![]() ![]() Libgfortran-devel_osx-arm64 11.0.0.dev0 h181927c_13 conda-forge/noarch Cached ![]() Libcxx 11.0.0 h7cf67bf_1 conda-forge/osx-arm64 Cached Ldid 2.1.2 h34db0f2_2 conda-forge/osx-arm64 Cached Gfortran_impl_osx-arm64 11.0.0.dev0 h2cdbfd1_13 conda-forge/osx-arm64 Cached Search the web for “How to Run Legacy Command Line Apps on Apple Silicon” to set up your Terminal sessions to prefer running Intel applications.įor the Python ecosystem, the conda-forge distribution is already supporting Apple Silicon in native ARM64 mode (without Rosetta 2). Geekbench has shown that the M1 processors are faster than many Mac portables that came before it, even when running Intel apps. In any case, you could probably run your data science workloads under Rosetta 2 (i.e. No FORTRAN also means a lot of numerical libraries are being held back (e.g. The other notable FORTRAN compiler is Intel’s, and the latter is very unlikely to be ported to Apple Silicon. Lack of GCC implies lack of FORTRAN support. Work on that is in progress, but as with all open-source efforts, there is “no timeline” since commitments are done on a “time-available” basis. The compiler hasn’t supported Apple’s ARM architecture (instruction set, calling convention, object format, etc) since an ancient version of iOS. The biggest hurdle for Data Science on Apple Silicon is gcc (the GNU Compiler Collection). Does this promise any further optimization and support for Python on Apple Silicon? Data scientists are more than just pro consumers needing an Adobe update for the new architecture (though for Matlab or Stata, the situation is similar), but less than full-blown developers who will use Swift anyway.Ĭonverters from coremltools can save some models (say, scikit-learn under Python) to use in apps. BLAS and LAPACK from veclib, though these are not the officially supported default for the Mac build).Īs we are investing into these platforms (both Apple hardware and our own codebase, not to mention human capital), it would be great to get more advance guidance on what performance we can expect on what front. R can also be much faster on the Mac with the Accelerate framework (esp. ![]() R became much more efficient with Revolution (now Microsoft) bundling Intel's Math Kernel Library (and more) into R. Intel has specifically built tools for Python lately. But I'd like to see some clarity how these ecosystems will transition from Intel to Apple Silicon. I am a data scientist working *in* these languages. This question does not come from a developer working on any of these languages. ![]()
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