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,
)
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)
def test_auto_ml_input(sagemaker_session):
inputs = AutoMLInput(
inputs=DEFAULT_S3_INPUT_DATA, target_attribute_name="target", compression="Gzip"
)
auto_ml = AutoML(
role=ROLE, target_attribute_name=TARGET_ATTRIBUTE_NAME, sagemaker_session=sagemaker_session
)
auto_ml.fit(inputs)
_, args = sagemaker_session.auto_ml.call_args
assert args["input_config"] == [
{
"CompressionType": "Gzip",
"DataSource": {
"S3DataSource": {"S3DataType": "S3Prefix", "S3Uri": DEFAULT_S3_INPUT_DATA}
},
"TargetAttributeName": TARGET_ATTRIBUTE_NAME,
}
]