def registry(region_name, framework=None):
"""
Return docker registry for the given AWS region for the given framework.
This is only used for SparkML and Scikit-learn for now.
"""
try:
account_id = image_registry_map[region_name][framework]
return get_ecr_image_uri_prefix(account_id, region_name)
except KeyError:
logging.error("The specific image or region does not exist")
raise
def container_image(sagemaker_session):
""" Create a Multi-Model container image for use with integration testcases
since 1P containers supporting multiple models are not available yet"""
region = sagemaker_session.boto_region_name
ecr_client = sagemaker_session.boto_session.client("ecr",
region_name=region)
sts_client = sagemaker_session.boto_session.client(
"sts",
region_name=region,
endpoint_url=utils.sts_regional_endpoint(region))
account_id = sts_client.get_caller_identity()["Account"]
algorithm_name = "sagemaker-multimodel-integ-test-{}".format(
sagemaker_timestamp())
ecr_image_uri_prefix = get_ecr_image_uri_prefix(account=account_id,
region=region)
ecr_image = "{prefix}/{algorithm_name}:latest".format(
prefix=ecr_image_uri_prefix, algorithm_name=algorithm_name)
# Build and tag docker image locally
docker_client = docker.from_env()
image, build_log = docker_client.images.build(path=os.path.join(
DATA_DIR, "multimodel", "container"),
tag=algorithm_name,
rm=True)
image.tag(ecr_image, tag="latest")
# Create AWS ECR and push the local docker image to it
_create_repository(ecr_client, algorithm_name)
username, password = _ecr_login(ecr_client)
# Retry docker image push
for _ in retries(3, "Upload docker image to ECR repo",
seconds_to_sleep=10):
try:
docker_client.images.push(ecr_image,
auth_config={
"username": username,
"password": password
})
break
except requests.exceptions.ConnectionError:
# This can happen when we try to create multiple repositories in parallel, so we retry
pass
yield ecr_image
# Delete repository after the multi model integration tests complete
_delete_repository(ecr_client, algorithm_name)
def registry(region_name, algorithm=None):
"""Return docker registry for the given AWS region
Note: Not all the algorithms listed below have an Amazon Estimator implemented. For full list of
pre-implemented Estimators, look at:
https://github.com/aws/sagemaker-python-sdk/tree/master/src/sagemaker/amazon
"""
if algorithm in [
None,
"pca",
"kmeans",
"linear-learner",
"factorization-machines",
"ntm",
"randomcutforest",
"knn",
"object2vec",
"ipinsights",
]:
account_id = {
"us-east-1": "382416733822",
"us-east-2": "404615174143",
"us-west-2": "174872318107",
"eu-west-1": "438346466558",
"eu-central-1": "664544806723",
"ap-northeast-1": "351501993468",
"ap-northeast-2": "835164637446",
"ap-southeast-2": "712309505854",
"us-gov-west-1": "226302683700",
"ap-southeast-1": "475088953585",
"ap-south-1": "991648021394",
"ca-central-1": "469771592824",
"eu-west-2": "644912444149",
"us-west-1": "632365934929",
"us-iso-east-1": "490574956308",
}[region_name]
elif algorithm in ["lda"]:
account_id = {
"us-east-1": "766337827248",
"us-east-2": "999911452149",
"us-west-2": "266724342769",
"eu-west-1": "999678624901",
"eu-central-1": "353608530281",
"ap-northeast-1": "258307448986",
"ap-northeast-2": "293181348795",
"ap-southeast-2": "297031611018",
"us-gov-west-1": "226302683700",
"ap-southeast-1": "475088953585",
"ap-south-1": "991648021394",
"ca-central-1": "469771592824",
"eu-west-2": "644912444149",
"us-west-1": "632365934929",
"us-iso-east-1": "490574956308",
}[region_name]
elif algorithm in ["forecasting-deepar"]:
account_id = {
"us-east-1": "522234722520",
"us-east-2": "566113047672",
"us-west-2": "156387875391",
"eu-west-1": "224300973850",
"eu-central-1": "495149712605",
"ap-northeast-1": "633353088612",
"ap-northeast-2": "204372634319",
"ap-southeast-2": "514117268639",
"us-gov-west-1": "226302683700",
"ap-southeast-1": "475088953585",
"ap-south-1": "991648021394",
"ca-central-1": "469771592824",
"eu-west-2": "644912444149",
"us-west-1": "632365934929",
"us-iso-east-1": "490574956308",
}[region_name]
elif algorithm in [
"xgboost",
"seq2seq",
"image-classification",
"blazingtext",
"object-detection",
"semantic-segmentation",
]:
account_id = {
"us-east-1": "811284229777",
"us-east-2": "825641698319",
"us-west-2": "433757028032",
"eu-west-1": "685385470294",
"eu-central-1": "813361260812",
"ap-northeast-1": "501404015308",
"ap-northeast-2": "306986355934",
"ap-southeast-2": "544295431143",
"us-gov-west-1": "226302683700",
"ap-southeast-1": "475088953585",
"ap-south-1": "991648021394",
"ca-central-1": "469771592824",
"eu-west-2": "644912444149",
"us-west-1": "632365934929",
"us-iso-east-1": "490574956308",
}[region_name]
elif algorithm in ["image-classification-neo", "xgboost-neo"]:
account_id = {
"us-west-2": "301217895009",
"us-east-1": "785573368785",
"eu-west-1": "802834080501",
"us-east-2": "007439368137",
}[region_name]
else:
raise ValueError(
"Algorithm class:{} does not have mapping to account_id with images"
.format(algorithm))
return get_ecr_image_uri_prefix(account_id, region_name)
def create_image_uri(
region,
framework,
instance_type,
framework_version,
py_version=None,
account=None,
accelerator_type=None,
optimized_families=None,
):
"""Return the ECR URI of an image.
Args:
region (str): AWS region where the image is uploaded.
framework (str): framework used by the image.
instance_type (str): SageMaker instance type. Used to determine device
type (cpu/gpu/family-specific optimized).
framework_version (str): The version of the framework.
py_version (str): Optional. Python version. If specified, should be one
of 'py2' or 'py3'. If not specified, image uri will not include a
python component.
account (str): AWS account that contains the image. (default:
'520713654638')
accelerator_type (str): SageMaker Elastic Inference accelerator type.
optimized_families (str): Instance families for which there exist
specific optimized images.
Returns:
str: The appropriate image URI based on the given parameters.
"""
optimized_families = optimized_families or []
if py_version and py_version not in VALID_PY_VERSIONS:
raise ValueError("invalid py_version argument: {}".format(py_version))
if _accelerator_type_valid_for_framework(
framework=framework,
accelerator_type=accelerator_type,
optimized_families=optimized_families,
):
framework += "-eia"
# Handle Account Number for Gov Cloud and frameworks with DLC merged images
if account is None:
account = _registry_id(
region=region,
framework=framework,
py_version=py_version,
account=DEFAULT_ACCOUNT,
framework_version=framework_version,
)
# Handle Local Mode
if instance_type.startswith("local"):
device_type = "cpu" if instance_type == "local" else "gpu"
elif not instance_type.startswith("ml."):
raise ValueError(
"{} is not a valid SageMaker instance type. See: "
"https://aws.amazon.com/sagemaker/pricing/instance-types/".format(
instance_type))
else:
family = instance_type.split(".")[1]
# For some frameworks, we have optimized images for specific families, e.g c5 or p3.
# In those cases, we use the family name in the image tag. In other cases, we use
# 'cpu' or 'gpu'.
if family in optimized_families:
device_type = family
elif family[0] in ["g", "p"]:
device_type = "gpu"
else:
device_type = "cpu"
using_merged_images = _using_merged_images(region, framework, py_version,
framework_version)
if not py_version or (using_merged_images
and framework == "tensorflow-serving-eia"):
tag = "{}-{}".format(framework_version, device_type)
else:
tag = "{}-{}-{}".format(framework_version, device_type, py_version)
if using_merged_images:
return "{}/{}:{}".format(get_ecr_image_uri_prefix(account, region),
MERGED_FRAMEWORKS_REPO_MAP[framework], tag)
return "{}/sagemaker-{}:{}".format(
get_ecr_image_uri_prefix(account, region), framework, tag)
def create_image_uri(
region,
framework,
instance_type,
framework_version,
py_version=None,
account=None,
accelerator_type=None,
optimized_families=None,
):
"""Return the ECR URI of an image.
Args:
region (str): AWS region where the image is uploaded.
framework (str): framework used by the image.
instance_type (str): SageMaker instance type. Used to determine device
type (cpu/gpu/family-specific optimized).
framework_version (str): The version of the framework.
py_version (str): Optional. Python version. If specified, should be one
of 'py2' or 'py3'. If not specified, image uri will not include a
python component.
account (str): AWS account that contains the image. (default:
'520713654638')
accelerator_type (str): SageMaker Elastic Inference accelerator type.
optimized_families (str): Instance families for which there exist
specific optimized images.
Returns:
str: The appropriate image URI based on the given parameters.
"""
logger.warning(
"'create_image_uri' will be deprecated in favor of 'ImageURIProvider' class "
"in SageMaker Python SDK v2."
)
optimized_families = optimized_families or []
if py_version and py_version not in VALID_PY_VERSIONS:
raise ValueError("invalid py_version argument: {}".format(py_version))
if py_version == "py37" and framework not in PY37_SUPPORTED_FRAMEWORKS:
raise ValueError("{} does not support Python 3.7 at this time.".format(framework))
if _accelerator_type_valid_for_framework(
framework=framework,
py_version=py_version,
accelerator_type=accelerator_type,
optimized_families=optimized_families,
):
framework += "-eia"
# Handle account number for specific cases (e.g. GovCloud, opt-in regions, DLC images etc.)
if account is None:
account = _registry_id(
region=region,
framework=framework,
py_version=py_version,
account=DEFAULT_ACCOUNT,
framework_version=framework_version,
)
# Handle Local Mode
if instance_type.startswith("local"):
device_type = "cpu" if instance_type == "local" else "gpu"
elif not instance_type.startswith("ml."):
raise ValueError(
"{} is not a valid SageMaker instance type. See: "
"https://aws.amazon.com/sagemaker/pricing/instance-types/".format(instance_type)
)
else:
family = instance_type.split(".")[1]
# For some frameworks, we have optimized images for specific families, e.g c5 or p3.
# In those cases, we use the family name in the image tag. In other cases, we use
# 'cpu' or 'gpu'.
if family in optimized_families:
device_type = family
elif family.startswith("inf"):
device_type = "inf"
elif family[0] in ["g", "p"]:
device_type = "gpu"
else:
device_type = "cpu"
if device_type == "inf":
if region not in INFERENTIA_SUPPORTED_REGIONS:
raise ValueError(
"Inferentia is not supported in region {}. Supported regions are {}".format(
region, ", ".join(INFERENTIA_SUPPORTED_REGIONS)
)
)
if framework not in INFERENTIA_VERSION_RANGES:
raise ValueError(
"Inferentia does not support {}. Currently it supports "
"MXNet and TensorFlow with more frameworks coming soon.".format(
framework.split("-")[-1]
)
)
if not _is_inferentia_supported(framework, framework_version):
raise ValueError(
"Inferentia is not supported with {} version {}.".format(
framework.split("-")[-1], framework_version
)
)
use_dlc_image = _is_dlc_version(framework, framework_version, py_version)
if not py_version or (use_dlc_image and framework == "tensorflow-serving-eia"):
tag = "{}-{}".format(framework_version, device_type)
else:
tag = "{}-{}-{}".format(framework_version, device_type, py_version)
if use_dlc_image:
ecr_repo = MERGED_FRAMEWORKS_REPO_MAP[framework]
else:
ecr_repo = "sagemaker-{}".format(framework)
return "{}/{}:{}".format(get_ecr_image_uri_prefix(account, region), ecr_repo, tag)
def registry(region_name, algorithm=None):
"""Return docker registry for the given AWS region
Note: Not all the algorithms listed below have an Amazon Estimator implemented. For full list of
pre-implemented Estimators, look at:
https://github.com/aws/sagemaker-python-sdk/tree/master/src/sagemaker/amazon
"""
if algorithm in [
None, 'pca', 'kmeans', 'linear-learner', 'factorization-machines',
'ntm', 'randomcutforest', 'knn', 'object2vec', 'ipinsights'
]:
account_id = {
'us-east-1': '382416733822',
'us-east-2': '404615174143',
'us-west-2': '174872318107',
'eu-west-1': '438346466558',
'eu-central-1': '664544806723',
'ap-northeast-1': '351501993468',
'ap-northeast-2': '835164637446',
'ap-southeast-2': '712309505854',
'us-gov-west-1': '226302683700',
'ap-southeast-1': '475088953585',
'ap-south-1': '991648021394',
'ca-central-1': '469771592824',
'eu-west-2': '644912444149',
'us-west-1': '632365934929',
'us-iso-east-1': '490574956308',
}[region_name]
elif algorithm in ['lda']:
account_id = {
'us-east-1': '766337827248',
'us-east-2': '999911452149',
'us-west-2': '266724342769',
'eu-west-1': '999678624901',
'eu-central-1': '353608530281',
'ap-northeast-1': '258307448986',
'ap-northeast-2': '293181348795',
'ap-southeast-2': '297031611018',
'us-gov-west-1': '226302683700',
'ap-southeast-1': '475088953585',
'ap-south-1': '991648021394',
'ca-central-1': '469771592824',
'eu-west-2': '644912444149',
'us-west-1': '632365934929',
'us-iso-east-1': '490574956308',
}[region_name]
elif algorithm in ['forecasting-deepar']:
account_id = {
'us-east-1': '522234722520',
'us-east-2': '566113047672',
'us-west-2': '156387875391',
'eu-west-1': '224300973850',
'eu-central-1': '495149712605',
'ap-northeast-1': '633353088612',
'ap-northeast-2': '204372634319',
'ap-southeast-2': '514117268639',
'us-gov-west-1': '226302683700',
'ap-southeast-1': '475088953585',
'ap-south-1': '991648021394',
'ca-central-1': '469771592824',
'eu-west-2': '644912444149',
'us-west-1': '632365934929',
'us-iso-east-1': '490574956308',
}[region_name]
elif algorithm in [
'xgboost', 'seq2seq', 'image-classification', 'blazingtext',
'object-detection', 'semantic-segmentation'
]:
account_id = {
'us-east-1': '811284229777',
'us-east-2': '825641698319',
'us-west-2': '433757028032',
'eu-west-1': '685385470294',
'eu-central-1': '813361260812',
'ap-northeast-1': '501404015308',
'ap-northeast-2': '306986355934',
'ap-southeast-2': '544295431143',
'us-gov-west-1': '226302683700',
'ap-southeast-1': '475088953585',
'ap-south-1': '991648021394',
'ca-central-1': '469771592824',
'eu-west-2': '644912444149',
'us-west-1': '632365934929',
'us-iso-east-1': '490574956308',
}[region_name]
elif algorithm in ['image-classification-neo', 'xgboost-neo']:
account_id = {
'us-west-2': '301217895009',
'us-east-1': '785573368785',
'eu-west-1': '802834080501',
'us-east-2': '007439368137',
}[region_name]
else:
raise ValueError(
'Algorithm class:{} does not have mapping to account_id with images'
.format(algorithm))
return get_ecr_image_uri_prefix(account_id, region_name)
def registry(region_name, algorithm=None):
"""Return docker registry for the given AWS region
Note: Not all the algorithms listed below have an Amazon Estimator
implemented. For full list of pre-implemented Estimators, look at:
https://github.com/aws/sagemaker-python-sdk/tree/master/src/sagemaker/amazon
Args:
region_name (str): The region name for the account.
algorithm (str): The algorithm for the account.
Raises:
ValueError: If invalid algorithm passed in or if mapping does not exist for given algorithm
and region.
"""
region_to_accounts = {}
if algorithm in [
None,
"pca",
"kmeans",
"linear-learner",
"factorization-machines",
"ntm",
"randomcutforest",
"knn",
"object2vec",
"ipinsights",
]:
region_to_accounts = {
"us-east-1": "382416733822",
"us-east-2": "404615174143",
"us-west-2": "174872318107",
"eu-west-1": "438346466558",
"eu-central-1": "664544806723",
"ap-northeast-1": "351501993468",
"ap-northeast-2": "835164637446",
"ap-southeast-2": "712309505854",
"us-gov-west-1": "226302683700",
"ap-southeast-1": "475088953585",
"ap-south-1": "991648021394",
"ca-central-1": "469771592824",
"eu-west-2": "644912444149",
"us-west-1": "632365934929",
"us-iso-east-1": "490574956308",
"ap-east-1": "286214385809",
"eu-north-1": "669576153137",
"eu-west-3": "749696950732",
"sa-east-1": "855470959533",
"me-south-1": "249704162688",
"cn-north-1": "390948362332",
"cn-northwest-1": "387376663083",
}
elif algorithm in ["lda"]:
region_to_accounts = {
"us-east-1": "766337827248",
"us-east-2": "999911452149",
"us-west-2": "266724342769",
"eu-west-1": "999678624901",
"eu-central-1": "353608530281",
"ap-northeast-1": "258307448986",
"ap-northeast-2": "293181348795",
"ap-southeast-2": "297031611018",
"us-gov-west-1": "226302683700",
"ap-southeast-1": "475088953585",
"ap-south-1": "991648021394",
"ca-central-1": "469771592824",
"eu-west-2": "644912444149",
"us-west-1": "632365934929",
"us-iso-east-1": "490574956308",
}
elif algorithm in ["forecasting-deepar"]:
region_to_accounts = {
"us-east-1": "522234722520",
"us-east-2": "566113047672",
"us-west-2": "156387875391",
"eu-west-1": "224300973850",
"eu-central-1": "495149712605",
"ap-northeast-1": "633353088612",
"ap-northeast-2": "204372634319",
"ap-southeast-2": "514117268639",
"us-gov-west-1": "226302683700",
"ap-southeast-1": "475088953585",
"ap-south-1": "991648021394",
"ca-central-1": "469771592824",
"eu-west-2": "644912444149",
"us-west-1": "632365934929",
"us-iso-east-1": "490574956308",
"ap-east-1": "286214385809",
"eu-north-1": "669576153137",
"eu-west-3": "749696950732",
"sa-east-1": "855470959533",
"me-south-1": "249704162688",
"cn-north-1": "390948362332",
"cn-northwest-1": "387376663083",
}
elif algorithm in [
"xgboost",
"seq2seq",
"image-classification",
"blazingtext",
"object-detection",
"semantic-segmentation",
]:
region_to_accounts = {
"us-east-1": "811284229777",
"us-east-2": "825641698319",
"us-west-2": "433757028032",
"eu-west-1": "685385470294",
"eu-central-1": "813361260812",
"ap-northeast-1": "501404015308",
"ap-northeast-2": "306986355934",
"ap-southeast-2": "544295431143",
"us-gov-west-1": "226302683700",
"ap-southeast-1": "475088953585",
"ap-south-1": "991648021394",
"ca-central-1": "469771592824",
"eu-west-2": "644912444149",
"us-west-1": "632365934929",
"us-iso-east-1": "490574956308",
"ap-east-1": "286214385809",
"eu-north-1": "669576153137",
"eu-west-3": "749696950732",
"sa-east-1": "855470959533",
"me-south-1": "249704162688",
"cn-north-1": "390948362332",
"cn-northwest-1": "387376663083",
}
elif algorithm in ["image-classification-neo", "xgboost-neo"]:
region_to_accounts = NEO_IMAGE_ACCOUNT
else:
raise ValueError(
"Algorithm class:{} does not have mapping to account_id with images"
.format(algorithm))
if region_name in region_to_accounts:
account_id = region_to_accounts[region_name]
return get_ecr_image_uri_prefix(account_id, region_name)
raise ValueError(
"Algorithm ({algorithm}) is unsupported for region ({region_name}).".
format(algorithm=algorithm, region_name=region_name))
def create_image_uri(region,
framework,
instance_type,
framework_version,
py_version=None,
account='520713654638',
accelerator_type=None,
optimized_families=None):
"""Return the ECR URI of an image.
Args:
region (str): AWS region where the image is uploaded.
framework (str): framework used by the image.
instance_type (str): SageMaker instance type. Used to determine device type (cpu/gpu/family-specific optimized).
framework_version (str): The version of the framework.
py_version (str): Optional. Python version. If specified, should be one of 'py2' or 'py3'.
If not specified, image uri will not include a python component.
account (str): AWS account that contains the image. (default: '520713654638')
accelerator_type (str): SageMaker Elastic Inference accelerator type.
optimized_families (str): Instance families for which there exist specific optimized images.
Returns:
str: The appropriate image URI based on the given parameters.
"""
optimized_families = optimized_families or []
if py_version and py_version not in VALID_PY_VERSIONS:
raise ValueError('invalid py_version argument: {}'.format(py_version))
# Handle Account Number for Gov Cloud
account = VALID_ACCOUNTS_BY_REGION.get(region, account)
# Handle Local Mode
if instance_type.startswith('local'):
device_type = 'cpu' if instance_type == 'local' else 'gpu'
elif not instance_type.startswith('ml.'):
raise ValueError(
'{} is not a valid SageMaker instance type. See: '
'https://aws.amazon.com/sagemaker/pricing/instance-types/'.format(
instance_type))
else:
family = instance_type.split('.')[1]
# For some frameworks, we have optimized images for specific families, e.g c5 or p3. In those cases,
# we use the family name in the image tag. In other cases, we use 'cpu' or 'gpu'.
if family in optimized_families:
device_type = family
elif family[0] in ['g', 'p']:
device_type = 'gpu'
else:
device_type = 'cpu'
if py_version:
tag = "{}-{}-{}".format(framework_version, device_type, py_version)
else:
tag = "{}-{}".format(framework_version, device_type)
if _accelerator_type_valid_for_framework(
framework=framework,
accelerator_type=accelerator_type,
optimized_families=optimized_families):
framework += '-eia'
return "{}/sagemaker-{}:{}" \
.format(get_ecr_image_uri_prefix(account, region), framework, tag)
