earthly VS Dagger.jl

Also CACHE keyword, for cache mounts. Makes incremental tools like compilers work well in the context of dockerfiles and layer caches.

That can extend beyond just producing docker iamges as well. Under the covers the CACHE keyword is how lib/rust in Earthly makes building Rust artifacts in CI faster.

https://github.com/earthly/earthly/issues/1399

earthly

A few of us will be around to answer questions if anyone has any. I myself worked only worked on the chmod feature which was pretty trivial.

https://github.com/earthly/earthly/pull/1821

[2] https://github.com/earthly/earthly/releases/tag/v0.7.0

# cat Makefile BIN_PATH = $(shell pwd)/bin $(shell mkdir $(BIN_PATH) &>/dev/null) EARTHLY = $(BIN_PATH)/earthly earthly: ifeq (,$(wildcard $(EARTHLY))) curl -L https://github.com/earthly/earthly/releases/download/v0.6.23/earthly-linux-amd64 -o $(EARTHLY) chmod +x $(EARTHLY) endif

I started to bring awareness to the Earthfiles goofy license, but it seems they've switched to MPL! https://github.com/earthly/earthly/releases/tag/v0.6.15

The (unfortunately named) Dagger is also an entry into that space: https://github.com/dagger/dagger#readme (Apache 2)

- https://github.com/earthly/earthly/tree/main/analytics

Another *monster* difference is that Dagger is (at least currently) Apache 2: https://github.com/dagger/dagger/blob/v0.2.4/LICENSE but Earthly went with BSL: https://github.com/earthly/earthly/blob/v0.6.12/LICENSE

That means I'm more likely to submit bugs and patches to Dagger, and I won't touch Earthly

Example build steps for a go application

Firstly, I'll say that we already have work started to implement out-of-core directly in Dagger: https://github.com/JuliaParallel/Dagger.jl/pull/289.

With that PR in place, it should be possible to define a "storage device" which is backed by a database. I haven't had a chance to actually try this, since the PR still needs quite some work and testing, but it's definitely something on my radar!

I'm not sure that's necessarily the domain of a low-level package like CUDA.jl though (which I assume you're referring to). That kind of interface is more the domain of higher-level packages like https://github.com/JuliaParallel/Dagger.jl/ and to a lesser extent https://juliagpu.github.io/KernelAbstractions.jl/stable/. Moreover, the jury is still out on whether the built-in Distributed module is an ideal abstraction for every use-case (clusters, heterogeneous compute, etc.)

WRT Nx, my biggest question is how they'll crack the problem of still needing big balls of C++ and the shims everywhere to get acceleration. Creating a compiler that generates efficient GPU or other accelerator code is a massive research project with no clear winners, never mind the challenge of reconciling the very mutation-heavy needs of GPU compute with a mostly immutable language model.