What is envd?
ɪnˈvdɪ) is a command-line tool that helps you create the container-based development environment for AI/ML.
Development environments are full of python and system dependencies, CUDA, BASH scripts, Dockerfiles, SSH configurations, Kubernetes YAMLs, and many other clunky things that are always breaking. envd is to solve the problem:
- Declare the list of dependencies (CUDA, python packages, your favorite IDE, and so on) in
- Simply run
- Develop in the isolated environment.
Environments built with
envd provide the following features out-of-the-box:
❤️ Knowledge reuse in your team
envd build functions can be reused. Use
include function to import any git repositories. No more copy/paste Dockerfile instructions, let's reuse them.
envdlib = include("https://github.com/tensorchord/envdlib") def build(): base(os="ubuntu20.04", language="python") envdlib.tensorboard(8888)
envdlib.tensorboard is defined in github.com/tensorchord/envdlib
def tensorboard(envd_port=6006, envd_dir="/home/envd/logs", host_port=0, host_dir="/var/log/tensorboard"): """Configure TensorBoard. Make sure you have permission for `host_dir` Args: envd_port (Optional[int]): port used by envd container envd_dir (Optional[str]): log storage mount path in the envd container host_port (Optional[int]): port used by the host, if not specified or equals to 0, envd will randomly choose a free port host_dir (Optional[str]): log storage mount path in the host """ install.python_packages(["tensorboard"]) runtime.mount(host_path=host_dir, envd_path=envd_dir) runtime.daemon( commands=[ [ "tensorboard", "--logdir", "/home/envd/logs", "--port", str(envd_port), "--host", "0.0.0.0", ">>tensorboard.log", "2>&1", ], ] ) runtime.expose(envd_port=envd_port, host_port=host_port, service="tensorboard")
⏱️ BuiltKit native, build up to 6x faster
BuildKit supports parallel builds and software cache (e.g. pip index cache and apt cache). You can enjoy the benefits without knowledge of it.
For example, the PyPI cache is shared across builds and thus the package will be cached if it has been downloaded before.
Setup your first
envd environment in 3 minutes
- Docker (20.10.0 or above)
Install and bootstrap
# envd can be installed with pip. pip3 install --upgrade envd
# If you are on MacOS, envd can be installed with homebrew. brew install envd
# envd can be installed with pipx. pipx install envd
# Run the following command in your terminal to install the latest release of envd. curl -sSfL https://envd.tensorchord.ai/install.sh | sudo bash
After the installation, please run
envd bootstrap to bootstrap:
You can add
-m flag when running
envd bootstrap, to configure the mirror for docker.io registry:
envd bootstrap --dockerhub-mirror https://docker.mirrors.sjtug.sjtu.edu.cn
Please clone the
git clone https://github.com/tensorchord/envd-quick-start.git
The build manifest
build.envd looks like:
def build(): config.repo(url="https://github.com/tensorchord/envd", description="envd quick start example") base(os="ubuntu20.04", language="python3") # Configure pip index if needed. # config.pip_index(url = "https://pypi.tuna.tsinghua.edu.cn/simple") install.python_packages(name = [ "numpy", ]) shell("zsh")
Note that we use Python here as an example but please check out examples for other languages such as R and Julia here.
Then please run the command below to set up a new environment:
cd envd-quick-start && envd up
$ cd envd-quick-start && envd up [+] ⌚ parse build.envd and download/cache dependencies 2.8s ✅ (finished) => download oh-my-zsh 2.8s [+] 🐋 build envd environment 18.3s (25/25) ✅ (finished) => create apt source dir 0.0s => local://cache-dir 0.1s => => transferring cache-dir: 5.12MB 0.1s ... => pip install numpy 13.0s => copy /oh-my-zsh /home/envd/.oh-my-zsh 0.1s => mkfile /home/envd/install.sh 0.0s => install oh-my-zsh 0.1s => mkfile /home/envd/.zshrc 0.0s => install shell 0.0s => install PyPI packages 0.0s => merging all components into one 0.3s => => merging 0.3s => mkfile /home/envd/.gitconfig 0.0s => exporting to oci image format 2.4s => => exporting layers 2.0s => => exporting manifest sha256:7dbe9494d2a7a39af16d514b997a5a8f08b637f 0.0s => => exporting config sha256:1da06b907d53cf8a7312c138c3221e590dedc2717 0.0s => => sending tarball 0.4s envd-quick-start via Py v3.9.13 via 🅒 envd ⬢ [envd]❯ # You are in the container-based environment!
Set up Jupyter notebook
Please edit the
build.envd to enable jupyter notebook:
def build(): config.repo(url="https://github.com/tensorchord/envd", description="envd quick start example") base(os="ubuntu20.04", language="python3") # Configure pip index if needed. # config.pip_index(url = "https://pypi.tuna.tsinghua.edu.cn/simple") install.python_packages(name = [ "numpy", ]) shell("zsh") config.jupyter()
You can get the endpoint of the running Jupyter notebook via
envd envs ls.
$ envd up --detach $ envd envs ls NAME JUPYTER SSH TARGET CONTEXT IMAGE GPU CUDA CUDNN STATUS CONTAINER ID envd-quick-start http://localhost:42779 envd-quick-start.envd /home/gaocegege/code/envd-quick-start envd-quick-start:dev false <none> <none> Up 54 seconds bd3f6a729e94
Please check out ROADMAP.
We welcome all kinds of contributions from the open-source community, individuals, and partners.
- Join our discord community!
- To build from the source, please read our contributing documentation and development tutorial.
Talk with us
💬 Interested in talking with us about your experience building or managing AI/ML applications?