nnetsauce with and without jax for GPU acceleration
[This article was first published on T. Moudiki's Webpage - R, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
In the new version (0.51.2) of nnetsauce (for Python, but also for R), available on PyPI and for conda, I removed jax and jaxlib (for GPU) from the default version, because jaxlib is heavy.
It means that if you want to use GPUs with nnetsauce (as in https://www.researchgate.net/publication/382589729_Probabilistic_Forecasting_with_nnetsauce_using_Density_Estimation_Bayesian_inference_Conformal_prediction_and_Vine_copulas), you’d want to explicitly install jax:
pip install nnetsauce[jax]
or
uv pip install nnetsauce[jax]
or
conda install -c conda-forge nnetsauce jax jaxlib
To leave a comment for the author, please follow the link and comment on their blog: T. Moudiki's Webpage - R.
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.