仅使用GPU时用253的容器

backend/Dockerfile

@@ -1,32 +1,19 @@
-# 使用本地已有的沐曦 maca + PyTorch 2.8 + Python 3.10 镜像
-# 驱动版本 maca 3.5.3.502，PyTorch 2.8，兼容性好
-FROM cr.metax-tech.com/public-ai-release/maca/vllm-metax:0.19.0-maca.ai3.5.3.502-torch2.8-py310-ubuntu22.04-amd64
+# 主节点（151）后端 — 轻量级 Python 镜像，不含 GPU 依赖
+# 仅负责 API/DB/WebSocket/SSH 调度，实际训练/推理在 253 算力节点执行
+FROM python:3.10-slim
 
 WORKDIR /app
 
-# 设置 conda Python 路径（镜像使用 /opt/conda）
-ENV PATH="/opt/conda/bin:$PATH"
-
 RUN apt-get update && apt-get install -y git openssh-client sshpass && rm -rf /var/lib/apt/lists/*
 
-# 升级 pip
-RUN /opt/conda/bin/pip install --no-cache-dir --upgrade pip
-
-# 复制依赖文件并安装（跳过 torch，镜像已自带）
 COPY requirements.txt .
-RUN /opt/conda/bin/pip install --no-cache-dir -r requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
 
-# 复制应用代码
 COPY . .
 
-# 沐曦 maca 环境变量（镜像中通常已设置，这里显式声明）
-ENV MACA_PATH=/opt/maca
-ENV LD_LIBRARY_PATH=/opt/maca/lib:/opt/maca/mxgpu_llvm/lib:/opt/maca/ompi/lib:${LD_LIBRARY_PATH}
-ENV MACA_CLANG_PATH=/opt/maca/mxgpu_llvm/bin
-
 EXPOSE 8010
 
 HEALTHCHECK --interval=30s --timeout=10s --start-period=60s --retries=3 \
-    CMD /opt/conda/bin/python -c "import urllib.request; urllib.request.urlopen('http://localhost:8010/health')" || exit 1
+    CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:8010/health')" || exit 1
 
 CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8010"]

backend/app/config.py

@@ -95,6 +95,7 @@ class Settings(BaseSettings):
     compute_node_ssh_user: str = "root"
     compute_node_ssh_password: str = ""  # SSH 密码（与密钥二选一）
     compute_node_ssh_key: str = ""  # SSH 私钥路径
+    compute_node_docker_container: str = "finetune-trainer"  # 算力节点上的训练容器名
     compute_node_python: str = "/opt/conda/bin/python"
     compute_node_workdir: str = "/root/Fine-tuning/backend"
     compute_node_remote_data_dir: str = "/root/Fine-tuning/backend/data"

backend/app/core/job_queue.py

@@ -1,4 +1,5 @@
 import asyncio
+import json
 from datetime import datetime, timezone
 from enum import Enum
 from typing import Any, Callable, Coroutine, Optional
@@ -193,17 +194,15 @@ class JobQueue:
                 self.update_job(job_id, status=JobStatus.TRAINING)
                 await self._notify_callbacks()
 
-                from app.core.remote_executor import run_training_remote
-                success = run_training_remote(job_id, model_id, model_type, dataset_id, config)
+                from app.core.remote_executor import run_training_remote, is_process_running
+                pid = run_training_remote(job_id, model_id, model_type, dataset_id, config)
 
-                if not success:
+                if not pid:
                     raise RuntimeError("Failed to launch remote training")
 
-                # 远程模式下，训练完成后通过日志解析或轮询获取 adapter_path
-                # 这里暂时标记为完成，adapter_path 由远程脚本回写
-                self.update_job(job_id, status=JobStatus.COMPLETED,
-                                adapter_path=str(settings.adapters_dir / job_id))
-                await self._notify_callbacks()
+                # 轮询共享日志文件解析进度
+                await self._poll_remote_progress(job_id, pid)
+
                 logger.info(f"Remote training launched for job {job_id}")
             else:
                 # 本地训练模式
@@ -276,6 +275,119 @@ class JobQueue:
             from app.engines.text_engine import text_engine
             return text_engine
 
+    async def _poll_remote_progress(self, job_id: str, pid: str):
+        """轮询共享日志文件，解析远程训练进度并通过 WebSocket 推送。"""
+        from app.config import get_settings
+        from app.core.websocket import send_progress, send_epoch_done, send_completed, send_error
+
+        settings = get_settings()
+        log_file = settings.data_dir / "logs" / f"{job_id}.jsonl"
+        last_offset = 0
+        poll_interval = 5  # 每 5 秒轮询一次
+        max_polls = 8640  # 最多轮询 12 小时 (8640 * 5s)
+
+        for _ in range(max_polls):
+            if self.is_cancelled(job_id):
+                # 取消容器内的远程进程
+                from app.core.remote_executor import ssh_exec
+                from app.config import get_settings
+                _s = get_settings()
+                ssh_exec(f"docker exec {_s.compute_node_docker_container} bash -c 'kill {pid} 2>/dev/null'", timeout=10)
+                self.update_job(job_id, status=JobStatus.CANCELLED)
+                await self._notify_callbacks()
+                await send_error(job_id, "Training cancelled")
+                return
+
+            # 检查进程是否还在运行
+            from app.core.remote_executor import is_process_running
+            process_alive = is_process_running(pid)
+
+            # 读取新的日志行
+            if log_file.exists():
+                try:
+                    with open(log_file, "r", encoding="utf-8") as f:
+                        f.seek(last_offset)
+                        new_lines = f.readlines()
+                        last_offset = f.tell()
+
+                    for line in new_lines:
+                        line = line.strip()
+                        if not line:
+                            continue
+                        try:
+                            entry = json.loads(line)
+                        except json.JSONDecodeError:
+                            continue
+
+                        entry_type = entry.get("type")
+                        if entry_type == "progress":
+                            self.update_job(job_id,
+                                            epoch=entry.get("epoch", 0),
+                                            current_step=entry.get("step", 0),
+                                            total_steps=entry.get("total_steps", 0),
+                                            loss=entry.get("loss"),
+                                            progress=round(entry.get("step", 0) / max(entry.get("total_steps", 1), 1) * 100, 1))
+                            await self._notify_callbacks()
+                            await send_progress(job_id, **{k: v for k, v in entry.items() if k != "type"})
+
+                        elif entry_type == "epoch_begin":
+                            self.update_job(job_id, current_epoch=entry.get("epoch", 0))
+                            await self._notify_callbacks()
+
+                        elif entry_type == "epoch_done":
+                            await self._notify_callbacks()
+                            await send_epoch_done(job_id, **{k: v for k, v in entry.items() if k not in ("type", "ts")})
+
+                        elif entry_type == "completed":
+                            adapter_path = entry.get("adapter_path", str(settings.adapters_dir / job_id))
+                            self.update_job(job_id,
+                                            status=JobStatus.COMPLETED,
+                                            adapter_path=adapter_path,
+                                            progress=100.0)
+                            await self._notify_callbacks()
+                            await send_completed(job_id, **{k: v for k, v in entry.items() if k not in ("type", "ts")})
+                            return
+
+                        elif entry_type == "error":
+                            self.update_job(job_id,
+                                            status=JobStatus.FAILED,
+                                            error_message=entry.get("message", "Unknown error"))
+                            await self._notify_callbacks()
+                            await send_error(job_id, entry.get("message", "Unknown error"))
+                            return
+                except Exception as e:
+                    logger.warning(f"Error reading remote log file: {e}")
+
+            # 进程已退出但日志里没有 completed/error，可能异常退出
+            if not process_alive:
+                # 再等一轮确认
+                await asyncio.sleep(2)
+                if not is_process_running(pid):
+                    # 检查日志里是否有最终状态
+                    if log_file.exists():
+                        try:
+                            with open(log_file, "r", encoding="utf-8") as f:
+                                content = f.read()
+                                if "completed" in content or "error" in content:
+                                    # 上面已经处理过了
+                                    continue
+                        except Exception:
+                            pass
+                    # 进程退出但没有最终状态，视为失败
+                    self.update_job(job_id,
+                                    status=JobStatus.FAILED,
+                                    error_message=f"Remote process exited unexpectedly (pid={pid})")
+                    await self._notify_callbacks()
+                    await send_error(job_id, f"Remote process exited unexpectedly (pid={pid})")
+                    return
+
+            await asyncio.sleep(poll_interval)
+
+        # 超时
+        self.update_job(job_id, status=JobStatus.FAILED, error_message="Remote training timed out")
+        await self._notify_callbacks()
+        await send_error(job_id, "Remote training timed out")
+
     @property
     def jobs(self) -> dict[str, TrainingJob]:
         return dict(self._jobs)

backend/app/core/remote_executor.py

@@ -56,34 +56,40 @@ def run_training_remote(
     model_type: str,
     dataset_id: str,
     config: dict[str, Any],
-) -> bool:
-    """在算力节点启动训练任务（后台执行，不阻塞）。
+) -> str | None:
+    """在算力节点启动训练任务（通过 docker exec，后台执行）。
 
-    使用 nohup + & 让训练在后台运行，通过 WebSocket 回传进度。
+    在容器内用 nohup 启动训练，返回 PID 以便后续检测。
     """
     config_json = json.dumps(config, ensure_ascii=False)
-    # 转义双引号避免 shell 解析问题
     config_escaped = config_json.replace('"', '\\"')
 
-    log_path = os.path.join(settings.compute_node_workdir, f"logs/{job_id}.log")
-    log_dir = os.path.dirname(log_path)
-
     remote_cmd = (
-        f"mkdir -p {log_dir} && "
-        f"cd {settings.compute_node_workdir} && "
-        f"nohup {settings.compute_node_python} -m app.engines.remote_train "
+        f"docker exec {settings.compute_node_docker_container} "
+        f"bash -c 'nohup {settings.compute_node_python} -m app.engines.remote_train "
         f"'{job_id}' '{model_id}' '{model_type}' '{dataset_id}' '{config_escaped}' "
-        f"> {log_path} 2>&1 & echo $!"
+        f">/tmp/train_{job_id}.log 2>&1 & echo $!'"
     )
 
     code, stdout, stderr = ssh_exec(remote_cmd, timeout=30)
 
     if code != 0:
         logger.error(f"Remote training launch failed: {stderr}")
-        return False
+        return None
+
+    pid = stdout.strip()
+    logger.info(f"Remote training launched in container: job={job_id}, container_pid={pid}")
+    return pid
+
 
-    logger.info(f"Remote training launched: job={job_id}, pid={stdout.strip()}")
-    return True
+def is_process_running(pid: str) -> bool:
+    """检查远程训练进程是否还在运行。
+
+    通过 docker exec 进入容器检查 PID 是否存在。
+    """
+    cmd = f"docker exec {settings.compute_node_docker_container} bash -c 'kill -0 {pid} 2>/dev/null && echo running || echo stopped'"
+    code, stdout, stderr = ssh_exec(cmd, timeout=10)
+    return code == 0 and "running" in stdout
 
 
 def run_inference_remote(
@@ -100,7 +106,7 @@ def run_inference_remote(
     safe_prompt = prompt.replace('"', '\\"').replace("'", "\\'").replace("\n", "\\n")
 
     remote_cmd = (
-        f"cd {settings.compute_node_workdir} && "
+        f"docker exec {settings.compute_node_docker_container} "
         f"{settings.compute_node_python} -c \""
         "import asyncio, json; "
         "from app.config import get_settings; "

backend/app/engines/multimodal_engine.py

@@ -73,6 +73,7 @@ class MultimodalEngine(BaseEngine):
         dataset_path: str,
         peft_config: Any,
         training_args: dict[str, Any],
+        callbacks: list | None = None,
     ) -> str:
         from peft import get_peft_model
         from transformers import Trainer, TrainingArguments
@@ -125,13 +126,13 @@ class MultimodalEngine(BaseEngine):
             report_to="none",
         )
 
-        callback = _ProgressCallback(job_id)
+        all_callbacks = callbacks if callbacks else [_ProgressCallback(job_id)]
         trainer = Trainer(
             model=self._model,
             args=tr_args,
             train_dataset=hf_dataset,
             data_collator=collate_fn,
-            callbacks=[callback],
+            callbacks=all_callbacks,
         )
 
         try:

backend/app/engines/remote_train.py

@@ -4,12 +4,73 @@ import json
 import os
 import sys
 import signal
+import time
+import traceback
+from datetime import datetime, timezone
 from pathlib import Path
 
 # 禁用 FlashAttention
 os.environ["PYTORCH_NO_FLASH"] = "1"
 os.environ["FLASH_ATTENTION_ENABLED"] = "0"
 
+_progress_log_file = None
+
+
+def _init_log_file(data_dir: Path, job_id: str):
+    """初始化进度日志文件（通过 SSHFS 共享给主节点读取）。"""
+    global _progress_log_file
+    log_dir = data_dir / "logs"
+    log_dir.mkdir(parents=True, exist_ok=True)
+    _progress_log_file = log_dir / f"{job_id}.jsonl"
+    _write_log(type="start", job_id=job_id)
+
+
+def _write_log(**kwargs):
+    """追加一行 JSON 到共享日志文件。"""
+    if _progress_log_file:
+        entry = {"ts": datetime.now(timezone.utc).isoformat(), **kwargs}
+        with open(_progress_log_file, "a", encoding="utf-8") as f:
+            f.write(json.dumps(entry, ensure_ascii=False) + "\n")
+            f.flush()
+
+
+class FileProgressCallback:
+    """HuggingFace Trainer 回调 — 写进度到共享日志文件。"""
+
+    def __init__(self, job_id: str):
+        self.job_id = job_id
+
+    def on_log(self, args, state, control, logs=None, **kwargs):
+        if logs and "loss" in logs:
+            _write_log(type="progress", epoch=int(state.epoch or 0),
+                       step=state.global_step, total_steps=state.max_steps or 0,
+                       loss=round(logs["loss"], 4),
+                       learning_rate=round(logs.get("learning_rate", 0), 8))
+
+    def on_epoch_begin(self, args, state, control, **kwargs):
+        _write_log(type="epoch_begin", epoch=int(state.epoch or 0))
+
+    def on_epoch_end(self, args, state, control, metrics=None, **kwargs):
+        _write_log(type="epoch_done", epoch=int(state.epoch or 0),
+                   eval_loss=metrics.get("eval_loss") if metrics and hasattr(metrics, "get") else None,
+                   eval_accuracy=metrics.get("eval_accuracy") if metrics and hasattr(metrics, "get") else None)
+
+    def on_train_end(self, args, state, control, **kwargs):
+        _write_log(type="completed", total_time_seconds=getattr(state, "train_runtime", 0),
+                   adapter_path=args.output_dir)
+
+    def on_train_begin(self, args, state, control, **kwargs):
+        _write_log(type="status", status="training")
+
+    def on_save(self, args, state, control, **kwargs):
+        _write_log(type="save", step=state.global_step)
+
+    def on_evaluate(self, args, state, control, metrics=None, **kwargs):
+        if metrics:
+            _write_log(type="evaluate", epoch=int(state.epoch or 0),
+                       eval_loss=metrics.get("eval_loss"),
+                       eval_accuracy=metrics.get("eval_accuracy"))
+
 
 async def run_training(job_id: str, model_id: str, model_type: str, dataset_id: str, config: dict):
     """执行单个训练任务（远程调用入口）。"""
@@ -17,66 +78,84 @@ async def run_training(job_id: str, model_id: str, model_type: str, dataset_id:
     from app.core.logging import logger
 
     settings = get_settings()
-
-    # 查找数据集
-    from app.core.db import async_session, DatasetRecord
-    from sqlalchemy import select
-
-    dataset_path = None
-    async with async_session() as session:
-        result = await session.execute(select(DatasetRecord).where(
-            (DatasetRecord.id == dataset_id) | (DatasetRecord.name == dataset_id)
-        ))
-        record = result.scalar_one_or_none()
-        if record:
-            dataset_path = record.file_path
-
-    if not dataset_path:
-        # 尝试 uploads 目录
-        upload_path = settings.uploads_dir / dataset_id
-        if upload_path.exists():
-            dataset_path = str(upload_path)
-
-    if not dataset_path:
-        raise FileNotFoundError(f"Dataset not found: {dataset_id}")
-
-    # 预处理
-    processed_path = str(settings.processed_dir / f"{job_id}_processed.jsonl")
-    task_type = config.get("task_type", "sft")
-    template = config.get("dataset_template", "alpaca")
-
-    # 选择引擎
-    if model_type == "vision":
-        from app.engines.vision_engine import vision_engine
-        engine = vision_engine
-    elif model_type == "multimodal":
-        from app.engines.multimodal_engine import multimodal_engine
-        engine = multimodal_engine
-    else:
-        from app.engines.text_engine import text_engine
-        engine = text_engine
-
-    peft_method = config.get("peft_method", "lora")
-
-    # 预处理数据集
-    await engine.preprocess_dataset(dataset_path, processed_path, task_type=task_type, template=template)
-
-    # 加载模型
-    await engine.load_model(model_id, quantization="4bit" if peft_method == "qlora" else None)
-
-    # 构建 PEFT 配置
-    peft_config = engine.get_peft_config(peft_method, config)
-
-    # 训练
-    adapter_path = await engine.train(
-        job_id=job_id,
-        dataset_path=processed_path,
-        peft_config=peft_config,
-        training_args=config,
-    )
-
-    logger.info(f"Remote training completed: {job_id} -> {adapter_path}")
-    return adapter_path
+    _init_log_file(settings.data_dir, job_id)
+
+    try:
+        # 查找数据集
+        from app.core.db import async_session, DatasetRecord
+        from sqlalchemy import select
+
+        dataset_path = None
+        async with async_session() as session:
+            result = await session.execute(select(DatasetRecord).where(
+                (DatasetRecord.id == dataset_id) | (DatasetRecord.name == dataset_id)
+            ))
+            record = result.scalar_one_or_none()
+            if record:
+                dataset_path = record.file_path
+
+        if not dataset_path:
+            upload_path = settings.uploads_dir / dataset_id
+            if upload_path.exists():
+                dataset_path = str(upload_path)
+
+        if not dataset_path:
+            raise FileNotFoundError(f"Dataset not found: {dataset_id}")
+
+        _write_log(type="status", status="preprocessing")
+
+        # 预处理
+        processed_path = str(settings.processed_dir / f"{job_id}_processed.jsonl")
+        task_type = config.get("task_type", "sft")
+        template = config.get("dataset_template", "alpaca")
+
+        # 选择引擎
+        if model_type == "vision":
+            from app.engines.vision_engine import vision_engine
+            engine = vision_engine
+        elif model_type == "multimodal":
+            from app.engines.multimodal_engine import multimodal_engine
+            engine = multimodal_engine
+        else:
+            from app.engines.text_engine import text_engine
+            engine = text_engine
+
+        peft_method = config.get("peft_method", "lora")
+
+        await engine.preprocess_dataset(dataset_path, processed_path, task_type=task_type, template=template)
+
+        _write_log(type="status", status="loading_model")
+
+        # 加载模型
+        await engine.load_model(model_id, quantization="4bit" if peft_method == "qlora" else None)
+
+        # 构建 PEFT 配置
+        peft_config = engine.get_peft_config(peft_method, config)
+
+        _write_log(type="status", status="training")
+
+        # 训练 — 传入文件日志回调替代 WebSocket 回调
+        start_time = time.time()
+        file_cb = FileProgressCallback(job_id)
+
+        adapter_path = await engine.train(
+            job_id=job_id,
+            dataset_path=processed_path,
+            peft_config=peft_config,
+            training_args=config,
+            callbacks=[file_cb],
+        )
+
+        elapsed = round(time.time() - start_time, 2)
+        _write_log(type="completed", adapter_path=str(adapter_path), total_time=elapsed)
+
+        logger.info(f"Remote training completed: {job_id} -> {adapter_path} ({elapsed}s)")
+        return adapter_path
+
+    except Exception as e:
+        _write_log(type="error", message=str(e), traceback=traceback.format_exc())
+        logger.error(f"Remote training failed: {job_id} - {e}")
+        raise
 
 
 def main():

backend/app/engines/text_engine.py

@@ -126,6 +126,7 @@ class TextEngine(BaseEngine):
         dataset_path: str,
         peft_config: Any,
         training_args: dict[str, Any],
+        callbacks: list | None = None,
     ) -> str:
         """执行训练。"""
         from peft import get_peft_model
@@ -167,7 +168,8 @@ class TextEngine(BaseEngine):
             **({"deepspeed": deepspeed_config} if deepspeed_config else {}),
         )
 
-        callback = _ProgressCallback(job_id)
+        # 本地模式用 WebSocket 回调，远程模式用传入的文件日志回调
+        all_callbacks = callbacks if callbacks else [_ProgressCallback(job_id)]
 
         if task_type == "sft":
             from transformers import Trainer
@@ -177,7 +179,7 @@ class TextEngine(BaseEngine):
                 args=tr_args,
                 train_dataset=dataset,
                 data_collator=DataCollatorForSeq2Seq(self._tokenizer),
-                callbacks=[callback],
+                callbacks=all_callbacks,
             )
         else:
             from trl import (
@@ -229,7 +231,7 @@ class TextEngine(BaseEngine):
                     args=tr_args,
                     train_dataset=dataset,
                     data_collator=DataCollatorForSeq2Seq(self._tokenizer),
-                    callbacks=[callback],
+                    callbacks=all_callbacks,
                 )
 
         try:

backend/app/engines/vision_engine.py

@@ -73,6 +73,7 @@ class VisionEngine(BaseEngine):
         dataset_path: str,
         peft_config: Any,
         training_args: dict[str, Any],
+        callbacks: list | None = None,
     ) -> str:
         from peft import get_peft_model
         from transformers import DataCollatorWithPadding, Trainer, TrainingArguments
@@ -125,13 +126,13 @@ class VisionEngine(BaseEngine):
             report_to="none",
         )
 
-        callback = _ProgressCallback(job_id)
+        all_callbacks = callbacks if callbacks else [_ProgressCallback(job_id)]
         trainer = Trainer(
             model=self._model,
             args=tr_args,
             train_dataset=hf_dataset,
             data_collator=DataCollatorWithPadding(self._processor),
-            callbacks=[callback],
+            callbacks=all_callbacks,
         )
 
         try:

docker-compose.yml

@@ -41,6 +41,7 @@ services:
       - COMPUTE_NODE_SSH_PASSWORD=ictrek
       # - COMPUTE_NODE_SSH_KEY=/root/.ssh/id_rsa  # 优先用密钥，密码为备选
       - COMPUTE_NODE_PYTHON=/opt/conda/bin/python
+      - COMPUTE_NODE_DOCKER_CONTAINER=finetune-trainer
       - COMPUTE_NODE_WORKDIR=/root/Fine-tuning/backend
       - COMPUTE_NODE_REMOTE_DATA_DIR=/root/Fine-tuning/backend/data
       - COMPUTE_NODE_REMOTE_ENV=production

result.txt

@@ -1,49 +1,19 @@
-(base) [root@localhost Fine-tuning]# docker exec finetune-backend pip install --upgrade datasets
-Looking in indexes: http://mirrors.aliyun.com/pypi/simple
-Requirement already satisfied: datasets in /opt/conda/lib/python3.10/site-packages (4.8.5)
-Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from datasets) (3.29.0)
-Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from datasets) (1.26.4)
-Requirement already satisfied: pyarrow>=21.0.0 in /opt/conda/lib/python3.10/site-packages (from datasets) (24.0.0)
-Requirement already satisfied: dill<0.4.2,>=0.3.0 in /opt/conda/lib/python3.10/site-packages (from datasets) (0.4.1)
-Requirement already satisfied: pandas in /opt/conda/lib/python3.10/site-packages (from datasets) (2.3.3)
-Requirement already satisfied: requests>=2.32.2 in /opt/conda/lib/python3.10/site-packages (from datasets) (2.32.3)
-Requirement already satisfied: httpx<1.0.0 in /opt/conda/lib/python3.10/site-packages (from datasets) (0.28.1)
-Requirement already satisfied: tqdm>=4.66.3 in /opt/conda/lib/python3.10/site-packages (from datasets) (4.67.1)
-Requirement already satisfied: xxhash in /opt/conda/lib/python3.10/site-packages (from datasets) (3.7.0)
-Requirement already satisfied: multiprocess<0.70.20 in /opt/conda/lib/python3.10/site-packages (from datasets) (0.70.19)
-Requirement already satisfied: fsspec<=2026.2.0,>=2023.1.0 in /opt/conda/lib/python3.10/site-packages (from fsspec[http]<=2026.2.0,>=2023.1.0->datasets) (2025.5.1)
-Requirement already satisfied: huggingface-hub<2.0,>=0.25.0 in /opt/conda/lib/python3.10/site-packages (from datasets) (1.14.0)
-Requirement already satisfied: packaging in /opt/conda/lib/python3.10/site-packages (from datasets) (26.2)
-Requirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.10/site-packages (from datasets) (6.0.3)
-Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /opt/conda/lib/python3.10/site-packages (from fsspec[http]<=2026.2.0,>=2023.1.0->datasets) (3.13.5)
-Requirement already satisfied: anyio in /opt/conda/lib/python3.10/site-packages (from httpx<1.0.0->datasets) (4.13.0)
-Requirement already satisfied: certifi in /opt/conda/lib/python3.10/site-packages (from httpx<1.0.0->datasets) (2026.4.22)
-Requirement already satisfied: httpcore==1.* in /opt/conda/lib/python3.10/site-packages (from httpx<1.0.0->datasets) (1.0.9)
-Requirement already satisfied: idna in /opt/conda/lib/python3.10/site-packages (from httpx<1.0.0->datasets) (3.10)
-Requirement already satisfied: h11>=0.16 in /opt/conda/lib/python3.10/site-packages (from httpcore==1.*->httpx<1.0.0->datasets) (0.16.0)
-Requirement already satisfied: hf-xet<2.0.0,>=1.4.3 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub<2.0,>=0.25.0->datasets) (1.4.3)
-Requirement already satisfied: typer>=0.20.0 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub<2.0,>=0.25.0->datasets) (0.25.0)
-Requirement already satisfied: typing-extensions>=4.1.0 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub<2.0,>=0.25.0->datasets) (4.15.0)
-Requirement already satisfied: aiohappyeyeballs>=2.5.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2026.2.0,>=2023.1.0->datasets) (2.6.1)
-Requirement already satisfied: aiosignal>=1.4.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2026.2.0,>=2023.1.0->datasets) (1.4.0)
-Requirement already satisfied: async-timeout<6.0,>=4.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2026.2.0,>=2023.1.0->datasets) (5.0.1)
-Requirement already satisfied: attrs>=17.3.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2026.2.0,>=2023.1.0->datasets) (26.1.0)
-Requirement already satisfied: frozenlist>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2026.2.0,>=2023.1.0->datasets) (1.8.0)
-Requirement already satisfied: multidict<7.0,>=4.5 in /opt/conda/lib/python3.10/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2026.2.0,>=2023.1.0->datasets) (6.7.1)
-Requirement already satisfied: propcache>=0.2.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2026.2.0,>=2023.1.0->datasets) (0.4.1)
-Requirement already satisfied: yarl<2.0,>=1.17.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2026.2.0,>=2023.1.0->datasets) (1.23.0)
-Requirement already satisfied: charset_normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests>=2.32.2->datasets) (3.4.1)
-Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests>=2.32.2->datasets) (2.3.0)
-Requirement already satisfied: click>=8.2.1 in /opt/conda/lib/python3.10/site-packages (from typer>=0.20.0->huggingface-hub<2.0,>=0.25.0->datasets) (8.2.1)
-Requirement already satisfied: shellingham>=1.3.0 in /opt/conda/lib/python3.10/site-packages (from typer>=0.20.0->huggingface-hub<2.0,>=0.25.0->datasets) (1.5.4)
-Requirement already satisfied: rich>=13.8.0 in /opt/conda/lib/python3.10/site-packages (from typer>=0.20.0->huggingface-hub<2.0,>=0.25.0->datasets) (15.0.0)
-Requirement already satisfied: annotated-doc>=0.0.2 in /opt/conda/lib/python3.10/site-packages (from typer>=0.20.0->huggingface-hub<2.0,>=0.25.0->datasets) (0.0.4)
-Requirement already satisfied: markdown-it-py>=2.2.0 in /opt/conda/lib/python3.10/site-packages (from rich>=13.8.0->typer>=0.20.0->huggingface-hub<2.0,>=0.25.0->datasets) (4.0.0)
-Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /opt/conda/lib/python3.10/site-packages (from rich>=13.8.0->typer>=0.20.0->huggingface-hub<2.0,>=0.25.0->datasets) (2.19.2)
-Requirement already satisfied: mdurl~=0.1 in /opt/conda/lib/python3.10/site-packages (from markdown-it-py>=2.2.0->rich>=13.8.0->typer>=0.20.0->huggingface-hub<2.0,>=0.25.0->datasets) (0.1.2)
-Requirement already satisfied: exceptiongroup>=1.0.2 in /opt/conda/lib/python3.10/site-packages (from anyio->httpx<1.0.0->datasets) (1.3.0)
-Requirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/lib/python3.10/site-packages (from pandas->datasets) (2.9.0.post0)
-Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas->datasets) (2026.1.post1)
-Requirement already satisfied: tzdata>=2022.7 in /opt/conda/lib/python3.10/site-packages (from pandas->datasets) (2026.2)
-Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.17.0)
-WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.
+(base) [root@localhost backend]# sshpass -p 'lq123456!' ssh -o StrictHostKeyChecking=no lq@192.168.92.151 "ls -la /home/lq/Fine-tuning/"
+total 104
+drwxrwxr-x 5 lq lq  4096 May 19 03:50 .
+drwxr-xr-x 8 lq lq  4096 May 19 08:05 ..
+drwxrwxr-x 4 lq lq  4096 May 19 03:59 backend
+-rw-rw-r-- 1 lq lq  5050 May 19 03:49 CLAUDE.md
+-rw-rw-r-- 1 lq lq  2031 May 19 03:50 DEPLOY.md
+-rw-rw-r-- 1 lq lq  1892 May 19 03:50 docker-compose.yml
+-rw-rw-r-- 1 lq lq    86 May 19 03:49 .dockerignore
+-rw-rw-r-- 1 lq lq   543 May 19 03:49 .env
+-rw-rw-r-- 1 lq lq   375 May 19 03:49 .env.example
+drwxrwxr-x 3 lq lq  4096 May 19 03:49 frontend
+drwxrwxr-x 8 lq lq  4096 May 19 03:50 .git
+-rw-rw-r-- 1 lq lq   210 May 19 03:49 .gitignore
+-rw-rw-r-- 1 lq lq     0 May 19 03:49 peft-finetune-frontend@0.1.0
+-rw-rw-r-- 1 lq lq  3485 May 19 03:49 README.md
+-rw-rw-r-- 1 lq lq  7036 May 19 03:49 result.txt
+-rw-rw-r-- 1 lq lq 15237 May 19 03:50 样本中心提供API接口文档_外部.md
+-rw-rw-r-- 1 lq lq 20943 May 19 03:50 统一认证平台接入流程及API接口文档(1)(1).md