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Linlang
805f337fc4
* change_log_object * lint code * delete comments * change_log_object * change_log_object * fix import test error * update code * update code * fix bugs * skip mypy error * skip mypy error * skip mypy error * Start the flask server before running the demo. * achieve front and back interaction * fix github-advanced-security comments * fix github-advanced-security comments * tmp ignore * fix CI * move some logic * change format * adjust logic * log2json changes * tmp * fix * fix bug * refine log2json between 5 scenarios * fix * refine codes * fix logic * use localhost * add loop & all_duration param for old scenario startup * merge control logic * add README for server ui api * update README * reuse code in logger * add loop_n and all_duration param * fix upload * ui server now use port in setting * fix port setting * fix port setting * fix mypy check * refine logger and log storage * fix ruff error * fix CI * refine logger, loop, storage * bind one FileStorage with one logger * not truncate log storage * refine LoopBase.load(), use `checkout` instead of `output_path` and `do_truncate` * clear session folder when loading loop to run * move component info init step to ExpGen Class * Update rdagent/utils/workflow.py * move truncate_session function to LoopBase class * add checkout param for other scenarios * fix bug * move WebStorage to UI * change web_storage name * add randomname to requirements * add typer * fix requirements --------- Co-authored-by: WinstonLiyte <1957922024@qq.com> Co-authored-by: Bowen Xian <xianbowen@outlook.com> Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
74 lines
3.0 KiB
Python
74 lines
3.0 KiB
Python
from pathlib import Path
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import fire
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from rdagent.app.data_science.conf import DS_RD_SETTING
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from rdagent.core.utils import import_class
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from rdagent.log import rdagent_logger as logger
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from rdagent.scenarios.data_science.loop import DataScienceRDLoop
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def main(
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path: str | None = None,
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checkout: bool | str | Path = True,
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step_n: int | None = None,
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loop_n: int | None = None,
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competition="bms-molecular-translation",
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timeout=None,
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replace_timer=True,
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exp_gen_cls: str | None = None,
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):
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"""
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Parameters
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----------
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path :
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A path like `$LOG_PATH/__session__/1/0_propose`. This indicates that we restore the state after finishing step 0 in loop 1.
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checkout :
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Used only when a path is provided.
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Can be True, False, or a path.
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Default is True.
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- If True, the new loop will use the existing folder and clear logs for sessions after the one corresponding to the given path.
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- If False, the new loop will use the existing folder but keep the logs for sessions after the one corresponding to the given path.
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- If a path (or a str like Path) is provided, the new loop will be saved to that path, leaving the original path unchanged.
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step_n :
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Number of steps to run; if None, the process will run indefinitely until an error or KeyboardInterrupt occurs.
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loop_n :
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Number of loops to run; if None, the process will run indefinitely until an error or KeyboardInterrupt occurs.
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- If the current loop is incomplete, it will be counted as the first loop for completion.
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- If both step_n and loop_n are provided, the process will stop as soon as either condition is met.
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competition :
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Competition name.
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replace_timer :
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If a session is loaded, determines whether to replace the timer with session.timer.
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exp_gen_cls :
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When there are different stages, the exp_gen can be replaced with the new proposal.
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Auto R&D Evolving loop for models in a Kaggle scenario.
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You can continue running a session by using the command:
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.. code-block:: bash
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dotenv run -- python rdagent/app/data_science/loop.py [--competition titanic] $LOG_PATH/__session__/1/0_propose --step_n 1 # `step_n` is an optional parameter
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rdagent kaggle --competition playground-series-s4e8 # This command is recommended.
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"""
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if competition is not None:
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DS_RD_SETTING.competition = competition
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if not DS_RD_SETTING.competition:
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logger.error("Please specify competition name.")
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if path is None:
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kaggle_loop = DataScienceRDLoop(DS_RD_SETTING)
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else:
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kaggle_loop: DataScienceRDLoop = DataScienceRDLoop.load(path, checkout=checkout, replace_timer=replace_timer)
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# replace exp_gen if we have new class
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if exp_gen_cls is not None:
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kaggle_loop.exp_gen = import_class(exp_gen_cls)(kaggle_loop.exp_gen.scen)
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kaggle_loop.run(step_n=step_n, loop_n=loop_n, all_duration=timeout)
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if __name__ == "__main__":
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fire.Fire(main)
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