A development environment management tool for data scientists.

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  • License: Apache Software License (Apache License 2.0)
  • Author: TensorChord
  • Requires: Python >=3.6
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Project description

envd cat wink envd cat wink

Development environment for AI/ML

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explain

explain

What is envd?

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:

  1. Declare the list of dependencies (CUDA, python packages, your favorite IDE, and so on) in build.envd
  2. Simply run envd up.
  3. Develop in the isolated environment.

Why use envd?

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(host_port=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="/tmp",
):
    """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",
                envd_dir,
                "--port",
                str(envd_port),
                "--host",
                "0.0.0.0",
            ],
        ]
    )
    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.

Getting Started 🚀

Requirements

  • Docker (20.10.0 or above)

Install and bootstrap envd

envd can be installed with pip, or you can download the binary release directly. After the installation, please run envd bootstrap to bootstrap.

# install with local pip
pip install --pre --upgrade envd
# or install with pipx
pipx install envd --pip-args="--pre"
# or download the latest release artifact with GitHub CLI
bash -c "gh release download -R tensorchord/envd -p $(echo envd_\*_$(uname)_$(uname -p))"

# bootstrap
envd bootstrap

You can add --dockerhub-mirror or -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

Create an envd environment

Please clone the envd-quick-start:

git clone https://github.com/tensorchord/envd-quick-start.git

The build manifest build.envd looks like:

def build():
    base(os="ubuntu20.04", language="python3")
    # Configure the 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():
    base(os="ubuntu20.04", language="python3")
    # Configure the 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

More on documentation 📝

See envd documentation.

Roadmap 🗂️

Please checkout ROADMAP.

Contribute 😊

We welcome all kinds of contributions from the open-source community, individuals, and partners.

Open in Gitpod

Contributors ✨

Thanks goes to these wonderful people (emoji key):

Friends A.
Friends A.
📖 🎨		 Aaron Sun
Aaron Sun
📓 💻		 Aka.Fido
Aka.Fido
📦 📖 💻		 Alex Xi
Alex Xi
💻		 Bingyi Sun
Bingyi Sun
💻		 Ce Gao
Ce Gao
💻 📖 🎨 📆		 Frost Ming
Frost Ming
💻
Guangyang Li
Guangyang Li
💻		 Gui-Yue
Gui-Yue
💻		 Haiker Sun
Haiker Sun
💻		 Ikko Ashimine
Ikko Ashimine
💻		 Isaac
Isaac
💻		 JasonZhu
JasonZhu
💻		 Jian Zeng
Jian Zeng
🎨 🤔 🔬
Jinjing Zhou
Jinjing Zhou
🐛 💻 🎨 📖		 Jun
Jun
📦 💻		 Keming
Keming
💻 📖 🤔 🚇		 Kevin Su
Kevin Su
💻		 Ling Jin
Ling Jin
🐛 🚇		 Manjusaka
Manjusaka
💻		 Nino
Nino
🎨 💻
Pengyu Wang
Pengyu Wang
📖		 Sepush
Sepush
📖		 Siyuan Wang
Siyuan Wang
💻 🚇 🚧		 Suyan
Suyan
📖		 To My
To My
📖		 Tumushimire Yves
Tumushimire Yves
💻		 Wei Zhang
Wei Zhang
💻
Weizhen Wang
Weizhen Wang
💻		 XRW
XRW
💻		 Xu Jin
Xu Jin
💻		 Xuanwo
Xuanwo
💬 🎨 🤔 👀		 Yijiang Liu
Yijiang Liu
💻		 Yilong Li
Yilong Li
📖 🐛 💻		 Yuan Tang
Yuan Tang
💻 🎨 📖 🤔
Yuchen Cheng
Yuchen Cheng
🐛 🚇 🚧 🔧		 Yuedong Wu
Yuedong Wu
💻		 Yunchuan Zheng
Yunchuan Zheng
💻		 Zheming Li
Zheming Li
💻		 Zhenguo.Li
Zhenguo.Li
💻 📖		 Zhenzhen Zhao
Zhenzhen Zhao
🚇 📓 💻		 Zhizhen He
Zhizhen He
💻 📖
cutecutecat
cutecutecat
💻		 dqhl76
dqhl76
📖 💻		 jimoosciuc
jimoosciuc
📓		 kenwoodjw
kenwoodjw
💻		 nullday
nullday
🤔 💻		 tison
tison
💻		 wangxiaolei
wangxiaolei
💻
wyq
wyq
🐛 🎨 💻		 x0oo0x
x0oo0x
💻		 xiangtianyu
xiangtianyu
📖		 xieydd
xieydd
💻		 xing0821
xing0821
🤔 📓 💻		 zhyon404
zhyon404
💻		 杨成锴
杨成锴
💻

This project follows the all-contributors specification. Contributions of any kind welcome!

Star History

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License 📋

Apache 2.0

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  • License: Apache Software License (Apache License 2.0)
  • Author: TensorChord
  • Requires: Python >=3.6
Classifiers

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