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I recently wanted to run some Python benchmarks, and was surprised to find that some major Python packages (i.e. Numba) didn't work on the latest Python release
by celrod 4y ago
I recently wanted to run some Python benchmarks, and was surprised to find that some major Python packages (i.e. Numba) didn't work on the latest Python release.
In Julia, all open source packages that pass tests on the latest release are confirmed to also pass on a release candidate before that candidate can become the next release.
A major package being incompatible with a new Julia version just doesn't happen.
Julia takes its stability guarantees seriously.
- ChrisRackauckas 4y agoIndeed. There's a nice example from yesterday where someone asked about whether anyone is planning to update ParallelDataTransfer.jl because it's so old (last update August 2018) that it doesn't have any of the modern package development features that became standard post v1.0. But I was like, that doesn't mean it's not working... Julia is stable. And indeed if you run it, it still passes tests on Julia v1.8, more than 4 years later. That's a testimony of just how stable post-v1.0 has been. And mind you, this is a library of tooling for distributed computing, so it's touching that is considered a deeper feature. Source: https://discourse.julialang.org/t/discontinuation-of-paralleldatatransfer-jl/91294 https://discourse.julialang.org/t/discontinuation-of-paralle...