CogVLM2 本地部署体验(Docker容器版)

智普AI推出新一代的 CogVLM2 系列模型，并开源了两款基于 Meta-Llama-3-8B-Instruct 开源模型。与上一代的 CogVLM 开源模型相比，CogVLM2 系列开源模型具有以下改进：

1. 在许多关键指标上有了显著提升，例如 TextVQA, DocVQA。
2. 支持 8K 文本长度。
3. 支持高达 1344 * 1344 的图像分辨率。
4. 提供支持中英文双语的开源模型版本

硬件要求（模型推理）：
INT4 ： RTX3090*1，显存16GB，内存32GB，系统盘200GB
模型微调硬件要求更高。

环境准备

源码下载

git clone https://github.com/THUDM/CogVLM2.git; 
cd CogVLM
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模型下载

手动下载模型
下载地址：https://hf-mirror.com/THUDM
git clone https://hf-mirror.com/THUDM/cogvlm2-llama3-chat-19B

Docker 容器化部署

构建镜像

• 修改源码模型地址

构建镜像先把模型的地址修改为本地模型，避免从huggingface临时下载。

修改： basic_demo/web_demo.py
:::info
MODEL_PATH = “/app/CogVLM2/models/cogvlm2-llama3-chinese-chat-19B-int4”
:::

:::info
model = AutoModelForCausalLM.from_pretrained(
~~ MODEL_PATH,~~
~~ load_in_4bit=True,~~
~~ torch_dtype=TORCH_TYPE,~~
~~ trust_remote_code=True,~~
~~ low_cpu_mem_usage=LOWUSAGE).eval()~~
:::

• Dockerfile文件编写

注意 COPY CogVLM2/ /app/CogVLM2/ 这行执行需要根据世纪CogVLM源码下载存放位置。

FROM pytorch/pytorch:2.3.0-cuda12.1-cudnn8-runtime
ARG DEBIAN_FRONTEND=noninteractive

WORKDIR /app

RUN sudo apt-get --fix-broken install 
RUN sudo apt-get install -y --no-install-recommends \
    python3-mpi4py  mpich gcc libopenmpi-dev

RUN pip config set global.index-url http://mirrors.aliyun.com/pypi/simple
RUN pip config set install.trusted-host mirrors.aliyun.com
# RUN pip install -i https://pypi.tuna.tsinghua.edu.cn/simple/ mpi4py
RUN pip install mpi4py

COPY CogVLM2/ /app/CogVLM2/
WORKDIR /app/CogVLM2

RUN pip install --use-pep517 -r basic_demo/requirements.txt
RUN pip install --use-pep517 -r finetune_demo/requirements.txt

EXPOSE 8000
CMD [ "chainlit","run","basic_demo/web_demo.py" ]

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本文采用基础镜像pytorch/pytorch:2.3.0-cuda12.1-cudnn8-runtime
系统预置了部分python 库，为避免冲突，需要注释掉源码中的部分依赖包。（torch，torchvision）

• 修改后requirements.txt文件：

xformers>=0.0.26.post1
torch>=2.3.0
torchvision>=0.18.0
transformers>=4.40.2
huggingface-hub>=0.23.0
pillow>=10.3.0
chainlit>=1.0.506
pydantic>=2.7.1
timm>=0.9.16
openai>=1.30.1
loguru>=0.7.2
pydantic>=2.7.1
einops>=0.7.0
sse-starlette>=2.1.0
bitsandbytes>=0.43.1

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• 执行构建

docker build -t qingcloudtech/cogvlm:v1.4 .
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运行

Docker webui运行

第一步：执行启动指令

docker run -it --gpus all \
  -p 8000:8000 \
  -v /u01/workspace/models/cogvlm2-llama3-chinese-chat-19B-int4:/app/CogVLM2/models/cogvlm2-llama3-chinese-chat-19B-int4 \
  -v /u01/workspace/cogvlm/images:/u01/workspace/images \
  -e MODEL_PATH=/app/CogVLM2/models/cogvlm2-llama3-chinese-chat-19B-int4 \
  -e QUANT=4 \
  qingcloudtech/cogvlm:v1.4 chainlit run basic_demo/web_demo.py
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注意提前准备好模型，并挂载好模型路径，否则可能会因为网络导致模型无法动态下载成功。

量化版启动后GPU大致16G：

+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.67                 Driver Version: 550.67         CUDA Version: 12.4     |
|-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA GeForce RTX 4090        Off |   00000000:01:00.0  On |                  Off |
|  0%   46C    P8             24W /  450W |   19266MiB /  24564MiB |      1%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
                                                                                         
+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|    0   N/A  N/A      1462      G   /usr/lib/xorg/Xorg                            231MiB |
|    0   N/A  N/A      1778      G   /usr/bin/gnome-shell                           69MiB |
|    0   N/A  N/A      8214      G   clash-verge                                     6MiB |
|    0   N/A  N/A      9739      G   ...seed-version=20240523-210831.182000        115MiB |
|    0   N/A  N/A     10687      G   ...erProcess --variations-seed-version         36MiB |
|    0   N/A  N/A    287819      C   /opt/conda/bin/python                        2986MiB |
|    0   N/A  N/A    291020      C   /opt/conda/bin/python                       15790MiB |
+-----------------------------------------------------------------------------------------+

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第一步：访问验证：127.0.0.1:8000

Q：按原文列表格式输出文本信息

Q：用列表形式描述图中的关键步骤。

openai api 方式运行

第一步：执行启动指令

docker run -itd --gpus all \
    -p 8000:8000 \
  -v /u01/workspace/models/cogvlm2-llama3-chinese-chat-19B-int4:/app/CogVLM2/models/cogvlm2-llama3-chinese-chat-19B-int4 \
  -v /u01/workspace/cogvlm/images:/u01/workspace/images \
  -e MODEL_PATH=/app/CogVLM2/models/cogvlm2-llama3-chinese-chat-19B-int4 \
  qingcloudtech/cogvlm:v1.4 python basic_demo/openai_api_demo.py --quant=4
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第二步：测试验证
『693cce5688f2 』替换为自己的容器ID

docker exec -it 0e691fd4153f /bin/bash
cd basic_demo
python openai_api_request.py
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root@itserver03:/u01/workspace/cogvlm/CogVLM2/basic_demo# docker exec -it 693cce5688f2 python openai_demo/openai_api_request.py 
This image captures a serene landscape featuring a wooden boardwalk that leads through a lush green field. The field is bordered by tall grasses, and the sky overhead is vast and blue, dotted with wispy clouds. The horizon reveals distant trees and a clear view of the sky, suggesting a calm and peaceful day.
root@itserver03:/u01/workspace/cogvlm/CogVLM2/basic_demo# 

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其他访问方式：

Restful API地址:
127.0.0.1:8000/v1/chat/completions

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