envd

README

envd

Development Environment for Data Scientists

⚠️ envd is still under heavy development, and subject to change. it is not feature-complete or production-ready. Please contact us in discord if there is any problem.

envd is a machine learning development environment for data scientists, AI/ML engineers, or teams.

🐍 No docker, only python - Write python code to build the development environment, we help you take care of Docker.

🖨️ Built-in jupyter/vscode - Jupyter and VSCode remote extension are first-class support.

⏱️ Save time - Better cache management to save your time, keep the focus on the model, instead of dependencies.

☁️ Local & cloud - envd integrates with Docker seamlessly, you can share, version, and publish envd environments with Docker Hub or any other OCI image registries.

🔁 Repeatable builds, reproducible results - You can reproduce the same dev environment, on your laptop, public cloud VMs, or Docker containers, without any setup or change.

Why use envd?

It is still too difficult to configure development environments and reproduce results in AI/ML scenarios.

envd is a machine learning development environment for data scientists, AI/ML engineers, or teams. Environments built with envd enjoy the following features out-of-the-box:

🐍 Life is short, we use Python[^1]

Development environments are full of Dockerfiles, bash scripts, Kubernetes YAML manifests, and many other clunky files. And they are always breaking. envd builds are isolated and clean. You can write simple instructions in Python, instead of Bash / Makefile / Dockerfile / ...

[^1]: The build language is starlark, which is a dialect of Python.

⏱️ Save you plenty of time

envd adopts a multi-level cache mechanism to accelerate the building process. For example, the PyPI cache is shared between different builds. Thus the package will be cached if it is downloaded before. It saves plenty of time, especially when you update the environment by trial and error.

envd	Docker[^2]
$ envd build => pip install tensorflow 5s + => Using cached tensorflow-...-.whl (511.7 MB)	$ docker build => pip install tensorflow 278s - => Downloading tensorflow-...-.whl (511.7 MB)

[^2]: Docker without buildkit

☁️ Local & cloud native

envd integrates with Docker seamlessly, you can share, version, and publish envd environments with Docker Hub or any other OCI image registries. And the envd environments can be run on Docker, or Kubernetes.

🔁 Repeatable builds, reproducible results

You can reproduce the same dev environment, on your laptop, public cloud VMs, or Docker containers, without any setup or change. And you can also collaborate with your colleagues without "let me configure the environment in your machine".

🖨️ Seamless experience of jupyter/vsocde

Jupyter and VSCode remote extension are first-class support. You benefit without sacrificing any developer experience.

Documentation

See envd documentation.

Getting Started

Get started by creating a new envd environment.

What you'll need

• Docker (20.10.0 or above)

Install envd

You can download the binary from the latest release page, and add it in $PATH.

After the download, please run envd bootstrap to bootstrap.

You can add --dockerhub-mirror or -m flag when running envd boostrap, 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")
    install.python_packages(name = [
        "numpy",
    ])
    shell("zsh")

Then please run the command below to setup 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) ➜  demo git:(master) ✗ # You are in the container-based environment!

Play with the environment

You can run ssh envd-quick-start.envd to reconnect if you exit from the environment. Or you can execute git or python commands inside.

$ python demo.py
[2 3 4]
$ git fetch
$

Setup jupyter notebook

Please edit the build.envd to enable jupyter notebook:

def build():
    base(os="ubuntu20.04", language="python3")
    install.python_packages(name = [
        "numpy",
    ])
    shell("zsh")
    config.jupyter(password="", port=8888)

You can get the endpoint of jupyter notebook via envd get envs.

$ envd up --detach
$ envd get env
NAME                    JUPYTER                 SSH TARGET              CONTEXT                                 IMAGE                   GPU     CUDA    CUDNN   STATUS          CONTAINER ID 
envd-quick-start        http://localhost:8888   envd-quick-start.envd   /home/gaocegege/code/envd-quick-start   envd-quick-start:dev    false   <none>  <none>  Up 54 seconds   bd3f6a729e94

Features

Pause and resume

$ envd pause --env mnist
mnist
$ env get envs
NAME         JUPYTER                 SSH TARGET   CONTEXT  IMAGE      GPU  CUDA  CUDNN  STATUS              CONTAINER ID 
mnist        http://localhost:9999   mnist.envd   /mnist   mnist:dev  true 11.6  8      Up 23 hours(Paused) 74a9f1007004
$ envd resume --env mnist
$ ssh mnist.envd
(envd 🐳) $ # The environment is resumed!

Configure mirrors

envd supports PyPI mirror and apt source configuration. You can configure them in build.env or $HOME/.config/envd/config.envd to set up in all environments.

cat ~/.config/envd/config.envd
config.apt_source(source="""
deb https://mirror.sjtu.edu.cn/ubuntu focal main restricted
deb https://mirror.sjtu.edu.cn/ubuntu focal-updates main restricted
deb https://mirror.sjtu.edu.cn/ubuntu focal universe
deb https://mirror.sjtu.edu.cn/ubuntu focal-updates universe
deb https://mirror.sjtu.edu.cn/ubuntu focal multiverse
deb https://mirror.sjtu.edu.cn/ubuntu focal-updates multiverse
deb https://mirror.sjtu.edu.cn/ubuntu focal-backports main restricted universe multiverse
deb http://archive.canonical.com/ubuntu focal partner
deb https://mirror.sjtu.edu.cn/ubuntu focal-security main restricted universe multiverse
""")
config.pip_index(url = "https://mirror.sjtu.edu.cn/pypi/web/simple")
install.vscode_extensions([
    "ms-python.python",
    "github.copilot"
])

Contribute

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

• Join our discord community!
• To build from the source, check the contributing page.