def test_transform_mxnet_tags(sagemaker_session, mxnet_full_version):
data_path = os.path.join(DATA_DIR, "mxnet_mnist")
script_path = os.path.join(data_path, "mnist.py")
tags = [{"Key": "some-tag", "Value": "value-for-tag"}]
mx = MXNet(
entry_point=script_path,
role="SageMakerRole",
train_instance_count=1,
train_instance_type="ml.c4.xlarge",
sagemaker_session=sagemaker_session,
framework_version=mxnet_full_version,
)
train_input = mx.sagemaker_session.upload_data(
path=os.path.join(data_path, "train"), key_prefix="integ-test-data/mxnet_mnist/train"
)
test_input = mx.sagemaker_session.upload_data(
path=os.path.join(data_path, "test"), key_prefix="integ-test-data/mxnet_mnist/test"
)
job_name = unique_name_from_base("test-mxnet-transform")
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
mx.fit({"train": train_input, "test": test_input}, job_name=job_name)
transform_input_path = os.path.join(data_path, "transform", "data.csv")
transform_input_key_prefix = "integ-test-data/mxnet_mnist/transform"
transform_input = mx.sagemaker_session.upload_data(
path=transform_input_path, key_prefix=transform_input_key_prefix
)
transformer = mx.transformer(1, "ml.m4.xlarge", tags=tags)
transformer.transform(transform_input, content_type="text/csv")
with timeout_and_delete_model_with_transformer(
transformer, sagemaker_session, minutes=TRANSFORM_DEFAULT_TIMEOUT_MINUTES
):
transformer.wait()
model_desc = sagemaker_session.sagemaker_client.describe_model(
ModelName=transformer.model_name
)
model_tags = sagemaker_session.sagemaker_client.list_tags(
ResourceArn=model_desc["ModelArn"]
)["Tags"]
assert tags == model_tags
def test_stop_transform_job(sagemaker_session, mxnet_full_version,
cpu_instance_type):
data_path = os.path.join(DATA_DIR, "mxnet_mnist")
script_path = os.path.join(data_path, "mnist.py")
tags = [{"Key": "some-tag", "Value": "value-for-tag"}]
mx = MXNet(
entry_point=script_path,
role="SageMakerRole",
train_instance_count=1,
train_instance_type=cpu_instance_type,
sagemaker_session=sagemaker_session,
framework_version=mxnet_full_version,
)
train_input = mx.sagemaker_session.upload_data(
path=os.path.join(data_path, "train"),
key_prefix="integ-test-data/mxnet_mnist/train")
test_input = mx.sagemaker_session.upload_data(
path=os.path.join(data_path, "test"),
key_prefix="integ-test-data/mxnet_mnist/test")
job_name = unique_name_from_base("test-mxnet-transform")
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
mx.fit({"train": train_input, "test": test_input}, job_name=job_name)
transform_input_path = os.path.join(data_path, "transform", "data.csv")
transform_input_key_prefix = "integ-test-data/mxnet_mnist/transform"
transform_input = mx.sagemaker_session.upload_data(
path=transform_input_path, key_prefix=transform_input_key_prefix)
transformer = mx.transformer(1, cpu_instance_type, tags=tags)
transformer.transform(transform_input, content_type="text/csv")
time.sleep(15)
latest_transform_job_name = transformer.latest_transform_job.name
print("Attempting to stop {}".format(latest_transform_job_name))
transformer.stop_transform_job()
desc = transformer.latest_transform_job.sagemaker_session.sagemaker_client.describe_transform_job(
TransformJobName=latest_transform_job_name)
assert desc["TransformJobStatus"] == "Stopped"
def test_local_transform_mxnet(sagemaker_local_session, tmpdir,
mxnet_full_version):
data_path = os.path.join(DATA_DIR, 'mxnet_mnist')
script_path = os.path.join(data_path, 'mnist.py')
mx = MXNet(entry_point=script_path,
role='SageMakerRole',
train_instance_count=1,
train_instance_type='ml.c4.xlarge',
framework_version=mxnet_full_version,
sagemaker_session=sagemaker_local_session)
train_input = mx.sagemaker_session.upload_data(
path=os.path.join(data_path, 'train'),
key_prefix='integ-test-data/mxnet_mnist/train')
test_input = mx.sagemaker_session.upload_data(
path=os.path.join(data_path, 'test'),
key_prefix='integ-test-data/mxnet_mnist/test')
with timeout(minutes=15):
mx.fit({'train': train_input, 'test': test_input})
transform_input_path = os.path.join(data_path, 'transform')
transform_input_key_prefix = 'integ-test-data/mxnet_mnist/transform'
transform_input = mx.sagemaker_session.upload_data(
path=transform_input_path, key_prefix=transform_input_key_prefix)
output_path = 'file://%s' % (str(tmpdir))
transformer = mx.transformer(1,
'local',
assemble_with='Line',
max_payload=1,
strategy='SingleRecord',
output_path=output_path)
with local_mode_utils.lock():
transformer.transform(transform_input,
content_type='text/csv',
split_type='Line')
transformer.wait()
assert os.path.exists(os.path.join(str(tmpdir), 'data.csv.out'))
def test_transform_mxnet_tags(sagemaker_session, mxnet_full_version):
data_path = os.path.join(DATA_DIR, 'mxnet_mnist')
script_path = os.path.join(data_path, 'mnist.py')
tags = [{'Key': 'some-tag', 'Value': 'value-for-tag'}]
mx = MXNet(entry_point=script_path,
role='SageMakerRole',
train_instance_count=1,
train_instance_type='ml.c4.xlarge',
sagemaker_session=sagemaker_session,
framework_version=mxnet_full_version)
train_input = mx.sagemaker_session.upload_data(
path=os.path.join(data_path, 'train'),
key_prefix='integ-test-data/mxnet_mnist/train')
test_input = mx.sagemaker_session.upload_data(
path=os.path.join(data_path, 'test'),
key_prefix='integ-test-data/mxnet_mnist/test')
job_name = unique_name_from_base('test-mxnet-transform')
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
mx.fit({'train': train_input, 'test': test_input}, job_name=job_name)
transform_input_path = os.path.join(data_path, 'transform', 'data.csv')
transform_input_key_prefix = 'integ-test-data/mxnet_mnist/transform'
transform_input = mx.sagemaker_session.upload_data(
path=transform_input_path, key_prefix=transform_input_key_prefix)
transformer = mx.transformer(1, 'ml.m4.xlarge', tags=tags)
transformer.transform(transform_input, content_type='text/csv')
with timeout_and_delete_model_with_transformer(
transformer,
sagemaker_session,
minutes=TRANSFORM_DEFAULT_TIMEOUT_MINUTES):
transformer.wait()
model_desc = sagemaker_session.sagemaker_client.describe_model(
ModelName=transformer.model_name)
model_tags = sagemaker_session.sagemaker_client.list_tags(
ResourceArn=model_desc['ModelArn'])['Tags']
assert tags == model_tags
