# Installer envd avec Homebrew

Consultez les chemins d'installation, exécutables, métadonnées et notes de sécurité de envd pour les workflows d'agents IA.

## installation

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

Commandes d'installation supplémentaires:

### macOS

- Homebrew (100%):

```sh
brew install envd
```

  Preuve: local Homebrew formula metadata

## Faits du paquet

- **Clé du paquet:** brew:envd
- **Gestionnaire de paquets:** Homebrew
- **Version:** 1.3.4
- **Résumé source:** Reproducible development environment for AI/ML
- **Page d'accueil:** <https://envd.tensorchord.ai>
- **Dépôt:** <https://github.com/tensorchord/envd>
- **Dernière mise à jour:** 2026-07-26T04:11:38+02:00
- **Généré:** 2026-08-03T19:37:03+00:00

## exécutables

- envd (alias)

## Comportement d'installation

- Bouteille: non disponible

## Version et fraîcheur

- page générée: 2026-08-03
- version du gestionnaire: 1.3.4
## Historique du projet et usages

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.

### Historique du projet

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.

### Historique d'adoption

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.

### Modes d'utilisation

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.

### Pourquoi les passionnés de paquets s'y intéressent

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.

### Chronologie

- 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.

### Sources

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


## Notes de sécurité

narrow executable package without higher-risk signals.

- **Risque Geiger:** vert / faible
- 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)


## Sources

- 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