β οΈ 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 (ΙͺnΛvdΙͺ) is a machine learning development environment for data science and AI/ML engineering teams.
π No Docker, only Python - Focus on writing Python code, we will take care of Docker and development environment setup.
π¨οΈ Built-in Jupyter/VSCode - First-class support for Jupyter and VSCode remote extension.
β±οΈ Save time - Better cache management to save your time, keep the focus on the model, instead of dependencies.
βοΈ 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.
π 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.
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:
π Life is short, use Python1
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 / ...
β±οΈ 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 |
Docker2 |
$ 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) |
βοΈ 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.
π 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".
π¨οΈ Seamless experience of Jupyter/VSCode
envd provides first-class support for Jupyter and VSCode remote extension. You benefit without sacrificing any developer experience.
Weβre focused on helping data scientists and teams that develop AI/ML models. And they may suffer from:
- building the development environments with Python, CUDA, Docker, SSH, and so on. Do you have a complicated Dockerfile or build script that sets up all your dev environments, but is always breaking?
- Updating the environment. Do you always need to ask infrastructure engineers how to add a new python package in the Dockerfile?
- Managing environments and machines. Do you always forget which machines are used for the specific project, because you handle multiple projects concurrently?
Talk with us
π¬ Interested in talking with us about your experience building or managing AI/ML applications?
| Before envd | After envd |
|---|---|
 |
 |
See envd documentation.
- Docker (20.10.0 or above)
envd can be installed with pip. After the installation, please run envd bootstrap to bootstrap.
pip install --pre --upgrade envd
envd bootstrapYou 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
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
[+] β parse build.envd and download/cache dependencies 2.8s β
(finished)
=> download oh-my-zsh 2.8s
[+] π build envd environment 18.3s (25/25) β
(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) β demo git:(master) β # You are in the container-based environment!
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="")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:42779 envd-quick-start.envd /home/gaocegege/code/envd-quick-start envd-quick-start:dev false <none> <none> Up 54 seconds bd3f6a729e94Please checkout 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.
Thanks goes to these wonderful people (emoji key):
This project follows the all-contributors specification. Contributions of any kind welcome!