chore: raise exception when no instances available in region

Author: evakravi

src/sagemaker/instance_types.py

@@ -15,7 +15,7 @@
 from __future__ import absolute_import
 
 import logging
-from typing import List, Optional
+from typing import List
 
 from sagemaker.jumpstart import utils as jumpstart_utils
 from sagemaker.jumpstart import artifacts
@@ -30,7 +30,7 @@ def retrieve_default(
     scope=None,
     tolerate_vulnerable_model: bool = False,
     tolerate_deprecated_model: bool = False,
-) -> Optional[str]:
+) -> str:
     """Retrieves the default instance type for the model matching the given arguments.
 
     Args:
@@ -50,7 +50,7 @@ def retrieve_default(
             (exception not raised). False if these models should raise an exception.
             (Default: False).
     Returns:
-        dict: The default instance type to use for the model.
+        str: The default instance type to use for the model.
 
     Raises:
         ValueError: If the combination of arguments specified is not supported.
@@ -80,7 +80,7 @@ def retrieve_supported(
     scope=None,
     tolerate_vulnerable_model: bool = False,
     tolerate_deprecated_model: bool = False,
-) -> Optional[List[str]]:
+) -> List[str]:
     """Retrieves the supported training instance types for the model matching the given arguments.
 
     Args:
@@ -98,7 +98,7 @@ def retrieve_supported(
             (exception not raised). False if these models should raise an exception.
             (Default: False).
     Returns:
-        dict: The supported instance types to use for the model.
+        list: The supported instance types to use for the model.
 
     Raises:
         ValueError: If the combination of arguments specified is not supported.

src/sagemaker/jumpstart/artifacts.py

@@ -15,6 +15,7 @@
 import os
 from typing import Dict, List, Optional
 from sagemaker import image_uris
+from sagemaker.jumpstart.exceptions import NO_AVAILABLE_INSTANCES_ERROR_MSG
 from sagemaker.jumpstart.constants import (
     ENV_VARIABLE_JUMPSTART_MODEL_ARTIFACT_BUCKET_OVERRIDE,
     ENV_VARIABLE_JUMPSTART_SCRIPT_ARTIFACT_BUCKET_OVERRIDE,
@@ -408,7 +409,7 @@ def _retrieve_default_instance_type(
     region: Optional[str] = None,
     tolerate_vulnerable_model: bool = False,
     tolerate_deprecated_model: bool = False,
-) -> Optional[str]:
+) -> str:
     """Retrieves the default instance type for the model.
 
     Args:
@@ -428,7 +429,11 @@ def _retrieve_default_instance_type(
             specifications should be tolerated (exception not raised). If False, raises
             an exception if the version of the model is deprecated. (Default: False).
     Returns:
-        list: the default instance type to use for the model or None.
+        str: the default instance type to use for the model or None.
+
+    Raises:
+        ValueError: If the model is not available in the
+            specified region due to lack of supported computing instances.
     """
 
     if region is None:
@@ -444,12 +449,17 @@ def _retrieve_default_instance_type(
     )
 
     if scope == JumpStartScriptScope.INFERENCE:
-        return model_specs.default_inference_instance_type
-    if scope == JumpStartScriptScope.TRAINING:
-        return model_specs.default_training_instance_type
-    raise NotImplementedError(
-        f"Unsupported script scope for retrieving default instance type: '{scope}'"
-    )
+        default_instance_type = model_specs.default_inference_instance_type
+    elif scope == JumpStartScriptScope.TRAINING:
+        default_instance_type = model_specs.default_training_instance_type
+    else:
+        raise NotImplementedError(
+            f"Unsupported script scope for retrieving default instance type: '{scope}'"
+        )
+
+    if default_instance_type in {None, ""}:
+        raise ValueError(NO_AVAILABLE_INSTANCES_ERROR_MSG.format(model_id=model_id, region=region))
+    return default_instance_type
 
 
 def _retrieve_supported_instance_type(
@@ -459,7 +469,7 @@ def _retrieve_supported_instance_type(
     region: Optional[str] = None,
     tolerate_vulnerable_model: bool = False,
     tolerate_deprecated_model: bool = False,
-) -> Optional[List[str]]:
+) -> List[str]:
     """Retrieves the supported instance types for the model.
 
     Args:
@@ -480,6 +490,10 @@ def _retrieve_supported_instance_type(
             an exception if the version of the model is deprecated. (Default: False).
     Returns:
         list: the supported instance types to use for the model or None.
+
+    Raises:
+        ValueError: If the model is not available in the
+            specified region due to lack of supported computing instances.
     """
 
     if region is None:
@@ -495,9 +509,15 @@ def _retrieve_supported_instance_type(
     )
 
     if scope == JumpStartScriptScope.INFERENCE:
-        return model_specs.supported_inference_instance_types
-    if scope == JumpStartScriptScope.TRAINING:
-        return model_specs.supported_training_instance_types
-    raise NotImplementedError(
-        f"Unsupported script scope for retrieving supported instance types: '{scope}'"
-    )
+        instance_types = model_specs.supported_inference_instance_types
+    elif scope == JumpStartScriptScope.TRAINING:
+        instance_types = model_specs.supported_training_instance_types
+    else:
+        raise NotImplementedError(
+            f"Unsupported script scope for retrieving supported instance types: '{scope}'"
+        )
+
+    if instance_types is None or len(instance_types) == 0:
+        raise ValueError(NO_AVAILABLE_INSTANCES_ERROR_MSG.format(model_id=model_id, region=region))
+
+    return instance_types

src/sagemaker/jumpstart/exceptions.py

@@ -16,8 +16,13 @@
 
 from sagemaker.jumpstart.constants import JumpStartScriptScope
 
+NO_AVAILABLE_INSTANCES_ERROR_MSG = (
+    "No instances available in {region} that can support model id '{model_id}'. "
+    "Please try another region."
+)
 
-class JumpStartHyperparametersError(Exception):
+
+class JumpStartHyperparametersError(ValueError):
     """Exception raised for bad hyperparameters of a JumpStart model."""
 
     def __init__(
@@ -29,7 +34,7 @@ def __init__(
         super().__init__(self.message)
 
 
-class VulnerableJumpStartModelError(Exception):
+class VulnerableJumpStartModelError(ValueError):
     """Exception raised when trying to access a JumpStart model specs flagged as vulnerable.
 
     Raise this exception only if the scope of attributes accessed in the specifications have
@@ -61,7 +66,7 @@ def __init__(
             self.message = message
         else:
             if None in [model_id, version, vulnerabilities, scope]:
-                raise ValueError(
+                raise RuntimeError(
                     "Must specify `model_id`, `version`, `vulnerabilities`, " "and scope arguments."
                 )
             if scope == JumpStartScriptScope.INFERENCE:
@@ -87,7 +92,7 @@ def __init__(
         super().__init__(self.message)
 
 
-class DeprecatedJumpStartModelError(Exception):
+class DeprecatedJumpStartModelError(ValueError):
     """Exception raised when trying to access a JumpStart model deprecated specifications.
 
     A deprecated specification for a JumpStart model does not mean the whole model is
@@ -107,7 +112,7 @@ def __init__(
             self.message = message
         else:
             if None in [model_id, version]:
-                raise ValueError("Must specify `model_id` and `version` arguments.")
+                raise RuntimeError("Must specify `model_id` and `version` arguments.")
             self.message = (
                 f"Version '{version}' of JumpStart model '{model_id}' is deprecated. "
                 "Please try targetting a higher version of the model."

tests/unit/sagemaker/instance_types/jumpstart/test_default.py renamed to tests/unit/sagemaker/instance_types/jumpstart/test_instance_types.py

@@ -18,7 +18,7 @@
 
 from sagemaker import instance_types
 
-from tests.unit.sagemaker.jumpstart.utils import get_spec_from_base_spec
+from tests.unit.sagemaker.jumpstart.utils import get_spec_from_base_spec, get_special_model_spec
 
 
 @patch("sagemaker.jumpstart.accessors.JumpStartModelsAccessor.get_model_specs")
@@ -132,3 +132,31 @@ def test_jumpstart_instance_types(patched_get_model_specs):
 
     with pytest.raises(ValueError):
         instance_types.retrieve_supported(model_id=model_id, scope="training")
+
+
+@patch("sagemaker.jumpstart.accessors.JumpStartModelsAccessor.get_model_specs")
+def test_jumpstart_no_supported_instance_types(patched_get_model_specs):
+    patched_get_model_specs.side_effect = get_special_model_spec
+
+    model_id, model_version = "no-supported-instance-types-model", "*"
+    region = "us-west-2"
+
+    with pytest.raises(ValueError):
+        instance_types.retrieve_default(
+            region=region, model_id=model_id, model_version=model_version, scope="training"
+        )
+
+    with pytest.raises(ValueError):
+        instance_types.retrieve_default(
+            region=region, model_id=model_id, model_version=model_version, scope="inference"
+        )
+
+    with pytest.raises(ValueError):
+        instance_types.retrieve_supported(
+            region=region, model_id=model_id, model_version=model_version, scope="training"
+        )
+
+    with pytest.raises(ValueError):
+        instance_types.retrieve_supported(
+            region=region, model_id=model_id, model_version=model_version, scope="inference"
+        )

tests/unit/sagemaker/jumpstart/constants.py

@@ -12,6 +12,127 @@
 # language governing permissions and limitations under the License.
 from __future__ import absolute_import
 
+
+SPECIAL_MODEL_SPECS_DICT = {
+    "no-supported-instance-types-model": {
+        "model_id": "pytorch-ic-mobilenet-v2",
+        "url": "https://pytorch.org/hub/pytorch_vision_mobilenet_v2/",
+        "version": "1.0.0",
+        "min_sdk_version": "2.49.0",
+        "training_supported": True,
+        "incremental_training_supported": True,
+        "hosting_ecr_specs": {
+            "framework": "pytorch",
+            "framework_version": "1.5.0",
+            "py_version": "py3",
+        },
+        "training_ecr_specs": {
+            "framework": "pytorch",
+            "framework_version": "1.5.0",
+            "py_version": "py3",
+        },
+        "hosting_artifact_key": "pytorch-infer/infer-pytorch-ic-mobilenet-v2.tar.gz",
+        "training_artifact_key": "pytorch-training/train-pytorch-ic-mobilenet-v2.tar.gz",
+        "hosting_script_key": "source-directory-tarballs/pytorch/inference/ic/v1.0.0/sourcedir.tar.gz",
+        "training_script_key": "source-directory-tarballs/pytorch/transfer_learning/ic/v1.0.0/sourcedir.tar.gz",
+        "hyperparameters": [
+            {
+                "name": "epochs",
+                "type": "int",
+                "default": 3,
+                "min": 1,
+                "max": 1000,
+                "scope": "algorithm",
+            },
+            {
+                "name": "adam-learning-rate",
+                "type": "float",
+                "default": 0.05,
+                "min": 1e-08,
+                "max": 1,
+                "scope": "algorithm",
+            },
+            {
+                "name": "batch-size",
+                "type": "int",
+                "default": 4,
+                "min": 1,
+                "max": 1024,
+                "scope": "algorithm",
+            },
+            {
+                "name": "sagemaker_submit_directory",
+                "type": "text",
+                "default": "/opt/ml/input/data/code/sourcedir.tar.gz",
+                "scope": "container",
+            },
+            {
+                "name": "sagemaker_program",
+                "type": "text",
+                "default": "transfer_learning.py",
+                "scope": "container",
+            },
+            {
+                "name": "sagemaker_container_log_level",
+                "type": "text",
+                "default": "20",
+                "scope": "container",
+            },
+        ],
+        "inference_environment_variables": [
+            {
+                "name": "SAGEMAKER_PROGRAM",
+                "type": "text",
+                "default": "inference.py",
+                "scope": "container",
+            },
+            {
+                "name": "SAGEMAKER_SUBMIT_DIRECTORY",
+                "type": "text",
+                "default": "/opt/ml/model/code",
+                "scope": "container",
+            },
+            {
+                "name": "SAGEMAKER_CONTAINER_LOG_LEVEL",
+                "type": "text",
+                "default": "20",
+                "scope": "container",
+            },
+            {
+                "name": "MODEL_CACHE_ROOT",
+                "type": "text",
+                "default": "/opt/ml/model",
+                "scope": "container",
+            },
+            {"name": "SAGEMAKER_ENV", "type": "text", "default": "1", "scope": "container"},
+            {
+                "name": "SAGEMAKER_MODEL_SERVER_WORKERS",
+                "type": "text",
+                "default": "1",
+                "scope": "container",
+            },
+            {
+                "name": "SAGEMAKER_MODEL_SERVER_TIMEOUT",
+                "type": "text",
+                "default": "3600",
+                "scope": "container",
+            },
+        ],
+        "inference_vulnerable": False,
+        "inference_dependencies": [],
+        "inference_vulnerabilities": [],
+        "training_vulnerable": False,
+        "training_dependencies": [],
+        "training_vulnerabilities": [],
+        "deprecated": False,
+        "default_inference_instance_type": "",
+        "supported_inference_instance_types": None,
+        "default_training_instance_type": None,
+        "supported_training_instance_types": [],
+    }
+}
+
+
 PROTOTYPICAL_MODEL_SPECS_DICT = {
     "pytorch-eqa-bert-base-cased": {
         "model_id": "pytorch-eqa-bert-base-cased",

tests/unit/sagemaker/jumpstart/utils.py

@@ -29,6 +29,7 @@
     BASE_MANIFEST,
     BASE_SPEC,
     BASE_HEADER,
+    SPECIAL_MODEL_SPECS_DICT,
 )
 
 
@@ -92,6 +93,18 @@ def get_prototype_model_spec(
     return specs
 
 
+def get_special_model_spec(
+    region: str = None, model_id: str = None, version: str = None
+) -> JumpStartModelSpecs:
+    """This function mocks cache accessor functions. For this mock,
+    we only retrieve model specs based on the model ID. This is reserved
+    for special specs.
+    """
+
+    specs = JumpStartModelSpecs(SPECIAL_MODEL_SPECS_DICT[model_id])
+    return specs
+
+
 def get_spec_from_base_spec(
     _obj: JumpStartModelsCache = None,
     region: str = None,