mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-28 16:07:46 +00:00
Linlang
32a29a7479
* feat: parameterize cache paths with USER to avoid conflicts * guide for missing training_hyperparameters * guidance for KeyError: 'concise_reason' * fixed three bugs in the test * fix general_model task bug * fixed some bugs in the med_model scenario * delete comments * format with black * fix mypy error * fix ruff error * fix isort error * sync code * revert cache_path code * revert cache_path code * delete data mining scenario * fix factor report loop * fix LiteLLMAPIBackend log_llm_chat_content setting * refine fin factor report scenario * remove unused LogColors * fix UI * remove medical scenario docs * change **kaggle** to **data_science** * remove default dataset_path in create_debug_data * remove KAGGLE_SETTINGS in kaggle_crawler * limit litellm versions * reformat with black * change README * fix_data_science_docs * make hypothesis observations string * Hiding old versions of kaggle docs * hidding kaggle agent docs --------- Co-authored-by: Young <afe.young@gmail.com> Co-authored-by: Bowen Xian <xianbowen@outlook.com> Co-authored-by: yuanteli <1957922024@qq.com>
69 lines
2.0 KiB
Python
69 lines
2.0 KiB
Python
"""
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CLI entrance for all rdagent application.
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This will
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- make rdagent a nice entry and
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- autoamtically load dotenv
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"""
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from dotenv import load_dotenv
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load_dotenv(".env")
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# 1) Make sure it is at the beginning of the script so that it will load dotenv before initializing BaseSettings.
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# 2) The ".env" argument is necessary to make sure it loads `.env` from the current directory.
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import subprocess
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from importlib.resources import path as rpath
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import fire
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from rdagent.app.data_science.loop import main as data_science
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from rdagent.app.general_model.general_model import (
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extract_models_and_implement as general_model,
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)
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from rdagent.app.qlib_rd_loop.factor import main as fin_factor
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from rdagent.app.qlib_rd_loop.factor_from_report import main as fin_factor_report
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from rdagent.app.qlib_rd_loop.model import main as fin_model
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from rdagent.app.qlib_rd_loop.quant import main as fin_quant
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from rdagent.app.utils.health_check import health_check
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from rdagent.app.utils.info import collect_info
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def ui(port=19899, log_dir="", debug=False):
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"""
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start web app to show the log traces.
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"""
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with rpath("rdagent.log.ui", "app.py") as app_path:
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cmds = ["streamlit", "run", app_path, f"--server.port={port}"]
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if log_dir or debug:
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cmds.append("--")
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if log_dir:
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cmds.append(f"--log_dir={log_dir}")
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if debug:
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cmds.append("--debug")
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subprocess.run(cmds)
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def server_ui(port=19899):
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"""
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start web app to show the log traces in real time
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"""
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subprocess.run(["python", "rdagent/log/server/app.py", f"--port={port}"])
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def app():
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fire.Fire(
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{
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"fin_factor": fin_factor,
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"fin_factor_report": fin_factor_report,
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"fin_model": fin_model,
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"fin_quant": fin_quant,
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"general_model": general_model,
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"ui": ui,
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"health_check": health_check,
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"collect_info": collect_info,
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"data_science": data_science,
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"server_ui": server_ui,
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}
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)
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