feature: pluggable instance fallback mechanism, add CapacityError

src/sagemaker/exceptions.py

@@ -23,6 +23,10 @@ def __init__(self, message, allowed_statuses, actual_status):
         super(UnexpectedStatusException, self).__init__(message)
 
 
+class CapacityError(UnexpectedStatusException):
+    """Raised when resource status is not expected and fails with a reason of CapacityError"""
+
+
 class AsyncInferenceError(Exception):
     """The base exception class for Async Inference exceptions."""
 

src/sagemaker/session.py

@@ -1721,6 +1721,7 @@ def wait_for_auto_ml_job(self, job, poll=5):
             (dict): Return value from the ``DescribeAutoMLJob`` API.
 
         Raises:
+            exceptions.CapacityError: If the auto ml job fails with CapacityError.
             exceptions.UnexpectedStatusException: If the auto ml job fails.
         """
         desc = _wait_until(lambda: _auto_ml_job_status(self.sagemaker_client, job), poll)
@@ -1743,7 +1744,8 @@ def logs_for_auto_ml_job(  # noqa: C901 - suppress complexity warning for this m
                 completion (default: 5).
 
         Raises:
-            exceptions.UnexpectedStatusException: If waiting and the training job fails.
+            exceptions.CapacityError: If waiting and auto ml job fails with CapacityError.
+            exceptions.UnexpectedStatusException: If waiting and auto ml job fails.
         """
 
         description = self.sagemaker_client.describe_auto_ml_job(AutoMLJobName=job_name)
@@ -2845,6 +2847,10 @@ def wait_for_model_package(self, model_package_name, poll=5):
 
         Returns:
             dict: Return value from the ``DescribeEndpoint`` API.
+
+        Raises:
+            exceptions.CapacityError: If the Model Package job fails with CapacityError.
+            exceptions.UnexpectedStatusException: If waiting and the Model Package job fails.
         """
         desc = _wait_until(
             lambda: _create_model_package_status(self.sagemaker_client, model_package_name), poll
@@ -2853,10 +2859,17 @@ def wait_for_model_package(self, model_package_name, poll=5):
 
         if status != "Completed":
             reason = desc.get("FailureReason", None)
+            message = "Error creating model package {package}: {status} Reason: {reason}".format(
+                package=model_package_name, status=status, reason=reason
+            )
+            if "CapacityError" in str(reason):
+                raise exceptions.CapacityError(
+                    message=message,
+                    allowed_statuses=["InService"],
+                    actual_status=status,
+                )
             raise exceptions.UnexpectedStatusException(
-                message="Error creating model package {package}: {status} Reason: {reason}".format(
-                    package=model_package_name, status=status, reason=reason
-                ),
+                message=message,
                 allowed_statuses=["Completed"],
                 actual_status=status,
             )
@@ -3147,6 +3160,7 @@ def wait_for_job(self, job, poll=5):
             (dict): Return value from the ``DescribeTrainingJob`` API.
 
         Raises:
+            exceptions.CapacityError: If the training job fails with CapacityError.
             exceptions.UnexpectedStatusException: If the training job fails.
         """
         desc = _wait_until_training_done(
@@ -3166,7 +3180,8 @@ def wait_for_processing_job(self, job, poll=5):
             (dict): Return value from the ``DescribeProcessingJob`` API.
 
         Raises:
-            exceptions.UnexpectedStatusException: If the compilation job fails.
+            exceptions.CapacityError: If the processing job fails with CapacityError.
+            exceptions.UnexpectedStatusException: If the processing job fails.
         """
         desc = _wait_until(lambda: _processing_job_status(self.sagemaker_client, job), poll)
         self._check_job_status(job, desc, "ProcessingJobStatus")
@@ -3183,6 +3198,7 @@ def wait_for_compilation_job(self, job, poll=5):
             (dict): Return value from the ``DescribeCompilationJob`` API.
 
         Raises:
+            exceptions.CapacityError: If the compilation job fails with CapacityError.
             exceptions.UnexpectedStatusException: If the compilation job fails.
         """
         desc = _wait_until(lambda: _compilation_job_status(self.sagemaker_client, job), poll)
@@ -3200,7 +3216,8 @@ def wait_for_edge_packaging_job(self, job, poll=5):
             (dict): Return value from the ``DescribeEdgePackagingJob`` API.
 
         Raises:
-            exceptions.UnexpectedStatusException: If the compilation job fails.
+            exceptions.CapacityError: If the edge packaging job fails with CapacityError.
+            exceptions.UnexpectedStatusException: If the edge packaging job fails.
         """
         desc = _wait_until(lambda: _edge_packaging_job_status(self.sagemaker_client, job), poll)
         self._check_job_status(job, desc, "EdgePackagingJobStatus")
@@ -3217,6 +3234,7 @@ def wait_for_tuning_job(self, job, poll=5):
             (dict): Return value from the ``DescribeHyperParameterTuningJob`` API.
 
         Raises:
+            exceptions.CapacityError: If the hyperparameter tuning job fails with CapacityError.
             exceptions.UnexpectedStatusException: If the hyperparameter tuning job fails.
         """
         desc = _wait_until(lambda: _tuning_job_status(self.sagemaker_client, job), poll)
@@ -3245,6 +3263,7 @@ def wait_for_transform_job(self, job, poll=5):
             (dict): Return value from the ``DescribeTransformJob`` API.
 
         Raises:
+            exceptions.CapacityError: If the transform job fails with CapacityError.
             exceptions.UnexpectedStatusException: If the transform job fails.
         """
         desc = _wait_until(lambda: _transform_job_status(self.sagemaker_client, job), poll)
@@ -3283,6 +3302,7 @@ def _check_job_status(self, job, desc, status_key_name):
             status_key_name (str): Status key name to check for.
 
         Raises:
+            exceptions.CapacityError: If the training job fails with CapacityError.
             exceptions.UnexpectedStatusException: If the training job fails.
         """
         status = desc[status_key_name]
@@ -3298,10 +3318,17 @@ def _check_job_status(self, job, desc, status_key_name):
         elif status != "Completed":
             reason = desc.get("FailureReason", "(No reason provided)")
             job_type = status_key_name.replace("JobStatus", " job")
+            message = "Error for {job_type} {job_name}: {status}. Reason: {reason}".format(
+                job_type=job_type, job_name=job, status=status, reason=reason
+            )
+            if "CapacityError" in str(reason):
+                raise exceptions.CapacityError(
+                    message=message,
+                    allowed_statuses=["Completed", "Stopped"],
+                    actual_status=status,
+                )
             raise exceptions.UnexpectedStatusException(
-                message="Error for {job_type} {job_name}: {status}. Reason: {reason}".format(
-                    job_type=job_type, job_name=job, status=status, reason=reason
-                ),
+                message=message,
                 allowed_statuses=["Completed", "Stopped"],
                 actual_status=status,
             )
@@ -3313,6 +3340,10 @@ def wait_for_endpoint(self, endpoint, poll=30):
             endpoint (str): Name of the ``Endpoint`` to wait for.
             poll (int): Polling interval in seconds (default: 5).
 
+        Raises:
+            exceptions.CapacityError: If the endpoint creation job fails with CapacityError.
+            exceptions.UnexpectedStatusException: If the endpoint creation job fails.
+
         Returns:
             dict: Return value from the ``DescribeEndpoint`` API.
         """
@@ -3321,10 +3352,17 @@ def wait_for_endpoint(self, endpoint, poll=30):
 
         if status != "InService":
             reason = desc.get("FailureReason", None)
+            message = "Error hosting endpoint {endpoint}: {status}. Reason: {reason}.".format(
+                endpoint=endpoint, status=status, reason=reason
+            )
+            if "CapacityError" in str(reason):
+                raise exceptions.CapacityError(
+                    message=message,
+                    allowed_statuses=["InService"],
+                    actual_status=status,
+                )
             raise exceptions.UnexpectedStatusException(
-                message="Error hosting endpoint {endpoint}: {status}. Reason: {reason}.".format(
-                    endpoint=endpoint, status=status, reason=reason
-                ),
+                message=message,
                 allowed_statuses=["InService"],
                 actual_status=status,
             )
@@ -3649,6 +3687,7 @@ def logs_for_job(  # noqa: C901 - suppress complexity warning for this method
                 completion (default: 5).
 
         Raises:
+            exceptions.CapacityError: If the training job fails with CapacityError.
             exceptions.UnexpectedStatusException: If waiting and the training job fails.
         """
 

tests/integ/test_huggingface.py

@@ -15,15 +15,14 @@
 import os
 
 import pytest
-import logging
 
 from sagemaker.huggingface import HuggingFace, HuggingFaceProcessor
 from sagemaker.huggingface.model import HuggingFaceModel, HuggingFacePredictor
 from sagemaker.utils import unique_name_from_base
 from tests import integ
+from tests.integ.utils import gpu_list, retry_with_instance_list
 from tests.integ import DATA_DIR, TRAINING_DEFAULT_TIMEOUT_MINUTES
 from tests.integ.timeout import timeout, timeout_and_delete_endpoint_by_name
-from sagemaker.exceptions import UnexpectedStatusException
 
 ROLE = "SageMakerRole"
 
@@ -34,43 +33,34 @@
     and integ.test_region() in integ.TRAINING_NO_P3_REGIONS,
     reason="no ml.p2 or ml.p3 instances in this region",
 )
+@retry_with_instance_list(gpu_list(integ.test_region()))
 def test_framework_processing_job_with_deps(
     sagemaker_session,
-    gpu_instance_type_list,
     huggingface_training_latest_version,
     huggingface_training_pytorch_latest_version,
     huggingface_pytorch_latest_training_py_version,
+    **kwargs,
 ):
-    for i_type in gpu_instance_type_list:
-        logging.info("Using the instance type: {}".format(i_type))
-        with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
-            code_path = os.path.join(DATA_DIR, "dummy_code_bundle_with_reqs")
-            entry_point = "main_script.py"
-
-            processor = HuggingFaceProcessor(
-                transformers_version=huggingface_training_latest_version,
-                pytorch_version=huggingface_training_pytorch_latest_version,
-                py_version=huggingface_pytorch_latest_training_py_version,
-                role=ROLE,
-                instance_count=1,
-                instance_type=i_type,
-                sagemaker_session=sagemaker_session,
-                base_job_name="test-huggingface",
-            )
-            try:
-                processor.run(
-                    code=entry_point,
-                    source_dir=code_path,
-                    inputs=[],
-                    wait=True,
-                )
-            except UnexpectedStatusException as e:
-                if "CapacityError" in str(e) and i_type != gpu_instance_type_list[-1]:
-                    logging.warning("Failure using instance type: {}. {}".format(i_type, str(e)))
-                    continue
-                else:
-                    raise
-        break
+    with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
+        code_path = os.path.join(DATA_DIR, "dummy_code_bundle_with_reqs")
+        entry_point = "main_script.py"
+
+        processor = HuggingFaceProcessor(
+            transformers_version=huggingface_training_latest_version,
+            pytorch_version=huggingface_training_pytorch_latest_version,
+            py_version=huggingface_pytorch_latest_training_py_version,
+            role=ROLE,
+            instance_count=1,
+            instance_type=kwargs["instance_type"],
+            sagemaker_session=sagemaker_session,
+            base_job_name="test-huggingface",
+        )
+        processor.run(
+            code=entry_point,
+            source_dir=code_path,
+            inputs=[],
+            wait=True,
+        )
 
 
 @pytest.mark.release

tests/integ/test_tf.py

@@ -15,7 +15,6 @@
 import numpy as np
 import os
 import time
-import logging
 
 import pytest
 
@@ -25,8 +24,8 @@
 import tests.integ
 from tests.integ import DATA_DIR, TRAINING_DEFAULT_TIMEOUT_MINUTES, kms_utils, timeout
 from tests.integ.retry import retries
+from tests.integ.utils import gpu_list, retry_with_instance_list
 from tests.integ.s3_utils import assert_s3_file_patterns_exist
-from sagemaker.exceptions import UnexpectedStatusException
 
 
 ROLE = "SageMakerRole"
@@ -48,41 +47,32 @@
     and tests.integ.test_region() in tests.integ.TRAINING_NO_P3_REGIONS,
     reason="no ml.p2 or ml.p3 instances in this region",
 )
+@retry_with_instance_list(gpu_list(tests.integ.test_region()))
 def test_framework_processing_job_with_deps(
     sagemaker_session,
-    gpu_instance_type_list,
     tensorflow_training_latest_version,
     tensorflow_training_latest_py_version,
+    **kwargs,
 ):
-    for i_type in gpu_instance_type_list:
-        logging.info("Using the instance type: {}".format(i_type))
-        with timeout.timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
-            code_path = os.path.join(DATA_DIR, "dummy_code_bundle_with_reqs")
-            entry_point = "main_script.py"
-
-            processor = TensorFlowProcessor(
-                framework_version=tensorflow_training_latest_version,
-                py_version=tensorflow_training_latest_py_version,
-                role=ROLE,
-                instance_count=1,
-                instance_type=i_type,
-                sagemaker_session=sagemaker_session,
-                base_job_name="test-tensorflow",
-            )
-            try:
-                processor.run(
-                    code=entry_point,
-                    source_dir=code_path,
-                    inputs=[],
-                    wait=True,
-                )
-            except UnexpectedStatusException as e:
-                if "CapacityError" in str(e) and i_type != gpu_instance_type_list[-1]:
-                    logging.warning("Failure using instance type: {}. {}".format(i_type, str(e)))
-                    continue
-                else:
-                    raise
-        break
+    with timeout.timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
+        code_path = os.path.join(DATA_DIR, "dummy_code_bundle_with_reqs")
+        entry_point = "main_script.py"
+
+        processor = TensorFlowProcessor(
+            framework_version=tensorflow_training_latest_version,
+            py_version=tensorflow_training_latest_py_version,
+            role=ROLE,
+            instance_count=1,
+            instance_type=kwargs["instance_type"],
+            sagemaker_session=sagemaker_session,
+            base_job_name="test-tensorflow",
+        )
+        processor.run(
+            code=entry_point,
+            source_dir=code_path,
+            inputs=[],
+            wait=True,
+        )
 
 
 def test_mnist_with_checkpoint_config(