/rdagent/app/finetune/llm/job

FT Job Runner

run_ft_job.sh launches multiple FT-Agent runs in parallel under one job directory. It is a convenience helper for multi-GPU machines; for a single run, prefer the command in ../README.md.

Quick Start

From the RD-Agent repository root:

# 1. Prepare the normal FT-Agent config first.
# See rdagent/app/finetune/llm/README.md for required values.
cp .env rdagent/app/finetune/llm/job/.env

# 2. Prepare task definitions.
cp rdagent/app/finetune/llm/job/tasks.json.example rdagent/app/finetune/llm/job/tasks.json

# 3. Run the job.
bash rdagent/app/finetune/llm/job/run_ft_job.sh rdagent/app/finetune/llm/job/tasks.json

The script opens one tmux window per task and writes logs under log/<job_id>/.

Requirements

• jq
• tmux
• conda
• an RD-Agent conda environment, named rdagent by default

If your RD-Agent environment has a different name, set CONDA_ENV_NAME before running the script or add it to job/.env:

CONDA_ENV_NAME=my-rdagent-env bash rdagent/app/finetune/llm/job/run_ft_job.sh

The FT training/evaluation backend is still controlled by FT_Coder_CoSTEER_env_type in job/.env. In docker mode, the job runner skips conda readiness checks for the training and OpenCompass environments. In conda mode, it waits for the llm_finetune and opencompass environments after the first task initializes them.

Task Config

tasks.json contains a list of independent runs:

{
  "tasks": [
    {
      "model": "Qwen/Qwen2.5-7B-Instruct",
      "benchmark": "aime25",
      "gpus": "0,1",
      "timeout": "12h"
    },
    {
      "model": "Qwen/Qwen2.5-7B-Instruct",
      "benchmark": "chemcotbench_mol_edit",
      "gpus": "2,3",
      "benchmark_description": "Molecule Editing - perform valid SMILES edits and return JSON: {\"output\": \"<valid SMILES>\"}."
    }
  ]
}

Field	Required	Default	Description
model	Yes	-	Hugging Face model id.
benchmark	Yes	-	Benchmark key, such as aime25 or chemcotbench_mol_edit.
gpus	No	"0"	Value for CUDA_VISIBLE_DEVICES.
timeout	No	"12h"	Wall-clock budget passed to the FT-Agent loop.
port	No	-	Optional local API port; writes OPENAI_API_BASE=http://localhost:<port> for that task.
benchmark_description	No	from scenarios.json	Task and output-format description.

If benchmark_description is omitted, the runner looks it up in scenarios.json.

Monitoring

tmux attach -t rdagent
tail -f log/<job_id>/*.log
streamlit run rdagent/app/finetune/llm/ui/app.py

In the Streamlit UI, select the generated job folder as the log source.