mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-27 23:47:46 +00:00
Tim
46dec7b624
* custom data * fix: simplify competition check and log local description file * no sample data * feat: add test evaluation module with error handling support * fix: update eval path to use eval_sub_dir and add valid_check TODO * refactor: add MLETestEval check to conditionally run grading steps * avoid blank stdout * valid in testeval * rename test.csv to avoid conflict * Support Disabling sample submission * refactoring * fix: remove DS_KAGGLE_DATA and update prompt instructions * add try for grade * ignore submission * fix: remove tee from eval command and warn about pipeline exit code detection * optional to use raw description * support old data * add execution result to stdout * add metric to raw description * custom data explain * add debug_path * rst update --------- Co-authored-by: Young <afe.young@gmail.com>
72 lines
2.2 KiB
Python
72 lines
2.2 KiB
Python
from typing import Literal
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from rdagent.app.data_science.conf import DS_RD_SETTING
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from rdagent.components.coder.CoSTEER.config import CoSTEERSettings
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from rdagent.utils.env import (
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CondaConf,
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DockerEnv,
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DSDockerConf,
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Env,
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LocalEnv,
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MLEBDockerConf,
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MLECondaConf,
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)
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class DSCoderCoSTEERSettings(CoSTEERSettings):
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"""Data Science CoSTEER settings"""
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class Config:
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env_prefix = "DS_Coder_CoSTEER_"
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max_seconds: int = 2400
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env_type: str = "docker"
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# TODO: extract a function for env and conf.
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def get_ds_env(
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conf_type: Literal["kaggle", "mlebench"] = "kaggle",
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extra_volumes: dict = {},
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running_timeout_period: int = (
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DS_RD_SETTING.debug_timeout if DS_RD_SETTING.sample_data else DS_RD_SETTING.full_timeout
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),
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) -> Env:
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"""
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Retrieve the appropriate environment configuration based on the env_type setting.
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Returns:
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Env: An instance of the environment configured either as DockerEnv or LocalEnv.
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Raises:
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ValueError: If the env_type is not recognized.
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"""
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conf = DSCoderCoSTEERSettings()
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assert conf_type in ["kaggle", "mlebench"], f"Unknown conf_type: {conf_type}"
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if conf.env_type == "docker":
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env_conf = DSDockerConf() if conf_type == "kaggle" else MLEBDockerConf()
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env = DockerEnv(conf=env_conf)
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elif conf.env_type == "conda":
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env = LocalEnv(
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conf=(
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CondaConf(conda_env_name=conf_type) if conf_type == "kaggle" else MLECondaConf(conda_env_name=conf_type)
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)
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)
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else:
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raise ValueError(f"Unknown env type: {conf.env_type}")
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env.conf.extra_volumes = extra_volumes
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env.conf.running_timeout_period = running_timeout_period
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return env
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def get_clear_ws_cmd(stage: Literal["before_training", "before_inference"] = "before_training") -> str:
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"""
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Clean the files in workspace to a specific stage
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"""
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assert stage in ["before_training", "before_inference"], f"Unknown stage: {stage}"
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if DS_RD_SETTING.enable_model_dump and stage == "before_training":
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cmd = "rm -r submission.csv scores.csv models"
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else:
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cmd = "rm submission.csv scores.csv"
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return cmd
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