deepseek linux安装

### DeepSeek 在 Linux 系统上的信息与使用 #### 安装准备 为了在 Ubuntu Linux 上部署 DeepSeek，操作流程类似于 Docker 的工作方式。对于有容器经验的用户来说，这将是一个熟悉的过程。具体而言，访问 Ollama 官方模型库中的 DeepSeek-R1 页面[^1]，从中挑选适合本地硬件配置的版本。 #### 下载模型 通过命令行工具可以轻松获取所需模型。例如，执行 `ollama pull deepseek-r1:14b` 命令来下载特定标签下的 DeepSeek 模型实例。 #### 查看已安装模型 要查看当前系统上已经存在的 DeepSeek 及其他相关模型列表，可以通过运行 `ollama list` 或者 `ollama ls` 来实现。这两个命令都会展示名称、ID、大小以及最近修改时间等信息[^2]。 ```bash [root@bogon ~]# ollama list NAME ID SIZE MODIFIED qwen2:latest e0d4e1163c58 4.4 GB 10 minutes ago deepseek-coder:latest 3ddd2d3fc8d2 776 MB 3 hours ago qwen2:0.5b 6f48b936a09f 352 MB 8 hours ago [root@bogon ~]# ollama ls NAME ID SIZE MODIFIED qwen2:latest e0d4e1163c58 4.4 GB 10 minutes ago deepseek-coder:latest 3ddd2d3fc8d2 776 MB 3 hours ago qwen2:0.5b 6f48b936a09f 352 MB 8 hours ago ``` 这些命令返回的结果可以帮助管理员管理不同版本之间的切换和更新。

### DeepSeek on Linux: Installation and Usage Guide For installing and using DeepSeek on a Linux system, the process involves several key steps focusing on environment setup, software installation, configuration, and validation. However, specific instructions for DeepSeek are not provided in the given reference material[^1]. Instead, based on common practices for deploying machine learning or AI-based applications like DeepSeek on Linux systems, here is an illustrative guide: #### Environment Setup Before proceeding with any installations, ensure that the operating system meets all prerequisites such as having Python installed along with pip (Python package installer). For most modern distributions of Linux, these can be easily obtained through their respective package managers. ```bash sudo apt-get update && sudo apt-get install python3-pip -y # Debian/Ubuntu sudo yum install python3-pip -y # CentOS/RHEL/Fedora ``` #### Installing Dependencies Assuming DeepSeek has dependencies similar to other data science tools, it might require additional libraries which should also be installed via `pip` or directly from source depending upon availability. ```bash pip3 install numpy pandas scikit-learn tensorflow matplotlib seaborn jupyterlab ``` #### Obtaining DeepSeek Since direct download links or repository information about DeepSeek aren't available within the referenced content, one would typically look towards official documentation or GitHub repositories associated with this tool. Once located, follow the project-specific guidelines for downloading either pre-built binaries or cloning the git repo followed by building locally if necessary. #### Configuration After acquiring DeepSeek, configuring environmental variables may become essential especially when dealing with paths related to executables or datasets used during operation. This step varies widely between projects but generally includes setting up PATH variable modifications so commands can execute globally without specifying full file locations each time they're invoked. ```bash export DEEPSEEK_HOME=/path/to/deepseek export PATH=$PATH:$DEEPSEEK_HOME/bin ``` #### Validation Finally, after completing previous stages successfully, validating whether everything works correctly becomes crucial before moving forward into more complex tasks involving actual analysis workloads. Running simple test cases supplied alongside the application serves well here ensuring expected behavior matches reality closely enough. ```python import deepseek as ds print(ds.__version__) ds.test() # Assuming there's a built-in function to run tests. ``` --related questions-- 1. What versions of Python does DeepSeek support? 2. Are there alternative methods besides pip for managing required packages? 3. Can you provide examples where DeepSeek was utilized effectively in research papers? 4. How frequently do developers release updates for fixing bugs found post-deployment? 5. Is containerization supported natively within DeepSeek?