# we define a fixture function below and it will be "used" by # referencing its name from tests import os import pytest from attr import dataclass os.environ["AWS_DEFAULT_REGION"] = "us-east-1" # defaults region @dataclass class SageMakerTestEnvironment: framework: str role = "arn:aws:iam::558105141721:role/sagemaker_execution_role" hyperparameters = { "task_name": "mnli", "per_device_train_batch_size": 32, "per_device_eval_batch_size": 32, "do_train": True, "do_eval": True, "do_predict": True, "output_dir": "/opt/ml/model", "overwrite_output_dir": True, "max_steps": 500, "save_steps": 5500, } distributed_hyperparameters = {**hyperparameters, "max_steps": 1000} @property def metric_definitions(self) -> str: if self.framework == "pytorch": return [ {"Name": "train_runtime", "Regex": "train_runtime.*=\D*(.*?)$"}, {"Name": "eval_accuracy", "Regex": "eval_accuracy.*=\D*(.*?)$"}, {"Name": "eval_loss", "Regex": "eval_loss.*=\D*(.*?)$"}, ] else: return [ {"Name": "train_runtime", "Regex": "train_runtime.*=\D*(.*?)$"}, {"Name": "eval_accuracy", "Regex": "loss.*=\D*(.*?)]?$"}, {"Name": "eval_loss", "Regex": "sparse_categorical_accuracy.*=\D*(.*?)]?$"}, ] @property def base_job_name(self) -> str: return f"{self.framework}-transfromers-test" @property def test_path(self) -> str: return f"./tests/sagemaker/scripts/{self.framework}" @property def image_uri(self) -> str: if self.framework == "pytorch": return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.6.0-transformers4.4.2-gpu-py36-cu110-ubuntu18.04" else: return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.4.2-gpu-py37-cu110-ubuntu18.04" @pytest.fixture(scope="class") def sm_env(request): request.cls.env = SageMakerTestEnvironment(framework=request.cls.framework)