adcomp
pynixify
| adcomp | pynixify | |
|---|---|---|
| 2 | 1 | |
| 164 | 62 | |
| - | - | |
| 7.9 | 3.9 | |
| 25 days ago | 10 months ago | |
| C++ | Python | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 only |
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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.
pynixify
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Nix: Taming Unix with Functional Programming
There are some sifferent/new tools for adding your own Python packages these days. It's still not truly solved, but one of these new package generation tools might serve your better:
poetry2nix: https://github.com/nix-community/poetry2nix
dream2nix: https://nix-community.github.io/dream2nix/guides/getting-sta...
mach-nix: https://github.com/DavHau/mach-nix
pip2nix: https://github.com/nix-community/pip2nix
pynixify: https://github.com/cript0nauta/pynixify
The tools available to you at the time (pypi2nix and maybe python2nix, if it was a long time ago) have been abandoned in favor of the newer tools, I think chiefly poetry2nix but I'm not sure.
There's still the Nixpkgs buildPythonPackage stuff, I think, if your goal is to upstream a lib into Nixpkgs. But if you just want to build your own Python applications and vendorize the deps (e.g., for work), you might try one of the tools above, which weren't available 3+ years ago. Maybe Nixy Python users and developers can reply with some of their experiences using those tools :)
What are some alternatives?
std - A DevOps framework for the SDLC with the power of Nix and Flakes. Good for keeping deadlines!
mach-nix - Create highly reproducible python environments
hydra - Hydra, the Nix-based continuous build system
deploy-rs - A simple multi-profile Nix-flake deploy tool.
boostrap - my personal ricing setup (WIP)
pip2nix - Freeze pip-installable packages into Nix expressions [maintainer=@datakurre]