Jinjing Zhou

I found the `sending tarball` still take ~30s on my machine. Is this expected? 

nerdctl uses a newer buildkit https://github.com/containerd/nerdctl/blob/e77e05b5fd252274e3727e0439e9a2d45622ccb9/Dockerfile.d/SHA256SUMS.d/buildkit-v0.10.3. Can we leverage this?

@aseaday I think https://github.com/tensorchord/envd/issues/91 is the high priority now. `load` syntax is somehow builtin functionality of starlark to load function from other file. There're some default behaviors we can enable...

I'm actually thinking of rename `config.jupyter` to a new namespace, such as `packages.jupyter`, and try to use starlark implement it (like how we did in tensorboard and jupyter lab). Because...

Some ideas: We can provide a way for user override `base` function in their own environment, with some methods marked as internal (starting with `_`) now. For example ``` def...

Also there are some ambiguity in the current API design. `config.ssh(mode='disable')` doesn't sounds good. Other options: - Introduce `install.dev_packages(..)` - Add `dev` as the argument to the base function, for...

> @VoVAllen > > I am wondering how to support different bases on different targets. For example, I want to use `nvidia/cuda:runtime` for serving, and `nvidia/cuda:devel` for development. Probably we...

``` def dev(): base("ubuntu20.04", default_dev()+cuda("11.1", cudnn="8", version="devel")) def serving(): base("ubuntu20.04", cuda("11.1", cudnn="8", version="runtime")) ``` Then envd can handle the logic inside `base` function For `dev` function, envd will use nvidia...

Also user may not want to include `config.envd` when building serving image

The problem I'm concerning is the composition for multiple dependency. For example, It's not efficient to construct over all available cuda image to satisfy user requirements. Therefore I'm thinking of...