def test_jumpstart_deprecated_model(patched_get_model_specs):
def make_deprecated_spec(*largs, **kwargs):
spec = get_spec_from_base_spec(*largs, **kwargs)
spec.deprecated = True
return spec
patched_get_model_specs.side_effect = make_deprecated_spec
with pytest.raises(DeprecatedJumpStartModelError) as e:
utils.verify_model_region_and_return_specs(
model_id="pytorch-eqa-bert-base-cased",
version="*",
scope=JumpStartScriptScope.INFERENCE.value,
region="us-west-2",
)
assert "Version '*' of JumpStart model 'pytorch-eqa-bert-base-cased' is deprecated. "
"Please try targetting a higher version of the model." == str(
e.value.message)
with patch("logging.Logger.warning") as mocked_warning_log:
assert (utils.verify_model_region_and_return_specs(
model_id="pytorch-eqa-bert-base-cased",
version="*",
scope=JumpStartScriptScope.INFERENCE.value,
region="us-west-2",
tolerate_deprecated_model=True,
) is not None)
mocked_warning_log.assert_called_once_with(
"Using deprecated JumpStart model '%s' and version '%s'.",
"pytorch-eqa-bert-base-cased",
"*",
)
def _retrieve_script_uri(
model_id: str,
model_version: str,
script_scope: Optional[str],
region: Optional[str],
tolerate_vulnerable_model: bool,
tolerate_deprecated_model: bool,
):
"""Retrieves the script S3 URI associated with the model matching the given arguments.
Args:
model_id (str): JumpStart model ID of the JumpStart model for which to
retrieve the script S3 URI.
model_version (str): Version of the JumpStart model for which to
retrieve the model script S3 URI.
script_scope (str): The script type, i.e. what it is used for.
Valid values: "training" and "inference".
region (str): Region for which to retrieve model script S3 URI.
tolerate_vulnerable_model (bool): True if vulnerable versions of model
specifications should be tolerated (exception not raised). If False, raises an
exception if the script used by this version of the model has dependencies with known
security vulnerabilities.
tolerate_deprecated_model (bool): True if deprecated versions of model
specifications should be tolerated (exception not raised). If False, raises
an exception if the version of the model is deprecated.
Returns:
str: the model script URI for the corresponding model.
Raises:
ValueError: If the combination of arguments specified is not supported.
VulnerableJumpStartModelError: If any of the dependencies required by the script have
known security vulnerabilities.
DeprecatedJumpStartModelError: If the version of the model is deprecated.
"""
if region is None:
region = JUMPSTART_DEFAULT_REGION_NAME
model_specs = verify_model_region_and_return_specs(
model_id=model_id,
version=model_version,
scope=script_scope,
region=region,
tolerate_vulnerable_model=tolerate_vulnerable_model,
tolerate_deprecated_model=tolerate_deprecated_model,
)
if script_scope == JumpStartScriptScope.INFERENCE:
model_script_key = model_specs.hosting_script_key
elif script_scope == JumpStartScriptScope.TRAINING:
model_script_key = model_specs.training_script_key
bucket = get_jumpstart_content_bucket(region)
script_s3_uri = f"s3://{bucket}/{model_script_key}"
return script_s3_uri
def test_jumpstart_vulnerable_model(patched_get_model_specs):
def make_vulnerable_inference_spec(*largs, **kwargs):
spec = get_spec_from_base_spec(*largs, **kwargs)
spec.inference_vulnerable = True
spec.inference_vulnerabilities = ["some", "vulnerability"]
return spec
patched_get_model_specs.side_effect = make_vulnerable_inference_spec
with pytest.raises(VulnerableJumpStartModelError) as e:
utils.verify_model_region_and_return_specs(
model_id="pytorch-eqa-bert-base-cased",
version="*",
scope=JumpStartScriptScope.INFERENCE.value,
region="us-west-2",
)
assert (
"Version '*' of JumpStart model 'pytorch-eqa-bert-base-cased' has at least 1 "
"vulnerable dependency in the inference script. "
"Please try targetting a higher version of the model. "
"List of vulnerabilities: some, vulnerability") == str(e.value.message)
with patch("logging.Logger.warning") as mocked_warning_log:
assert (utils.verify_model_region_and_return_specs(
model_id="pytorch-eqa-bert-base-cased",
version="*",
scope=JumpStartScriptScope.INFERENCE.value,
region="us-west-2",
tolerate_vulnerable_model=True,
) is not None)
mocked_warning_log.assert_called_once_with(
"Using vulnerable JumpStart model '%s' and version '%s' (inference).",
"pytorch-eqa-bert-base-cased",
"*",
)
def make_vulnerable_training_spec(*largs, **kwargs):
spec = get_spec_from_base_spec(*largs, **kwargs)
spec.training_vulnerable = True
spec.training_vulnerabilities = ["some", "vulnerability"]
return spec
patched_get_model_specs.side_effect = make_vulnerable_training_spec
with pytest.raises(VulnerableJumpStartModelError) as e:
utils.verify_model_region_and_return_specs(
model_id="pytorch-eqa-bert-base-cased",
version="*",
scope=JumpStartScriptScope.TRAINING.value,
region="us-west-2",
)
assert (
"Version '*' of JumpStart model 'pytorch-eqa-bert-base-cased' has at least 1 "
"vulnerable dependency in the training script. "
"Please try targetting a higher version of the model. "
"List of vulnerabilities: some, vulnerability") == str(e.value.message)
with patch("logging.Logger.warning") as mocked_warning_log:
assert (utils.verify_model_region_and_return_specs(
model_id="pytorch-eqa-bert-base-cased",
version="*",
scope=JumpStartScriptScope.TRAINING.value,
region="us-west-2",
tolerate_vulnerable_model=True,
) is not None)
mocked_warning_log.assert_called_once_with(
"Using vulnerable JumpStart model '%s' and version '%s' (training).",
"pytorch-eqa-bert-base-cased",
"*",
)
def _retrieve_image_uri(
model_id: str,
model_version: str,
image_scope: str,
framework: Optional[str],
region: Optional[str],
version: Optional[str],
py_version: Optional[str],
instance_type: Optional[str],
accelerator_type: Optional[str],
container_version: Optional[str],
distribution: Optional[str],
base_framework_version: Optional[str],
training_compiler_config: Optional[str],
tolerate_vulnerable_model: bool,
tolerate_deprecated_model: bool,
):
"""Retrieves the container image URI for JumpStart models.
Only `model_id`, `model_version`, and `image_scope` are required;
the rest of the fields are auto-populated.
Args:
model_id (str): JumpStart model ID for which to retrieve image URI.
model_version (str): Version of the JumpStart model for which to retrieve
the image URI.
image_scope (str): The image type, i.e. what it is used for.
Valid values: "training", "inference", "eia". If ``accelerator_type`` is set,
``image_scope`` is ignored.
framework (str): The name of the framework or algorithm.
region (str): The AWS region.
version (str): The framework or algorithm version. This is required if there is
more than one supported version for the given framework or algorithm.
py_version (str): The Python version. This is required if there is
more than one supported Python version for the given framework version.
instance_type (str): The SageMaker instance type. For supported types, see
https://aws.amazon.com/sagemaker/pricing/instance-types. This is required if
there are different images for different processor types.
accelerator_type (str): Elastic Inference accelerator type. For more, see
https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html.
container_version (str): the version of docker image.
Ideally the value of parameter should be created inside the framework.
For custom use, see the list of supported container versions:
https://github.com/aws/deep-learning-containers/blob/master/available_images.md.
distribution (dict): A dictionary with information on how to run distributed training
training_compiler_config (:class:`~sagemaker.training_compiler.TrainingCompilerConfig`):
A configuration class for the SageMaker Training Compiler.
tolerate_vulnerable_model (bool): True if vulnerable versions of model
specifications should be tolerated (exception not raised). If False, raises an
exception if the script used by this version of the model has dependencies with known
security vulnerabilities.
tolerate_deprecated_model (bool): True if deprecated versions of model
specifications should be tolerated (exception not raised). If False, raises
an exception if the version of the model is deprecated.
Returns:
str: the ECR URI for the corresponding SageMaker Docker image.
Raises:
ValueError: If the combination of arguments specified is not supported.
VulnerableJumpStartModelError: If any of the dependencies required by the script have
known security vulnerabilities.
DeprecatedJumpStartModelError: If the version of the model is deprecated.
"""
if region is None:
region = JUMPSTART_DEFAULT_REGION_NAME
model_specs = verify_model_region_and_return_specs(
model_id=model_id,
version=model_version,
scope=image_scope,
region=region,
tolerate_vulnerable_model=tolerate_vulnerable_model,
tolerate_deprecated_model=tolerate_deprecated_model,
)
if image_scope == JumpStartScriptScope.INFERENCE:
ecr_specs = model_specs.hosting_ecr_specs
elif image_scope == JumpStartScriptScope.TRAINING:
ecr_specs = model_specs.training_ecr_specs
if framework is not None and framework != ecr_specs.framework:
raise ValueError(
f"Incorrect container framework '{framework}' for JumpStart model ID '{model_id}' "
f"and version '{model_version}'.")
if version is not None and version != ecr_specs.framework_version:
raise ValueError(
f"Incorrect container framework version '{version}' for JumpStart model ID "
f"'{model_id}' and version '{model_version}'.")
if py_version is not None and py_version != ecr_specs.py_version:
raise ValueError(
f"Incorrect python version '{py_version}' for JumpStart model ID '{model_id}' "
f"and version '{model_version}'.")
base_framework_version_override: Optional[str] = None
version_override: Optional[str] = None
if ecr_specs.framework == ModelFramework.HUGGINGFACE:
base_framework_version_override = ecr_specs.framework_version
version_override = ecr_specs.huggingface_transformers_version
if image_scope == JumpStartScriptScope.TRAINING:
return image_uris.get_training_image_uri(
region=region,
framework=ecr_specs.framework,
framework_version=version_override or ecr_specs.framework_version,
py_version=ecr_specs.py_version,
image_uri=None,
distribution=None,
compiler_config=None,
tensorflow_version=None,
pytorch_version=base_framework_version_override
or base_framework_version,
instance_type=instance_type,
)
if base_framework_version_override is not None:
base_framework_version_override = f"pytorch{base_framework_version_override}"
return image_uris.retrieve(
framework=ecr_specs.framework,
region=region,
version=version_override or ecr_specs.framework_version,
py_version=ecr_specs.py_version,
instance_type=instance_type,
accelerator_type=accelerator_type,
image_scope=image_scope,
container_version=container_version,
distribution=distribution,
base_framework_version=base_framework_version_override
or base_framework_version,
training_compiler_config=training_compiler_config,
)