README
¶
envd
Development Environment for data science and AI/ML
⚠️ 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 science and AI/ML engineering teams.
🐍 No Docker, only Python - Focus on writing Python code, we will take care of Docker and development environment setup.
🖨️ Built-in Jupyter/VSCode - First-class support for Jupyter and VSCode remote extension.
⏱️ Save time - Better cache management to save your time, keep the focus on the model, instead of dependencies.
☁️ Local & cloud - envd integrates seamlessly with Docker so that you can easily 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 change in setup.
Why use envd?
It is still too difficult to configure development environments and reproduce results in AI/ML applications.
envd is a machine learning development environment for data science and AI/ML engineering teams. Environments built with envd provide the following features out-of-the-box:
🐍 Life is short, use Python[^1]
Development environments are full of Dockerfiles, bash scripts, Kubernetes YAML manifests, and many other clunky files that 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 across builds and thus the package will be cached if it has been downloaded before. It saves plenty of time, especially when you update the environment by trial and error.
| envd |
Docker[^2] |
|
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[^2]: Docker without buildkit
☁️ Local & cloud native
envd integrates seamlessly with Docker, you can share, version, and publish envd environments with Docker Hub or any other OCI image registries. 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 change in setup. You can also collaborate with your colleagues without "let me configure the environment in your machine".
🖨️ Seamless experience of Jupyter/VSCode
envd provides first-class support for Jupyter and VSCode remote extension. 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
envd can be installed with pip. After the installation, please run envd bootstrap to bootstrap.
pip install --pre envd
envd bootstrap
You can add
--dockerhub-mirroror-mflag when runningenvd 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")
install.python_packages(name = [
"numpy",
])
shell("zsh")
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) ➜ 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
$
Set up 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 the running 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"
])
Contribute
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.
Contributors ✨
Thanks goes to these wonderful people (emoji key):
This project follows the all-contributors specification. Contributions of any kind welcome!
License
Directories
¶
| Path | Synopsis |
|---|---|
|
cmd
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envd-ssh
ssh is the CLI running in the container as the sshd.
|
ssh is the CLI running in the container as the sshd. |
|
pkg
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buildkitd/mock
Package mock is a generated GoMock package.
|
Package mock is a generated GoMock package. |
|
lang/frontend/starlark/mock
Package mock is a generated GoMock package.
|
Package mock is a generated GoMock package. |
|
progress/compileui/mock
Package mock is a generated GoMock package.
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Package mock is a generated GoMock package. |
|
ssh
https://gist.github.com/stefanprodan/2d20d0c6fdab6f14ce8219464e8b4b9a Refer to okteto/pkg/ssh/exec.go
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https://gist.github.com/stefanprodan/2d20d0c6fdab6f14ce8219464e8b4b9a Refer to okteto/pkg/ssh/exec.go |