std
adcomp
std | adcomp | |
---|---|---|
5 | 2 | |
364 | 164 | |
3.0% | - | |
8.8 | 7.9 | |
7 days ago | 19 days ago | |
Nix | C++ | |
- | GNU General Public License v3.0 or later |
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std
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Nix is a better Docker image builder than Docker's image builder
Newcomer here. Could anyone tell if std [0] is a good way to bring more sanity into flake design, esp. in avoiding ivory towery custom approaches? Using devenv.sh is another option, but I liked emphasis on having a common mental picture and focus on SLDC that std provides.
[0] https://std.divnix.com
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Show HN: Flox 1.0 – Open-source dev env as code with Nix
Agreed on the experience, hard to onboard. I looked at devenv.sh as easier way to get going. Implemented all with Nix, less lock-in. Just found std [0] and that quote looks promising too.
[0] https://std.divnix.com/
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NixOS + Haskell best practices circa March 2023
std/paisano for large projects.
- Std: The Nix Flakes framework for perfectionists with deadlines
- Nix: Taming Unix with Functional Programming
adcomp
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Nix: Taming Unix with Functional Programming
Are any of your R users TMB users? Because I ran into that in multiple setups (Guix SD and Guix on foreign distros) where only a specific ordering worked even when using guix shell --pure. This was back in the R 4.1.2 days. In theory it should be impossible and yet I ran into it. No I was not mixing packages installed via. install.packages.
Right now with the latest version of Guix and R 4.2.1 TMB is not usable. Try running:
"guix shell --container r r-tmb make gcc-toolchain gfortran-toolchain"
then try running the linreg.R (with the corresponding cpp file, or any of the examples) example from https://github.com/kaskr/adcomp/tree/master/tmb_examples
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Useful Algorithms That Are Not Optimized by Jax, PyTorch, or TensorFlow
There is no free lunch:).
I remember spending a summer using Template Model Builder (TMB), which is a useful R/C++ automatic differentiation (AD) framework, for working with accelerated failure time models. For these models, the survival to time T given covariates X is defined by S(t|X) = P(T>t|X) = S_0(t exp(-beta^T X)) for baseline survival S_0(t). I wanted to use splines for the baseline survival and then use AD for gradients and random effects. Unfortunately, after implementing the splines in template C++, I found a web page entitled "Things you should NOT do in TMB" (https://github.com/kaskr/adcomp/wiki/Things-you-should-NOT-d...) - which included using if statements that are based on coefficients. In this case, the splines for S_0 depend on beta, which is this specific excluded case:(. An older framework (ADMB) did not have this constraint, but dissemination of code was more difficult. Finally, PyTorch did not have an implementation of B-splines or an implementation for Laplace's approximation. Returning to my opening comment, there is no free lunch.
What are some alternatives?
deploy-rs - A simple multi-profile Nix-flake deploy tool.
mach-nix - Create highly reproducible python environments
awesome-nix - 😎 A curated list of the best resources in the Nix community [maintainer=@cyntheticfox]
hydra - Hydra, the Nix-based continuous build system
boostrap - my personal ricing setup (WIP)
poetry2nix - Convert poetry projects to nix automagically [maintainer=@adisbladis]
nixpkgs - Nix Packages collection & NixOS
pip2nix - Freeze pip-installable packages into Nix expressions [maintainer=@datakurre]
nixkell - A simple Nix-Haskell skeleton
micro-editor - A modern and intuitive terminal-based text editor