feat: Evaluator handshake to trainer #5420
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rsareddy0329 merged 3 commits intoaws:masterfrom Dec 17, 2025
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Can we also make type info available here (instead of Any) for all available options?
| SAGEMAKER_REGION env var or default region. | ||
| sagemaker_session (Optional[Any]): SageMaker session object. If not provided, a default | ||
| session will be created automatically. | ||
| model (Union[str, Any]): Model for evaluation. Can be: |
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Need to update the documentation, on additional values allowed
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| try: | ||
| # Handle BaseTrainer type | ||
| if hasattr(v, '__class__') and v.__class__.__name__ == 'BaseTrainer' or hasattr(v, '_latest_training_job'): |
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With the conditioning of v.__class__.__name__ == 'BaseTrainer' will I able to supply SFTTrainer?
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Previous jobs were submitted fine, so might have worked.
I moved the code to model_resolution.py to keep it central in this method _resolve_base_model and updated it to isinstance to be sure.
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mufaddal-rohawala
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Dec 17, 2025
jam-jee
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| benchmark: _Benchmark | ||
| dataset: Union[str, Any] # Required field, must come before optional fields | ||
| subtasks: Optional[Union[str, List[str]]] = None | ||
| evaluate_base_model: bool = True |
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Lets make evaluate_base_model false for all evaluations.
jam-jee
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Dec 17, 2025
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arn:aws:sagemaker:us-west-2:729646638167:training-job/pipelines-ayzeo4u9l04n-EvaluateCustomModel-XO0F6U2FMJarn:aws:sagemaker:us-west-2:729646638167:training-job/pipelines-1ufg9wznju1o-EvaluateCustomModel-o2gwjaKdc2arn:aws:sagemaker:us-west-2:729646638167:training-job/CustomInference-wgvcp7tvxhex-cEW1geWEAABy submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.