151 lines
4.5 KiB
Markdown
151 lines
4.5 KiB
Markdown
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## 1. 测试平台
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以下为本指南测试使用的平台信息，供参考：
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```
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os: Ubuntu 22.04
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cpu: INTEL x86_64
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gpu: C500
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driver: 2.12.13
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docker: 28.1.1
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```
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## 2. 环境准备
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>[!NOTE]
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>maca加速卡支持使用`vllm`或`lmdeploy`进行VLM模型推理加速。请根据实际需求选择安装和使用其中之一:
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### 2.1 使用metax官方镜像作为基础镜像构建vllm环境镜像
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1. 从metax官方仓库拉取基础镜像
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- 1.1 镜像获取地址：[https://developer.metax-tech.com/softnova/docker](https://developer.metax-tech.com/softnova/docker)
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- 1.2 在镜像网站选择`AI`分类，软件包类型选择`vllm`，操作系统选择`ubuntu`
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- 1.3 找到`vllm:maca.ai3.1.0.7-torch2.6-py310-ubuntu22.04-amd64`镜像，复制拉取命令并在本地终端执行
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2. 使用 Dockerfile 构建镜像 （vllm）
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```bash
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wget https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/docker/china/maca.Dockerfile
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docker build --network=host -t mineru:maca-vllm-latest -f maca.Dockerfile .
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```
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### 2.2 使用 Dockerfile 构建镜像 （lmdeploy）
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```bash
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wget https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/docker/china/maca.Dockerfile
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# 将基础镜像从 vllm 切换为 lmdeploy
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sed -i '3s/^/# /' maca.Dockerfile && sed -i '5s/^# //' maca.Dockerfile
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docker build --network=host -t mineru:maca-lmdeploy-latest -f maca.Dockerfile .
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```
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## 3. 启动 Docker 容器
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```bash
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docker run --ipc host \
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--cap-add SYS_PTRACE \
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--privileged=true \
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--device=/dev/mem \
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--device=/dev/dri \
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--device=/dev/mxcd \
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--device=/dev/infiniband \
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--group-add video \
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--network=host \
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--shm-size '100gb' \
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--ulimit memlock=-1 \
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--security-opt seccomp=unconfined \
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--security-opt apparmor=unconfined \
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--name mineru_docker \
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-v /datapool:/datapool \
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-e MINERU_MODEL_SOURCE=local \
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-e MINERU_LMDEPLOY_DEVICE=maca \
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-it mineru:maca-vllm-latest \
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/bin/bash
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```
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>[!TIP]
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> 请根据实际情况选择使用`vllm`或`lmdeploy`版本的镜像，如需使用lmdeploy，替换上述命令中的`mineru:maca-vllm-latest`为`mineru:maca-lmdeploy-latest`即可。
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执行该命令后，您将进入到Docker容器的交互式终端，您可以直接在容器内运行MinerU相关命令来使用MinerU的功能。
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您也可以直接通过替换`/bin/bash`为服务启动命令来启动MinerU服务，详细说明请参考[通过命令启动服务](https://opendatalab.github.io/MinerU/zh/usage/quick_usage/#apiwebuihttp-clientserver)。
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## 4. 注意事项
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不同环境下，MinerU对maca加速卡的支持情况如下表所示：
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<table border="1">
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<thead>
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<tr>
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<th rowspan="2" colspan="2">使用场景</th>
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<th colspan="2">容器环境</th>
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</tr>
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<tr>
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<th>vllm</th>
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<th>lmdeploy</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td rowspan="3">命令行工具(mineru)</td>
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<td>pipeline</td>
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<td>🟢</td>
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<td>🟢</td>
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</tr>
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<tr>
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<td><vlm/hybrid>-auto-engine</td>
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<td>🟢</td>
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<td>🟢</td>
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</tr>
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<tr>
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<td><vlm/hybrid>-http-client</td>
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<td>🟢</td>
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<td>🟢</td>
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</tr>
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<tr>
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<td rowspan="3">fastapi服务(mineru-api)</td>
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<td>pipeline</td>
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<td>🟢</td>
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<td>🟢</td>
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</tr>
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<tr>
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<td><vlm/hybrid>-auto-engine</td>
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<td>🟢</td>
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<td>🟢</td>
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</tr>
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<tr>
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<td><vlm/hybrid>-http-client</td>
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<td>🟢</td>
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<td>🟢</td>
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</tr>
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<tr>
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<td rowspan="3">gradio界面(mineru-gradio)</td>
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<td>pipeline</td>
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<td>🟢</td>
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<td>🟢</td>
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</tr>
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<tr>
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<td><vlm/hybrid>-auto-engine</td>
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<td>🟢</td>
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<td>🟢</td>
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</tr>
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<tr>
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<td><vlm/hybrid>-http-client</td>
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<td>🟢</td>
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<td>🟢</td>
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</tr>
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<tr>
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<td colspan="2">openai-server服务（mineru-openai-server）</td>
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<td>🟢</td>
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<td>🟢</td>
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</tr>
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<tr>
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<td colspan="2">数据并行 (--data-parallel-size/--dp)</td>
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<td>🔴</td>
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<td>🔴</td>
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</tr>
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</tbody>
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</table>
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注：
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🟢: 支持，运行较稳定，精度与Nvidia GPU基本一致
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🟡: 支持但较不稳定，在某些场景下可能出现异常，或精度存在一定差异
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🔴: 不支持，无法运行，或精度存在较大差异
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>[!TIP]
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>MACA加速卡指定可用加速卡的方式与NVIDIA GPU类似，请参考[使用指定GPU设备](https://opendatalab.github.io/MinerU/zh/usage/advanced_cli_parameters/#cuda_visible_devices)章节说明。
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