def test_object2vec(sagemaker_session, cpu_instance_type):
job_name = unique_name_from_base("object2vec")
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
data_path = os.path.join(DATA_DIR, "object2vec")
data_filename = "train.jsonl"
with open(os.path.join(data_path, data_filename), "r") as f:
num_records = len(f.readlines())
object2vec = Object2Vec(
role="SageMakerRole",
instance_count=1,
instance_type=cpu_instance_type,
epochs=3,
enc0_max_seq_len=20,
enc0_vocab_size=45000,
enc_dim=16,
num_classes=3,
negative_sampling_rate=0,
comparator_list="hadamard,concat,abs_diff",
tied_token_embedding_weight=False,
token_embedding_storage_type="dense",
sagemaker_session=sagemaker_session,
)
record_set = prepare_record_set_from_local_files(
data_path, object2vec.data_location, num_records, FEATURE_NUM,
sagemaker_session)
object2vec.fit(records=record_set, job_name=job_name)
with timeout_and_delete_endpoint_by_name(job_name, sagemaker_session):
model = Object2VecModel(object2vec.model_data,
role="SageMakerRole",
sagemaker_session=sagemaker_session)
predictor = model.deploy(1, cpu_instance_type, endpoint_name=job_name)
assert isinstance(predictor, Predictor)
predict_input = {"instances": [{"in0": [354, 623], "in1": [16]}]}
result = predictor.predict(predict_input)
assert len(result) == 1
for record in result:
assert record.label["scores"] is not None
def test_object2vec(sagemaker_session):
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
data_path = os.path.join(DATA_DIR, 'object2vec')
data_filename = 'train.jsonl'
with open(os.path.join(data_path, data_filename), 'r') as f:
num_records = len(f.readlines())
object2vec = Object2Vec(role='SageMakerRole',
train_instance_count=1,
train_instance_type='ml.c4.xlarge',
epochs=3,
enc0_max_seq_len=20,
enc0_vocab_size=45000,
enc_dim=16,
num_classes=3,
sagemaker_session=sagemaker_session,
base_job_name='test-object2vec')
record_set = prepare_record_set_from_local_files(
data_path, object2vec.data_location, num_records, FEATURE_NUM,
sagemaker_session)
object2vec.fit(record_set, None)
endpoint_name = unique_name_from_base('object2vec')
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
model = Object2VecModel(object2vec.model_data,
role='SageMakerRole',
sagemaker_session=sagemaker_session)
predictor = model.deploy(1,
'ml.c4.xlarge',
endpoint_name=endpoint_name)
assert isinstance(predictor, RealTimePredictor)
predict_input = {'instances': [{"in0": [354, 623], "in1": [16]}]}
result = predictor.predict(predict_input)
assert len(result) == 1
for record in result:
assert record.label["scores"] is not None