Basic Support for Endpoints, EndpointConfigs and TrainingJobs (#3142)
* Basic upport for Endpoints, EndpointConfigs and TrainingJobs * Dropped extraneous pass statement. Co-authored-by: Joseph Weitekamp <jweite@amazon.com>
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5 changed files with 1007 additions and 6 deletions
246
tests/test_sagemaker/test_sagemaker_endpoint.py
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246
tests/test_sagemaker/test_sagemaker_endpoint.py
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# -*- coding: utf-8 -*-
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from __future__ import unicode_literals
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import datetime
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import boto3
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from botocore.exceptions import ClientError, ParamValidationError
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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
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TEST_REGION_NAME = "us-east-1"
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FAKE_ROLE_ARN = "arn:aws:iam::{}:role/FakeRole".format(ACCOUNT_ID)
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GENERIC_TAGS_PARAM = [
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{"Key": "newkey1", "Value": "newval1"},
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{"Key": "newkey2", "Value": "newval2"},
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]
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@mock_sagemaker
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def test_create_endpoint_config():
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sagemaker = boto3.client("sagemaker", region_name=TEST_REGION_NAME)
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model_name = "MyModel"
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production_variants = [
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{
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"VariantName": "MyProductionVariant",
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"ModelName": model_name,
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"InitialInstanceCount": 1,
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"InstanceType": "ml.t2.medium",
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},
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]
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endpoint_config_name = "MyEndpointConfig"
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with assert_raises(ClientError) as e:
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sagemaker.create_endpoint_config(
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EndpointConfigName=endpoint_config_name,
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ProductionVariants=production_variants,
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)
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assert_true(
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e.exception.response["Error"]["Message"].startswith("Could not find model")
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)
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_create_model(sagemaker, model_name)
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resp = sagemaker.create_endpoint_config(
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EndpointConfigName=endpoint_config_name, ProductionVariants=production_variants
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)
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resp["EndpointConfigArn"].should.match(
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r"^arn:aws:sagemaker:.*:.*:endpoint-config/{}$".format(endpoint_config_name)
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)
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resp = sagemaker.describe_endpoint_config(EndpointConfigName=endpoint_config_name)
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resp["EndpointConfigArn"].should.match(
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r"^arn:aws:sagemaker:.*:.*:endpoint-config/{}$".format(endpoint_config_name)
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)
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resp["EndpointConfigName"].should.equal(endpoint_config_name)
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resp["ProductionVariants"].should.equal(production_variants)
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@mock_sagemaker
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def test_delete_endpoint_config():
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sagemaker = boto3.client("sagemaker", region_name=TEST_REGION_NAME)
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model_name = "MyModel"
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_create_model(sagemaker, model_name)
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endpoint_config_name = "MyEndpointConfig"
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production_variants = [
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{
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"VariantName": "MyProductionVariant",
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"ModelName": model_name,
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"InitialInstanceCount": 1,
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"InstanceType": "ml.t2.medium",
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},
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]
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resp = sagemaker.create_endpoint_config(
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EndpointConfigName=endpoint_config_name, ProductionVariants=production_variants
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)
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resp["EndpointConfigArn"].should.match(
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r"^arn:aws:sagemaker:.*:.*:endpoint-config/{}$".format(endpoint_config_name)
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)
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resp = sagemaker.describe_endpoint_config(EndpointConfigName=endpoint_config_name)
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resp["EndpointConfigArn"].should.match(
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r"^arn:aws:sagemaker:.*:.*:endpoint-config/{}$".format(endpoint_config_name)
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)
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resp = sagemaker.delete_endpoint_config(EndpointConfigName=endpoint_config_name)
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with assert_raises(ClientError) as e:
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sagemaker.describe_endpoint_config(EndpointConfigName=endpoint_config_name)
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assert_true(
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e.exception.response["Error"]["Message"].startswith(
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"Could not find endpoint configuration"
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)
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)
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with assert_raises(ClientError) as e:
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sagemaker.delete_endpoint_config(EndpointConfigName=endpoint_config_name)
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assert_true(
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e.exception.response["Error"]["Message"].startswith(
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"Could not find endpoint configuration"
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)
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)
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pass
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@mock_sagemaker
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def test_create_endpoint_invalid_instance_type():
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sagemaker = boto3.client("sagemaker", region_name=TEST_REGION_NAME)
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model_name = "MyModel"
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_create_model(sagemaker, model_name)
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instance_type = "InvalidInstanceType"
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production_variants = [
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{
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"VariantName": "MyProductionVariant",
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"ModelName": model_name,
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"InitialInstanceCount": 1,
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"InstanceType": instance_type,
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},
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]
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endpoint_config_name = "MyEndpointConfig"
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with assert_raises(ClientError) as e:
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sagemaker.create_endpoint_config(
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EndpointConfigName=endpoint_config_name,
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ProductionVariants=production_variants,
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)
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assert_equal(e.exception.response["Error"]["Code"], "ValidationException")
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expected_message = "Value '{}' at 'instanceType' failed to satisfy constraint: Member must satisfy enum value set: [".format(
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instance_type
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)
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assert_true(expected_message in e.exception.response["Error"]["Message"])
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@mock_sagemaker
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def test_create_endpoint():
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sagemaker = boto3.client("sagemaker", region_name=TEST_REGION_NAME)
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endpoint_name = "MyEndpoint"
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with assert_raises(ClientError) as e:
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sagemaker.create_endpoint(
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EndpointName=endpoint_name, EndpointConfigName="NonexistentEndpointConfig"
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)
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assert_true(
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e.exception.response["Error"]["Message"].startswith(
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"Could not find endpoint configuration"
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)
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)
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model_name = "MyModel"
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_create_model(sagemaker, model_name)
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endpoint_config_name = "MyEndpointConfig"
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_create_endpoint_config(sagemaker, endpoint_config_name, model_name)
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resp = sagemaker.create_endpoint(
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EndpointName=endpoint_name,
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EndpointConfigName=endpoint_config_name,
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Tags=GENERIC_TAGS_PARAM,
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)
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resp["EndpointArn"].should.match(
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r"^arn:aws:sagemaker:.*:.*:endpoint/{}$".format(endpoint_name)
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)
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resp = sagemaker.describe_endpoint(EndpointName=endpoint_name)
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resp["EndpointArn"].should.match(
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r"^arn:aws:sagemaker:.*:.*:endpoint/{}$".format(endpoint_name)
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)
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resp["EndpointName"].should.equal(endpoint_name)
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resp["EndpointConfigName"].should.equal(endpoint_config_name)
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resp["EndpointStatus"].should.equal("InService")
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assert_true(isinstance(resp["CreationTime"], datetime.datetime))
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assert_true(isinstance(resp["LastModifiedTime"], datetime.datetime))
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resp["ProductionVariants"][0]["VariantName"].should.equal("MyProductionVariant")
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resp = sagemaker.list_tags(ResourceArn=resp["EndpointArn"])
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assert_equal(resp["Tags"], GENERIC_TAGS_PARAM)
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@mock_sagemaker
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def test_delete_endpoint():
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sagemaker = boto3.client("sagemaker", region_name=TEST_REGION_NAME)
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model_name = "MyModel"
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_create_model(sagemaker, model_name)
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endpoint_config_name = "MyEndpointConfig"
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_create_endpoint_config(sagemaker, endpoint_config_name, model_name)
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endpoint_name = "MyEndpoint"
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_create_endpoint(sagemaker, endpoint_name, endpoint_config_name)
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sagemaker.delete_endpoint(EndpointName=endpoint_name)
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with assert_raises(ClientError) as e:
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sagemaker.describe_endpoint(EndpointName=endpoint_name)
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assert_true(
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e.exception.response["Error"]["Message"].startswith("Could not find endpoint")
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)
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with assert_raises(ClientError) as e:
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sagemaker.delete_endpoint(EndpointName=endpoint_name)
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assert_true(
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e.exception.response["Error"]["Message"].startswith("Could not find endpoint")
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)
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def _create_model(boto_client, model_name):
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resp = boto_client.create_model(
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ModelName=model_name,
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PrimaryContainer={
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"Image": "382416733822.dkr.ecr.us-east-1.amazonaws.com/factorization-machines:1",
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"ModelDataUrl": "s3://MyBucket/model.tar.gz",
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},
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ExecutionRoleArn=FAKE_ROLE_ARN,
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)
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assert_equal(resp["ResponseMetadata"]["HTTPStatusCode"], 200)
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def _create_endpoint_config(boto_client, endpoint_config_name, model_name):
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production_variants = [
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{
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"VariantName": "MyProductionVariant",
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"ModelName": model_name,
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"InitialInstanceCount": 1,
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"InstanceType": "ml.t2.medium",
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},
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]
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resp = boto_client.create_endpoint_config(
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EndpointConfigName=endpoint_config_name, ProductionVariants=production_variants
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)
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resp["EndpointConfigArn"].should.match(
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r"^arn:aws:sagemaker:.*:.*:endpoint-config/{}$".format(endpoint_config_name)
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)
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def _create_endpoint(boto_client, endpoint_name, endpoint_config_name):
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resp = boto_client.create_endpoint(
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EndpointName=endpoint_name, EndpointConfigName=endpoint_config_name
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)
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resp["EndpointArn"].should.match(
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r"^arn:aws:sagemaker:.*:.*:endpoint/{}$".format(endpoint_name)
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)
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127
tests/test_sagemaker/test_sagemaker_training.py
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127
tests/test_sagemaker/test_sagemaker_training.py
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# -*- 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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