204 lines
5.8 KiB
Python
204 lines
5.8 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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import sys
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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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from typing import Optional
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import typer
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from typing_extensions import Annotated
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from rdagent.app.data_science.loop import main as data_science
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from rdagent.app.finetune.llm.loop import main as llm_finetune
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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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from rdagent.log.mle_summary import grade_summary as grade_summary
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app = typer.Typer()
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CheckoutOption = Annotated[bool, typer.Option("--checkout/--no-checkout", "-c/-C")]
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CheckEnvOption = Annotated[bool, typer.Option("--check-env/--no-check-env", "-e/-E")]
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CheckDockerOption = Annotated[bool, typer.Option("--check-docker/--no-check-docker", "-d/-D")]
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CheckPortsOption = Annotated[bool, typer.Option("--check-ports/--no-check-ports", "-p/-P")]
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def ui(port=19899, log_dir="", debug: bool = False, data_science: bool = 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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if data_science:
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with rpath("rdagent.log.ui", "dsapp.py") as app_path:
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cmds = ["streamlit", "run", app_path, f"--server.port={port}"]
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subprocess.run(cmds)
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return
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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 the Flask log server in real time
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"""
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from rdagent.log.server.app import main as log_server_main
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log_server_main(port=port)
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def ds_user_interact(port=19900):
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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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commands = ["streamlit", "run", "rdagent/log/ui/ds_user_interact.py", f"--server.port={port}"]
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subprocess.run(commands)
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@app.command(name="fin_factor")
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def fin_factor_cli(
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path: Optional[str] = None,
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step_n: Optional[int] = None,
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loop_n: Optional[int] = None,
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all_duration: Optional[str] = None,
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checkout: CheckoutOption = True,
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):
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fin_factor(path=path, step_n=step_n, loop_n=loop_n, all_duration=all_duration, checkout=checkout)
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@app.command(name="fin_model")
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def fin_model_cli(
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path: Optional[str] = None,
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step_n: Optional[int] = None,
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loop_n: Optional[int] = None,
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all_duration: Optional[str] = None,
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checkout: CheckoutOption = True,
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):
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fin_model(path=path, step_n=step_n, loop_n=loop_n, all_duration=all_duration, checkout=checkout)
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@app.command(name="fin_quant")
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def fin_quant_cli(
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path: Optional[str] = None,
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step_n: Optional[int] = None,
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loop_n: Optional[int] = None,
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all_duration: Optional[str] = None,
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checkout: CheckoutOption = True,
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):
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fin_quant(path=path, step_n=step_n, loop_n=loop_n, all_duration=all_duration, checkout=checkout)
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@app.command(name="fin_factor_report")
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def fin_factor_report_cli(
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report_folder: Optional[str] = None,
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path: Optional[str] = None,
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all_duration: Optional[str] = None,
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checkout: CheckoutOption = True,
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):
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fin_factor_report(report_folder=report_folder, path=path, all_duration=all_duration, checkout=checkout)
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@app.command(name="general_model")
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def general_model_cli(report_file_path: str):
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general_model(report_file_path)
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@app.command(name="data_science")
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def data_science_cli(
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path: Optional[str] = None,
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checkout: CheckoutOption = True,
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step_n: Optional[int] = None,
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loop_n: Optional[int] = None,
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timeout: Optional[str] = None,
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competition: Optional[str] = None,
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):
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data_science(
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path=path,
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checkout=checkout,
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step_n=step_n,
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loop_n=loop_n,
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timeout=timeout,
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competition=competition,
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)
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@app.command(name="llm_finetune")
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def llm_finetune_cli(
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path: Optional[str] = None,
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checkout: CheckoutOption = True,
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benchmark: Optional[str] = None,
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benchmark_description: Optional[str] = None,
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dataset: Optional[str] = None,
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base_model: Optional[str] = None,
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upper_data_size_limit: Optional[int] = None,
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step_n: Optional[int] = None,
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loop_n: Optional[int] = None,
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timeout: Optional[str] = None,
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):
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llm_finetune(
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path=path,
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checkout=checkout,
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benchmark=benchmark,
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benchmark_description=benchmark_description,
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dataset=dataset,
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base_model=base_model,
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upper_data_size_limit=upper_data_size_limit,
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step_n=step_n,
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loop_n=loop_n,
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timeout=timeout,
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)
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@app.command(name="grade_summary")
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def grade_summary_cli(log_folder: str):
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grade_summary(log_folder)
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app.command(name="ui")(ui)
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app.command(name="server_ui")(server_ui)
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@app.command(name="health_check")
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def health_check_cli(
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check_env: CheckEnvOption = True,
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check_docker: CheckDockerOption = True,
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check_ports: CheckPortsOption = True,
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):
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health_check(check_env=check_env, check_docker=check_docker, check_ports=check_ports)
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@app.command(name="collect_info")
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def collect_info_cli():
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collect_info()
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app.command(name="ds_user_interact")(ds_user_interact)
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if __name__ == "__main__":
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app()
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