File size: 13,205 Bytes
476455e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 | # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
# the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file is
# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
# ANY KIND, either express or implied. See the License for the specific
# language governing permissions and limitations under the License.
from __future__ import absolute_import
import os
import pytest
from botocore.exceptions import ClientError
import tests.integ
from sagemaker import AutoML, AutoMLInput, CandidateEstimator
from sagemaker.utils import unique_name_from_base
from tests.integ import AUTO_ML_DEFAULT_TIMEMOUT_MINUTES, DATA_DIR, auto_ml_utils
from tests.integ.timeout import timeout
ROLE = "SageMakerRole"
PREFIX = "sagemaker/beta-automl-xgboost"
AUTO_ML_INSTANCE_TYPE = "ml.m5.2xlarge"
INSTANCE_COUNT = 1
RESOURCE_POOLS = [{"InstanceType": AUTO_ML_INSTANCE_TYPE, "PoolSize": INSTANCE_COUNT}]
TARGET_ATTRIBUTE_NAME = "virginica"
DATA_DIR = os.path.join(DATA_DIR, "automl", "data")
TRAINING_DATA = os.path.join(DATA_DIR, "iris_training.csv")
TEST_DATA = os.path.join(DATA_DIR, "iris_test.csv")
TRANSFORM_DATA = os.path.join(DATA_DIR, "iris_transform.csv")
PROBLEM_TYPE = "MultiClassClassification"
BASE_JOB_NAME = "auto-ml"
# use a succeeded AutoML job to test describe and list candidates method, otherwise tests will run too long
AUTO_ML_JOB_NAME = "python-sdk-integ-test-base-job"
DEFAULT_MODEL_NAME = "python-sdk-automl"
EXPECTED_DEFAULT_JOB_CONFIG = {
"CompletionCriteria": {"MaxCandidates": 3},
"SecurityConfig": {"EnableInterContainerTrafficEncryption": False},
}
@pytest.mark.slow_test
@pytest.mark.skipif(
tests.integ.test_region() in tests.integ.NO_AUTO_ML_REGIONS,
reason="AutoML is not supported in the region yet.",
)
@pytest.mark.release
def test_auto_ml_fit(sagemaker_session):
auto_ml = AutoML(
role=ROLE,
target_attribute_name=TARGET_ATTRIBUTE_NAME,
sagemaker_session=sagemaker_session,
max_candidates=1,
)
job_name = unique_name_from_base("auto-ml", max_length=32)
inputs = sagemaker_session.upload_data(path=TRAINING_DATA, key_prefix=PREFIX + "/input")
with timeout(minutes=AUTO_ML_DEFAULT_TIMEMOUT_MINUTES):
auto_ml.fit(inputs, job_name=job_name)
@pytest.mark.skipif(
tests.integ.test_region() in tests.integ.NO_AUTO_ML_REGIONS,
reason="AutoML is not supported in the region yet.",
)
def test_auto_ml_fit_local_input(sagemaker_session):
auto_ml = AutoML(
role=ROLE,
target_attribute_name=TARGET_ATTRIBUTE_NAME,
sagemaker_session=sagemaker_session,
max_candidates=1,
generate_candidate_definitions_only=True,
)
inputs = TRAINING_DATA
job_name = unique_name_from_base("auto-ml", max_length=32)
with timeout(minutes=AUTO_ML_DEFAULT_TIMEMOUT_MINUTES):
auto_ml.fit(inputs, job_name=job_name)
@pytest.mark.skipif(
tests.integ.test_region() in tests.integ.NO_AUTO_ML_REGIONS,
reason="AutoML is not supported in the region yet.",
)
def test_auto_ml_input_object_fit(sagemaker_session):
auto_ml = AutoML(
role=ROLE,
target_attribute_name=TARGET_ATTRIBUTE_NAME,
sagemaker_session=sagemaker_session,
max_candidates=1,
generate_candidate_definitions_only=True,
)
job_name = unique_name_from_base("auto-ml", max_length=32)
s3_input = sagemaker_session.upload_data(path=TRAINING_DATA, key_prefix=PREFIX + "/input")
inputs = AutoMLInput(inputs=s3_input, target_attribute_name=TARGET_ATTRIBUTE_NAME)
with timeout(minutes=AUTO_ML_DEFAULT_TIMEMOUT_MINUTES):
auto_ml.fit(inputs, job_name=job_name)
@pytest.mark.skipif(
tests.integ.test_region() in tests.integ.NO_AUTO_ML_REGIONS,
reason="AutoML is not supported in the region yet.",
)
def test_auto_ml_fit_optional_args(sagemaker_session):
output_path = "s3://{}/{}".format(sagemaker_session.default_bucket(), "specified_ouput_path")
problem_type = "MulticlassClassification"
job_objective = {"MetricName": "Accuracy"}
auto_ml = AutoML(
role=ROLE,
target_attribute_name=TARGET_ATTRIBUTE_NAME,
sagemaker_session=sagemaker_session,
max_candidates=1,
output_path=output_path,
problem_type=problem_type,
job_objective=job_objective,
generate_candidate_definitions_only=True,
)
inputs = TRAINING_DATA
with timeout(minutes=AUTO_ML_DEFAULT_TIMEMOUT_MINUTES):
auto_ml.fit(inputs, job_name=unique_name_from_base(BASE_JOB_NAME))
auto_ml_desc = auto_ml.describe_auto_ml_job(job_name=auto_ml.latest_auto_ml_job.job_name)
assert auto_ml_desc["AutoMLJobStatus"] == "Completed"
assert auto_ml_desc["AutoMLJobName"] == auto_ml.latest_auto_ml_job.job_name
assert auto_ml_desc["AutoMLJobObjective"] == job_objective
assert auto_ml_desc["ProblemType"] == problem_type
assert auto_ml_desc["OutputDataConfig"]["S3OutputPath"] == output_path
@pytest.mark.skipif(
tests.integ.test_region() in tests.integ.NO_AUTO_ML_REGIONS,
reason="AutoML is not supported in the region yet.",
)
def test_auto_ml_invalid_target_attribute(sagemaker_session):
auto_ml = AutoML(
role=ROLE, target_attribute_name="y", sagemaker_session=sagemaker_session, max_candidates=1
)
job_name = unique_name_from_base("auto-ml", max_length=32)
inputs = sagemaker_session.upload_data(path=TRAINING_DATA, key_prefix=PREFIX + "/input")
with pytest.raises(
ClientError,
match=r"An error occurred \(ValidationException\) when calling the CreateAutoMLJob "
"operation: Target attribute name y does not exist in header.",
):
auto_ml.fit(inputs, job_name=job_name)
@pytest.mark.skipif(
tests.integ.test_region() in tests.integ.NO_AUTO_ML_REGIONS,
reason="AutoML is not supported in the region yet.",
)
def test_auto_ml_describe_auto_ml_job(sagemaker_session):
expected_default_input_config = [
{
"DataSource": {
"S3DataSource": {
"S3DataType": "S3Prefix",
"S3Uri": "s3://{}/{}/input/iris_training.csv".format(
sagemaker_session.default_bucket(), PREFIX
),
}
},
"TargetAttributeName": TARGET_ATTRIBUTE_NAME,
"ContentType": "text/csv;header=present",
"ChannelType": "training",
}
]
expected_default_output_config = {
"S3OutputPath": "s3://{}/".format(sagemaker_session.default_bucket())
}
auto_ml_utils.create_auto_ml_job_if_not_exist(sagemaker_session)
auto_ml = AutoML(
role=ROLE, target_attribute_name=TARGET_ATTRIBUTE_NAME, sagemaker_session=sagemaker_session
)
desc = auto_ml.describe_auto_ml_job(job_name=AUTO_ML_JOB_NAME)
assert desc["AutoMLJobName"] == AUTO_ML_JOB_NAME
assert desc["AutoMLJobStatus"] == "Completed"
assert isinstance(desc["BestCandidate"], dict)
assert desc["InputDataConfig"] == expected_default_input_config
assert desc["AutoMLJobConfig"] == EXPECTED_DEFAULT_JOB_CONFIG
assert desc["OutputDataConfig"] == expected_default_output_config
@pytest.mark.skipif(
tests.integ.test_region() in tests.integ.NO_AUTO_ML_REGIONS,
reason="AutoML is not supported in the region yet.",
)
def test_auto_ml_attach(sagemaker_session):
expected_default_input_config = [
{
"DataSource": {
"S3DataSource": {
"S3DataType": "S3Prefix",
"S3Uri": "s3://{}/{}/input/iris_training.csv".format(
sagemaker_session.default_bucket(), PREFIX
),
}
},
"TargetAttributeName": TARGET_ATTRIBUTE_NAME,
"ContentType": "text/csv;header=present",
"ChannelType": "training",
}
]
expected_default_output_config = {
"S3OutputPath": "s3://{}/".format(sagemaker_session.default_bucket())
}
auto_ml_utils.create_auto_ml_job_if_not_exist(sagemaker_session)
attached_automl_job = AutoML.attach(
auto_ml_job_name=AUTO_ML_JOB_NAME, sagemaker_session=sagemaker_session
)
attached_desc = attached_automl_job.describe_auto_ml_job()
assert attached_desc["AutoMLJobName"] == AUTO_ML_JOB_NAME
assert attached_desc["AutoMLJobStatus"] == "Completed"
assert isinstance(attached_desc["BestCandidate"], dict)
assert attached_desc["InputDataConfig"] == expected_default_input_config
assert attached_desc["AutoMLJobConfig"] == EXPECTED_DEFAULT_JOB_CONFIG
assert attached_desc["OutputDataConfig"] == expected_default_output_config
@pytest.mark.skipif(
tests.integ.test_region() in tests.integ.NO_AUTO_ML_REGIONS,
reason="AutoML is not supported in the region yet.",
)
def test_list_candidates(sagemaker_session):
auto_ml_utils.create_auto_ml_job_if_not_exist(sagemaker_session)
auto_ml = AutoML(
role=ROLE, target_attribute_name=TARGET_ATTRIBUTE_NAME, sagemaker_session=sagemaker_session
)
candidates = auto_ml.list_candidates(job_name=AUTO_ML_JOB_NAME)
assert len(candidates) == 3
@pytest.mark.skipif(
tests.integ.test_region() in tests.integ.NO_AUTO_ML_REGIONS,
reason="AutoML is not supported in the region yet.",
)
def test_best_candidate(sagemaker_session):
auto_ml_utils.create_auto_ml_job_if_not_exist(sagemaker_session)
auto_ml = AutoML(
role=ROLE, target_attribute_name=TARGET_ATTRIBUTE_NAME, sagemaker_session=sagemaker_session
)
best_candidate = auto_ml.best_candidate(job_name=AUTO_ML_JOB_NAME)
assert len(best_candidate["InferenceContainers"]) == 3
assert len(best_candidate["CandidateSteps"]) == 4
assert best_candidate["CandidateStatus"] == "Completed"
@pytest.mark.skipif(
tests.integ.test_region() in tests.integ.NO_AUTO_ML_REGIONS,
reason="AutoML is not supported in the region yet.",
)
@pytest.mark.release
def test_deploy_best_candidate(sagemaker_session, cpu_instance_type):
auto_ml_utils.create_auto_ml_job_if_not_exist(sagemaker_session)
auto_ml = AutoML(
role=ROLE, target_attribute_name=TARGET_ATTRIBUTE_NAME, sagemaker_session=sagemaker_session
)
best_candidate = auto_ml.best_candidate(job_name=AUTO_ML_JOB_NAME)
endpoint_name = unique_name_from_base("sagemaker-auto-ml-best-candidate-test")
with timeout(minutes=AUTO_ML_DEFAULT_TIMEMOUT_MINUTES):
auto_ml.deploy(
candidate=best_candidate,
initial_instance_count=INSTANCE_COUNT,
instance_type=cpu_instance_type,
endpoint_name=endpoint_name,
)
endpoint_status = sagemaker_session.sagemaker_client.describe_endpoint(
EndpointName=endpoint_name
)["EndpointStatus"]
assert endpoint_status == "InService"
sagemaker_session.sagemaker_client.delete_endpoint(EndpointName=endpoint_name)
@pytest.mark.skipif(
tests.integ.test_region() in tests.integ.NO_AUTO_ML_REGIONS,
reason="AutoML is not supported in the region yet.",
)
@pytest.mark.skip(
reason="",
)
def test_candidate_estimator_default_rerun_and_deploy(sagemaker_session, cpu_instance_type):
auto_ml_utils.create_auto_ml_job_if_not_exist(sagemaker_session)
auto_ml = AutoML(
role=ROLE, target_attribute_name=TARGET_ATTRIBUTE_NAME, sagemaker_session=sagemaker_session
)
candidates = auto_ml.list_candidates(job_name=AUTO_ML_JOB_NAME)
candidate = candidates[1]
candidate_estimator = CandidateEstimator(candidate, sagemaker_session)
inputs = sagemaker_session.upload_data(path=TEST_DATA, key_prefix=PREFIX + "/input")
endpoint_name = unique_name_from_base("sagemaker-auto-ml-rerun-candidate-test")
with timeout(minutes=AUTO_ML_DEFAULT_TIMEMOUT_MINUTES):
candidate_estimator.fit(inputs)
auto_ml.deploy(
initial_instance_count=INSTANCE_COUNT,
instance_type=cpu_instance_type,
candidate=candidate,
endpoint_name=endpoint_name,
)
endpoint_status = sagemaker_session.sagemaker_client.describe_endpoint(
EndpointName=endpoint_name
)["EndpointStatus"]
assert endpoint_status == "InService"
sagemaker_session.sagemaker_client.delete_endpoint(EndpointName=endpoint_name)
@pytest.mark.skipif(
tests.integ.test_region() in tests.integ.NO_AUTO_ML_REGIONS,
reason="AutoML is not supported in the region yet.",
)
def test_candidate_estimator_get_steps(sagemaker_session):
auto_ml_utils.create_auto_ml_job_if_not_exist(sagemaker_session)
auto_ml = AutoML(
role=ROLE, target_attribute_name=TARGET_ATTRIBUTE_NAME, sagemaker_session=sagemaker_session
)
candidates = auto_ml.list_candidates(job_name=AUTO_ML_JOB_NAME)
candidate = candidates[1]
candidate_estimator = CandidateEstimator(candidate, sagemaker_session)
steps = candidate_estimator.get_steps()
assert len(steps) == 3
|