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@ -16,5 +16,5 @@ RUN pip install --upgrade pip && \
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pip install -e ./gradio-5.35.0
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# 验证安装：打印版本号
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CMD ["python", "-c", "import gradio; print('✅ Gradio version =', gradio.__version__)"]
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CMD ["python", "evalscope_ui.py"]
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@ -40,8 +40,8 @@ cd ..
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# ======== 构建 Docker 镜像 ========
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echo "🐳 开始构建 Docker 镜像..."
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docker build \
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--build-arg proxy="${PROXY_URL}" \
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--network=host \
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# --build-arg proxy="${PROXY_URL}" \
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# --network=host \
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-t "${IMAGE_NAME}" \
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-f Dockerfile . # 你应当把 Dockerfile 放在当前目录（或加路径）
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@ -49,7 +49,7 @@ docker build \
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echo "🚀 启动 Gradio 容器（后台运行）..."
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docker run -d \
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--name "${CONTAINER_NAME}" \
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--network=host \
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# --network=host \
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"${IMAGE_NAME}"
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echo "✅ Gradio 容器已启动成功，监听端口 ${PORT}"
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@ -0,0 +1,141 @@
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"""
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evalscope_ui.py
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Gradio 端到端配置面板（针对 EvalScope ≥5.35.0）
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运行方式：python evalscope_ui.py
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"""
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import json, subprocess, tempfile, os, datetime
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import gradio as gr
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# ---------- 常量 ----------
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DEFAULT_LIMIT = 5
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DEFAULT_PARALLEL = 1
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PORT = 7860 # 与 Dockerfile 的 EXPOSE 保持一致
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REPORT_DIR = "./reports" # 评测报告输出目录
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os.makedirs(REPORT_DIR, exist_ok=True)
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# ---------- 核心回调 ----------
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def run_eval(
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model, api_mode, api_url, api_key, local_device_map, # 模型 / API
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datasets, limit, gen_cfg_json, ds_cfg_json, # 数据集 & 参数
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backend, save_wandb, save_swanlab, save_gradio, # 可视化输出
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stress_parallel, stress_number, stress_stream, # 性能压测
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extra_yaml_json # 高级自定义
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):
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"""
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• 将表单参数组织成 EvalScope TaskConfig 字典
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• 写入临时 YAML
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• subprocess 调用 evalscope.run.run_task
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"""
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try:
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task_cfg = {
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"model" : model.strip(),
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"datasets": [d.strip() for d in datasets.split()] if datasets else [],
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"limit" : limit or None,
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"backend" : backend,
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}
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# ---------- Model / API 细节 ----------
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if api_mode != "local":
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task_cfg["api"] = api_mode
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if api_url: task_cfg["url"] = api_url
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if api_key: task_cfg["api_key"] = api_key
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else:
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task_cfg["model_args"] = {
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"device_map": local_device_map or "auto"
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}
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# ---------- 生成与数据集高级 JSON ----------
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if gen_cfg_json:
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task_cfg["generation_config"] = json.loads(gen_cfg_json)
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if ds_cfg_json:
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task_cfg["dataset_args"] = json.loads(ds_cfg_json)
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# ---------- 性能压测 ----------
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task_cfg["stress_test"] = {
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"parallel": stress_parallel,
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"number" : stress_number,
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"stream" : stress_stream
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}
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# ---------- 额外 YAML/JSON 直接 merge ----------
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if extra_yaml_json:
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extra_dict = json.loads(extra_yaml_json)
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task_cfg.update(extra_dict)
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# ---------- 写入临时 YAML 并执行 ----------
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ts = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
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yaml_path = os.path.join("/tmp", f"task_{ts}.yaml")
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with open(yaml_path, "w") as f:
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import yaml; yaml.safe_dump(task_cfg, f, allow_unicode=True)
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# 结果输出路径
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report_path = os.path.join(REPORT_DIR, f"report_{ts}.json")
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cmd = [
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"python", "-m", "evalscope.run",
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"--task-cfg", yaml_path,
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"--report-path", report_path
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]
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# 可选第三方可视化
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if save_wandb: cmd += ["--wandb"]
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if save_swanlab: cmd += ["--swanlab"]
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if save_gradio: cmd += ["--gradio"]
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completed = subprocess.run(cmd, capture_output=True, text=True, check=True)
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return f"✅ 评测完成！报告位于: {report_path}\n\n{completed.stdout}"
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except subprocess.CalledProcessError as e:
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return f"❌ EvalScope 执行失败\nSTDERR:\n{e.stderr}"
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except Exception as eg:
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return f"❌ 脚本内部异常: {eg}"
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# ---------- Gradio UI ----------
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with gr.Blocks(title="EvalScope 全量配置面板") as demo:
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gr.Markdown("## EvalScope 评测配置界面（Gradio@5.35.0）")
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with gr.Tab("模型与 API"):
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model = gr.Textbox(label="模型 ID / 本地路径")
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api_mode = gr.Radio(["openai", "dashscope", "local", "local_vllm"], value="local", label="API / 模式")
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api_url = gr.Textbox(label="API URL（remote 模式必填）", placeholder="http://host:port/chat/completion")
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api_key = gr.Textbox(label="API Key（可选）", type="password")
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local_device = gr.Textbox(label="device_map（local 模式）", value="auto")
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with gr.Tab("数据集与参数"):
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datasets = gr.Textbox(label="Datasets（空格分隔）", placeholder="gsm8k arc mmlu")
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limit = gr.Number(label="limit", value=DEFAULT_LIMIT, precision=0)
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gen_cfg_json = gr.JSON(label="generation_config（JSON）", value={})
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ds_cfg_json = gr.JSON(label="dataset_args（JSON）", value={})
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with gr.Tab("后端与可视化"):
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backend = gr.Dropdown(["native", "opencompass", "vlmevalkit", "ragas", "mteb"], value="native", label="Evaluation Backend")
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save_wandb = gr.Checkbox(label="推送 WandB", value=False)
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save_swanlab = gr.Checkbox(label="推送 SwanLab", value=False)
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save_gradio = gr.Checkbox(label="生成本地 Gradio 报告", value=True)
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with gr.Tab("性能压测（可选）"):
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stress_parallel = gr.Number(label="并发 parallel", value=DEFAULT_PARALLEL, precision=0)
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stress_number = gr.Number(label="请求数 number", value=1000, precision=0)
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stress_stream = gr.Checkbox(label="开启 stream", value=True)
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with gr.Tab("高级配置 YAML/JSON 合并"):
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extra_yaml_json = gr.JSON(label="额外 TaskConfig 字段", value={})
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run_btn = gr.Button("🚀 运行 EvalScope")
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output = gr.Textbox(label="控制台输出 / 错误信息", lines=15)
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run_btn.click(
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run_eval,
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inputs=[model, api_mode, api_url, api_key, local_device,
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datasets, limit, gen_cfg_json, ds_cfg_json,
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backend, save_wandb, save_swanlab, save_gradio,
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stress_parallel, stress_number, stress_stream,
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extra_yaml_json],
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outputs=output
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)
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# 启动
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if __name__ == "__main__":
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demo.launch(server_name="0.0.0.0", server_port=PORT)
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