
 event_bus_arn=event_bus_arn,
 )

 @_telemetry_emitter(
 feature=Feature.MODEL_CUSTOMIZATION,
 func_name="BaseTrainer.stream_logs",
 telemetry_params=[
 ("compute", TelemetryParamType.ATTR_TYPE),
 ],
 )
 def stream_logs(self, poll: int = 5, start_time: Optional[Any] = None, tail_lines: Optional[int] = None) -> None:
 """Stream CloudWatch logs in real-time (like ``kubectl logs -f``).

Expand Down

3 changes: 3 additions & 0 deletions
sagemaker-train/src/sagemaker/train/common_utils/telemetry_params.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,8 +9,10 @@
 ("kms_key_id", TelemetryParamType.ATTR_EXISTS),
 ("mlflow_resource_arn", TelemetryParamType.ATTR_EXISTS),
 ("stopping_condition", TelemetryParamType.ATTR_EXISTS),
 ("notification_rule_arn", TelemetryParamType.ATTR_EXISTS),
 ("validation_dataset", TelemetryParamType.KWARG_EXISTS),
 ("wait", TelemetryParamType.KWARG_EXISTS),
 ("dry_run", TelemetryParamType.KWARG_EXISTS),
]

# Common params for all evaluators
Expand All@@ -20,4 +22,5 @@
 ("networking", TelemetryParamType.ATTR_EXISTS),
 ("kms_key_id", TelemetryParamType.ATTR_EXISTS),
 ("mlflow_resource_arn", TelemetryParamType.ATTR_EXISTS),
 ("dry_run", TelemetryParamType.KWARG_EXISTS),
]

9 changes: 9 additions & 0 deletions
sagemaker-train/src/sagemaker/train/evaluate/base_evaluator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -35,6 +35,8 @@
 _is_nova_model,
)
from sagemaker.train.common_utils.cloudwatch_metrics import _get_smhp_log_group
from sagemaker.core.telemetry.telemetry_logging import _telemetry_emitter, TelemetryParamType
from sagemaker.core.telemetry.constants import Feature
from sagemaker.train.common_utils.log_streamer import (
 LogStreamer,
 _format_timestamp,
Expand DownExpand Up@@ -1096,6 +1098,13 @@ def evaluate(self, dry_run: bool = False) -> Any:
 """
 raise NotImplementedError("Subclasses must implement evaluate method")

 @_telemetry_emitter(
 feature=Feature.MODEL_CUSTOMIZATION,
 func_name="BaseEvaluator.stream_logs",
 telemetry_params=[
 ("compute", TelemetryParamType.ATTR_TYPE),
 ],
 )
 def stream_logs(self, poll: int = 5, start_time=None) -> None:
 """Stream CloudWatch logs for the latest evaluation execution.

Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
feat(telemetry): Add tracking for dry_run, notifications, reuse#6159

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 Merged

zhaoqizqwang
merged 8 commits into

from

Aug 5, 2026
 Merged


feat(telemetry): Add tracking for dry_run, notifications, reuse
#6159
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691db06

fix: usuability update from feedback


zhaoqizqwang Aug 4, 2026
5ef9216

update documentation


zhaoqizqwang Aug 4, 2026
d4e2042

docs: organize model-customization examples into serverless/serverful…


zhaoqizqwang Aug 4, 2026
1675758

add job notification integ test


zhaoqizqwang Aug 4, 2026
33d52af

feat(telemetry): Add tracking for dry_run, notifications, reuse_resou…


zhaoqizqwang Aug 5, 2026
02a8843

Merge remote-tracking branch 'upstream/master-nova-follow-ups' into s…


zhaoqizqwang Aug 5, 2026
4e0a944

update telemetry


zhaoqizqwang Aug 5, 2026
c637ebb

update telemetry


zhaoqizqwang Aug 5, 2026

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2 changes: 2 additions & 0 deletions
sagemaker-serve/src/sagemaker/serve/bedrock_model_builder.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -258,7 +258,9 @@ def _is_nova_model_for_telemetry(self) -> bool:
 func_name="BedrockModelBuilder.deploy",
 telemetry_params=[
 ("model_package", TelemetryParamType.ATTR_EXISTS),
 ("_is_nova_model_for_telemetry", TelemetryParamType.ATTR_CALL),
@mujtaba1747mujtaba1747Aug 5, 2026

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how does this work? What if bedrock model builder doesn't call _is_nova_model_for_telemetry() and calls _is_nova_model instead?
@zhaoqizqwangzhaoqizqwangAug 5, 2026

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so every time BedrockModelBuilder.deploy() is called, the decorator runs _is_nova_model_for_telemetry before the actual deploy() body executes. The actual method called inside deploy doesn't matter
@mujtaba1747mujtaba1747Aug 5, 2026

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Thanks for explaining.
 ("imported_model_kms_key_id", TelemetryParamType.KWARG_EXISTS),
 ("reuse_resources", TelemetryParamType.KWARG_EXISTS),
 ],
 )
 def deploy(
Expand Down

4 changes: 4 additions & 0 deletions
sagemaker-serve/src/sagemaker/serve/model_builder.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -3948,9 +3948,11 @@ def _reset_build_state(self):
 func_name="model_builder.build",
 telemetry_params=[
 ("mode", TelemetryParamType.ATTR_VALUE),
 ("_is_nova_model_for_telemetry", TelemetryParamType.ATTR_CALL),
 ("network", TelemetryParamType.ATTR_EXISTS),
 ("source_code", TelemetryParamType.ATTR_EXISTS),
 ("inference_spec", TelemetryParamType.ATTR_EXISTS),
 ("reuse_resources", TelemetryParamType.KWARG_EXISTS),
 ],
 )
 @runnable_by_pipeline
Expand DownExpand Up@@ -5326,9 +5328,11 @@ def _deploy_recommendation(
 ("mode", TelemetryParamType.ATTR_VALUE),
 ("instance_type", TelemetryParamType.ATTR_VALUE),
 ("_is_model_customization", TelemetryParamType.ATTR_CALL),
 ("_is_nova_model_for_telemetry", TelemetryParamType.ATTR_CALL),
 ("network", TelemetryParamType.ATTR_EXISTS),
 ("compute", TelemetryParamType.ATTR_EXISTS),
 ("update_endpoint", TelemetryParamType.KWARG_EXISTS),
 ("reuse_resources", TelemetryParamType.KWARG_EXISTS),
 ],
 )
 def deploy(
Expand Down

16 changes: 16 additions & 0 deletions
sagemaker-train/src/sagemaker/train/base_trainer.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -41,6 +41,8 @@
from sagemaker.train.common_utils.notifications import enable_notifications, delete_notification_rule, list_notification_rules
from sagemaker.train.common_utils.validator import validate_hyperpod_compute
from sagemaker.train.common_utils.cloudwatch_metrics import fetch_and_plot_metrics, _get_smhp_log_group
from sagemaker.core.telemetry.telemetry_logging import _telemetry_emitter, TelemetryParamType
from sagemaker.core.telemetry.constants import Feature
from sagemaker.train.defaults import TrainDefaults
from sagemaker.train.model_trainer import ModelTrainer
from sagemaker.train.utils import _get_unique_name
Expand DownExpand Up@@ -365,6 +367,13 @@ def _apply_recipe_to_hyperparameters(

 return final_hyperparameters

 @_telemetry_emitter(
 feature=Feature.MODEL_CUSTOMIZATION,
 func_name="BaseTrainer.show_metrics",
 telemetry_params=[
 ("compute", TelemetryParamType.ATTR_TYPE),
 ],
 )
 def show_metrics(
 self,
 metrics: Optional[List[str]] = None,
Expand DownExpand Up@@ -627,6 +636,13 @@ def list_notification_rules(
 event_bus_arn=event_bus_arn,
 )

 @_telemetry_emitter(
 feature=Feature.MODEL_CUSTOMIZATION,
 func_name="BaseTrainer.stream_logs",
 telemetry_params=[
 ("compute", TelemetryParamType.ATTR_TYPE),
 ],
 )
 def stream_logs(self, poll: int = 5, start_time: Optional[Any] = None, tail_lines: Optional[int] = None) -> None:
 """Stream CloudWatch logs in real-time (like ``kubectl logs -f``).

Expand Down

3 changes: 3 additions & 0 deletions
sagemaker-train/src/sagemaker/train/common_utils/telemetry_params.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,8 +9,10 @@
 ("kms_key_id", TelemetryParamType.ATTR_EXISTS),
 ("mlflow_resource_arn", TelemetryParamType.ATTR_EXISTS),
 ("stopping_condition", TelemetryParamType.ATTR_EXISTS),
 ("notification_rule_arn", TelemetryParamType.ATTR_EXISTS),
 ("validation_dataset", TelemetryParamType.KWARG_EXISTS),
 ("wait", TelemetryParamType.KWARG_EXISTS),
 ("dry_run", TelemetryParamType.KWARG_EXISTS),
]

# Common params for all evaluators
Expand All@@ -20,4 +22,5 @@
 ("networking", TelemetryParamType.ATTR_EXISTS),
 ("kms_key_id", TelemetryParamType.ATTR_EXISTS),
 ("mlflow_resource_arn", TelemetryParamType.ATTR_EXISTS),
 ("dry_run", TelemetryParamType.KWARG_EXISTS),
]

9 changes: 9 additions & 0 deletions
sagemaker-train/src/sagemaker/train/evaluate/base_evaluator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -35,6 +35,8 @@
 _is_nova_model,
)
from sagemaker.train.common_utils.cloudwatch_metrics import _get_smhp_log_group
from sagemaker.core.telemetry.telemetry_logging import _telemetry_emitter, TelemetryParamType
from sagemaker.core.telemetry.constants import Feature
from sagemaker.train.common_utils.log_streamer import (
 LogStreamer,
 _format_timestamp,
Expand DownExpand Up@@ -1096,6 +1098,13 @@ def evaluate(self, dry_run: bool = False) -> Any:
 """
 raise NotImplementedError("Subclasses must implement evaluate method")

 @_telemetry_emitter(
 feature=Feature.MODEL_CUSTOMIZATION,
 func_name="BaseEvaluator.stream_logs",
 telemetry_params=[
 ("compute", TelemetryParamType.ATTR_TYPE),
 ],
 )
 def stream_logs(self, poll: int = 5, start_time=None) -> None:
 """Stream CloudWatch logs for the latest evaluation execution.

Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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feat(telemetry): Add tracking for dry_run, notifications, reuse#6159

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 Merged

zhaoqizqwang
merged 8 commits into

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Aug 5, 2026
 Merged


feat(telemetry): Add tracking for dry_run, notifications, reuse
#6159
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691db06

fix: usuability update from feedback


zhaoqizqwang Aug 4, 2026
5ef9216

update documentation


zhaoqizqwang Aug 4, 2026
d4e2042

docs: organize model-customization examples into serverless/serverful…


zhaoqizqwang Aug 4, 2026
1675758

add job notification integ test


zhaoqizqwang Aug 4, 2026
33d52af

feat(telemetry): Add tracking for dry_run, notifications, reuse_resou…


zhaoqizqwang Aug 5, 2026
02a8843

Merge remote-tracking branch 'upstream/master-nova-follow-ups' into s…


zhaoqizqwang Aug 5, 2026
4e0a944

update telemetry


zhaoqizqwang Aug 5, 2026
c637ebb

update telemetry


zhaoqizqwang Aug 5, 2026

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2 changes: 2 additions & 0 deletions
sagemaker-serve/src/sagemaker/serve/bedrock_model_builder.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -258,7 +258,9 @@ def _is_nova_model_for_telemetry(self) -> bool:
 func_name="BedrockModelBuilder.deploy",
 telemetry_params=[
 ("model_package", TelemetryParamType.ATTR_EXISTS),
 ("_is_nova_model_for_telemetry", TelemetryParamType.ATTR_CALL),
@mujtaba1747mujtaba1747Aug 5, 2026

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how does this work? What if bedrock model builder doesn't call _is_nova_model_for_telemetry() and calls _is_nova_model instead?
@zhaoqizqwangzhaoqizqwangAug 5, 2026

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so every time BedrockModelBuilder.deploy() is called, the decorator runs _is_nova_model_for_telemetry before the actual deploy() body executes. The actual method called inside deploy doesn't matter
@mujtaba1747mujtaba1747Aug 5, 2026

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Thanks for explaining.
 ("imported_model_kms_key_id", TelemetryParamType.KWARG_EXISTS),
 ("reuse_resources", TelemetryParamType.KWARG_EXISTS),
 ],
 )
 def deploy(
Expand Down

4 changes: 4 additions & 0 deletions
sagemaker-serve/src/sagemaker/serve/model_builder.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -3948,9 +3948,11 @@ def _reset_build_state(self):
 func_name="model_builder.build",
 telemetry_params=[
 ("mode", TelemetryParamType.ATTR_VALUE),
 ("_is_nova_model_for_telemetry", TelemetryParamType.ATTR_CALL),
 ("network", TelemetryParamType.ATTR_EXISTS),
 ("source_code", TelemetryParamType.ATTR_EXISTS),
 ("inference_spec", TelemetryParamType.ATTR_EXISTS),
 ("reuse_resources", TelemetryParamType.KWARG_EXISTS),
 ],
 )
 @runnable_by_pipeline
Expand DownExpand Up@@ -5326,9 +5328,11 @@ def _deploy_recommendation(
 ("mode", TelemetryParamType.ATTR_VALUE),
 ("instance_type", TelemetryParamType.ATTR_VALUE),
 ("_is_model_customization", TelemetryParamType.ATTR_CALL),
 ("_is_nova_model_for_telemetry", TelemetryParamType.ATTR_CALL),
 ("network", TelemetryParamType.ATTR_EXISTS),
 ("compute", TelemetryParamType.ATTR_EXISTS),
 ("update_endpoint", TelemetryParamType.KWARG_EXISTS),
 ("reuse_resources", TelemetryParamType.KWARG_EXISTS),
 ],
 )
 def deploy(
Expand Down

16 changes: 16 additions & 0 deletions
sagemaker-train/src/sagemaker/train/base_trainer.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -41,6 +41,8 @@
from sagemaker.train.common_utils.notifications import enable_notifications, delete_notification_rule, list_notification_rules
from sagemaker.train.common_utils.validator import validate_hyperpod_compute
from sagemaker.train.common_utils.cloudwatch_metrics import fetch_and_plot_metrics, _get_smhp_log_group
from sagemaker.core.telemetry.telemetry_logging import _telemetry_emitter, TelemetryParamType
from sagemaker.core.telemetry.constants import Feature
from sagemaker.train.defaults import TrainDefaults
from sagemaker.train.model_trainer import ModelTrainer
from sagemaker.train.utils import _get_unique_name
Expand DownExpand Up@@ -365,6 +367,13 @@ def _apply_recipe_to_hyperparameters(

 return final_hyperparameters

 @_telemetry_emitter(
 feature=Feature.MODEL_CUSTOMIZATION,
 func_name="BaseTrainer.show_metrics",
 telemetry_params=[
 ("compute", TelemetryParamType.ATTR_TYPE),
 ],
 )
 def show_metrics(
 self,
 metrics: Optional[List[str]] = None,
Expand DownExpand Up@@ -627,6 +636,13 @@ def list_notification_rules(
 event_bus_arn=event_bus_arn,
 )

 @_telemetry_emitter(
 feature=Feature.MODEL_CUSTOMIZATION,
 func_name="BaseTrainer.stream_logs",
 telemetry_params=[
 ("compute", TelemetryParamType.ATTR_TYPE),
 ],
 )
 def stream_logs(self, poll: int = 5, start_time: Optional[Any] = None, tail_lines: Optional[int] = None) -> None:
 """Stream CloudWatch logs in real-time (like ``kubectl logs -f``).

Expand Down

3 changes: 3 additions & 0 deletions
sagemaker-train/src/sagemaker/train/common_utils/telemetry_params.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,8 +9,10 @@
 ("kms_key_id", TelemetryParamType.ATTR_EXISTS),
 ("mlflow_resource_arn", TelemetryParamType.ATTR_EXISTS),
 ("stopping_condition", TelemetryParamType.ATTR_EXISTS),
 ("notification_rule_arn", TelemetryParamType.ATTR_EXISTS),
 ("validation_dataset", TelemetryParamType.KWARG_EXISTS),
 ("wait", TelemetryParamType.KWARG_EXISTS),
 ("dry_run", TelemetryParamType.KWARG_EXISTS),
]

# Common params for all evaluators
Expand All@@ -20,4 +22,5 @@
 ("networking", TelemetryParamType.ATTR_EXISTS),
 ("kms_key_id", TelemetryParamType.ATTR_EXISTS),
 ("mlflow_resource_arn", TelemetryParamType.ATTR_EXISTS),
 ("dry_run", TelemetryParamType.KWARG_EXISTS),
]

9 changes: 9 additions & 0 deletions
sagemaker-train/src/sagemaker/train/evaluate/base_evaluator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -35,6 +35,8 @@
 _is_nova_model,
)
from sagemaker.train.common_utils.cloudwatch_metrics import _get_smhp_log_group
from sagemaker.core.telemetry.telemetry_logging import _telemetry_emitter, TelemetryParamType
from sagemaker.core.telemetry.constants import Feature
from sagemaker.train.common_utils.log_streamer import (
 LogStreamer,
 _format_timestamp,
Expand DownExpand Up@@ -1096,6 +1098,13 @@ def evaluate(self, dry_run: bool = False) -> Any:
 """
 raise NotImplementedError("Subclasses must implement evaluate method")

 @_telemetry_emitter(
 feature=Feature.MODEL_CUSTOMIZATION,
 func_name="BaseEvaluator.stream_logs",
 telemetry_params=[
 ("compute", TelemetryParamType.ATTR_TYPE),
 ],
 )
 def stream_logs(self, poll: int = 5, start_time=None) -> None:
 """Stream CloudWatch logs for the latest evaluation execution.

Expand Down

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

Skip to content
feat(telemetry): Add tracking for dry_run, notifications, reuse#6159

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 Merged

zhaoqizqwang
merged 8 commits into

from

Aug 5, 2026
 Merged


feat(telemetry): Add tracking for dry_run, notifications, reuse
#6159
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691db06

fix: usuability update from feedback


zhaoqizqwang Aug 4, 2026
5ef9216

update documentation


zhaoqizqwang Aug 4, 2026
d4e2042

docs: organize model-customization examples into serverless/serverful…


zhaoqizqwang Aug 4, 2026
1675758

add job notification integ test


zhaoqizqwang Aug 4, 2026
33d52af

feat(telemetry): Add tracking for dry_run, notifications, reuse_resou…


zhaoqizqwang Aug 5, 2026
02a8843

Merge remote-tracking branch 'upstream/master-nova-follow-ups' into s…


zhaoqizqwang Aug 5, 2026
4e0a944

update telemetry


zhaoqizqwang Aug 5, 2026
c637ebb

update telemetry


zhaoqizqwang Aug 5, 2026

File filter

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2 changes: 2 additions & 0 deletions
sagemaker-serve/src/sagemaker/serve/bedrock_model_builder.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -258,7 +258,9 @@ def _is_nova_model_for_telemetry(self) -> bool:
 func_name="BedrockModelBuilder.deploy",
 telemetry_params=[
 ("model_package", TelemetryParamType.ATTR_EXISTS),
 ("_is_nova_model_for_telemetry", TelemetryParamType.ATTR_CALL),
@mujtaba1747mujtaba1747Aug 5, 2026

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how does this work? What if bedrock model builder doesn't call _is_nova_model_for_telemetry() and calls _is_nova_model instead?
@zhaoqizqwangzhaoqizqwangAug 5, 2026

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so every time BedrockModelBuilder.deploy() is called, the decorator runs _is_nova_model_for_telemetry before the actual deploy() body executes. The actual method called inside deploy doesn't matter
@mujtaba1747mujtaba1747Aug 5, 2026

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Thanks for explaining.
 ("imported_model_kms_key_id", TelemetryParamType.KWARG_EXISTS),
 ("reuse_resources", TelemetryParamType.KWARG_EXISTS),
 ],
 )
 def deploy(
Expand Down

4 changes: 4 additions & 0 deletions
sagemaker-serve/src/sagemaker/serve/model_builder.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -3948,9 +3948,11 @@ def _reset_build_state(self):
 func_name="model_builder.build",
 telemetry_params=[
 ("mode", TelemetryParamType.ATTR_VALUE),
 ("_is_nova_model_for_telemetry", TelemetryParamType.ATTR_CALL),
 ("network", TelemetryParamType.ATTR_EXISTS),
 ("source_code", TelemetryParamType.ATTR_EXISTS),
 ("inference_spec", TelemetryParamType.ATTR_EXISTS),
 ("reuse_resources", TelemetryParamType.KWARG_EXISTS),
 ],
 )
 @runnable_by_pipeline
Expand DownExpand Up@@ -5326,9 +5328,11 @@ def _deploy_recommendation(
 ("mode", TelemetryParamType.ATTR_VALUE),
 ("instance_type", TelemetryParamType.ATTR_VALUE),
 ("_is_model_customization", TelemetryParamType.ATTR_CALL),
 ("_is_nova_model_for_telemetry", TelemetryParamType.ATTR_CALL),
 ("network", TelemetryParamType.ATTR_EXISTS),
 ("compute", TelemetryParamType.ATTR_EXISTS),
 ("update_endpoint", TelemetryParamType.KWARG_EXISTS),
 ("reuse_resources", TelemetryParamType.KWARG_EXISTS),
 ],
 )
 def deploy(
Expand Down

16 changes: 16 additions & 0 deletions
sagemaker-train/src/sagemaker/train/base_trainer.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -41,6 +41,8 @@
from sagemaker.train.common_utils.notifications import enable_notifications, delete_notification_rule, list_notification_rules
from sagemaker.train.common_utils.validator import validate_hyperpod_compute
from sagemaker.train.common_utils.cloudwatch_metrics import fetch_and_plot_metrics, _get_smhp_log_group
from sagemaker.core.telemetry.telemetry_logging import _telemetry_emitter, TelemetryParamType
from sagemaker.core.telemetry.constants import Feature
from sagemaker.train.defaults import TrainDefaults
from sagemaker.train.model_trainer import ModelTrainer
from sagemaker.train.utils import _get_unique_name
Expand DownExpand Up@@ -365,6 +367,13 @@ def _apply_recipe_to_hyperparameters(

 return final_hyperparameters

 @_telemetry_emitter(
 feature=Feature.MODEL_CUSTOMIZATION,
 func_name="BaseTrainer.show_metrics",
 telemetry_params=[
 ("compute", TelemetryParamType.ATTR_TYPE),
 ],
 )
 def show_metrics(
 self,
 metrics: Optional[List[str]] = None,
Expand DownExpand Up@@ -627,6 +636,13 @@ def list_notification_rules(
 event_bus_arn=event_bus_arn,
 )

 @_telemetry_emitter(
 feature=Feature.MODEL_CUSTOMIZATION,
 func_name="BaseTrainer.stream_logs",
 telemetry_params=[
 ("compute", TelemetryParamType.ATTR_TYPE),
 ],
 )
 def stream_logs(self, poll: int = 5, start_time: Optional[Any] = None, tail_lines: Optional[int] = None) -> None:
 """Stream CloudWatch logs in real-time (like ``kubectl logs -f``).

Expand Down

3 changes: 3 additions & 0 deletions
sagemaker-train/src/sagemaker/train/common_utils/telemetry_params.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -9,8 +9,10 @@
 ("kms_key_id", TelemetryParamType.ATTR_EXISTS),
 ("mlflow_resource_arn", TelemetryParamType.ATTR_EXISTS),
 ("stopping_condition", TelemetryParamType.ATTR_EXISTS),
 ("notification_rule_arn", TelemetryParamType.ATTR_EXISTS),
 ("validation_dataset", TelemetryParamType.KWARG_EXISTS),
 ("wait", TelemetryParamType.KWARG_EXISTS),
 ("dry_run", TelemetryParamType.KWARG_EXISTS),
]

# Common params for all evaluators
Expand All@@ -20,4 +22,5 @@
 ("networking", TelemetryParamType.ATTR_EXISTS),
 ("kms_key_id", TelemetryParamType.ATTR_EXISTS),
 ("mlflow_resource_arn", TelemetryParamType.ATTR_EXISTS),
 ("dry_run", TelemetryParamType.KWARG_EXISTS),
]

9 changes: 9 additions & 0 deletions
sagemaker-train/src/sagemaker/train/evaluate/base_evaluator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -35,6 +35,8 @@
 _is_nova_model,
)
from sagemaker.train.common_utils.cloudwatch_metrics import _get_smhp_log_group
from sagemaker.core.telemetry.telemetry_logging import _telemetry_emitter, TelemetryParamType
from sagemaker.core.telemetry.constants import Feature
from sagemaker.train.common_utils.log_streamer import (
 LogStreamer,
 _format_timestamp,
Expand DownExpand Up@@ -1096,6 +1098,13 @@ def evaluate(self, dry_run: bool = False) -> Any:
 """
 raise NotImplementedError("Subclasses must implement evaluate method")

 @_telemetry_emitter(
 feature=Feature.MODEL_CUSTOMIZATION,
 func_name="BaseEvaluator.stream_logs",
 telemetry_params=[
 ("compute", TelemetryParamType.ATTR_TYPE),
 ],
 )
 def stream_logs(self, poll: int = 5, start_time=None) -> None:
 """Stream CloudWatch logs for the latest evaluation execution.

Expand Down