mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-29 08:27:43 +00:00
Xu Yang
768229427d
* simplify RDAgent conf * add unified cacher(untested) * fix small bugs * fix a bug * fix a small bug in runner * use hash_key = None to skip cache * fix CI * in factor execution, ignore cache when raise exception * add file locker to avoid mp calling * fix CI * use function __module__ name as folder in cache
39 lines
1.5 KiB
Python
39 lines
1.5 KiB
Python
from rdagent.components.runner import CachedRunner
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from rdagent.core.exception import ModelEmptyError
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from rdagent.core.utils import cache_with_pickle
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from rdagent.scenarios.qlib.experiment.model_experiment import QlibModelExperiment
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class QlibModelRunner(CachedRunner[QlibModelExperiment]):
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"""
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Docker run
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Everything in a folder
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- config.yaml
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- Pytorch `model.py`
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- results in `mlflow`
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https://github.com/microsoft/qlib/blob/main/qlib/contrib/model/pytorch_nn.py
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- pt_model_uri: hard-code `model.py:Net` in the config
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- let LLM modify model.py
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"""
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@cache_with_pickle(CachedRunner.get_cache_key, CachedRunner.assign_cached_result)
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def develop(self, exp: QlibModelExperiment) -> QlibModelExperiment:
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if exp.sub_workspace_list[0].code_dict.get("model.py") is None:
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raise ModelEmptyError("model.py is empty")
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# to replace & inject code
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exp.experiment_workspace.inject_code(**{"model.py": exp.sub_workspace_list[0].code_dict["model.py"]})
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env_to_use = {"PYTHONPATH": "./"}
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if exp.sub_tasks[0].model_type == "TimeSeries":
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env_to_use.update({"dataset_cls": "TSDatasetH", "step_len": 20, "num_timesteps": 20})
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elif exp.sub_tasks[0].model_type == "Tabular":
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env_to_use.update({"dataset_cls": "DatasetH"})
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result = exp.experiment_workspace.execute(qlib_config_name="conf.yaml", run_env=env_to_use)
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exp.result = result
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return exp
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