ba99c61477
* Basic upport for Endpoints, EndpointConfigs and TrainingJobs * Dropped extraneous pass statement. Co-authored-by: Joseph Weitekamp <jweite@amazon.com>
128 lines
5.0 KiB
Python
128 lines
5.0 KiB
Python
# -*- coding: utf-8 -*-
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from __future__ import unicode_literals
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import boto3
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import datetime
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import sure # noqa
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from moto import mock_sagemaker
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from moto.sts.models import ACCOUNT_ID
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from nose.tools import assert_true, assert_equal, assert_raises, assert_regexp_matches
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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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@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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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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params = {
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"RoleArn": FAKE_ROLE_ARN,
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"TrainingJobName": training_job_name,
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"AlgorithmSpecification": {
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"TrainingImage": container,
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"TrainingInputMode": "File",
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},
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"ResourceConfig": {
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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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"InputDataConfig": [
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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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"OutputDataConfig": {"S3OutputPath": "s3://{}/{}/".format(bucket, prefix)},
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"HyperParameters": {
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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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"StoppingCondition": {"MaxRuntimeInSeconds": 60 * 60},
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}
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resp = sagemaker.create_training_job(**params)
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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_true(
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resp["ModelArtifacts"]["S3ModelArtifacts"].startswith(
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params["OutputDataConfig"]["S3OutputPath"]
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)
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)
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assert_true(training_job_name in (resp["ModelArtifacts"]["S3ModelArtifacts"]))
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assert_true(
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resp["ModelArtifacts"]["S3ModelArtifacts"].endswith("output/model.tar.gz")
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)
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assert_equal(resp["TrainingJobStatus"], "Completed")
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assert_equal(resp["SecondaryStatus"], "Completed")
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assert_equal(resp["HyperParameters"], params["HyperParameters"])
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assert_equal(
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resp["AlgorithmSpecification"]["TrainingImage"],
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params["AlgorithmSpecification"]["TrainingImage"],
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)
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assert_equal(
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resp["AlgorithmSpecification"]["TrainingInputMode"],
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params["AlgorithmSpecification"]["TrainingInputMode"],
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)
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assert_true("MetricDefinitions" in resp["AlgorithmSpecification"])
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assert_true("Name" in resp["AlgorithmSpecification"]["MetricDefinitions"][0])
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assert_true("Regex" in resp["AlgorithmSpecification"]["MetricDefinitions"][0])
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assert_equal(resp["RoleArn"], FAKE_ROLE_ARN)
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assert_equal(resp["InputDataConfig"], params["InputDataConfig"])
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assert_equal(resp["OutputDataConfig"], params["OutputDataConfig"])
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assert_equal(resp["ResourceConfig"], params["ResourceConfig"])
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assert_equal(resp["StoppingCondition"], params["StoppingCondition"])
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assert_true(isinstance(resp["CreationTime"], datetime.datetime))
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assert_true(isinstance(resp["TrainingStartTime"], datetime.datetime))
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assert_true(isinstance(resp["TrainingEndTime"], datetime.datetime))
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assert_true(isinstance(resp["LastModifiedTime"], datetime.datetime))
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assert_true("SecondaryStatusTransitions" in resp)
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assert_true("Status" in resp["SecondaryStatusTransitions"][0])
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assert_true("StartTime" in resp["SecondaryStatusTransitions"][0])
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assert_true("EndTime" in resp["SecondaryStatusTransitions"][0])
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assert_true("StatusMessage" in resp["SecondaryStatusTransitions"][0])
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assert_true("FinalMetricDataList" in resp)
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assert_true("MetricName" in resp["FinalMetricDataList"][0])
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assert_true("Value" in resp["FinalMetricDataList"][0])
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assert_true("Timestamp" in resp["FinalMetricDataList"][0])
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pass
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