本站首页 收藏本站
广告位联系

APP正在开发中...
返回顶部
您的位置： 主页 > 网络编程 > 相关技巧 >

收藏
分享到

在linux服务器本地部署Deepseek及在mac远程web-ui访问的操作

相关技巧 来源：互联网 作者：佚名 发布时间：2025-02-19 21:29:58 人浏览
摘要

1. 在Linux服务器上部署DeepSeek模型 要在 Linux 上通过 Ollama 安装和使用模型，您可以按照以下步骤进行操作： 步骤 1：安装 Ollama 安装 Ollama： 使用以下命令安装 Ollama： 1 curl -sSfL https://ollama.com/

1. 在Linux服务器上部署DeepSeek模型

要在 Linux 上通过 Ollama 安装和使用模型，您可以按照以下步骤进行操作：

步骤 1：安装 Ollama

安装 Ollama：
使用以下命令安装 Ollama：

1

curl -sSfL https://ollama.com/install.sh | sh

验证安装：
安装完成后，您可以通过以下命令验证 Ollama 是否安装成功：

1

ollama --version

步骤 2：下载模型

1

ollama run deepseek-r1:32b

这将下载并启动DeepSeek R1 32B模型。

DeepSeek R1 蒸馏模型列表

模型名称 参数量 基础架构 适用场景
DeepSeek-R1-Distill-Qwen-1.5B 1.5B Qwen2.5 适合移动设备或资源受限的终端
DeepSeek-R1-Distill-Qwen-7B 7B Qwen2.5 适合普通文本生成工具
DeepSeek-R1-Distill-Llama-8B 8B Llama3.1 适合小型企业日常文本处理
DeepSeek-R1-Distill-Qwen-14B 14B Qwen2.5 适合桌面级应用
DeepSeek-R1-Distill-Qwen-32B 32B Qwen2.5 适合专业领域知识问答系统
DeepSeek-R1-Distill-Llama-70B 70B Llama3.3 适合科研、学术研究等高要求场景

RTX 4090 显卡显存为 24GB，32B 模型在 4-bit 量化下约需 22GB 显存，适合该硬件。32B 模型在推理基准测试中表现优异，接近 70B 模型的推理能力，但对硬件资源需求更低。

步骤 3：运行模型

1

ollama run deepseek-r1:32b

通过上面的步骤，已经可以直接在 Linux服务器通过命令行的形式使用Deepseek了。但是不够友好，下面介绍更方便的形式。

2. 在linux服务器配置Ollama服务

1. 设置Ollama服务配置

设置OLLAMA_HOST=0.0.0.0环境变量，这使得Ollama服务能够监听所有网络接口，从而允许远程访问。

1

sudo vi /etc/systemd/system/ollama.service

1

2

3

4

5

6

7

8

9

10

11

12

13

[Unit]

Description=Ollama Service

After=network-online.target

[Service]

ExecStart=/usr/local/bin/ollama serve

User=ollama

Group=ollama

Restart=always

RestartSec=3

Environment="OLLAMA_HOST=0.0.0.0"

Environment="PATH=/usr/local/cuda/bin:/home/bytedance/miniconda3/bin:/home/bytedance/miniconda3/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin"

[Install]

WantedBy=default.target

2. 重新加载并重启Ollama服务

1

2

sudo systemctl daemon-reload

sudo systemctl restart ollama

3.验证Ollama服务是否正常运行

运行以下命令，确保Ollama服务正在监听所有网络接口：

1

sudo netstat -tulpn | grep ollama

您应该看到类似以下的输出，表明Ollama服务正在监听所有网络接口（0.0.0.0）：

tcp        0      0 0.0.0.0:11434           0.0.0.0:*               LISTEN      -                   ollama

4. 配置防火墙以允许远程访问

为了确保您的Linux服务器允许从外部访问Ollama服务，您需要配置防火墙以允许通过端口11434的流量。

1

2

sudo ufw allow 11434/tcp

sudo ufw reload

5. 验证防火墙规则

确保防火墙规则已正确添加，并且端口11434已开放。您可以使用以下命令检查防火墙状态：

1

sudo ufw status

1

2

3

4

5

6

7

状态： 激活

至                          动作          来自

-                          --          --

22/tcp                     ALLOW       Anywhere                 

11434/tcp                  ALLOW       Anywhere                 

22/tcp (v6)                ALLOW       Anywhere (v6)            

11434/tcp (v6)             ALLOW       Anywhere (v6)

6. 测试远程访问

在完成上述配置后，您可以通过远程设备（如Mac）测试对Ollama服务的访问。
在远程设备上测试连接：
在Mac上打开终端，运行以下命令以测试对Ollama服务的连接：

1

curl http://10.37.96.186:11434/api/version

显示

1

{"version":"0.5.7"}

测试问答

1

2

3

curl -X POST http://10.37.96.186:11434/api/generate \

     -H "Content-Type: application/json" \

     -d '{"model": "deepseek-r1:32b", "prompt": "你是谁?"}'

显示

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.118616168Z","response":"\u003cthink\u003e","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.150938966Z","response":"\n\n","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.175255854Z","response":"\u003c/think\u003e","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.199509353Z","response":"\n\n","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.223657359Z","response":"您好","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.24788375Z","response":"！","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.272068174Z","response":"我是","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.296163417Z","response":"由","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.320515728Z","response":"中国的","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.344646528Z","response":"深度","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.36880216Z","response":"求","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.393006489Z","response":"索","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.417115966Z","response":"（","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.441321254Z","response":"Deep","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.465439117Z","response":"Seek","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.489619415Z","response":"）","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.51381827Z","response":"公司","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.538012781Z","response":"开发","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.562186246Z","response":"的","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.586331325Z","response":"智能","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.610539651Z","response":"助手","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.634769989Z","response":"Deep","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.659134003Z","response":"Seek","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.683523205Z","response":"-R","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.70761762Z","response":"1","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.731953604Z","response":"。","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.756135462Z","response":"如","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.783480232Z","response":"您","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.807766337Z","response":"有任何","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.831964079Z","response":"任何","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.856229156Z","response":"问题","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.880487159Z","response":"，","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.904710537Z","response":"我会","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.929026993Z","response":"尽","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.953239249Z","response":"我","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:15.977496819Z","response":"所能","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:16.001763128Z","response":"为您提供","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:16.026068523Z","response":"帮助","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:16.050242581Z","response":"。","done":false}

{"model":"deepseek-r1:32b","created_at":"2025-02-06T00:47:16.074454593Z","response":"","done":true,"done_reason":"stop","context":[151644,105043,100165,30,151645,151648,271,151649,198,198,111308,6313,104198,67071,105538,102217,30918,50984,9909,33464,39350,7552,73218,100013,9370,100168,110498,33464,39350,12,49,16,1773,29524,87026,110117,99885,86119,3837,105351,99739,35946,111079,113445,100364,1773],"total_duration":3872978599,"load_duration":2811407308,"prompt_eval_count":6,"prompt_eval_duration":102000000,"eval_count":40,"eval_duration":958000000}

通过上述步骤，已经成功在Linux服务器上配置了Ollama服务，并通过Mac远程访问了DeepSeek模型。接下来，将介绍如何在Mac上安装Web UI，以便更方便地与模型进行交互。

3. 在Mac上安装Web UI

为了更方便地与远程Linux服务器上的DeepSeek模型进行交互，可以在Mac上安装一个Web UI工具。这里我们推荐使用 Open Web UI，它是一个基于Web的界面，支持多种AI模型，包括Ollama。

1. 通过conda安装open-webui

打开终端，运行以下命令创建一个新的conda环境，并指定Python版本为3.11：

1

2

3

conda create -n open-webui-env python=3.11

conda activate open-webui-env

pip install open-webui

2. 启动open-webui

1

open-webui serve

3. 浏览器访问

http://localhost:8080/

使用管理员身份（第一个注册用户）登录

在Open webui界面中，依次点击“展开左侧栏”（左上角三道杠）–>“头像”（左下角）–>管理员面板–>设置（上侧）–>外部连接

在外部连接的Ollama API一栏将switch开关打开，在栏中填上http://10.37.96.186:11434（这是我的服务器地址）

点击右下角“保存”按钮

点击“新对话”（左上角），确定是否正确刷出模型列表，如果正确刷出，则设置完毕。

4. 愉快的使用本地deepseek模型
版权声明 : 本文内容来源于互联网或用户自行发布贡献，该文观点仅代表原作者本人。本站仅提供信息存储空间服务和不拥有所有权，不承担相关法律责任。如发现本站有涉嫌抄袭侵权， 违法违规的内容， 请发送邮件至2530232025#qq.cn(#换@)举报，一经查实，本站将立刻删除。

您可能感兴趣的文章 :

原文链接 :
相关文章
推荐阅读
最新更新
友情链接
  • 本站所有内容来源于互联网或用户自行发布，本站仅提供信息存储空间服务，不拥有版权，不承担法律责任。如有侵犯您的权益，请您联系站长处理！
  • Copyright © 2017-2022 F11.CN All Rights Reserved. F11站长开发者网 版权所有 | 苏ICP备2022031554号-1 | 51LA统计