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#
# 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.huggingface import HuggingFace, HuggingFaceProcessor
from sagemaker.huggingface.model import HuggingFaceModel, HuggingFacePredictor
from sagemaker.utils import unique_name_from_base
from tests import integ
from tests.integ.utils import gpu_list, retry_with_instance_list
from tests.integ import DATA_DIR, TRAINING_DEFAULT_TIMEOUT_MINUTES
from tests.integ.timeout import timeout, timeout_and_delete_endpoint_by_name
ROLE = "SageMakerRole"
@pytest.mark.release
@pytest.mark.skipif(
integ.test_region() in integ.TRAINING_NO_P2_REGIONS
and integ.test_region() in integ.TRAINING_NO_P3_REGIONS,
reason="no ml.p2 or ml.p3 instances in this region",
)
@retry_with_instance_list(gpu_list(integ.test_region()))
def test_framework_processing_job_with_deps(
sagemaker_session,
huggingface_training_latest_version,
huggingface_training_pytorch_latest_version,
huggingface_pytorch_latest_training_py_version,
**kwargs,
):
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
code_path = os.path.join(DATA_DIR, "dummy_code_bundle_with_reqs")
entry_point = "main_script.py"
processor = HuggingFaceProcessor(
transformers_version=huggingface_training_latest_version,
pytorch_version=huggingface_training_pytorch_latest_version,
py_version=huggingface_pytorch_latest_training_py_version,
role=ROLE,
instance_count=1,
instance_type=kwargs["instance_type"],
sagemaker_session=sagemaker_session,
base_job_name="test-huggingface",
)
processor.run(
code=entry_point,
source_dir=code_path,
inputs=[],
wait=True,
)
@pytest.mark.release
@pytest.mark.skipif(
integ.test_region() in integ.TRAINING_NO_P2_REGIONS
and integ.test_region() in integ.TRAINING_NO_P3_REGIONS,
reason="no ml.p2 or ml.p3 instances in this region",
)
@retry_with_instance_list(gpu_list(integ.test_region()))
def test_huggingface_training(
sagemaker_session,
huggingface_training_latest_version,
huggingface_training_pytorch_latest_version,
huggingface_pytorch_latest_training_py_version,
**kwargs,
):
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
data_path = os.path.join(DATA_DIR, "huggingface")
hf = HuggingFace(
py_version=huggingface_pytorch_latest_training_py_version,
entry_point=os.path.join(data_path, "run_glue.py"),
role="SageMakerRole",
transformers_version=huggingface_training_latest_version,
pytorch_version=huggingface_training_pytorch_latest_version,
instance_count=1,
instance_type=kwargs["instance_type"],
hyperparameters={
"model_name_or_path": "distilbert-base-cased",
"task_name": "wnli",
"do_train": True,
"do_eval": True,
"max_seq_length": 128,
"fp16": True,
"per_device_train_batch_size": 128,
"output_dir": "/opt/ml/model",
},
sagemaker_session=sagemaker_session,
disable_profiler=True,
)
train_input = hf.sagemaker_session.upload_data(
path=os.path.join(data_path, "train"),
key_prefix="integ-test-data/huggingface/train",
)
hf.fit(train_input)
@pytest.mark.release
@pytest.mark.skipif(
integ.test_region() in integ.TRAINING_NO_P2_REGIONS
and integ.test_region() in integ.TRAINING_NO_P3_REGIONS,
reason="no ml.p2 or ml.p3 instances in this region",
)
@pytest.mark.skip(
reason="need to re enable it later t.corp:V609860141",
)
def test_huggingface_training_tf(
sagemaker_session,
gpu_instance_type,
huggingface_training_latest_version,
huggingface_training_tensorflow_latest_version,
huggingface_tensorflow_latest_training_py_version,
):
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
data_path = os.path.join(DATA_DIR, "huggingface")
hf = HuggingFace(
py_version=huggingface_tensorflow_latest_training_py_version,
entry_point=os.path.join(data_path, "run_tf.py"),
role=ROLE,
transformers_version=huggingface_training_latest_version,
tensorflow_version=huggingface_training_tensorflow_latest_version,
instance_count=1,
instance_type=gpu_instance_type,
hyperparameters={
"model_name_or_path": "distilbert-base-cased",
"per_device_train_batch_size": 128,
"per_device_eval_batch_size": 128,
"output_dir": "/opt/ml/model",
"overwrite_output_dir": True,
"save_steps": 5500,
},
sagemaker_session=sagemaker_session,
disable_profiler=True,
)
train_input = hf.sagemaker_session.upload_data(
path=os.path.join(data_path, "train"), key_prefix="integ-test-data/huggingface/train"
)
hf.fit(train_input)
@pytest.mark.skip(
reason="need to re enable it later",
)
def test_huggingface_inference(
sagemaker_session,
gpu_instance_type,
huggingface_inference_latest_version,
huggingface_inference_pytorch_latest_version,
huggingface_pytorch_latest_inference_py_version,
):
env = {
"HF_MODEL_ID": "philschmid/tiny-distilbert-classification",
"HF_TASK": "text-classification",
}
endpoint_name = unique_name_from_base("test-hf-inference")
model = HuggingFaceModel(
sagemaker_session=sagemaker_session,
role="SageMakerRole",
env=env,
py_version=huggingface_pytorch_latest_inference_py_version,
transformers_version=huggingface_inference_latest_version,
pytorch_version=huggingface_inference_pytorch_latest_version,
)
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
model.deploy(
instance_type=gpu_instance_type, initial_instance_count=1, endpoint_name=endpoint_name
)
predictor = HuggingFacePredictor(endpoint_name=endpoint_name)
data = {
"inputs": "Camera - You are awarded a SiPix Digital Camera!"
"call 09061221066 fromm landline. Delivery within 28 days."
}
output = predictor.predict(data)
assert "score" in output[0]
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