linting, moved env, updated contrib credit

environments/community/pytorch_optimizer_coding/wrapper.py

@@ -0,0 +1,60 @@
+from evaluator import OptimizerEvaluator
+from FOB.optimizer_benchmark_env import OptimizerBenchmarkEnv
+from modal.functions import Function
+
+
+def score_optimizer(optimizer_code: str, architecture: str):
+    """
+    Test an optimizer implementation by sending it to the Modal sandbox environment.
+
+    Args:
+        optimizer_code (str): The optimizer code to test
+
+    Returns:
+        dict: Results containing stdout, stderr, code and filename
+    """
+    send_code = Function.from_name("optimizer-test", "send_code")
+
+    response_obj = send_code.remote(optimizer_code)
+
+    print(response_obj)
+
+    evaluator = OptimizerEvaluator()
+
+    validity = evaluator.check_validity(
+        optimizer_code=optimizer_code,
+        stdout=response_obj["stdout"],
+        stderr=response_obj["stderr"],
+    )
+
+    if validity:
+        return 0
+    else:
+        # Get evaluator score
+        score = evaluator.score(
+            optimizer_code=optimizer_code, architecture=architecture
+        )
+        # Use OptimizerBenchmarkEnv to get the reward
+        env = OptimizerBenchmarkEnv()
+        env.submit_optimizer(optimizer_code, "custom_optimizer")
+        env.generate_experiment_yaml()
+        env.run_benchmark()
+        reward = env.get_reward()
+        # Return the sum of both
+        return score + reward
+
+
+if __name__ == "__main__":
+    test_code = """
+from lightning.pytorch.utilities.types import OptimizerLRScheduler
+from torch.optim import SGD
+from pytorch_fob.engine.parameter_groups import GroupedModel
+from pytorch_fob.engine.configs import OptimizerConfig
+
+def configure_optimizers(model: GroupedModel, config: OptimizerConfig) -> OptimizerLRScheduler:
+    lr = config.learning_rate
+    optimizer = SGD(model.grouped_parameters(lr=lr), lr=lr)
+    return {"optimizer": optimizer}
+"""
+    result = score_optimizer(test_code, "mnist")
+    print(result)