init

transformers/tests/sagemaker/conftest.py

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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
+
+
+@dataclass
+class SageMakerTestEnvironment:
+    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}
+
+    @property
+    def metric_definitions(self) -> str:
+        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*(.*?)$"},
+        ]
+
+    @property
+    def base_job_name(self) -> str:
+        return "pytorch-transformers-test"
+
+    @property
+    def test_path(self) -> str:
+        return "./tests/sagemaker/scripts/pytorch"
+
+    @property
+    def image_uri(self) -> str:
+        return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
+
+
+@pytest.fixture(scope="class")
+def sm_env(request):
+    request.cls.env = SageMakerTestEnvironment()