wehub-resource-sync
53 lines
1.5 KiB
Python
53 lines
1.5 KiB
Python
from typing import Optional
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import typer
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from rdagent.app.finetune.llm.conf import FT_RD_SETTING
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from rdagent.components.coder.finetune.conf import get_ft_env
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from rdagent.utils.agent.tpl import T
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app = typer.Typer(help="Run LLM fine-tuning environment commands.")
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@app.command()
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def run(
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dataset: str,
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model: str,
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cmd: str,
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local_path: str = "./",
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mount_path: str | None = None,
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):
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"""
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Launch the LLM fine-tuning environment for a specific dataset and model, then run the
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provided command.
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Example:
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1) start the container:
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dotenv run -- python -m rdagent.app.utils.ws_ft alpaca_gpt4_zh qwen2-7b "sleep 3600" --local-path your_workspace
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2) then run the following command to enter the latest container:
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- docker exec -it `docker ps --filter 'status=running' -l --format '{{.Names}}'` bash
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Or you can attach to the container by specifying the container name (find it in the run info)
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- docker exec -it sweet_robinson bash
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Arguments:
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dataset: The dataset name for fine-tuning.
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model: The base model name for fine-tuning.
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cmd: The shell command or script entry point to execute inside
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the environment.
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"""
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# Don't set time limitation and always disable cache
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env = get_ft_env(
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running_timeout_period=None,
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enable_cache=False,
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)
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if mount_path is not None:
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env.conf.mount_path = mount_path
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env.run(entry=cmd, local_path=local_path)
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if __name__ == "__main__": # pragma: no cover
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app()
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