fix: forwarded role to Evaluator#6196

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fix: forwarded role to Evaluator #6196

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49 changes: 43 additions & 6 deletions sagemaker-train/src/sagemaker/train/rlvr_trainer.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -37,6 +37,7 @@
from sagemaker.train.common_utils.rlvr_reward_verifier import verify_reward_function
from sagemaker.core.telemetry.constants import Feature
from sagemaker.train.constants import get_sagemaker_hub_name
from sagemaker.core.helper.iam_role_resolver import RoleValidationError

logger = logging.getLogger(__name__)

Expand DownExpand Up@@ -110,6 +111,25 @@ class RLVRTrainer(BaseTrainer):
(e.g., "arn:aws:lambda:us-east-1:123456789012:function:my-reward"), an Evaluator
will be automatically created in the AI Registry.
Required for RLVR training to provide reward signals.

When providing a Lambda function, it must accept a list of sample dicts and return
a list of dicts with the following required fields:

- ``id`` (str): The sample identifier from the input.
- ``aggregate_reward_score`` (float): The reward score for the sample.

Optionally, each dict may include:

- ``metrics_list`` (list): A list of metric dicts, each with ``name`` (str),
``value`` (float), and ``type`` ("Metric" or "Reward").

Example Lambda return value::

[
{"id": "sample-1", "aggregate_reward_score": 0.85},
{"id": "sample-2", "aggregate_reward_score": 0.42,
"metrics_list": [{"name": "accuracy", "value": 0.9, "type": "Metric"}]}
]
mlflow_resource_arn (Optional[Union[str, MlflowTrackingServer]]):
The MLflow tracking server ARN for experiment tracking.
If not specified, uses default MLflow experience.
Expand DownExpand Up@@ -474,12 +494,29 @@ def train(self, training_dataset: Optional[Union[str, DataSet]] = None,
lambda_arn = self.custom_reward_function
evaluator_name = _get_unique_name(f"rlvr-reward-{self._model_name}")
logger.info(f"Creating Evaluator from Lambda ARN: {lambda_arn}")
evaluator_obj = Evaluator.create(
name=evaluator_name,
type="RewardFunction",
source=lambda_arn,
sagemaker_session=sagemaker_session,
)
try:
evaluator_obj = Evaluator.create(
name=evaluator_name,
type="RewardFunction",
source=lambda_arn,
role=role,
sagemaker_session=sagemaker_session,
)
except RoleValidationError as e:
raise RoleValidationError(
f"Failed to create Evaluator from Lambda ARN during training. "
f"The IAM role could not be resolved for the Evaluator. "
f"To fix this, either pass 'role' to the trainer, or pre-create the "
f"Evaluator with an explicit role:\n\n"
f" evaluator = Evaluator.create(\n"
f" name=\"my-evaluator\",\n"
f" type=\"RewardFunction\",\n"
f" source=\"{lambda_arn}\",\n"
f" role=\"<your-sagemaker-execution-role-arn>\",\n"
f" )\n"
f" trainer = RLVRTrainer(..., custom_reward_function=evaluator)\n\n"
f"Original error: {e}"
) from e
evaluator_arn = _extract_evaluator_arn(evaluator_obj)
logger.info(f"Created Evaluator with ARN: {evaluator_arn}")
else:
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);
});
};
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" + '

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fix: forwarded role to Evaluator#6196

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49 changes: 43 additions & 6 deletions
sagemaker-train/src/sagemaker/train/rlvr_trainer.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -37,6 +37,7 @@
from sagemaker.train.common_utils.rlvr_reward_verifier import verify_reward_function
from sagemaker.core.telemetry.constants import Feature
from sagemaker.train.constants import get_sagemaker_hub_name
from sagemaker.core.helper.iam_role_resolver import RoleValidationError

logger = logging.getLogger(__name__)

Expand DownExpand Up@@ -110,6 +111,25 @@ class RLVRTrainer(BaseTrainer):
 (e.g., "arn:aws:lambda:us-east-1:123456789012:function:my-reward"), an Evaluator
 will be automatically created in the AI Registry.
 Required for RLVR training to provide reward signals.

 When providing a Lambda function, it must accept a list of sample dicts and return
 a list of dicts with the following required fields:

 - ``id`` (str): The sample identifier from the input.
 - ``aggregate_reward_score`` (float): The reward score for the sample.

 Optionally, each dict may include:

 - ``metrics_list`` (list): A list of metric dicts, each with ``name`` (str),
 ``value`` (float), and ``type`` ("Metric" or "Reward").

 Example Lambda return value::

 [
 {"id": "sample-1", "aggregate_reward_score": 0.85},
 {"id": "sample-2", "aggregate_reward_score": 0.42,
 "metrics_list": [{"name": "accuracy", "value": 0.9, "type": "Metric"}]}
 ]
 mlflow_resource_arn (Optional[Union[str, MlflowTrackingServer]]):
 The MLflow tracking server ARN for experiment tracking.
 If not specified, uses default MLflow experience.
Expand DownExpand Up@@ -474,12 +494,29 @@ def train(self, training_dataset: Optional[Union[str, DataSet]] = None,
 lambda_arn = self.custom_reward_function
 evaluator_name = _get_unique_name(f"rlvr-reward-{self._model_name}")
 logger.info(f"Creating Evaluator from Lambda ARN: {lambda_arn}")
 evaluator_obj = Evaluator.create(
 name=evaluator_name,
 type="RewardFunction",
 source=lambda_arn,
 sagemaker_session=sagemaker_session,
 )
 try:
 evaluator_obj = Evaluator.create(
 name=evaluator_name,
 type="RewardFunction",
 source=lambda_arn,
 role=role,
 sagemaker_session=sagemaker_session,
 )
 except RoleValidationError as e:
 raise RoleValidationError(
 f"Failed to create Evaluator from Lambda ARN during training. "
 f"The IAM role could not be resolved for the Evaluator. "
 f"To fix this, either pass 'role' to the trainer, or pre-create the "
 f"Evaluator with an explicit role:\n\n"
 f" evaluator = Evaluator.create(\n"
 f" name=\"my-evaluator\",\n"
 f" type=\"RewardFunction\",\n"
 f" source=\"{lambda_arn}\",\n"
 f" role=\"<your-sagemaker-execution-role-arn>\",\n"
 f" )\n"
 f" trainer = RLVRTrainer(..., custom_reward_function=evaluator)\n\n"
 f"Original error: {e}"
 ) from e
 evaluator_arn = _extract_evaluator_arn(evaluator_obj)
 logger.info(f"Created Evaluator with ARN: {evaluator_arn}")
 else:
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; }
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';
document.head.appendChild(style);
})();
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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fix: forwarded role to Evaluator#6196

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49 changes: 43 additions & 6 deletions
sagemaker-train/src/sagemaker/train/rlvr_trainer.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -37,6 +37,7 @@
from sagemaker.train.common_utils.rlvr_reward_verifier import verify_reward_function
from sagemaker.core.telemetry.constants import Feature
from sagemaker.train.constants import get_sagemaker_hub_name
from sagemaker.core.helper.iam_role_resolver import RoleValidationError

logger = logging.getLogger(__name__)

Expand DownExpand Up@@ -110,6 +111,25 @@ class RLVRTrainer(BaseTrainer):
 (e.g., "arn:aws:lambda:us-east-1:123456789012:function:my-reward"), an Evaluator
 will be automatically created in the AI Registry.
 Required for RLVR training to provide reward signals.

 When providing a Lambda function, it must accept a list of sample dicts and return
 a list of dicts with the following required fields:

 - ``id`` (str): The sample identifier from the input.
 - ``aggregate_reward_score`` (float): The reward score for the sample.

 Optionally, each dict may include:

 - ``metrics_list`` (list): A list of metric dicts, each with ``name`` (str),
 ``value`` (float), and ``type`` ("Metric" or "Reward").

 Example Lambda return value::

 [
 {"id": "sample-1", "aggregate_reward_score": 0.85},
 {"id": "sample-2", "aggregate_reward_score": 0.42,
 "metrics_list": [{"name": "accuracy", "value": 0.9, "type": "Metric"}]}
 ]
 mlflow_resource_arn (Optional[Union[str, MlflowTrackingServer]]):
 The MLflow tracking server ARN for experiment tracking.
 If not specified, uses default MLflow experience.
Expand DownExpand Up@@ -474,12 +494,29 @@ def train(self, training_dataset: Optional[Union[str, DataSet]] = None,
 lambda_arn = self.custom_reward_function
 evaluator_name = _get_unique_name(f"rlvr-reward-{self._model_name}")
 logger.info(f"Creating Evaluator from Lambda ARN: {lambda_arn}")
 evaluator_obj = Evaluator.create(
 name=evaluator_name,
 type="RewardFunction",
 source=lambda_arn,
 sagemaker_session=sagemaker_session,
 )
 try:
 evaluator_obj = Evaluator.create(
 name=evaluator_name,
 type="RewardFunction",
 source=lambda_arn,
 role=role,
 sagemaker_session=sagemaker_session,
 )
 except RoleValidationError as e:
 raise RoleValidationError(
 f"Failed to create Evaluator from Lambda ARN during training. "
 f"The IAM role could not be resolved for the Evaluator. "
 f"To fix this, either pass 'role' to the trainer, or pre-create the "
 f"Evaluator with an explicit role:\n\n"
 f" evaluator = Evaluator.create(\n"
 f" name=\"my-evaluator\",\n"
 f" type=\"RewardFunction\",\n"
 f" source=\"{lambda_arn}\",\n"
 f" role=\"<your-sagemaker-execution-role-arn>\",\n"
 f" )\n"
 f" trainer = RLVRTrainer(..., custom_reward_function=evaluator)\n\n"
 f"Original error: {e}"
 ) from e
 evaluator_arn = _extract_evaluator_arn(evaluator_obj)
 logger.info(f"Created Evaluator with ARN: {evaluator_arn}")
 else:
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
(function() {
var ref = document.referrer;
var terms = [];
if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {
var url = new URL(ref);
var q = url.searchParams.get('q') || url.searchParams.get('p');
if (q) {
terms = q.split(/\s+/).filter(function(t) { return t.length > 2; });
}
}
if (terms.length === 0) return;
var style = document.createElement('style');
style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';
document.head.appendChild(style);
function highlight(node) {
if (node.nodeType === 3) { // text node
var text = node.textContent;
var found = false;
terms.forEach(function(term) {
var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\]\\]/g, '\\') + ')', 'gi');
if (regex.test(text)) {
found = true;
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frag.appendChild(document.createTextNode(part));
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if (!skipTags.includes(node.tagName)) {
Array.from(node.childNodes).forEach(highlight);
}
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}
highlight(document.body);
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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('^' + ".*" + '

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fix: forwarded role to Evaluator#6196

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49 changes: 43 additions & 6 deletions
sagemaker-train/src/sagemaker/train/rlvr_trainer.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -37,6 +37,7 @@
from sagemaker.train.common_utils.rlvr_reward_verifier import verify_reward_function
from sagemaker.core.telemetry.constants import Feature
from sagemaker.train.constants import get_sagemaker_hub_name
from sagemaker.core.helper.iam_role_resolver import RoleValidationError

logger = logging.getLogger(__name__)

Expand DownExpand Up@@ -110,6 +111,25 @@ class RLVRTrainer(BaseTrainer):
 (e.g., "arn:aws:lambda:us-east-1:123456789012:function:my-reward"), an Evaluator
 will be automatically created in the AI Registry.
 Required for RLVR training to provide reward signals.

 When providing a Lambda function, it must accept a list of sample dicts and return
 a list of dicts with the following required fields:

 - ``id`` (str): The sample identifier from the input.
 - ``aggregate_reward_score`` (float): The reward score for the sample.

 Optionally, each dict may include:

 - ``metrics_list`` (list): A list of metric dicts, each with ``name`` (str),
 ``value`` (float), and ``type`` ("Metric" or "Reward").

 Example Lambda return value::

 [
 {"id": "sample-1", "aggregate_reward_score": 0.85},
 {"id": "sample-2", "aggregate_reward_score": 0.42,
 "metrics_list": [{"name": "accuracy", "value": 0.9, "type": "Metric"}]}
 ]
 mlflow_resource_arn (Optional[Union[str, MlflowTrackingServer]]):
 The MLflow tracking server ARN for experiment tracking.
 If not specified, uses default MLflow experience.
Expand DownExpand Up@@ -474,12 +494,29 @@ def train(self, training_dataset: Optional[Union[str, DataSet]] = None,
 lambda_arn = self.custom_reward_function
 evaluator_name = _get_unique_name(f"rlvr-reward-{self._model_name}")
 logger.info(f"Creating Evaluator from Lambda ARN: {lambda_arn}")
 evaluator_obj = Evaluator.create(
 name=evaluator_name,
 type="RewardFunction",
 source=lambda_arn,
 sagemaker_session=sagemaker_session,
 )
 try:
 evaluator_obj = Evaluator.create(
 name=evaluator_name,
 type="RewardFunction",
 source=lambda_arn,
 role=role,
 sagemaker_session=sagemaker_session,
 )
 except RoleValidationError as e:
 raise RoleValidationError(
 f"Failed to create Evaluator from Lambda ARN during training. "
 f"The IAM role could not be resolved for the Evaluator. "
 f"To fix this, either pass 'role' to the trainer, or pre-create the "
 f"Evaluator with an explicit role:\n\n"
 f" evaluator = Evaluator.create(\n"
 f" name=\"my-evaluator\",\n"
 f" type=\"RewardFunction\",\n"
 f" source=\"{lambda_arn}\",\n"
 f" role=\"<your-sagemaker-execution-role-arn>\",\n"
 f" )\n"
 f" trainer = RLVRTrainer(..., custom_reward_function=evaluator)\n\n"
 f"Original error: {e}"
 ) from e
 evaluator_arn = _extract_evaluator_arn(evaluator_obj)
 logger.info(f"Created Evaluator with ARN: {evaluator_arn}")
 else:
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" + '

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fix: forwarded role to Evaluator#6196

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49 changes: 43 additions & 6 deletions
sagemaker-train/src/sagemaker/train/rlvr_trainer.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -37,6 +37,7 @@
from sagemaker.train.common_utils.rlvr_reward_verifier import verify_reward_function
from sagemaker.core.telemetry.constants import Feature
from sagemaker.train.constants import get_sagemaker_hub_name
from sagemaker.core.helper.iam_role_resolver import RoleValidationError

logger = logging.getLogger(__name__)

Expand DownExpand Up@@ -110,6 +111,25 @@ class RLVRTrainer(BaseTrainer):
 (e.g., "arn:aws:lambda:us-east-1:123456789012:function:my-reward"), an Evaluator
 will be automatically created in the AI Registry.
 Required for RLVR training to provide reward signals.

 When providing a Lambda function, it must accept a list of sample dicts and return
 a list of dicts with the following required fields:

 - ``id`` (str): The sample identifier from the input.
 - ``aggregate_reward_score`` (float): The reward score for the sample.

 Optionally, each dict may include:

 - ``metrics_list`` (list): A list of metric dicts, each with ``name`` (str),
 ``value`` (float), and ``type`` ("Metric" or "Reward").

 Example Lambda return value::

 [
 {"id": "sample-1", "aggregate_reward_score": 0.85},
 {"id": "sample-2", "aggregate_reward_score": 0.42,
 "metrics_list": [{"name": "accuracy", "value": 0.9, "type": "Metric"}]}
 ]
 mlflow_resource_arn (Optional[Union[str, MlflowTrackingServer]]):
 The MLflow tracking server ARN for experiment tracking.
 If not specified, uses default MLflow experience.
Expand DownExpand Up@@ -474,12 +494,29 @@ def train(self, training_dataset: Optional[Union[str, DataSet]] = None,
 lambda_arn = self.custom_reward_function
 evaluator_name = _get_unique_name(f"rlvr-reward-{self._model_name}")
 logger.info(f"Creating Evaluator from Lambda ARN: {lambda_arn}")
 evaluator_obj = Evaluator.create(
 name=evaluator_name,
 type="RewardFunction",
 source=lambda_arn,
 sagemaker_session=sagemaker_session,
 )
 try:
 evaluator_obj = Evaluator.create(
 name=evaluator_name,
 type="RewardFunction",
 source=lambda_arn,
 role=role,
 sagemaker_session=sagemaker_session,
 )
 except RoleValidationError as e:
 raise RoleValidationError(
 f"Failed to create Evaluator from Lambda ARN during training. "
 f"The IAM role could not be resolved for the Evaluator. "
 f"To fix this, either pass 'role' to the trainer, or pre-create the "
 f"Evaluator with an explicit role:\n\n"
 f" evaluator = Evaluator.create(\n"
 f" name=\"my-evaluator\",\n"
 f" type=\"RewardFunction\",\n"
 f" source=\"{lambda_arn}\",\n"
 f" role=\"<your-sagemaker-execution-role-arn>\",\n"
 f" )\n"
 f" trainer = RLVRTrainer(..., custom_reward_function=evaluator)\n\n"
 f"Original error: {e}"
 ) from e
 evaluator_arn = _extract_evaluator_arn(evaluator_obj)
 logger.info(f"Created Evaluator with ARN: {evaluator_arn}")
 else:
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('^' + ".*" + '

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fix: forwarded role to Evaluator#6196

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49 changes: 43 additions & 6 deletions
sagemaker-train/src/sagemaker/train/rlvr_trainer.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -37,6 +37,7 @@
from sagemaker.train.common_utils.rlvr_reward_verifier import verify_reward_function
from sagemaker.core.telemetry.constants import Feature
from sagemaker.train.constants import get_sagemaker_hub_name
from sagemaker.core.helper.iam_role_resolver import RoleValidationError

logger = logging.getLogger(__name__)

Expand DownExpand Up@@ -110,6 +111,25 @@ class RLVRTrainer(BaseTrainer):
 (e.g., "arn:aws:lambda:us-east-1:123456789012:function:my-reward"), an Evaluator
 will be automatically created in the AI Registry.
 Required for RLVR training to provide reward signals.

 When providing a Lambda function, it must accept a list of sample dicts and return
 a list of dicts with the following required fields:

 - ``id`` (str): The sample identifier from the input.
 - ``aggregate_reward_score`` (float): The reward score for the sample.

 Optionally, each dict may include:

 - ``metrics_list`` (list): A list of metric dicts, each with ``name`` (str),
 ``value`` (float), and ``type`` ("Metric" or "Reward").

 Example Lambda return value::

 [
 {"id": "sample-1", "aggregate_reward_score": 0.85},
 {"id": "sample-2", "aggregate_reward_score": 0.42,
 "metrics_list": [{"name": "accuracy", "value": 0.9, "type": "Metric"}]}
 ]
 mlflow_resource_arn (Optional[Union[str, MlflowTrackingServer]]):
 The MLflow tracking server ARN for experiment tracking.
 If not specified, uses default MLflow experience.
Expand DownExpand Up@@ -474,12 +494,29 @@ def train(self, training_dataset: Optional[Union[str, DataSet]] = None,
 lambda_arn = self.custom_reward_function
 evaluator_name = _get_unique_name(f"rlvr-reward-{self._model_name}")
 logger.info(f"Creating Evaluator from Lambda ARN: {lambda_arn}")
 evaluator_obj = Evaluator.create(
 name=evaluator_name,
 type="RewardFunction",
 source=lambda_arn,
 sagemaker_session=sagemaker_session,
 )
 try:
 evaluator_obj = Evaluator.create(
 name=evaluator_name,
 type="RewardFunction",
 source=lambda_arn,
 role=role,
 sagemaker_session=sagemaker_session,
 )
 except RoleValidationError as e:
 raise RoleValidationError(
 f"Failed to create Evaluator from Lambda ARN during training. "
 f"The IAM role could not be resolved for the Evaluator. "
 f"To fix this, either pass 'role' to the trainer, or pre-create the "
 f"Evaluator with an explicit role:\n\n"
 f" evaluator = Evaluator.create(\n"
 f" name=\"my-evaluator\",\n"
 f" type=\"RewardFunction\",\n"
 f" source=\"{lambda_arn}\",\n"
 f" role=\"<your-sagemaker-execution-role-arn>\",\n"
 f" )\n"
 f" trainer = RLVRTrainer(..., custom_reward_function=evaluator)\n\n"
 f"Original error: {e}"
 ) from e
 evaluator_arn = _extract_evaluator_arn(evaluator_obj)
 logger.info(f"Created Evaluator with ARN: {evaluator_arn}")
 else:
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('^' + ".*" + '

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fix: forwarded role to Evaluator#6196

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fix: forwarded role to Evaluator
#6196
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49 changes: 43 additions & 6 deletions
sagemaker-train/src/sagemaker/train/rlvr_trainer.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -37,6 +37,7 @@
from sagemaker.train.common_utils.rlvr_reward_verifier import verify_reward_function
from sagemaker.core.telemetry.constants import Feature
from sagemaker.train.constants import get_sagemaker_hub_name
from sagemaker.core.helper.iam_role_resolver import RoleValidationError

logger = logging.getLogger(__name__)

Expand DownExpand Up@@ -110,6 +111,25 @@ class RLVRTrainer(BaseTrainer):
 (e.g., "arn:aws:lambda:us-east-1:123456789012:function:my-reward"), an Evaluator
 will be automatically created in the AI Registry.
 Required for RLVR training to provide reward signals.

 When providing a Lambda function, it must accept a list of sample dicts and return
 a list of dicts with the following required fields:

 - ``id`` (str): The sample identifier from the input.
 - ``aggregate_reward_score`` (float): The reward score for the sample.

 Optionally, each dict may include:

 - ``metrics_list`` (list): A list of metric dicts, each with ``name`` (str),
 ``value`` (float), and ``type`` ("Metric" or "Reward").

 Example Lambda return value::

 [
 {"id": "sample-1", "aggregate_reward_score": 0.85},
 {"id": "sample-2", "aggregate_reward_score": 0.42,
 "metrics_list": [{"name": "accuracy", "value": 0.9, "type": "Metric"}]}
 ]
 mlflow_resource_arn (Optional[Union[str, MlflowTrackingServer]]):
 The MLflow tracking server ARN for experiment tracking.
 If not specified, uses default MLflow experience.
Expand DownExpand Up@@ -474,12 +494,29 @@ def train(self, training_dataset: Optional[Union[str, DataSet]] = None,
 lambda_arn = self.custom_reward_function
 evaluator_name = _get_unique_name(f"rlvr-reward-{self._model_name}")
 logger.info(f"Creating Evaluator from Lambda ARN: {lambda_arn}")
 evaluator_obj = Evaluator.create(
 name=evaluator_name,
 type="RewardFunction",
 source=lambda_arn,
 sagemaker_session=sagemaker_session,
 )
 try:
 evaluator_obj = Evaluator.create(
 name=evaluator_name,
 type="RewardFunction",
 source=lambda_arn,
 role=role,
 sagemaker_session=sagemaker_session,
 )
 except RoleValidationError as e:
 raise RoleValidationError(
 f"Failed to create Evaluator from Lambda ARN during training. "
 f"The IAM role could not be resolved for the Evaluator. "
 f"To fix this, either pass 'role' to the trainer, or pre-create the "
 f"Evaluator with an explicit role:\n\n"
 f" evaluator = Evaluator.create(\n"
 f" name=\"my-evaluator\",\n"
 f" type=\"RewardFunction\",\n"
 f" source=\"{lambda_arn}\",\n"
 f" role=\"<your-sagemaker-execution-role-arn>\",\n"
 f" )\n"
 f" trainer = RLVRTrainer(..., custom_reward_function=evaluator)\n\n"
 f"Original error: {e}"
 ) from e
 evaluator_arn = _extract_evaluator_arn(evaluator_obj)
 logger.info(f"Created Evaluator with ARN: {evaluator_arn}")
 else:
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); }
})();
})();

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fix: forwarded role to Evaluator#6196

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zhaoqizqwang
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fix: forwarded role to Evaluator
#6196
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49 changes: 43 additions & 6 deletions
sagemaker-train/src/sagemaker/train/rlvr_trainer.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -37,6 +37,7 @@
from sagemaker.train.common_utils.rlvr_reward_verifier import verify_reward_function
from sagemaker.core.telemetry.constants import Feature
from sagemaker.train.constants import get_sagemaker_hub_name
from sagemaker.core.helper.iam_role_resolver import RoleValidationError

logger = logging.getLogger(__name__)

Expand DownExpand Up@@ -110,6 +111,25 @@ class RLVRTrainer(BaseTrainer):
 (e.g., "arn:aws:lambda:us-east-1:123456789012:function:my-reward"), an Evaluator
 will be automatically created in the AI Registry.
 Required for RLVR training to provide reward signals.

 When providing a Lambda function, it must accept a list of sample dicts and return
 a list of dicts with the following required fields:

 - ``id`` (str): The sample identifier from the input.
 - ``aggregate_reward_score`` (float): The reward score for the sample.

 Optionally, each dict may include:

 - ``metrics_list`` (list): A list of metric dicts, each with ``name`` (str),
 ``value`` (float), and ``type`` ("Metric" or "Reward").

 Example Lambda return value::

 [
 {"id": "sample-1", "aggregate_reward_score": 0.85},
 {"id": "sample-2", "aggregate_reward_score": 0.42,
 "metrics_list": [{"name": "accuracy", "value": 0.9, "type": "Metric"}]}
 ]
 mlflow_resource_arn (Optional[Union[str, MlflowTrackingServer]]):
 The MLflow tracking server ARN for experiment tracking.
 If not specified, uses default MLflow experience.
Expand DownExpand Up@@ -474,12 +494,29 @@ def train(self, training_dataset: Optional[Union[str, DataSet]] = None,
 lambda_arn = self.custom_reward_function
 evaluator_name = _get_unique_name(f"rlvr-reward-{self._model_name}")
 logger.info(f"Creating Evaluator from Lambda ARN: {lambda_arn}")
 evaluator_obj = Evaluator.create(
 name=evaluator_name,
 type="RewardFunction",
 source=lambda_arn,
 sagemaker_session=sagemaker_session,
 )
 try:
 evaluator_obj = Evaluator.create(
 name=evaluator_name,
 type="RewardFunction",
 source=lambda_arn,
 role=role,
 sagemaker_session=sagemaker_session,
 )
 except RoleValidationError as e:
 raise RoleValidationError(
 f"Failed to create Evaluator from Lambda ARN during training. "
 f"The IAM role could not be resolved for the Evaluator. "
 f"To fix this, either pass 'role' to the trainer, or pre-create the "
 f"Evaluator with an explicit role:\n\n"
 f" evaluator = Evaluator.create(\n"
 f" name=\"my-evaluator\",\n"
 f" type=\"RewardFunction\",\n"
 f" source=\"{lambda_arn}\",\n"
 f" role=\"<your-sagemaker-execution-role-arn>\",\n"
 f" )\n"
 f" trainer = RLVRTrainer(..., custom_reward_function=evaluator)\n\n"
 f"Original error: {e}"
 ) from e
 evaluator_arn = _extract_evaluator_arn(evaluator_obj)
 logger.info(f"Created Evaluator with ARN: {evaluator_arn}")
 else:
Expand Down
Loading