# 使用 Homebrew 安装 envd

查看 envd 的安装路径、可执行文件、元数据以及面向 AI 代理工作流的安全说明。

## 安装

```sh
sudo av install brew:envd
```

其他安装命令:

### macOS

- Homebrew (100%):

```sh
brew install envd
```

  证据: local Homebrew formula metadata

## 软件包事实

- **软件包键:** brew:envd
- **软件包管理器:** Homebrew
- **版本:** 1.3.4
- **来源摘要:** Reproducible development environment for AI/ML
- **主页:** <https://envd.tensorchord.ai>
- **仓库:** <https://github.com/tensorchord/envd>
- **最后更新:** 2026-07-26T04:11:38+02:00
- **已生成:** 2026-08-03T19:37:03+00:00

## 可执行文件

- envd (别名)

## 安装行为

- Bottle: 不可用

## 版本和新鲜度

- 页面生成时间: 2026-08-03
- 管理器版本: 1.3.4
## 项目历史与用法

envd is TensorChord's command-line tool for creating container-based development environments for AI and machine-learning work. It uses a Python-like `build.envd` declaration to turn Python, CUDA, shell, Jupyter, and container setup into a repeatable environment.

### 项目历史

The README presents envd as a reaction to fragile AI/ML development setups, where Python packages, CUDA, shell scripts, and Dockerfiles often break together. Its core promise is to replace hand-written environment assembly with a simple declaration plus `envd up`.

The project builds OCI-compatible images and uses technologies such as Docker and BuildKit. Its documentation also covers local and Kubernetes-backed contexts, remote builds, package caches, Jupyter setup, and reusable build functions imported from Git repositories.

### 采用历史

envd is distributed through several developer channels: the README documents pip installation, direct GitHub release binaries, and bootstrap after installation, while Homebrew packages it as `envd`. Homebrew analytics showed low hundreds of annual installs during this run, consistent with a specialized AI/ML infrastructure tool.

### 使用方式

A typical workflow installs envd, runs `envd bootstrap`, creates or clones a project with a `build.envd` file, and runs `envd up` to build and attach to the containerized environment. The example manifest installs conda, Python, Python packages, a shell, and optional Jupyter support.

The package is especially relevant where reproducibility and GPU/container setup matter: local notebooks, remote build machines, Kubernetes clusters, and teams sharing environment definitions.

### 为什么软件包爱好者会关心

envd is a packaging-adjacent tool because it treats development environments themselves as declarative artifacts. For package nerds, it sits at the intersection of language package managers, OCI images, BuildKit caching, and reproducible developer onboarding.

### 时间线

- 2022: TensorChord copyright and envd documentation identify the project era.
- 2020s: envd documents pip, release-binary, and Homebrew installation paths.
- 2020s: envd expands beyond local containers with remote build, cache, and Kubernetes-oriented documentation.

### Related projects

- envd builds on Docker, BuildKit, OCI images, conda, Python packaging, Jupyter, and Kubernetes.
- envdlib is documented as a reusable library of envd build functions imported from Git repositories.

### 来源

- <https://github.com/tensorchord/envd/blob/main/README.md>
- <https://envd.tensorchord.ai/guide/getting-started>
- <https://formulae.brew.sh/formula/envd>


## 安全说明

narrow executable package without higher-risk signals.

- **Geiger 风险:** 绿色 / 低
- narrow executable package without higher-risk signals


## Configuration and credential file locations

These source-backed paths show where this package keeps local settings or durable credentials. Automic Vault can use them as review targets for secret scanning, migration, and command approval.


## Configuration files

- Unix: build.envd

## Combined YAML source

View the package source record on GitHub. [combined/envd.yml](https://github.com/mxcl/pkgdb/blob/main/combined/envd.yml)


## 来源

- pkg.so package database
- Geiger risk classifier
- curated configuration and credential file locations
- curated package history
- pkgdb category and tag curation
- cross-ecosystem install command graph