115 lines
3.0 KiB
Markdown
115 lines
3.0 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 22.04.3 LTS
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cpu: Hygon Hygon C86-4G(x86-64)
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dcu: BW200
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driver: 6.3.13-V1.12.0a
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docker: 20.10.24
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```
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## 2. 环境准备
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### 2.1 使用 Dockerfile 构建镜像
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```bash
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wget https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/docker/china/dcu.Dockerfile
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docker build --network=host -t mineru:dcu-vllm-latest -f dcu.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 -u root --name mineru_docker \
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--network=host \
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--ipc=host \
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--shm-size=16G \
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--device=/dev/kfd \
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--device=/dev/mkfd \
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--device=/dev/dri \
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-v /opt/hyhal:/opt/hyhal \
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--group-add video \
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--cap-add=SYS_PTRACE \
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--security-opt seccomp=unconfined \
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-e MINERU_MODEL_SOURCE=local \
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-it mineru:dcu-vllm-latest \
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/bin/bash
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```
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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Hygon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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</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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</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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</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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</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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</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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</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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</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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</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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</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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</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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</tr>
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<tr>
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<td colspan="2">数据并行 (--data-parallel-size)</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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>DCU加速卡指定可用加速卡的方式与AMD GPU类似，请参考[GPU isolation techniques](https://rocm.docs.amd.com/en/docs-6.2.4/conceptual/gpu-isolation.html)
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