DeepRacer workshop content. This Guidance demonstrates how software developers can use an Amazon SageMaker Notebook instance to directly train and evaluate AWS DeepRacer models with full control
region = "us-east-1"
dr_client = boto3.client('deepracer', region_name=region,
endpoint_url="https://deepracer-prod.{}.amazonaws.com".format(region))
models = dr_client.list_models(ModelType="REINFORCEMENT_LEARNING",MaxResults=100)["Models"]
for model in models:
if model["ModelName"]==model_name:
break
Boto3 : 1.15.143
Code at below, Boto3 raises UNKNOWNSERVICE Error
envroot = os.getcwd() aws_data_path = set(os.environ.get('AWS_DATA_PATH', '').split(os.pathsep)) aws_data_path.add(os.path.join(envroot, 'models')) os.environ.update({'AWS_DATA_PATH': os.pathsep.join(aws_data_path)})
region = "us-east-1" dr_client = boto3.client('deepracer', region_name=region, endpoint_url="https://deepracer-prod.{}.amazonaws.com".format(region)) models = dr_client.list_models(ModelType="REINFORCEMENT_LEARNING",MaxResults=100)["Models"] for model in models: if model["ModelName"]==model_name: break
This is Error Logs.
UnknownServiceError Traceback (most recent call last)