mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-28 16:07:46 +00:00
you-n-g
20778a9b87
* use conda to run kaggle and mlebench code * refactor: Simplify environment configuration and execution logic * add setting to use local env in ds * refine dockerfile * fix: Move MLEBDockerEnv initialization inside conditionals & fix condaenv * refactor: reformat code for better readability and consistency * feat: add conda env to all envs. * fix: fix bugs when run loop * refactor: Simplify DockerEnv configuration in mle_summary.py * fix image bug * style: reformat code for better readability and consistency * change commit * feat: Add entrypoint script for sing_docker scenario in rdagent * refactor: add Any type hints and comments for clarity in env.py * feat: Create log directory if it doesn't exist in entrypoint script * feat: Add debug mode and list root directory in entrypoint script * fix: Remove specific branch checkout in Dockerfile for RD-Agent * fix: Add competition argument to loop.py script execution * fix: Correct directory navigation and dependency installation in entrypoint.sh * fix: Correct user ownership assignment in entrypoint script * refactor: Comment out redundant log copying to RD_OUTPUT_DIR * fix: Unset LOG_TRACE_PATH to prevent log contamination in entrypoint.sh --------- Co-authored-by: Xu Yang <peteryang@vip.qq.com>
37 lines
1.3 KiB
Python
37 lines
1.3 KiB
Python
from typing import Dict, Optional
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from rdagent.components.coder.CoSTEER.task import CoSTEERTask
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# Because we use isinstance to distinguish between different types of tasks, we need to use sub classes to represent different types of tasks
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class ModelTask(CoSTEERTask):
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def __init__(
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self,
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name: str,
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description: str,
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architecture: str = "",
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*args,
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hyperparameters: Dict[str, str] = {},
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model_type: Optional[str] = None,
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**kwargs,
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) -> None:
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self.architecture: str = architecture
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self.hyperparameters: str = hyperparameters
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self.model_type: str | None = (
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model_type # Tabular for tabular model, TimesSeries for time series model, Graph for graph model, XGBoost for XGBoost model
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# TODO: More Models Supported
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)
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super().__init__(name=name, description=description, *args, **kwargs)
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def get_task_information(self):
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task_desc = f"""name: {self.name}
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description: {self.description}
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"""
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if self.architecture:
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task_desc += f"architecture: {self.architecture}\n"
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if self.hyperparameters:
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task_desc += f"hyperparameters: {self.hyperparameters}\n"
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if self.model_type:
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task_desc += f"model_type: {self.model_type}\n"
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return task_desc
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