JuliaDiff / JuliaDiff/Diffractor.jl
Diffractor as backend for Turing?
Nobody has claimed this yet.
- Dominant language
- Julia
- Stars
- 453
- Forks
- 33
- PR merge metrics
- No merged PRs in 30d
Description
Hi, this is more a naive question than an issue so apologies if posted at the wrong place.
I asked on Turing whether they had any plans/interest in supporting Diffractor as a backend, and they mentioned it would in-principle be feasible. I was just wondering if Diffractor could be an interesting option for Turing (with expected speedups, especially in the problematically slow areas of current existing AD backends)? And if so whether there were any long-term plans to work towards this integration, or whether Diffractor was made with different goals in mind than Bayesian sampling. Thanks for the hard work!
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the linked Turing discussion at https://github.com/TuringLang/Turing.jl/discussions/2100 and this issue’s comment thread to understand the question about using Diffractor as a Turing backend. The issue names no files or tests; done would require a clear decision on whether the integration is of interest and what goals or work it entails.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- backend
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100