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zhousha
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026_0010.jpg
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18
Dockerfile_kunlunxin
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Dockerfile_kunlunxin
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FROM r200-8f_xmlir-ubuntu_2004_x86_64:v0.27
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WORKDIR /workspace/
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COPY ./model_test_caltech_http_kunlunxin.py /workspace/
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COPY ./microsoft_beit_base_patch16_224_pt22k_ft22k /model
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# 安装transformers 4.46.3
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RUN /root/miniconda/envs/python38_torch201_cuda/bin/python3 -m pip install --upgrade pip
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RUN /root/miniconda/envs/python38_torch201_cuda/bin/python3 -m pip install --no-cache-dir -i https://pypi.tuna.tsinghua.edu.cn/simple transformers==4.46.3
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RUN /root/miniconda/envs/python38_torch201_cuda/bin/python3 -m pip install flask
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EXPOSE 80
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ENTRYPOINT ["/root/miniconda/envs/python38_torch201_cuda/bin/python3", "model_test_caltech_http_kunlunxin.py"]
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35
README.md
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README.md
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# enginex-r200-vc-cnn
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运行于昆仑芯 R200 算力卡的【视觉分类】引擎，基于 CNN 架构，支持 BEiT、MobileViT 等流行模型
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## QuickStart
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1、从 modelscope上下载视觉分类的模型，例如 microsoft/beit-base-patch16-224
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```python
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modelscope download --model microsoft/beit-base-patch16-224
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```
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2、使用Dockerfile生成镜像
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使用 Dockerfile 生成 镜像
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```python
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docker build -f Dockerfile_kunlunxin -t kunlunxin-my:v1 .
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```
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其中基础镜像 r200-8f_xmlir-ubuntu_2004_x86_64:v0.27 通过联系昆仑芯厂商技术支持可获取
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注意 Dockerfile 中已预先将模型 microsoft_beit_base_patch16_224_pt22k_ft22k 放在了 /model 下面
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3、启动docker
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```python
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docker run -it --rm \
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--privileged \
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-p 10086:80 \
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--device /dev/xpu0 \
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--device /dev/xpuctrl \
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--name kunlun-my-v4 \
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kunlunxin-my:v1
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```
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4、测试服务
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```python
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curl -X POST http://localhost:10086/v1/private/s782b4996 \
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> -F "image=@/home/zhoushasha/models/026_0010.jpg"
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```
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287
model_test_caltech_http_kunlunxin.py
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287
model_test_caltech_http_kunlunxin.py
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import requests
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import json
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import torch
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from PIL import Image
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from io import BytesIO
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from transformers import AutoImageProcessor, AutoModelForImageClassification
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import os
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import time
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import subprocess
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from flask import Flask, request, jsonify
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class ImageClassifier:
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def __init__(self, model_path: str, device: torch.device):
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"""初始化图像分类器，指定设备"""
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# 模型路径有效性校验
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if not os.path.exists(model_path):
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raise ValueError(f"模型路径不存在: {model_path}")
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if not os.path.isdir(model_path):
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raise ValueError(f"模型路径不是目录: {model_path}")
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# 检查模型必要文件
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required_files = ["config.json", "pytorch_model.bin"]
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missing_files = [f for f in required_files if not os.path.exists(os.path.join(model_path, f))]
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if missing_files:
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raise ValueError(f"模型路径缺少必要文件: {missing_files}")
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self.processor = AutoImageProcessor.from_pretrained(model_path)
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self.model = AutoModelForImageClassification.from_pretrained(model_path)
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# 将模型移动到指定设备
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self.model = self.model.to(device)
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self.device = device
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# 检查设备类型并打印信息
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if device.type == "cuda":
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if is_kunlunxin_gpu():
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print(f"模型是否在昆仑芯GPU上: {next(self.model.parameters()).is_cuda}")
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print("使用符号重写技术（CUDA兼容模式）")
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else:
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print(f"模型是否在NVIDIA GPU上: {next(self.model.parameters()).is_cuda}")
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else:
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print(f"模型在 {device.type.upper()} 上运行")
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# 多卡处理
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if device.type == "cuda" and torch.cuda.device_count() > 1:
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self.model = torch.nn.DataParallel(self.model)
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self.id2label = self.model.module.config.id2label if hasattr(self.model, 'module') else self.model.config.id2label
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def predict_single_image(self, image: Image.Image) -> dict:
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"""预测单张PIL图片"""
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try:
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# 预处理
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inputs = self.processor(images=image, return_tensors="pt")
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# 将输入数据移动到设备
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inputs = inputs.to(self.device)
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# 模型推理
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start_time = time.time()
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with torch.no_grad():
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ts = time.time()
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outputs = self.model(** inputs)
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print('kunlunxin T1', time.time() - ts, flush=True)
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ts = time.time()
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for i in range(1000):
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outputs = self.model(**inputs)
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print('kunlunxin T2', time.time() - ts, flush=True)
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processing_time = time.time() - start_time
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# 获取预测结果（只取置信度最高的一个）
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logits = outputs.logits
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probs = torch.nn.functional.softmax(logits, dim=1)
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top_probs, top_indices = probs.topk(1, dim=1)
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# 整理结果
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class_idx = top_indices[0, 0].item()
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confidence = top_probs[0, 0].item()
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return {
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"class_id": class_idx,
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"class_name": self.id2label[class_idx],
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"confidence": confidence,
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"device_used": str(self.device), # 修改为使用 str(device)
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"processing_time": processing_time,
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"hardware_info": get_hardware_info()
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}
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except Exception as e:
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print(f"处理图片时出错: {e}")
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return {
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"class_id": -1,
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"class_name": "error",
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"confidence": 0.0,
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"device_used": str(self.device), # 修改为使用 str(device)
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"processing_time": 0.0,
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"error": str(e),
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"hardware_info": get_hardware_info()
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}
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def is_kunlunxin_gpu():
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"""检查是否为昆仑芯GPU"""
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try:
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# 检查xpu-smi命令是否存在
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result = subprocess.run(['which', 'xpu-smi'], capture_output=True, text=True)
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if result.returncode == 0:
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return True
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# 检查PCI设备中是否有昆仑芯特征
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result = subprocess.run(['lspci'], capture_output=True, text=True)
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if 'Kunlun' in result.stdout or 'kunlun' in result.stdout or 'R200' in result.stdout:
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return True
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# 检查/dev目录下是否有xpu设备
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result = subprocess.run(['ls', '/dev/xpu*'], capture_output=True, text=True)
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if result.returncode == 0 and 'xpu' in result.stdout:
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return True
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# 检查是否有昆仑芯相关的环境变量或库加载
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if 'XPURT' in os.environ.get('LD_PRELOAD', '') or 'libxpurt' in os.environ.get('LD_PRELOAD', ''):
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return True
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except:
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pass
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return False
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def check_kunlunxin_available():
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"""检查昆仑芯GPU是否可用（通过CUDA接口和符号重写）"""
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if torch.cuda.is_available():
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try:
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# 检查设备名称
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if torch.cuda.device_count() > 0:
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device_name = torch.cuda.get_device_name(0)
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if 'R200' in device_name or '8F' in device_name:
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return True
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# 如果有符号重写的迹象，也认为是昆仑芯
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if is_kunlunxin_gpu():
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return True
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except:
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pass
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return False
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def get_hardware_info():
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"""获取硬件信息"""
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info = {
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"device_type": "昆仑芯GPU",
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"backend": "符号重写模式 (CUDA兼容)"
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}
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try:
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if torch.cuda.is_available() and torch.cuda.device_count() > 0:
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info.update({
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"device_name": torch.cuda.get_device_name(0),
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"device_count": torch.cuda.device_count(),
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"cuda_available": torch.cuda.is_available()
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})
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except:
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pass
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return info
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def get_device():
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"""获取最佳可用设备（优先昆仑芯GPU）"""
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# 首先检查昆仑芯GPU（通过符号重写）
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if check_kunlunxin_available():
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print("检测到昆仑芯GPU可用（符号重写模式）")
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try:
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device = torch.device("cuda:0")
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print(f"使用昆仑芯设备（CUDA兼容模式）: {device}")
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return device
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except Exception as e:
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print(f"设置昆仑芯设备时出错: {e}")
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return torch.device("cpu")
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# 然后检查NVIDIA GPU
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elif torch.cuda.is_available():
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print("检测到NVIDIA GPU可用")
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return torch.device("cuda:0")
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# 最后使用CPU
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else:
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print("未检测到加速设备，使用CPU")
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return torch.device("cpu")
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def setup_kunlunxin_environment():
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"""设置昆仑芯GPU环境"""
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if check_kunlunxin_available():
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print("正在设置昆仑芯GPU环境...")
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try:
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# 设置昆仑芯相关的环境变量
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os.environ['XPU_VISIBLE_DEVICES'] = '0' # 使用第一张昆仑芯卡
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print("昆仑芯GPU环境设置完成（符号重写模式）")
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except Exception as e:
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print(f"设置昆仑芯环境时出错: {e}")
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# 初始化服务
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app = Flask(__name__)
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MODEL_PATH = os.environ.get("MODEL_PATH", "/model") # 模型路径（环境变量或默认路径）
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# 设置昆仑芯环境并初始化分类器
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setup_kunlunxin_environment()
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device = get_device()
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classifier = ImageClassifier(MODEL_PATH, device)
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@app.route('/v1/private/s782b4996', methods=['POST'])
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def predict_single():
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"""接收单张图片并返回预测结果"""
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if 'image' not in request.files:
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return jsonify({
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"prediction": {
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"class_id": -1,
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"class_name": "error",
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"confidence": 0.0,
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"device_used": str(device),
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"processing_time": 0.0,
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"error": "请求中未包含图片",
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"hardware_info": get_hardware_info()
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},
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"status": "error"
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}), 400
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image_file = request.files['image']
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try:
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image = Image.open(BytesIO(image_file.read())).convert("RGB")
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# 获取预测结果
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prediction_result = classifier.predict_single_image(image)
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# 构建响应
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response = {
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"prediction": prediction_result,
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"status": "success"
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}
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return jsonify(response)
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except Exception as e:
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return jsonify({
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"prediction": {
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"class_id": -1,
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"class_name": "error",
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"confidence": 0.0,
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"device_used": str(device),
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"processing_time": 0.0,
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"error": str(e),
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"hardware_info": get_hardware_info()
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},
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"status": "error"
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}), 500
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@app.route('/health', methods=['GET'])
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def health_check():
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"""健康检查接口"""
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hardware_info = get_hardware_info()
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return jsonify({
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"status": "healthy",
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"kunlunxin_available": check_kunlunxin_available(),
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"device_used": str(device),
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"hardware_info": hardware_info,
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"model_loaded": True,
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"service": "昆仑芯GPU图像分类服务"
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}), 200
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@app.route('/device-info', methods=['GET'])
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def device_info():
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"""设备信息接口"""
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return jsonify(get_hardware_info())
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if __name__ == "__main__":
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# 打印启动信息
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print("=== 昆仑芯GPU图像分类服务启动 ===")
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print(f"模型路径: {MODEL_PATH}")
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print(f"使用设备: {device}")
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print(f"昆仑芯可用: {check_kunlunxin_available()}")
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if check_kunlunxin_available():
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print("✅ 服务将在昆仑芯GPU上运行（符号重写模式）")
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elif torch.cuda.is_available():
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print("⚠️ 服务在NVIDIA GPU上运行")
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else:
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print("⚠️ 服务在CPU上运行")
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print("服务启动完成，监听端口 80")
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app.run(host='0.0.0.0', port=80, debug=False)
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