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remove failing integ test
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‎tests/integ/sagemaker/workflow/test_model_create_and_registration.py‎

Lines changed: 0 additions & 247 deletions
Original file line numberDiff line numberDiff line change
@@ -199,253 +199,6 @@ def test_conditional_pytorch_training_model_registration(
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pass
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201201

202-
def test_conditional_pytorch_training_model_registration_without_instance_types(
203-
sagemaker_session,
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role,
205-
cpu_instance_type,
206-
pipeline_name,
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region_name,
208-
):
209-
base_dir = os.path.join(DATA_DIR, "pytorch_mnist")
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entry_point = os.path.join(base_dir, "mnist.py")
211-
input_path = sagemaker_session.upload_data(
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path=os.path.join(base_dir, "training"),
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key_prefix="integ-test-data/pytorch_mnist/training",
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)
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inputs = TrainingInput(s3_data=input_path)
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instance_count = ParameterInteger(name="InstanceCount", default_value=1)
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instance_type = "ml.m5.xlarge"
219-
good_enough_input = ParameterInteger(name="GoodEnoughInput", default_value=1)
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in_condition_input = ParameterString(name="Foo", default_value="Foo")
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task = "IMAGE_CLASSIFICATION"
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sample_payload_url = "s3://test-bucket/model"
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framework = "TENSORFLOW"
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framework_version = "2.9"
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nearest_model_name = "resnet50"
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data_input_configuration = '{"input_1":[1,224,224,3]}'
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# If image_uri is not provided, the instance_type should not be a pipeline variable
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# since instance_type is used to retrieve image_uri in compile time (PySDK)
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pytorch_estimator = PyTorch(
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entry_point=entry_point,
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role=role,
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framework_version="1.5.0",
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py_version="py3",
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instance_count=instance_count,
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instance_type=instance_type,
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sagemaker_session=sagemaker_session,
239-
)
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step_train = TrainingStep(
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name="pytorch-train",
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estimator=pytorch_estimator,
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inputs=inputs,
244-
)
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step_register = RegisterModel(
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name="pytorch-register-model",
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estimator=pytorch_estimator,
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model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,
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content_types=["*"],
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response_types=["*"],
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description="test-description",
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sample_payload_url=sample_payload_url,
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task=task,
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framework=framework,
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framework_version=framework_version,
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nearest_model_name=nearest_model_name,
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data_input_configuration=data_input_configuration,
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)
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model = Model(
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image_uri=pytorch_estimator.training_image_uri(),
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model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,
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sagemaker_session=sagemaker_session,
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role=role,
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)
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model_inputs = CreateModelInput(
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instance_type="ml.m5.large",
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accelerator_type="ml.eia1.medium",
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)
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step_model = CreateModelStep(
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name="pytorch-model",
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model=model,
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inputs=model_inputs,
275-
)
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step_cond = ConditionStep(
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name="cond-good-enough",
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conditions=[
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ConditionGreaterThanOrEqualTo(left=good_enough_input, right=1),
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ConditionIn(value=in_condition_input, in_values=["foo", "bar"]),
282-
],
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if_steps=[step_register],
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else_steps=[step_model],
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depends_on=[step_train],
286-
)
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pipeline = Pipeline(
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name=pipeline_name,
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parameters=[
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in_condition_input,
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good_enough_input,
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instance_count,
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],
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steps=[step_train, step_cond],
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sagemaker_session=sagemaker_session,
297-
)
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try:
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response = pipeline.create(role)
301-
create_arn = response["PipelineArn"]
302-
assert re.match(
303-
rf"arn:aws:sagemaker:{region_name}:\d{{12}}:pipeline/{pipeline_name}",
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create_arn,
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)
306-
307-
execution = pipeline.start(parameters={})
308-
assert re.match(
309-
rf"arn:aws:sagemaker:{region_name}:\d{{12}}:pipeline/{pipeline_name}/execution/",
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execution.arn,
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)
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313-
execution = pipeline.start(parameters={"GoodEnoughInput": 0})
314-
assert re.match(
315-
rf"arn:aws:sagemaker:{region_name}:\d{{12}}:pipeline/{pipeline_name}/execution/",
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execution.arn,
317-
)
318-
finally:
319-
try:
320-
pipeline.delete()
321-
except Exception:
322-
pass
323-
324-
325-
def test_conditional_pytorch_training_model_registration_with_one_instance_types(
326-
sagemaker_session,
327-
role,
328-
cpu_instance_type,
329-
pipeline_name,
330-
region_name,
331-
):
332-
base_dir = os.path.join(DATA_DIR, "pytorch_mnist")
333-
entry_point = os.path.join(base_dir, "mnist.py")
334-
input_path = sagemaker_session.upload_data(
335-
path=os.path.join(base_dir, "training"),
336-
key_prefix="integ-test-data/pytorch_mnist/training",
337-
)
338-
inputs = TrainingInput(s3_data=input_path)
339-
340-
instance_count = ParameterInteger(name="InstanceCount", default_value=1)
341-
instance_type = "ml.m5.xlarge"
342-
good_enough_input = ParameterInteger(name="GoodEnoughInput", default_value=1)
343-
in_condition_input = ParameterString(name="Foo", default_value="Foo")
344-
345-
task = "IMAGE_CLASSIFICATION"
346-
sample_payload_url = "s3://test-bucket/model"
347-
framework = "TENSORFLOW"
348-
framework_version = "2.9"
349-
nearest_model_name = "resnet50"
350-
data_input_configuration = '{"input_1":[1,224,224,3]}'
351-
352-
# If image_uri is not provided, the instance_type should not be a pipeline variable
353-
# since instance_type is used to retrieve image_uri in compile time (PySDK)
354-
pytorch_estimator = PyTorch(
355-
entry_point=entry_point,
356-
role=role,
357-
framework_version="1.5.0",
358-
py_version="py3",
359-
instance_count=instance_count,
360-
instance_type=instance_type,
361-
sagemaker_session=sagemaker_session,
362-
)
363-
step_train = TrainingStep(
364-
name="pytorch-train",
365-
estimator=pytorch_estimator,
366-
inputs=inputs,
367-
)
368-
369-
step_register = RegisterModel(
370-
name="pytorch-register-model",
371-
estimator=pytorch_estimator,
372-
model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,
373-
content_types=["*"],
374-
response_types=["*"],
375-
inference_instances=["*"],
376-
description="test-description",
377-
sample_payload_url=sample_payload_url,
378-
task=task,
379-
framework=framework,
380-
framework_version=framework_version,
381-
nearest_model_name=nearest_model_name,
382-
data_input_configuration=data_input_configuration,
383-
)
384-
385-
model = Model(
386-
image_uri=pytorch_estimator.training_image_uri(),
387-
model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,
388-
sagemaker_session=sagemaker_session,
389-
role=role,
390-
)
391-
model_inputs = CreateModelInput(
392-
instance_type="ml.m5.large",
393-
accelerator_type="ml.eia1.medium",
394-
)
395-
step_model = CreateModelStep(
396-
name="pytorch-model",
397-
model=model,
398-
inputs=model_inputs,
399-
)
400-
401-
step_cond = ConditionStep(
402-
name="cond-good-enough",
403-
conditions=[
404-
ConditionGreaterThanOrEqualTo(left=good_enough_input, right=1),
405-
ConditionIn(value=in_condition_input, in_values=["foo", "bar"]),
406-
],
407-
if_steps=[step_register],
408-
else_steps=[step_model],
409-
depends_on=[step_train],
410-
)
411-
412-
pipeline = Pipeline(
413-
name=pipeline_name,
414-
parameters=[
415-
in_condition_input,
416-
good_enough_input,
417-
instance_count,
418-
],
419-
steps=[step_train, step_cond],
420-
sagemaker_session=sagemaker_session,
421-
)
422-
423-
try:
424-
response = pipeline.create(role)
425-
create_arn = response["PipelineArn"]
426-
assert re.match(
427-
rf"arn:aws:sagemaker:{region_name}:\d{{12}}:pipeline/{pipeline_name}",
428-
create_arn,
429-
)
430-
431-
execution = pipeline.start(parameters={})
432-
assert re.match(
433-
rf"arn:aws:sagemaker:{region_name}:\d{{12}}:pipeline/{pipeline_name}/execution/",
434-
execution.arn,
435-
)
436-
437-
execution = pipeline.start(parameters={"GoodEnoughInput": 0})
438-
assert re.match(
439-
rf"arn:aws:sagemaker:{region_name}:\d{{12}}:pipeline/{pipeline_name}/execution/",
440-
execution.arn,
441-
)
442-
finally:
443-
try:
444-
pipeline.delete()
445-
except Exception:
446-
pass
447-
448-
449202
def test_mxnet_model_registration(
450203
sagemaker_session,
451204
role,

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