项目概述
A system-level, binary package and environment manager running on all major operating systems and platforms.
项目地址
https://github.com/conda/conda
项目页面预览

关键指标
- Stars:7273
- 主要语言:Python
- License:Other
- 最近更新:2026-01-14T15:03:25Z
- 默认分支:main
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安装部署要点(README 精选)
Installation
To bootstrap a minimal distribution, use a minimal installer such as Miniconda or Miniforge.
Conda is also included in the Anaconda Distribution.
Getting Started
If you install the Anaconda Distribution, you will already have hundreds of packages
installed. You can see what packages are installed by running:
$ conda list
to see all the packages that are available, use:
$ conda search
and to install a package, use
$ conda install <package-name>
The real power of conda comes from its ability to manage environments.
In conda, an environment can be thought of as a completely separate installation.
Conda installs packages into environments efficiently using hard links by default when it is possible, so
environments are space efficient, and take seconds to create.
The default environment, which conda itself is installed into, is called base.
To create another environment, use the conda create command.
For instance, to create an environment with PyTorch, you would run:
$ conda create --name ml-project pytorch
This creates an environment called ml-project with the latest version of PyTorch, and its dependencies.
We can now activate this environment:
$ conda activate ml-project
This puts the bin directory of the ml-project environment in the front of the PATH,
and sets it as the default environment for all subsequent conda commands.
To go back to the base environment, use:
$ conda deactivate
常用命令(从 README 提取)
$ conda update --name base conda
$ conda list
$ conda search
通用部署说明
- 下载源码并阅读 README
- 安装依赖(pip/npm/yarn 等)
- 配置环境变量(API Key、模型路径、数据库等)
- 启动服务并测试访问
- 上线建议:Nginx 反代 + HTTPS + 进程守护(systemd / pm2)
免责声明与版权说明
本文仅做开源项目整理与教程索引,源码版权归原作者所有,请遵循对应 License 合规使用。








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