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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) | |