408 lines
14 KiB
Python
408 lines
14 KiB
Python
import datetime
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import re
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import boto3
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import pytest
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from botocore.exceptions import ClientError
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from moto import mock_aws
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from moto.core import DEFAULT_ACCOUNT_ID as ACCOUNT_ID
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FAKE_ROLE_ARN = f"arn:aws:iam::{ACCOUNT_ID}:role/FakeRole"
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FAKE_PROCESSING_JOB_NAME = "MyProcessingJob"
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FAKE_CONTAINER = "382416733822.dkr.ecr.us-east-1.amazonaws.com/linear-learner:1"
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TEST_REGION_NAME = "us-east-1"
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@pytest.fixture(name="sagemaker_client")
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def fixture_sagemaker_client():
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with mock_aws():
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yield boto3.client("sagemaker", region_name=TEST_REGION_NAME)
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class MyProcessingJobModel:
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def __init__(
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self,
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processing_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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app_specification=None,
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network_config=None,
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processing_inputs=None,
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processing_output_config=None,
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processing_resources=None,
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stopping_condition=None,
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):
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self.processing_job_name = processing_job_name
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self.role_arn = role_arn
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self.container = (
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container
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or "683313688378.dkr.ecr.us-east-1.amazonaws.com/sagemaker-scikit-learn:0.23-1-cpu-py3"
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)
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self.bucket = bucket or "my-bucket"
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self.prefix = prefix or "sagemaker"
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self.app_specification = app_specification or {
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"ImageUri": self.container,
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"ContainerEntrypoint": ["python3"],
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}
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self.network_config = network_config or {
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"EnableInterContainerTrafficEncryption": False,
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"EnableNetworkIsolation": False,
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}
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self.processing_inputs = processing_inputs or [
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{
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"InputName": "input",
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"AppManaged": False,
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"S3Input": {
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"S3Uri": f"s3://{self.bucket}/{self.prefix}/processing/",
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"LocalPath": "/opt/ml/processing/input",
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"S3DataType": "S3Prefix",
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"S3InputMode": "File",
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"S3DataDistributionType": "FullyReplicated",
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"S3CompressionType": "None",
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},
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}
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]
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self.processing_output_config = processing_output_config or {
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"Outputs": [
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{
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"OutputName": "output",
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"S3Output": {
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"S3Uri": f"s3://{self.bucket}/{self.prefix}/processing/",
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"LocalPath": "/opt/ml/processing/output",
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"S3UploadMode": "EndOfJob",
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},
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"AppManaged": False,
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}
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]
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}
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self.processing_resources = processing_resources or {
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"ClusterConfig": {
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"InstanceCount": 1,
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"InstanceType": "ml.m5.large",
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"VolumeSizeInGB": 10,
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},
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}
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self.stopping_condition = stopping_condition or {
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"MaxRuntimeInSeconds": 3600,
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}
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def save(self, sagemaker_client):
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params = {
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"AppSpecification": self.app_specification,
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"NetworkConfig": self.network_config,
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"ProcessingInputs": self.processing_inputs,
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"ProcessingJobName": self.processing_job_name,
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"ProcessingOutputConfig": self.processing_output_config,
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"ProcessingResources": self.processing_resources,
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"RoleArn": self.role_arn,
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"StoppingCondition": self.stopping_condition,
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}
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return sagemaker_client.create_processing_job(**params)
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def test_create_processing_job(sagemaker_client):
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bucket = "my-bucket"
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prefix = "my-prefix"
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app_specification = {
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"ImageUri": FAKE_CONTAINER,
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"ContainerEntrypoint": ["python3", "app.py"],
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}
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processing_resources = {
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"ClusterConfig": {
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"InstanceCount": 2,
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"InstanceType": "ml.m5.xlarge",
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"VolumeSizeInGB": 20,
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},
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}
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stopping_condition = {"MaxRuntimeInSeconds": 60 * 60}
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job = MyProcessingJobModel(
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processing_job_name=FAKE_PROCESSING_JOB_NAME,
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role_arn=FAKE_ROLE_ARN,
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container=FAKE_CONTAINER,
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bucket=bucket,
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prefix=prefix,
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app_specification=app_specification,
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processing_resources=processing_resources,
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stopping_condition=stopping_condition,
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)
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resp = job.save(sagemaker_client)
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assert re.match(
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rf"^arn:aws:sagemaker:.*:.*:processing-job/{FAKE_PROCESSING_JOB_NAME}$",
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resp["ProcessingJobArn"],
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)
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resp = sagemaker_client.describe_processing_job(
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ProcessingJobName=FAKE_PROCESSING_JOB_NAME
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)
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assert resp["ProcessingJobName"] == FAKE_PROCESSING_JOB_NAME
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assert re.match(
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rf"^arn:aws:sagemaker:.*:.*:processing-job/{FAKE_PROCESSING_JOB_NAME}$",
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resp["ProcessingJobArn"],
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)
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assert "python3" in resp["AppSpecification"]["ContainerEntrypoint"]
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assert "app.py" in resp["AppSpecification"]["ContainerEntrypoint"]
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assert resp["RoleArn"] == FAKE_ROLE_ARN
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assert resp["ProcessingJobStatus"] == "Completed"
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assert isinstance(resp["CreationTime"], datetime.datetime)
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assert isinstance(resp["LastModifiedTime"], datetime.datetime)
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def test_list_processing_jobs(sagemaker_client):
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test_processing_job = MyProcessingJobModel(
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processing_job_name=FAKE_PROCESSING_JOB_NAME, role_arn=FAKE_ROLE_ARN
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)
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test_processing_job.save(sagemaker_client)
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processing_jobs = sagemaker_client.list_processing_jobs()
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assert len(processing_jobs["ProcessingJobSummaries"]) == 1
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assert (
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processing_jobs["ProcessingJobSummaries"][0]["ProcessingJobName"]
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== FAKE_PROCESSING_JOB_NAME
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)
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assert re.match(
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rf"^arn:aws:sagemaker:.*:.*:processing-job/{FAKE_PROCESSING_JOB_NAME}$",
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processing_jobs["ProcessingJobSummaries"][0]["ProcessingJobArn"],
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)
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assert processing_jobs.get("NextToken") is None
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def test_list_processing_jobs_multiple(sagemaker_client):
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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_processing_job_1 = MyProcessingJobModel(
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processing_job_name=name_job_1, role_arn=arn_job_1
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)
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test_processing_job_1.save(sagemaker_client)
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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_processing_job_2 = MyProcessingJobModel(
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processing_job_name=name_job_2, role_arn=arn_job_2
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)
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test_processing_job_2.save(sagemaker_client)
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processing_jobs_limit = sagemaker_client.list_processing_jobs(MaxResults=1)
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assert len(processing_jobs_limit["ProcessingJobSummaries"]) == 1
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processing_jobs = sagemaker_client.list_processing_jobs()
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assert len(processing_jobs["ProcessingJobSummaries"]) == 2
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assert processing_jobs.get("NextToken") is None
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def test_list_processing_jobs_none(sagemaker_client):
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processing_jobs = sagemaker_client.list_processing_jobs()
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assert len(processing_jobs["ProcessingJobSummaries"]) == 0
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def test_list_processing_jobs_should_validate_input(sagemaker_client):
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junk_status_equals = "blah"
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with pytest.raises(ClientError) as ex:
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sagemaker_client.list_processing_jobs(StatusEquals=junk_status_equals)
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expected_error = (
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f"1 validation errors detected: Value '{junk_status_equals}' at "
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"'statusEquals' failed to satisfy constraint: Member must satisfy "
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"enum value set: ['Completed', 'Stopped', 'InProgress', 'Stopping', "
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"'Failed']"
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)
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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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sagemaker_client.list_processing_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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def test_list_processing_jobs_with_name_filters(sagemaker_client):
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for i in range(5):
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name = f"xgboost-{i}"
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arn = f"arn:aws:sagemaker:us-east-1:000000000000:x-x/foobar-{i}"
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MyProcessingJobModel(processing_job_name=name, role_arn=arn).save(
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sagemaker_client
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)
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for i in range(5):
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name = f"vgg-{i}"
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arn = f"arn:aws:sagemaker:us-east-1:000000000000:x-x/barfoo-{i}"
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MyProcessingJobModel(processing_job_name=name, role_arn=arn).save(
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sagemaker_client
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)
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xgboost_processing_jobs = sagemaker_client.list_processing_jobs(
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NameContains="xgboost"
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)
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assert len(xgboost_processing_jobs["ProcessingJobSummaries"]) == 5
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processing_jobs_with_2 = sagemaker_client.list_processing_jobs(NameContains="2")
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assert len(processing_jobs_with_2["ProcessingJobSummaries"]) == 2
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def test_list_processing_jobs_paginated(sagemaker_client):
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for i in range(5):
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name = f"xgboost-{i}"
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arn = f"arn:aws:sagemaker:us-east-1:000000000000:x-x/foobar-{i}"
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MyProcessingJobModel(processing_job_name=name, role_arn=arn).save(
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sagemaker_client
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)
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xgboost_processing_job_1 = sagemaker_client.list_processing_jobs(
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NameContains="xgboost", MaxResults=1
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)
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assert len(xgboost_processing_job_1["ProcessingJobSummaries"]) == 1
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assert (
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xgboost_processing_job_1["ProcessingJobSummaries"][0]["ProcessingJobName"]
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== "xgboost-0"
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)
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assert xgboost_processing_job_1.get("NextToken") is not None
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xgboost_processing_job_next = sagemaker_client.list_processing_jobs(
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NameContains="xgboost",
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MaxResults=1,
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NextToken=xgboost_processing_job_1.get("NextToken"),
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)
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assert len(xgboost_processing_job_next["ProcessingJobSummaries"]) == 1
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assert (
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xgboost_processing_job_next["ProcessingJobSummaries"][0]["ProcessingJobName"]
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== "xgboost-1"
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)
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assert xgboost_processing_job_next.get("NextToken") is not None
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def test_list_processing_jobs_paginated_with_target_in_middle(sagemaker_client):
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for i in range(5):
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name = f"xgboost-{i}"
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arn = f"arn:aws:sagemaker:us-east-1:000000000000:x-x/foobar-{i}"
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MyProcessingJobModel(processing_job_name=name, role_arn=arn).save(
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sagemaker_client
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)
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for i in range(5):
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name = f"vgg-{i}"
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arn = f"arn:aws:sagemaker:us-east-1:000000000000:x-x/barfoo-{i}"
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MyProcessingJobModel(processing_job_name=name, role_arn=arn).save(
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sagemaker_client
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)
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vgg_processing_job_1 = sagemaker_client.list_processing_jobs(
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NameContains="vgg", MaxResults=1
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)
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assert len(vgg_processing_job_1["ProcessingJobSummaries"]) == 0
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assert vgg_processing_job_1.get("NextToken") is not None
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vgg_processing_job_6 = sagemaker_client.list_processing_jobs(
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NameContains="vgg", MaxResults=6
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)
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assert len(vgg_processing_job_6["ProcessingJobSummaries"]) == 1
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assert (
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vgg_processing_job_6["ProcessingJobSummaries"][0]["ProcessingJobName"]
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== "vgg-0"
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)
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assert vgg_processing_job_6.get("NextToken") is not None
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vgg_processing_job_10 = sagemaker_client.list_processing_jobs(
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NameContains="vgg", MaxResults=10
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)
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assert len(vgg_processing_job_10["ProcessingJobSummaries"]) == 5
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assert (
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vgg_processing_job_10["ProcessingJobSummaries"][-1]["ProcessingJobName"]
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== "vgg-4"
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)
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assert vgg_processing_job_10.get("NextToken") is None
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def test_list_processing_jobs_paginated_with_fragmented_targets(sagemaker_client):
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for i in range(5):
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name = f"xgboost-{i}"
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arn = f"arn:aws:sagemaker:us-east-1:000000000000:x-x/foobar-{i}"
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MyProcessingJobModel(processing_job_name=name, role_arn=arn).save(
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sagemaker_client
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)
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for i in range(5):
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name = f"vgg-{i}"
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arn = f"arn:aws:sagemaker:us-east-1:000000000000:x-x/barfoo-{i}"
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MyProcessingJobModel(processing_job_name=name, role_arn=arn).save(
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sagemaker_client
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)
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processing_jobs_with_2 = sagemaker_client.list_processing_jobs(
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NameContains="2", MaxResults=8
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)
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assert len(processing_jobs_with_2["ProcessingJobSummaries"]) == 2
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assert processing_jobs_with_2.get("NextToken") is not None
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processing_jobs_with_2_next = sagemaker_client.list_processing_jobs(
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NameContains="2",
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MaxResults=1,
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NextToken=processing_jobs_with_2.get("NextToken"),
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)
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assert len(processing_jobs_with_2_next["ProcessingJobSummaries"]) == 0
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assert processing_jobs_with_2_next.get("NextToken") is not None
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processing_jobs_with_2_next_next = sagemaker_client.list_processing_jobs(
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NameContains="2",
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MaxResults=1,
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NextToken=processing_jobs_with_2_next.get("NextToken"),
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)
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assert len(processing_jobs_with_2_next_next["ProcessingJobSummaries"]) == 0
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assert processing_jobs_with_2_next_next.get("NextToken") is None
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def test_add_and_delete_tags_in_training_job(sagemaker_client):
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processing_job_name = "MyProcessingJob"
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role_arn = f"arn:aws:iam::{ACCOUNT_ID}:role/FakeRole"
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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 = "my-prefix"
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app_specification = {
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"ImageUri": container,
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"ContainerEntrypoint": ["python3", "app.py"],
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}
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processing_resources = {
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"ClusterConfig": {
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"InstanceCount": 2,
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"InstanceType": "ml.m5.xlarge",
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"VolumeSizeInGB": 20,
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},
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}
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stopping_condition = {"MaxRuntimeInSeconds": 60 * 60}
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job = MyProcessingJobModel(
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processing_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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app_specification=app_specification,
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processing_resources=processing_resources,
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stopping_condition=stopping_condition,
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)
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resp = job.save(sagemaker_client)
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resource_arn = resp["ProcessingJobArn"]
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tags = [
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{"Key": "myKey", "Value": "myValue"},
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]
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response = sagemaker_client.add_tags(ResourceArn=resource_arn, Tags=tags)
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assert response["ResponseMetadata"]["HTTPStatusCode"] == 200
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response = sagemaker_client.list_tags(ResourceArn=resource_arn)
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assert response["Tags"] == tags
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tag_keys = [tag["Key"] for tag in tags]
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response = sagemaker_client.delete_tags(ResourceArn=resource_arn, TagKeys=tag_keys)
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assert response["ResponseMetadata"]["HTTPStatusCode"] == 200
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response = sagemaker_client.list_tags(ResourceArn=resource_arn)
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assert response["Tags"] == []
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