import json import os import subprocess import unittest from ast import literal_eval import pytest from parameterized import parameterized_class from . import is_sagemaker_available if is_sagemaker_available(): from sagemaker import Session, TrainingJobAnalytics from sagemaker.huggingface import HuggingFace @pytest.mark.skipif( literal_eval(os.getenv("TEST_SAGEMAKER", "False")) is not True, reason="Skipping test because should only be run when releasing minor transformers version", ) @pytest.mark.usefixtures("sm_env") @parameterized_class( [ { "framework": "pytorch", "script": "run_glue.py", "model_name_or_path": "distilbert-base-cased", "instance_type": "ml.g4dn.xlarge", "results": {"train_runtime": 650, "eval_accuracy": 0.6, "eval_loss": 0.9}, }, { "framework": "tensorflow", "script": "run_tf.py", "model_name_or_path": "distilbert-base-cased", "instance_type": "ml.g4dn.xlarge", "results": {"train_runtime": 600, "eval_accuracy": 0.3, "eval_loss": 0.9}, }, ] ) class SingleNodeTest(unittest.TestCase): def setUp(self): if self.framework == "pytorch": subprocess.run( f"cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py".split(), encoding="utf-8", check=True, ) assert hasattr(self, "env") def create_estimator(self, instance_count=1): # creates estimator return HuggingFace( entry_point=self.script, source_dir=self.env.test_path, role=self.env.role, image_uri=self.env.image_uri, base_job_name=f"{self.env.base_job_name}-single", instance_count=instance_count, instance_type=self.instance_type, debugger_hook_config=False, hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path}, metric_definitions=self.env.metric_definitions, py_version="py36", ) def save_results_as_csv(self, job_name): TrainingJobAnalytics(job_name).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv") def test_glue(self): # create estimator estimator = self.create_estimator() # run training estimator.fit() # result dataframe result_metrics_df = TrainingJobAnalytics(estimator.latest_training_job.name).dataframe() # extract kpis eval_accuracy = list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]) eval_loss = list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]) # get train time from SageMaker job, this includes starting, preprocessing, stopping train_runtime = ( Session().describe_training_job(estimator.latest_training_job.name).get("TrainingTimeInSeconds", 999999) ) # assert kpis assert train_runtime <= self.results["train_runtime"] assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy) assert all(t <= self.results["eval_loss"] for t in eval_loss) # dump tests result into json file to share in PR with open(f"{estimator.latest_training_job.name}.json", "w") as outfile: json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}, outfile)