启动ChatGPT+ChatGLM
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# How to build | 如何构建: docker build -t gpt-academic --network=host -f Dockerfile+ChatGLM .
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# How to run | 如何运行 (1) 直接运行: docker run --rm -it --net=host gpt-academic
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# How to run | 如何运行 (2) 我想运行之前进容器做一些调整: docker run --rm -it --net=host --gpu=all gpt-academic bash
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# How to run | 如何运行 (1) 直接运行: docker run --rm -it --net=host --gpus=all gpt-academic
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# How to run | 如何运行 (2) 我想运行之前进容器做一些调整: docker run --rm -it --net=host --gpus=all gpt-academic bash
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# 从NVIDIA源,从而支持显卡运损(检查宿主的nvidia-smi中的cuda版本必须>=11.3)
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FROM nvidia/cuda:11.3.1-runtime-ubuntu20.04
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@ -30,18 +30,21 @@ RUN $useProxyNetwork python3 -m pip install -r requirements.txt
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RUN $useProxyNetwork python3 -m pip install -r request_llm/requirements_chatglm.txt
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RUN $useProxyNetwork python3 -m pip install torch --extra-index-url https://download.pytorch.org/whl/cu113
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# 为chatgpt-academic配置代理和API-KEY (非必要 可选步骤)
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RUN echo ' \n\
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API_KEY = "sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" \n\
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USE_PROXY = True \n\
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proxies = { "http": "socks5h://localhost:10880", "https": "socks5h://localhost:10880", } ' >> config_private.py
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# 预热CHATGLM参数(非必要 可选步骤)
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RUN echo ' \n\
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from transformers import AutoModel, AutoTokenizer \n\
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chatglm_tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) \n\
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chatglm_model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).float() ' >> warm_up_chatglm.py
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RUN python3 -u warm_up_chatglm.py
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RUN $useProxyNetwork git pull
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# 为chatgpt-academic配置代理和API-KEY (非必要 可选步骤)
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RUN echo ' \n\
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API_KEY = "sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" \n\
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USE_PROXY = True \n\
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LLM_MODEL = "chatglm" \n\
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LOCAL_MODEL_DEVICE = "cuda" \n\
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proxies = { "http": "socks5h://localhost:10880", "https": "socks5h://localhost:10880", } ' >> config_private.py
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# 启动
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CMD ["python3", "-u", "main.py"]
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