31 lines
1.2 KiB
Plaintext
31 lines
1.2 KiB
Plaintext
# 使用 nvidia/cuda 作为基础镜像
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FROM nvidia/cuda:12.1.0-base-ubuntu22.04
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USER root
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# 设置环境变量
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ARG version=1.9.7
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ENV name="1"
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ENV num=30
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ENV token="MUHHZYNAUSWPQAPQVEEDPWVOIQTBJBBZFAJKZURUGBAIKFAPHRUYPUMFGFEG"
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ENV url="https://mine.qubic.li/"
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# 换源为清华源并更新源，创建目录并下载解压qli-Client，更改appsettings.json
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RUN apt-get update && apt-get install -y libicu-dev wget vim && \
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rm -rf /var/lib/apt/lists/* && \
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mkdir /q && \
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wget -O /q/qli-Client-${version}-Linux-x64.tar.gz https://dl.qubic.li/downloads/qli-Client-${version}-Linux-x64.tar.gz && \
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tar -xzvf /q/qli-Client-${version}-Linux-x64.tar.gz -C /q/ && \
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rm /q/qli-Client-${version}-Linux-x64.tar.gz && \
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mv /q/qli-Client /q/trainer
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# 设置工作目录
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WORKDIR /q
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# 在容器启动时运行命令
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CMD sh -c "echo '{\"Settings\": {\"baseUrl\": \"$url\",\"amountOfThreads\": $num,\"payoutId\": \"$token\",\"accessToken\": null,\"overwrites\": {\"CUDA\": \"12\"},\"allowHwInfoCollect\": true,\"alias\": \"$name\"}}' > appsettings.json && ./trainer"
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# 构建编译和推送
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# docker build -f gpu-dockerfile -t itgpt/qubic-gpu:latest .
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# docker push itgpt/qubic-gpu:latest
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