# 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.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]