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| # 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 | |
| class SageMakerTestEnvironment: | |
| framework: str | |
| role = "arn:aws:iam::558105141721:role/sagemaker_execution_role" | |
| hyperparameters = { | |
| "task_name": "mnli", | |
| "per_device_train_batch_size": 16, | |
| "per_device_eval_batch_size": 16, | |
| "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} | |
| def metric_definitions(self) -> str: | |
| if self.framework == "pytorch": | |
| return [ | |
| {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"}, | |
| {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"}, | |
| {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"}, | |
| ] | |
| else: | |
| return [ | |
| {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"}, | |
| {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"}, | |
| {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"}, | |
| ] | |
| def base_job_name(self) -> str: | |
| return f"{self.framework}-transfromers-test" | |
| def test_path(self) -> str: | |
| return f"./tests/sagemaker/scripts/{self.framework}" | |
| def image_uri(self) -> str: | |
| if self.framework == "pytorch": | |
| return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04" | |
| else: | |
| return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04" | |
| def sm_env(request): | |
| request.cls.env = SageMakerTestEnvironment(framework=request.cls.framework) | |