444 lines
17 KiB
Python
444 lines
17 KiB
Python
import boto3
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from botocore.exceptions import ClientError
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import datetime
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import sure # noqa # pylint: disable=unused-import
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import pytest
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from moto import mock_sagemaker
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from moto.sts.models import ACCOUNT_ID
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FAKE_ROLE_ARN = "arn:aws:iam::{}:role/FakeRole".format(ACCOUNT_ID)
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TEST_REGION_NAME = "us-east-1"
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class MyTrainingJobModel(object):
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def __init__(
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self,
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training_job_name,
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role_arn,
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container=None,
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bucket=None,
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prefix=None,
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algorithm_specification=None,
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resource_config=None,
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input_data_config=None,
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output_data_config=None,
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hyper_parameters=None,
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stopping_condition=None,
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):
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self.training_job_name = training_job_name
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self.role_arn = role_arn
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self.container = (
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container or "382416733822.dkr.ecr.us-east-1.amazonaws.com/linear-learner:1"
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)
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self.bucket = bucket or "my-bucket"
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self.prefix = prefix or "sagemaker/DEMO-breast-cancer-prediction/"
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self.algorithm_specification = algorithm_specification or {
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"TrainingImage": self.container,
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"TrainingInputMode": "File",
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}
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self.resource_config = resource_config or {
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"InstanceCount": 1,
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"InstanceType": "ml.c4.2xlarge",
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"VolumeSizeInGB": 10,
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}
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self.input_data_config = input_data_config or [
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{
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"ChannelName": "train",
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"DataSource": {
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"S3DataSource": {
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"S3DataType": "S3Prefix",
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"S3Uri": "s3://{}/{}/train/".format(self.bucket, self.prefix),
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"S3DataDistributionType": "ShardedByS3Key",
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}
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},
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"CompressionType": "None",
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"RecordWrapperType": "None",
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},
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{
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"ChannelName": "validation",
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"DataSource": {
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"S3DataSource": {
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"S3DataType": "S3Prefix",
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"S3Uri": "s3://{}/{}/validation/".format(
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self.bucket, self.prefix
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),
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"S3DataDistributionType": "FullyReplicated",
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}
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},
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"CompressionType": "None",
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"RecordWrapperType": "None",
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},
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]
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self.output_data_config = output_data_config or {
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"S3OutputPath": "s3://{}/{}/".format(self.bucket, self.prefix)
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}
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self.hyper_parameters = hyper_parameters or {
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"feature_dim": "30",
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"mini_batch_size": "100",
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"predictor_type": "regressor",
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"epochs": "10",
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"num_models": "32",
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"loss": "absolute_loss",
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}
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self.stopping_condition = stopping_condition or {"MaxRuntimeInSeconds": 60 * 60}
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def save(self):
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sagemaker = boto3.client("sagemaker", region_name=TEST_REGION_NAME)
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params = {
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"RoleArn": self.role_arn,
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"TrainingJobName": self.training_job_name,
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"AlgorithmSpecification": self.algorithm_specification,
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"ResourceConfig": self.resource_config,
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"InputDataConfig": self.input_data_config,
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"OutputDataConfig": self.output_data_config,
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"HyperParameters": self.hyper_parameters,
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"StoppingCondition": self.stopping_condition,
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}
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return sagemaker.create_training_job(**params)
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@mock_sagemaker
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def test_create_training_job():
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sagemaker = boto3.client("sagemaker", region_name=TEST_REGION_NAME)
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training_job_name = "MyTrainingJob"
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role_arn = "arn:aws:iam::{}:role/FakeRole".format(ACCOUNT_ID)
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container = "382416733822.dkr.ecr.us-east-1.amazonaws.com/linear-learner:1"
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bucket = "my-bucket"
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prefix = "sagemaker/DEMO-breast-cancer-prediction/"
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algorithm_specification = {
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"TrainingImage": container,
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"TrainingInputMode": "File",
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}
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resource_config = {
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"InstanceCount": 1,
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"InstanceType": "ml.c4.2xlarge",
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"VolumeSizeInGB": 10,
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}
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input_data_config = [
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{
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"ChannelName": "train",
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"DataSource": {
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"S3DataSource": {
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"S3DataType": "S3Prefix",
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"S3Uri": "s3://{}/{}/train/".format(bucket, prefix),
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"S3DataDistributionType": "ShardedByS3Key",
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}
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},
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"CompressionType": "None",
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"RecordWrapperType": "None",
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},
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{
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"ChannelName": "validation",
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"DataSource": {
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"S3DataSource": {
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"S3DataType": "S3Prefix",
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"S3Uri": "s3://{}/{}/validation/".format(bucket, prefix),
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"S3DataDistributionType": "FullyReplicated",
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}
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},
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"CompressionType": "None",
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"RecordWrapperType": "None",
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},
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]
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output_data_config = {"S3OutputPath": "s3://{}/{}/".format(bucket, prefix)}
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hyper_parameters = {
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"feature_dim": "30",
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"mini_batch_size": "100",
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"predictor_type": "regressor",
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"epochs": "10",
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"num_models": "32",
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"loss": "absolute_loss",
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}
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stopping_condition = {"MaxRuntimeInSeconds": 60 * 60}
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job = MyTrainingJobModel(
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training_job_name,
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role_arn,
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container=container,
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bucket=bucket,
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prefix=prefix,
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algorithm_specification=algorithm_specification,
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resource_config=resource_config,
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input_data_config=input_data_config,
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output_data_config=output_data_config,
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hyper_parameters=hyper_parameters,
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stopping_condition=stopping_condition,
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)
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resp = job.save()
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resp["TrainingJobArn"].should.match(
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r"^arn:aws:sagemaker:.*:.*:training-job/{}$".format(training_job_name)
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)
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resp = sagemaker.describe_training_job(TrainingJobName=training_job_name)
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resp["TrainingJobName"].should.equal(training_job_name)
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resp["TrainingJobArn"].should.match(
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r"^arn:aws:sagemaker:.*:.*:training-job/{}$".format(training_job_name)
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)
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assert resp["ModelArtifacts"]["S3ModelArtifacts"].startswith(
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output_data_config["S3OutputPath"]
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)
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assert training_job_name in (resp["ModelArtifacts"]["S3ModelArtifacts"])
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assert resp["ModelArtifacts"]["S3ModelArtifacts"].endswith("output/model.tar.gz")
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assert resp["TrainingJobStatus"] == "Completed"
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assert resp["SecondaryStatus"] == "Completed"
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assert resp["HyperParameters"] == hyper_parameters
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assert (
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resp["AlgorithmSpecification"]["TrainingImage"]
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== algorithm_specification["TrainingImage"]
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)
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assert (
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resp["AlgorithmSpecification"]["TrainingInputMode"]
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== algorithm_specification["TrainingInputMode"]
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)
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assert "MetricDefinitions" in resp["AlgorithmSpecification"]
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assert "Name" in resp["AlgorithmSpecification"]["MetricDefinitions"][0]
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assert "Regex" in resp["AlgorithmSpecification"]["MetricDefinitions"][0]
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assert resp["RoleArn"] == role_arn
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assert resp["InputDataConfig"] == input_data_config
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assert resp["OutputDataConfig"] == output_data_config
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assert resp["ResourceConfig"] == resource_config
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assert resp["StoppingCondition"] == stopping_condition
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assert isinstance(resp["CreationTime"], datetime.datetime)
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assert isinstance(resp["TrainingStartTime"], datetime.datetime)
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assert isinstance(resp["TrainingEndTime"], datetime.datetime)
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assert isinstance(resp["LastModifiedTime"], datetime.datetime)
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assert "SecondaryStatusTransitions" in resp
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assert "Status" in resp["SecondaryStatusTransitions"][0]
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assert "StartTime" in resp["SecondaryStatusTransitions"][0]
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assert "EndTime" in resp["SecondaryStatusTransitions"][0]
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assert "StatusMessage" in resp["SecondaryStatusTransitions"][0]
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assert "FinalMetricDataList" in resp
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assert "MetricName" in resp["FinalMetricDataList"][0]
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assert "Value" in resp["FinalMetricDataList"][0]
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assert "Timestamp" in resp["FinalMetricDataList"][0]
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pass
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@mock_sagemaker
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def test_list_training_jobs():
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client = boto3.client("sagemaker", region_name="us-east-1")
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name = "blah"
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arn = "arn:aws:sagemaker:us-east-1:000000000000:x-x/foobar"
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test_training_job = MyTrainingJobModel(training_job_name=name, role_arn=arn)
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test_training_job.save()
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training_jobs = client.list_training_jobs()
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assert len(training_jobs["TrainingJobSummaries"]).should.equal(1)
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assert training_jobs["TrainingJobSummaries"][0]["TrainingJobName"].should.equal(
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name
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)
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assert training_jobs["TrainingJobSummaries"][0]["TrainingJobArn"].should.match(
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r"^arn:aws:sagemaker:.*:.*:training-job/{}$".format(name)
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)
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assert training_jobs.get("NextToken") is None
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@mock_sagemaker
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def test_list_training_jobs_multiple():
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client = boto3.client("sagemaker", region_name="us-east-1")
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name_job_1 = "blah"
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arn_job_1 = "arn:aws:sagemaker:us-east-1:000000000000:x-x/foobar"
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test_training_job_1 = MyTrainingJobModel(
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training_job_name=name_job_1, role_arn=arn_job_1
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)
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test_training_job_1.save()
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name_job_2 = "blah2"
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arn_job_2 = "arn:aws:sagemaker:us-east-1:000000000000:x-x/foobar2"
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test_training_job_2 = MyTrainingJobModel(
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training_job_name=name_job_2, role_arn=arn_job_2
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)
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test_training_job_2.save()
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training_jobs_limit = client.list_training_jobs(MaxResults=1)
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assert len(training_jobs_limit["TrainingJobSummaries"]).should.equal(1)
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training_jobs = client.list_training_jobs()
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assert len(training_jobs["TrainingJobSummaries"]).should.equal(2)
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assert training_jobs.get("NextToken").should.be.none
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@mock_sagemaker
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def test_list_training_jobs_none():
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client = boto3.client("sagemaker", region_name="us-east-1")
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training_jobs = client.list_training_jobs()
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assert len(training_jobs["TrainingJobSummaries"]).should.equal(0)
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@mock_sagemaker
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def test_list_training_jobs_should_validate_input():
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client = boto3.client("sagemaker", region_name="us-east-1")
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junk_status_equals = "blah"
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with pytest.raises(ClientError) as ex:
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client.list_training_jobs(StatusEquals=junk_status_equals)
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expected_error = f"1 validation errors detected: Value '{junk_status_equals}' at 'statusEquals' failed to satisfy constraint: Member must satisfy enum value set: ['Completed', 'Stopped', 'InProgress', 'Stopping', 'Failed']"
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assert ex.value.response["Error"]["Code"] == "ValidationException"
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assert ex.value.response["Error"]["Message"] == expected_error
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junk_next_token = "asdf"
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with pytest.raises(ClientError) as ex:
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client.list_training_jobs(NextToken=junk_next_token)
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assert ex.value.response["Error"]["Code"] == "ValidationException"
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assert (
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ex.value.response["Error"]["Message"]
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== 'Invalid pagination token because "{0}".'
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)
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@mock_sagemaker
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def test_list_training_jobs_with_name_filters():
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client = boto3.client("sagemaker", region_name="us-east-1")
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for i in range(5):
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name = "xgboost-{}".format(i)
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arn = "arn:aws:sagemaker:us-east-1:000000000000:x-x/foobar-{}".format(i)
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MyTrainingJobModel(training_job_name=name, role_arn=arn).save()
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for i in range(5):
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name = "vgg-{}".format(i)
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arn = "arn:aws:sagemaker:us-east-1:000000000000:x-x/barfoo-{}".format(i)
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MyTrainingJobModel(training_job_name=name, role_arn=arn).save()
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xgboost_training_jobs = client.list_training_jobs(NameContains="xgboost")
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assert len(xgboost_training_jobs["TrainingJobSummaries"]).should.equal(5)
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training_jobs_with_2 = client.list_training_jobs(NameContains="2")
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assert len(training_jobs_with_2["TrainingJobSummaries"]).should.equal(2)
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@mock_sagemaker
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def test_list_training_jobs_paginated():
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client = boto3.client("sagemaker", region_name="us-east-1")
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for i in range(5):
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name = "xgboost-{}".format(i)
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arn = "arn:aws:sagemaker:us-east-1:000000000000:x-x/foobar-{}".format(i)
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MyTrainingJobModel(training_job_name=name, role_arn=arn).save()
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xgboost_training_job_1 = client.list_training_jobs(
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NameContains="xgboost", MaxResults=1
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)
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assert len(xgboost_training_job_1["TrainingJobSummaries"]).should.equal(1)
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assert xgboost_training_job_1["TrainingJobSummaries"][0][
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"TrainingJobName"
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].should.equal("xgboost-0")
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assert xgboost_training_job_1.get("NextToken").should_not.be.none
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xgboost_training_job_next = client.list_training_jobs(
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NameContains="xgboost",
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MaxResults=1,
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NextToken=xgboost_training_job_1.get("NextToken"),
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)
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assert len(xgboost_training_job_next["TrainingJobSummaries"]).should.equal(1)
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assert xgboost_training_job_next["TrainingJobSummaries"][0][
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"TrainingJobName"
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].should.equal("xgboost-1")
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assert xgboost_training_job_next.get("NextToken").should_not.be.none
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@mock_sagemaker
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def test_list_training_jobs_paginated_with_target_in_middle():
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client = boto3.client("sagemaker", region_name="us-east-1")
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for i in range(5):
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name = "xgboost-{}".format(i)
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arn = "arn:aws:sagemaker:us-east-1:000000000000:x-x/foobar-{}".format(i)
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MyTrainingJobModel(training_job_name=name, role_arn=arn).save()
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for i in range(5):
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name = "vgg-{}".format(i)
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arn = "arn:aws:sagemaker:us-east-1:000000000000:x-x/barfoo-{}".format(i)
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MyTrainingJobModel(training_job_name=name, role_arn=arn).save()
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vgg_training_job_1 = client.list_training_jobs(NameContains="vgg", MaxResults=1)
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assert len(vgg_training_job_1["TrainingJobSummaries"]).should.equal(0)
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assert vgg_training_job_1.get("NextToken").should_not.be.none
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vgg_training_job_6 = client.list_training_jobs(NameContains="vgg", MaxResults=6)
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assert len(vgg_training_job_6["TrainingJobSummaries"]).should.equal(1)
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assert vgg_training_job_6["TrainingJobSummaries"][0][
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"TrainingJobName"
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].should.equal("vgg-0")
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assert vgg_training_job_6.get("NextToken").should_not.be.none
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vgg_training_job_10 = client.list_training_jobs(NameContains="vgg", MaxResults=10)
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assert len(vgg_training_job_10["TrainingJobSummaries"]).should.equal(5)
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assert vgg_training_job_10["TrainingJobSummaries"][-1][
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"TrainingJobName"
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].should.equal("vgg-4")
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assert vgg_training_job_10.get("NextToken").should.be.none
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@mock_sagemaker
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def test_list_training_jobs_paginated_with_fragmented_targets():
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client = boto3.client("sagemaker", region_name="us-east-1")
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for i in range(5):
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name = "xgboost-{}".format(i)
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arn = "arn:aws:sagemaker:us-east-1:000000000000:x-x/foobar-{}".format(i)
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MyTrainingJobModel(training_job_name=name, role_arn=arn).save()
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for i in range(5):
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name = "vgg-{}".format(i)
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arn = "arn:aws:sagemaker:us-east-1:000000000000:x-x/barfoo-{}".format(i)
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MyTrainingJobModel(training_job_name=name, role_arn=arn).save()
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training_jobs_with_2 = client.list_training_jobs(NameContains="2", MaxResults=8)
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assert len(training_jobs_with_2["TrainingJobSummaries"]).should.equal(2)
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assert training_jobs_with_2.get("NextToken").should_not.be.none
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training_jobs_with_2_next = client.list_training_jobs(
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NameContains="2", MaxResults=1, NextToken=training_jobs_with_2.get("NextToken")
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)
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assert len(training_jobs_with_2_next["TrainingJobSummaries"]).should.equal(0)
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assert training_jobs_with_2_next.get("NextToken").should_not.be.none
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training_jobs_with_2_next_next = client.list_training_jobs(
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NameContains="2",
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MaxResults=1,
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NextToken=training_jobs_with_2_next.get("NextToken"),
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)
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assert len(training_jobs_with_2_next_next["TrainingJobSummaries"]).should.equal(0)
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assert training_jobs_with_2_next_next.get("NextToken").should.be.none
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@mock_sagemaker
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def test_add_tags_to_training_job():
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client = boto3.client("sagemaker", region_name=TEST_REGION_NAME)
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name = "blah"
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resource_arn = f"arn:aws:sagemaker:us-east-1:000000000000:training-job/{name}"
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test_training_job = MyTrainingJobModel(
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training_job_name=name, role_arn=resource_arn
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)
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test_training_job.save()
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tags = [
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{"Key": "myKey", "Value": "myValue"},
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]
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response = client.add_tags(ResourceArn=resource_arn, Tags=tags)
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assert response["ResponseMetadata"]["HTTPStatusCode"] == 200
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response = client.list_tags(ResourceArn=resource_arn)
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assert response["Tags"] == tags
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@mock_sagemaker
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def test_delete_tags_from_training_job():
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client = boto3.client("sagemaker", region_name=TEST_REGION_NAME)
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name = "blah"
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resource_arn = f"arn:aws:sagemaker:us-east-1:000000000000:training-job/{name}"
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test_training_job = MyTrainingJobModel(
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training_job_name=name, role_arn=resource_arn
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)
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test_training_job.save()
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tags = [
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{"Key": "myKey", "Value": "myValue"},
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]
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response = client.add_tags(ResourceArn=resource_arn, Tags=tags)
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assert response["ResponseMetadata"]["HTTPStatusCode"] == 200
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tag_keys = [tag["Key"] for tag in tags]
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response = client.delete_tags(ResourceArn=resource_arn, TagKeys=tag_keys)
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assert response["ResponseMetadata"]["HTTPStatusCode"] == 200
|
|
|
|
response = client.list_tags(ResourceArn=resource_arn)
|
|
assert response["Tags"] == []
|