File size: 4,554 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 | # 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 sagemaker.predictor import Predictor
from sagemaker import Object2Vec, Object2VecModel
from sagemaker.serverless import ServerlessInferenceConfig
from sagemaker.utils import unique_name_from_base
from tests.integ import DATA_DIR, TRAINING_DEFAULT_TIMEOUT_MINUTES
from tests.integ.timeout import timeout, timeout_and_delete_endpoint_by_name
from tests.integ.record_set import prepare_record_set_from_local_files
FEATURE_NUM = None
@pytest.mark.skip(
reason="This test has always failed, but the failure was masked by a bug. "
"This test should be fixed. Details in https://github.com/aws/sagemaker-python-sdk/pull/968"
)
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_serverless_inference(sagemaker_session, cpu_instance_type):
job_name = unique_name_from_base("object2vec-serverless")
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(
serverless_inference_config=ServerlessInferenceConfig(), endpoint_name=job_name
)
assert isinstance(predictor, Predictor)
|