def test_retrieve_different_config_per_python_version(config_for_framework, caplog):
config = {
"processors": ["cpu", "gpu"],
"scope": ["training", "inference"],
"versions": {
"1.0.0": {
"py3": {"registries": {"us-west-2": "123412341234"}, "repository": "foo"},
"py37": {"registries": {"us-west-2": "012345678901"}, "repository": "bar"},
},
},
}
config_for_framework.return_value = config
uri = image_uris.retrieve(
framework="useless-string",
version="1.0.0",
py_version="py3",
instance_type="ml.c4.xlarge",
region="us-west-2",
image_scope="training",
)
assert "123412341234.dkr.ecr.us-west-2.amazonaws.com/foo:1.0.0-cpu-py3" == uri
uri = image_uris.retrieve(
framework="useless-string",
version="1.0.0",
py_version="py37",
instance_type="ml.c4.xlarge",
region="us-west-2",
image_scope="training",
)
assert "012345678901.dkr.ecr.us-west-2.amazonaws.com/bar:1.0.0-cpu-py37" == uri
def test_retrieve_unsupported_version(config_for_framework):
with pytest.raises(ValueError) as e:
image_uris.retrieve(
framework="some-framework",
version="1",
py_version="py3",
instance_type="ml.c4.xlarge",
region="us-west-2",
image_scope="training",
)
assert "Unsupported some-framework version: 1." in str(e.value)
assert "Supported some-framework version(s): 1.0.0, 1.1.0." in str(e.value)
with pytest.raises(ValueError) as e:
image_uris.retrieve(
framework="some-framework",
py_version="py3",
instance_type="ml.c4.xlarge",
region="us-west-2",
image_scope="training",
)
assert "Unsupported some-framework version: None." in str(e.value)
assert "Supported some-framework version(s): 1.0.0, 1.1.0." in str(e.value)
def test_retrieve_processor_type_from_version_specific_processor_config(config_for_framework):
config = copy.deepcopy(BASE_CONFIG)
del config["processors"]
config["versions"]["1.0.0"]["processors"] = ["cpu"]
config_for_framework.return_value = config
uri = image_uris.retrieve(
framework="useless-string",
version="1.0.0",
py_version="py3",
instance_type="ml.c4.xlarge",
region="us-west-2",
image_scope="training",
)
assert "123412341234.dkr.ecr.us-west-2.amazonaws.com/dummy:1.0.0-cpu-py3" == uri
uri = image_uris.retrieve(
framework="useless-string",
version="1.1.0",
py_version="py3",
instance_type="ml.c4.xlarge",
region="us-west-2",
image_scope="training",
)
assert "123412341234.dkr.ecr.us-west-2.amazonaws.com/dummy:1.1.0-py3" == uri
def test_retrieve_unsupported_python_version(config_for_framework):
with pytest.raises(ValueError) as e:
image_uris.retrieve(
framework="useless-string",
version="1.0.0",
py_version="py2",
instance_type="ml.c4.xlarge",
region="us-west-2",
image_scope="training",
)
assert "Unsupported Python version: py2." in str(e.value)
assert "Supported Python version(s): py3, py37." in str(e.value)
with pytest.raises(ValueError) as e:
image_uris.retrieve(
framework="useless-string",
version="1.0.0",
instance_type="ml.c4.xlarge",
region="us-west-2",
image_scope="training",
)
assert "Unsupported Python version: None." in str(e.value)
assert "Supported Python version(s): py3, py37." in str(e.value)
def test_retrieve_unsupported_image_scope(config_for_framework):
with pytest.raises(ValueError) as e:
image_uris.retrieve(
framework="useless-string",
version="1.0.0",
py_version="py3",
instance_type="ml.c4.xlarge",
region="us-west-2",
image_scope="invalid-image-scope",
)
assert "Unsupported image scope: invalid-image-scope." in str(e.value)
assert "Supported image scope(s): training, inference." in str(e.value)
config = copy.deepcopy(BASE_CONFIG)
config["scope"].append("eia")
config_for_framework.return_value = config
with pytest.raises(ValueError) as e:
image_uris.retrieve(
framework="useless-string",
version="1.0.0",
py_version="py3",
instance_type="ml.c4.xlarge",
region="us-west-2",
)
assert "Unsupported image scope: None." in str(e.value)
assert "Supported image scope(s): training, inference, eia." in str(e.value)
def _test_neo_framework_uris(framework, version):
framework_in_config = f"neo-{framework}"
framework_in_uri = f"neo-{framework}" if framework == "tensorflow" else f"inference-{framework}"
for region in regions.regions():
if region in ACCOUNTS:
uri = image_uris.retrieve(framework_in_config,
region,
instance_type="ml_c5",
version=version)
assert _expected_framework_uri(framework_in_uri,
version,
region=region) == uri
else:
with pytest.raises(ValueError) as e:
image_uris.retrieve(framework_in_config,
region,
instance_type="ml_c5",
version=version)
assert "Unsupported region: {}.".format(region) in str(e.value)
uri = image_uris.retrieve(framework_in_config,
"us-west-2",
instance_type="ml_p2",
version=version)
assert _expected_framework_uri(framework_in_uri, version,
processor="gpu") == uri
def _test_image_uris(
framework,
fw_version,
py_version,
scope,
expected_fn,
expected_fn_args,
base_framework_version=None,
):
base_args = {
"framework": framework,
"version": fw_version,
"py_version": py_version,
"image_scope": scope,
}
for instance_type, processor in INSTANCE_TYPES_AND_PROCESSORS:
uri = image_uris.retrieve(region=REGION, instance_type=instance_type, **base_args)
expected = expected_fn(processor=processor, **expected_fn_args)
assert expected == uri
for region in SAGEMAKER_ALTERNATE_REGION_ACCOUNTS.keys():
uri = image_uris.retrieve(region=region, instance_type="ml.c4.xlarge", **base_args)
expected = expected_fn(region=region, **expected_fn_args)
assert expected == uri
def test_mxnet_eia(mxnet_eia_version, mxnet_py_version):
base_args = {
"framework": "mxnet",
"version": mxnet_eia_version,
"py_version": mxnet_py_version,
"image_scope": "inference",
"instance_type": "ml.c4.xlarge",
"accelerator_type": "ml.eia1.medium",
}
uri = image_uris.retrieve(region=REGION, **base_args)
expected = _expected_mxnet_inference_uri(mxnet_eia_version,
mxnet_py_version,
eia=True)
assert expected == uri
for region in SAGEMAKER_ALTERNATE_REGION_ACCOUNTS.keys():
uri = image_uris.retrieve(region=region, **base_args)
expected = _expected_mxnet_inference_uri(mxnet_eia_version,
mxnet_py_version,
region=region,
eia=True)
assert expected == uri
def test_pytorch_eia(pytorch_eia_version, pytorch_eia_py_version):
base_args = {
"framework": "pytorch",
"version": pytorch_eia_version,
"py_version": pytorch_eia_py_version,
"image_scope": "inference",
"instance_type": "ml.c4.xlarge",
"accelerator_type": "ml.eia1.medium",
}
uri = image_uris.retrieve(region=REGION, **base_args)
expected = expected_uris.framework_uri(
"pytorch-inference-eia",
pytorch_eia_version,
DLC_ACCOUNT,
py_version=pytorch_eia_py_version,
region=REGION,
)
assert expected == uri
for region, account in DLC_ALTERNATE_REGION_ACCOUNTS.items():
uri = image_uris.retrieve(region=region, **base_args)
expected = expected_uris.framework_uri(
"pytorch-inference-eia",
pytorch_eia_version,
account,
py_version=pytorch_eia_py_version,
region=region,
)
assert expected == uri
def test_retrieve_aliased_version(config_for_framework):
version = "1.0.0-build123"
config = copy.deepcopy(BASE_CONFIG)
config["version_aliases"] = {version: "1.0.0"}
config_for_framework.return_value = config
uri = image_uris.retrieve(
framework="useless-string",
version=version,
py_version="py3",
instance_type="ml.c4.xlarge",
region="us-west-2",
image_scope="training",
)
assert "123412341234.dkr.ecr.us-west-2.amazonaws.com/dummy:{}-cpu-py3".format(version) == uri
del config["versions"]["1.1.0"]
uri = image_uris.retrieve(
framework="useless-string",
version=version,
py_version="py3",
instance_type="ml.c4.xlarge",
region="us-west-2",
image_scope="training",
)
assert "123412341234.dkr.ecr.us-west-2.amazonaws.com/dummy:{}-cpu-py3".format(version) == uri
def test_retrieve_no_python_version(config_for_framework, caplog):
config = copy.deepcopy(BASE_CONFIG)
config["versions"]["1.0.0"]["py_versions"] = []
config_for_framework.return_value = config
uri = image_uris.retrieve(
framework="useless-string",
version="1.0.0",
instance_type="ml.c4.xlarge",
region="us-west-2",
image_scope="training",
)
assert "123412341234.dkr.ecr.us-west-2.amazonaws.com/dummy:1.0.0-cpu" == uri
caplog.set_level(logging.INFO)
uri = image_uris.retrieve(
framework="useless-string",
version="1.0.0",
py_version="py3",
instance_type="ml.c4.xlarge",
region="us-west-2",
image_scope="training",
)
assert "123412341234.dkr.ecr.us-west-2.amazonaws.com/dummy:1.0.0-cpu" == uri
assert "Ignoring unnecessary Python version: py3." in caplog.text
def test_jumpstart_xgboost_image_uri(patched_get_model_specs, session):
patched_get_model_specs.side_effect = get_prototype_model_spec
model_id, model_version = "xgboost-classification-model", "*"
instance_type = "ml.p2.xlarge"
region = "us-west-2"
model_specs = accessors.JumpStartModelsAccessor.get_model_specs(
region, model_id, model_version)
# inference
uri = image_uris.retrieve(
framework=None,
region=region,
image_scope="inference",
model_id=model_id,
model_version=model_version,
instance_type=instance_type,
)
framework_class_uri = XGBoostModel(
role="mock_role",
model_data="mock_data",
entry_point="mock_entry_point",
framework_version=model_specs.hosting_ecr_specs.framework_version,
py_version=model_specs.hosting_ecr_specs.py_version,
sagemaker_session=session,
).serving_image_uri(region, instance_type)
assert uri == framework_class_uri
assert uri == "246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-xgboost:1.3-1"
# training
uri = image_uris.retrieve(
framework=None,
region=region,
image_scope="training",
model_id=model_id,
model_version=model_version,
instance_type=instance_type,
)
framework_class_uri = XGBoost(
role="mock_role",
entry_point="mock_entry_point",
framework_version=model_specs.training_ecr_specs.framework_version,
py_version=model_specs.training_ecr_specs.py_version,
instance_type=instance_type,
instance_count=1,
image_uri_region=region,
sagemaker_session=session,
).training_image_uri(region=region)
assert uri == framework_class_uri
assert uri == "246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-xgboost:1.3-1"
def test_gpu_error(sklearn_version):
with pytest.raises(ValueError) as e:
image_uris.retrieve(
"sklearn",
region="us-west-2",
version=sklearn_version,
instance_type="ml.p2.xlarge",
)
assert "Unsupported processor: gpu." in str(e.value)
def test_algo_uris(algo):
for region in regions.regions():
if region in ACCOUNTS:
uri = image_uris.retrieve(algo, region)
expected = expected_uris.algo_uri(algo, ACCOUNTS[region], region, version="latest")
assert expected == uri
else:
with pytest.raises(ValueError) as e:
image_uris.retrieve(algo, region)
assert "Unsupported region: {}.".format(region) in str(e.value)
def test_jumpstart_catboost_image_uri(patched_get_model_specs, session):
patched_get_model_specs.side_effect = get_prototype_model_spec
model_id, model_version = "catboost-classification-model", "*"
instance_type = "ml.p2.xlarge"
region = "us-west-2"
model_specs = accessors.JumpStartModelsAccessor.get_model_specs(
region, model_id, model_version)
# inference
uri = image_uris.retrieve(
framework=None,
region=region,
image_scope="inference",
model_id=model_id,
model_version=model_version,
instance_type=instance_type,
)
framework_class_uri = PyTorchModel(
role="mock_role",
model_data="mock_data",
entry_point="mock_entry_point",
framework_version=model_specs.hosting_ecr_specs.framework_version,
py_version=model_specs.hosting_ecr_specs.py_version,
sagemaker_session=session,
).serving_image_uri(region, instance_type)
assert uri == framework_class_uri
assert uri == "763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-inference:1.9.0-gpu-py38"
# training
uri = image_uris.retrieve(
framework=None,
region=region,
image_scope="training",
model_id=model_id,
model_version=model_version,
instance_type=instance_type,
)
framework_class_uri = PyTorch(
role="mock_role",
entry_point="mock_entry_point",
framework_version=model_specs.training_ecr_specs.framework_version,
py_version=model_specs.training_ecr_specs.py_version,
instance_type=instance_type,
instance_count=1,
sagemaker_session=session,
).training_image_uri(region=region)
assert uri == framework_class_uri
assert uri == "763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-training:1.9.0-gpu-py38"
def test_gpu_error(version):
with pytest.raises(ValueError) as e:
image_uris.retrieve(
"autogluon",
region="us-west-2",
version=version,
image_scope="inference",
instance_type="ml.p2.xlarge",
)
assert "Unsupported processor: gpu." in str(e.value)
def test_jumpstart_tensorflow_image_uri(patched_get_model_specs, session):
patched_get_model_specs.side_effect = get_prototype_model_spec
model_id, model_version = "tensorflow-ic-bit-m-r101x1-ilsvrc2012-classification-1", "*"
instance_type = "ml.p2.xlarge"
region = "us-west-2"
model_specs = accessors.JumpStartModelsAccessor.get_model_specs(
region, model_id, model_version)
# inference
uri = image_uris.retrieve(
framework=None,
region=region,
image_scope="inference",
model_id=model_id,
model_version=model_version,
instance_type=instance_type,
)
framework_class_uri = TensorFlowModel(
role="mock_role",
model_data="mock_data",
entry_point="mock_entry_point",
framework_version=model_specs.hosting_ecr_specs.framework_version,
sagemaker_session=session,
).serving_image_uri(region, instance_type)
assert uri == framework_class_uri
assert uri == "763104351884.dkr.ecr.us-west-2.amazonaws.com/tensorflow-inference:2.3-gpu"
# training
uri = image_uris.retrieve(
framework=None,
region=region,
image_scope="training",
model_id=model_id,
model_version=model_version,
instance_type=instance_type,
)
framework_class_uri = TensorFlow(
role="mock_role",
entry_point="mock_entry_point",
framework_version=model_specs.training_ecr_specs.framework_version,
py_version=model_specs.training_ecr_specs.py_version,
instance_type=instance_type,
instance_count=1,
sagemaker_session=session,
).training_image_uri(region=region)
assert uri == framework_class_uri
assert uri == "763104351884.dkr.ecr.us-west-2.amazonaws.com/tensorflow-training:2.3-gpu-py37"
def test_py2_error(sklearn_version):
with pytest.raises(ValueError) as e:
image_uris.retrieve(
"sklearn",
region="us-west-2",
version=sklearn_version,
py_version="py2",
instance_type="ml.c4.xlarge",
)
assert "Unsupported Python version: py2." in str(e.value)
def get_algo_image_uri(algo_name, region, repo_version):
if algo_name == "xgboost":
return image_uris.retrieve(algo_name, region=region, version='1.2-2')
elif algo_name == "mlp":
mlp_image_uri = image_uris.retrieve("linear-learner", region=region, version=repo_version)
last_slash_index = mlp_image_uri.rfind('/')
return "{}/{}:{}".format(
mlp_image_uri[:last_slash_index], "mxnet-algorithms", repo_version
)
else:
return image_uris.retrieve(algo_name, region=region, version=repo_version)
def test_retrieve_invalid_accelerator(config_for_framework):
with pytest.raises(ValueError) as e:
image_uris.retrieve(
framework="useless-string",
version="1.0.0",
py_version="py3",
instance_type="ml.c4.xlarge",
accelerator_type="fake-accelerator",
region="us-west-2",
)
assert "Invalid SageMaker Elastic Inference accelerator type: fake-accelerator." in str(e.value)
def test_lda():
algo = "lda"
accounts = _accounts_for_algo(algo)
for region in regions.regions():
if region in accounts:
uri = image_uris.retrieve(algo, region)
assert expected_uris.algo_uri(algo, accounts[region], region) == uri
else:
with pytest.raises(ValueError) as e:
image_uris.retrieve(algo, region)
assert "Unsupported region: {}.".format(region) in str(e.value)
def test_py3_error(version):
with pytest.raises(ValueError) as e:
image_uris.retrieve(
"autogluon",
region="us-west-2",
version=version,
py_version="py3",
image_scope="training",
instance_type="ml.c4.xlarge",
)
assert "Unsupported Python version: py3." in str(e.value)
def test_retrieve_processor_type_neo(config_for_framework):
for cpu in ("ml_m4", "ml_m5", "ml_c4", "ml_c5"):
uri = image_uris.retrieve(
framework="useless-string",
version="1.0.0",
py_version="py3",
instance_type=cpu,
region="us-west-2",
image_scope="training",
)
assert "123412341234.dkr.ecr.us-west-2.amazonaws.com/dummy:1.0.0-cpu-py3" == uri
for gpu in ("ml_p2", "ml_p3"):
uri = image_uris.retrieve(
framework="useless-string",
version="1.0.0",
py_version="py3",
instance_type=gpu,
region="us-west-2",
image_scope="training",
)
assert "123412341234.dkr.ecr.us-west-2.amazonaws.com/dummy:1.0.0-gpu-py3" == uri
config = copy.deepcopy(BASE_CONFIG)
config["processors"] = ["inf"]
config_for_framework.return_value = config
uri = image_uris.retrieve(
framework="useless-string",
version="1.0.0",
py_version="py3",
instance_type="ml_inf1",
region="us-west-2",
image_scope="training",
)
assert "123412341234.dkr.ecr.us-west-2.amazonaws.com/dummy:1.0.0-inf-py3" == uri
config = copy.deepcopy(BASE_CONFIG)
config["processors"] = ["c5"]
config_for_framework.return_value = config
uri = image_uris.retrieve(
framework="useless-string",
version="1.0.0",
py_version="py3",
instance_type="ml_c5",
region="us-west-2",
image_scope="training",
)
assert "123412341234.dkr.ecr.us-west-2.amazonaws.com/dummy:1.0.0-c5-py3" == uri
def endpoint_name(sagemaker_session):
endpoint_name = unique_name_from_base("model-quality-monitor-integ")
xgb_model_data = sagemaker_session.upload_data(
path=os.path.join(XGBOOST_DATA_PATH, "xgb_model.tar.gz"),
key_prefix="integ-test-data/xgboost/model",
)
xgb_image = image_uris.retrieve("xgboost",
sagemaker_session.boto_region_name,
version="1",
image_scope="inference")
with tests.integ.timeout.timeout_and_delete_endpoint_by_name(
endpoint_name=endpoint_name,
sagemaker_session=sagemaker_session,
hours=2):
xgb_model = Model(
model_data=xgb_model_data,
image_uri=xgb_image,
name=endpoint_name, # model name
role=ROLE,
sagemaker_session=sagemaker_session,
)
xgb_model.deploy(
INSTANCE_COUNT,
INSTANCE_TYPE,
endpoint_name=endpoint_name,
data_capture_config=DataCaptureConfig(
True, sagemaker_session=sagemaker_session),
)
yield endpoint_name
def __init__(
self,
entry_point,
region,
framework_version,
py_version,
instance_type,
source_dir=None,
hyperparameters=None,
**kwargs,
):
self.framework_version = framework_version
self.py_version = py_version
self.image_uri = image_uris.retrieve(
"autogluon",
region=region,
version=framework_version,
py_version=py_version,
image_scope="training",
instance_type=instance_type,
)
super().__init__(
entry_point,
source_dir,
hyperparameters,
framework_version=framework_version,
instance_type=instance_type,
image_uri=self.image_uri,
**kwargs,
)
def serving_image_uri(self,
region_name,
instance_type,
accelerator_type=None):
"""Create a URI for the serving image.
Args:
region_name (str): AWS region where the image is uploaded.
instance_type (str): SageMaker instance type. Used to determine device type
(cpu/gpu/family-specific optimized).
accelerator_type (str): The Elastic Inference accelerator type to
deploy to the instance for loading and making inferences to the
model (default: None). For example, 'ml.eia1.medium'.
Returns:
str: The appropriate image URI based on the given parameters.
"""
return image_uris.retrieve(
self._framework_name,
region_name,
version=self.framework_version,
py_version=self.py_version,
instance_type=instance_type,
accelerator_type=accelerator_type,
image_scope="inference",
)
def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
"""Initialization for LinearLearnerModel.
Args:
model_data (str): The S3 location of a SageMaker model data
``.tar.gz`` file.
role (str): An AWS IAM role (either name or full ARN). The Amazon
SageMaker training jobs and APIs that create Amazon SageMaker
endpoints use this role to access training data and model
artifacts. After the endpoint is created, the inference code
might use the IAM role, if it needs to access an AWS resource.
sagemaker_session (sagemaker.session.Session): Session object which
manages interactions with Amazon SageMaker APIs and any other
AWS services needed. If not specified, the estimator creates one
using the default AWS configuration chain.
**kwargs: Keyword arguments passed to the ``FrameworkModel``
initializer.
"""
sagemaker_session = sagemaker_session or Session()
image_uri = image_uris.retrieve(
LinearLearner.repo_name,
sagemaker_session.boto_region_name,
version=LinearLearner.repo_version,
)
super(LinearLearnerModel,
self).__init__(image_uri,
model_data,
role,
predictor_cls=LinearLearnerPredictor,
sagemaker_session=sagemaker_session,
**kwargs)
def training_image_uri(self):
"""Placeholder docstring"""
return image_uris.retrieve(
self.repo_name,
self.sagemaker_session.boto_region_name,
version=self.repo_version,
)
def _validate_args(self, py_version):
"""Placeholder docstring"""
if py_version == "py2" and self._only_python_3_supported():
msg = (
"Python 2 containers are only available with {} and lower versions. "
"Please use a Python 3 container.".format(
defaults.LATEST_PY2_VERSION))
raise AttributeError(msg)
if self.image_uri is None and self._only_legacy_mode_supported():
legacy_image_uri = image_uris.retrieve(
"tensorflow",
self.sagemaker_session.boto_region_name,
instance_type=self.instance_type,
version=self.framework_version,
py_version=self.py_version,
image_scope="training",
)
msg = (
"TF {} supports only legacy mode. Please supply the image URI directly with "
"'image_uri={}' and set 'model_dir=False'. If you are using any legacy parameters "
"(training_steps, evaluation_steps, checkpoint_path, requirements_file), "
"make sure to pass them directly as hyperparameters instead. For more, see "
"https://sagemaker.readthedocs.io/en/v2.0.0.rc0/frameworks/tensorflow/upgrade_from_legacy.html."
).format(self.framework_version, legacy_image_uri)
raise ValueError(msg)
def _compilation_image_uri(self, region, target_instance_type, framework,
framework_version):
"""Retrieve the Neo or Inferentia image URI.
Args:
region (str): The AWS region.
target_instance_type (str): Identifies the device on which you want to run
your model after compilation, for example: ml_c5. For valid values, see
https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
framework (str): The framework name.
framework_version (str): The framework version.
"""
framework_prefix = ""
framework_suffix = ""
if framework == "xgboost":
framework_suffix = "-neo"
elif target_instance_type.startswith("ml_inf"):
framework_prefix = "inferentia-"
else:
framework_prefix = "neo-"
return image_uris.retrieve(
"{}{}{}".format(framework_prefix, framework, framework_suffix),
region,
instance_type=target_instance_type,
version=framework_version,
)