I am trying to run a simple model in sagemaker.
When trying to run the following code I keep getting this error.
It is a very simple code that I saw in some tutorials and in sagemaker examples that are included in the Jupiter notebook.
Does anyone know what should I do to make it work?
imput:
import sagemaker
sess = sagemaker.Session()
output:
---------------------------------------------------------------------------
UnknownServiceError Traceback (most recent call last)
/tmp/ipykernel_11350/4126940475.py in <cell line: 3>()
1 import sagemaker
2
----> 3 sess = sagemaker.Session()
~/anaconda3/envs/python3/lib/python3.8/site-packages/sagemaker/session.py in __init__(self, boto_session, sagemaker_client, sagemaker_runtime_client, sagemaker_featurestore_runtime_client, default_bucket, settings, sagemaker_metrics_client)
131 self.settings = settings
132
--> 133 self._initialize(
134 boto_session=boto_session,
135 sagemaker_client=sagemaker_client,
~/anaconda3/envs/python3/lib/python3.8/site-packages/sagemaker/session.py in _initialize(self, boto_session, sagemaker_client, sagemaker_runtime_client, sagemaker_featurestore_runtime_client, sagemaker_metrics_client)
183 self.sagemaker_metrics_client = sagemaker_metrics_client
184 else:
--> 185 self.sagemaker_metrics_client = self.boto_session.client("sagemaker-metrics")
186 prepend_user_agent(self.sagemaker_metrics_client)
187
~/anaconda3/envs/python3/lib/python3.8/site-packages/boto3/session.py in client(self, service_name, region_name, api_version, use_ssl, verify, endpoint_url, aws_access_key_id, aws_secret_access_key, aws_session_token, config)
297
298 """
--> 299 return self._session.create_client(
300 service_name,
301 region_name=region_name,
~/anaconda3/envs/python3/lib/python3.8/site-packages/botocore/session.py in create_client(self, service_name, region_name, api_version, use_ssl, verify, endpoint_url, aws_access_key_id, aws_secret_access_key, aws_session_token, config)
868 retryhandler, translate, response_parser_factory,
869 exceptions_factory, config_store)
--> 870 client = client_creator.create_client(
871 service_name=service_name, region_name=region_name,
872 is_secure=use_ssl, endpoint_url=endpoint_url, verify=verify,
~/anaconda3/envs/python3/lib/python3.8/site-packages/botocore/client.py in create_client(self, service_name, region_name, is_secure, endpoint_url, verify, credentials, scoped_config, api_version, client_config)
85 'choose-service-name', service_name=service_name)
86 service_name = first_non_none_response(responses, default=service_name)
---> 87 service_model = self._load_service_model(service_name, api_version)
88 cls = self._create_client_class(service_name, service_model)
89 region_name, client_config = self._normalize_fips_region(
~/anaconda3/envs/python3/lib/python3.8/site-packages/botocore/client.py in _load_service_model(self, service_name, api_version)
152
153 def _load_service_model(self, service_name, api_version=None):
--> 154 json_model = self._loader.load_service_model(service_name, 'service-2',
155 api_version=api_version)
156 service_model = ServiceModel(json_model, service_name=service_name)
~/anaconda3/envs/python3/lib/python3.8/site-packages/botocore/loaders.py in _wrapper(self, *args, **kwargs)
130 if key in self._cache:
131 return self._cache[key]
--> 132 data = func(self, *args, **kwargs)
133 self._cache[key] = data
134 return data
~/anaconda3/envs/python3/lib/python3.8/site-packages/botocore/loaders.py in load_service_model(self, service_name, type_name, api_version)
375 known_services = self.list_available_services(type_name)
376 if service_name not in known_services:
--> 377 raise UnknownServiceError(
378 service_name=service_name,
379 known_service_names=', '.join(sorted(known_services)))
UnknownServiceError: Unknown service: 'sagemaker-metrics'. Valid service names are: accessanalyzer, account, acm, acm-pca, alexaforbusiness, amp, amplify, amplifybackend, amplifyuibuilder, apigateway, apigatewaymanagementapi, apigatewayv2, appconfig, appconfigdata, appflow, appintegrations, application-autoscaling, application-insights, applicationcostprofiler, appmesh, apprunner, appstream, appsync, athena, auditmanager, autoscaling, autoscaling-plans, backup, backup-gateway, batch, braket, budgets, ce, chime, chime-sdk-identity, chime-sdk-meetings, chime-sdk-messaging, cloud9, cloudcontrol, clouddirectory, cloudformation, cloudfront, cloudhsm, cloudhsmv2, cloudsearch, cloudsearchdomain, cloudtrail, cloudwatch, codeartifact, codebuild, codecommit, codedeploy, codeguru-reviewer, codeguruprofiler, codepipeline, codestar, codestar-connections, codestar-notifications, cognito-identity, cognito-idp, cognito-sync, comprehend, comprehendmedical, compute-optimizer, config, connect, connect-contact-lens, connectparticipant, cur, customer-profiles, databrew, dataexchange, datapipeline, datasync, dax, detective, devicefarm, devops-guru, directconnect, discovery, dlm, dms, docdb, drs, ds, dynamodb, dynamodbstreams, ebs, ec2, ec2-instance-connect, ecr, ecr-public, ecs, efs, eks, elastic-inference, elasticache, elasticbeanstalk, elastictranscoder, elb, elbv2, emr, emr-containers, es, events, evidently, finspace, finspace-data, firehose, fis, fms, forecast, forecastquery, frauddetector, fsx, gamelift, glacier, globalaccelerator, glue, grafana, greengrass, greengrassv2, groundstation, guardduty, health, healthlake, honeycode, iam, identitystore, imagebuilder, importexport, inspector, inspector2, iot, iot-data, iot-jobs-data, iot1click-devices, iot1click-projects, iotanalytics, iotdeviceadvisor, iotevents, iotevents-data, iotfleethub, iotsecuretunneling, iotsitewise, iotthingsgraph, iottwinmaker, iotwireless, ivs, kafka, kafkaconnect, kendra, keyspaces, kinesis, kinesis-video-archived-media, kinesis-video-media, kinesis-video-signaling, kinesisanalytics, kinesisanalyticsv2, kinesisvideo, kms, lakeformation, lambda, lex-models, lex-runtime, lexv2-models, lexv2-runtime, license-manager, lightsail, location, logs, lookoutequipment, lookoutmetrics, lookoutvision, machinelearning, macie, macie2, managedblockchain, marketplace-catalog, marketplace-entitlement, marketplacecommerceanalytics, mediaconnect, mediaconvert, medialive, mediapackage, mediapackage-vod, mediastore, mediastore-data, mediatailor, memorydb, meteringmarketplace, mgh, mgn, migration-hub-refactor-spaces, migrationhub-config, migrationhubstrategy, mobile, mq, mturk, mwaa, neptune, network-firewall, networkmanager, nimble, opensearch, opsworks, opsworkscm, organizations, outposts, panorama, personalize, personalize-events, personalize-runtime, pi, pinpoint, pinpoint-email, pinpoint-sms-voice, polly, pricing, proton, qldb, qldb-session, quicksight, ram, rbin, rds, rds-data, redshift, redshift-data, rekognition, resiliencehub, resource-groups, resourcegroupstaggingapi, robomaker, route53, route53-recovery-cluster, route53-recovery-control-config, route53-recovery-readiness, route53domains, route53resolver, rum, s3, s3control, s3outposts, sagemaker, sagemaker-a2i-runtime, sagemaker-edge, sagemaker-featurestore-runtime, sagemaker-runtime, savingsplans, schemas, sdb, secretsmanager, securityhub, serverlessrepo, service-quotas, servicecatalog, servicecatalog-appregistry, servicediscovery, ses, sesv2, shield, signer, sms, sms-voice, snow-device-management, snowball, sns, sqs, ssm, ssm-contacts, ssm-incidents, sso, sso-admin, sso-oidc, stepfunctions, storagegateway, sts, support, swf, synthetics, textract, timestream-query, timestream-write, transcribe, transfer, translate, voice-id, waf, waf-regional, wafv2, wellarchitected, wisdom, workdocs, worklink, workmail, workmailmessageflow, workspaces, workspaces-web, xray
I was trying to update some libraries like sagemaker, boto, boto3, but nothing seems to help.
I came across the same issue trying to run a machine learning tutorial by AWS in a notebook instance. What I had to do was update sagemaker within the notebook instance like so:
import sys
!{sys.executable} -m pip install sagemaker -U
Hopefully this fixes your problem :)
I had the same issue, running
pip install sagemaker -U
solved it for me. Similar to the following issue: Sagemaker Studio UnkownServiceError for Session
I am trying to build a hyperparameter optimization job in Amazon Sagemaker, in python, but something is not working. Here is what I have:
sess = sagemaker.Session()
xgb = sagemaker.estimator.Estimator(containers[boto3.Session().region_name],
role,
train_instance_count=1,
train_instance_type='ml.m4.4xlarge',
output_path=output_path_1,
base_job_name='HPO-xgb',
sagemaker_session=sess)
from sagemaker.tuner import HyperparameterTuner, IntegerParameter, CategoricalParameter, ContinuousParameter
hyperparameter_ranges = {'eta': ContinuousParameter(0.01, 0.2),
'num_rounds': ContinuousParameter(100, 500),
'num_class': 4,
'max_depth': IntegerParameter(3, 9),
'gamma': IntegerParameter(0, 5),
'min_child_weight': IntegerParameter(2, 6),
'subsample': ContinuousParameter(0.5, 0.9),
'colsample_bytree': ContinuousParameter(0.5, 0.9)}
objective_metric_name = 'validation:mlogloss'
objective_type='minimize'
metric_definitions = [{'Name': 'validation-mlogloss',
'Regex': 'validation-mlogloss=([0-9\\.]+)'}]
tuner = HyperparameterTuner(xgb,
objective_metric_name,
objective_type,
hyperparameter_ranges,
metric_definitions,
max_jobs=9,
max_parallel_jobs=3)
tuner.fit({'train': s3_input_train, 'validation': s3_input_validation})
And the error I get is:
AttributeError: 'str' object has no attribute 'keys'
The error seems to come from the tuner.py file:
----> 1 tuner.fit({'train': s3_input_train, 'validation': s3_input_validation})
~/anaconda3/envs/python3/lib/python3.6/site-packages/sagemaker/tuner.py in fit(self, inputs, job_name, **kwargs)
144 self.estimator._prepare_for_training(job_name)
145
--> 146 self._prepare_for_training(job_name=job_name)
147 self.latest_tuning_job = _TuningJob.start_new(self, inputs)
148
~/anaconda3/envs/python3/lib/python3.6/site-packages/sagemaker/tuner.py in _prepare_for_training(self, job_name)
120
121 self.static_hyperparameters = {to_str(k): to_str(v) for (k, v) in self.estimator.hyperparameters().items()}
--> 122 for hyperparameter_name in self._hyperparameter_ranges.keys():
123 self.static_hyperparameters.pop(hyperparameter_name, None)
124
AttributeError: 'list' object has no attribute 'keys'
Your arguments when initializing the HyperparameterTuner object are in the wrong order. The constructor has the following signature:
HyperparameterTuner(estimator,
objective_metric_name,
hyperparameter_ranges,
metric_definitions=None,
strategy='Bayesian',
objective_type='Maximize',
max_jobs=1,
max_parallel_jobs=1,
tags=None,
base_tuning_job_name=None)
so in this case, your objective_type is in the wrong position. See the docs for more details.