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