sys | Powerful replacements for base : :system2 | Runtime Evironment library

It looks like setuptools>=58 breaks support for use_2to3:

setuptools changelog for v58

So you should update setuptools to setuptools<58 or avoid using packages with use_2to3 in the setup parameters.

I was having the same problem, pip==19.3.1

The current release of pyodbc (4.0.32) does not have pre-built wheel files for Python 3.10. The easiest way to get it installed at the moment is to download the appropriate wheel from

https://www.lfd.uci.edu/~gohlke/pythonlibs/#pyodbc

and then install it. For example, if you are running 64-bit Python then you would download the 64-bit wheel and use

It is a bug, you can downgrade using:

pip install "pip<22"

https://github.com/jazzband/pip-tools/issues/1558

Currently (2022-02-05), CRAN builds R binaries for Apple silicon using Apple clang (from Command Line Tools for Xcode 12.4) and an experimental build of gfortran.

If you obtain R from CRAN (i.e., here), then you need to replicate CRAN's compiler setup on your system before building R packages that contain C/C++/Fortran code from their sources (and before using Rcpp, etc.). This requirement ensures that your package builds are compatible with R itself.

A further complication is the fact that Apple clang doesn't support OpenMP, so you need to do even more work to compile programs that make use of multithreading. You could circumvent the issue by building R itself and all R packages from sources with LLVM clang, which does support OpenMP, but this approach is onerous and "for experts only". There is another approach that has been tested by a few people, including Simon Urbanek, the maintainer of R for macOS. It is experimental and also "for experts only", but seems to work on my machine and is simpler than trying to build R yourself.

Warning: These instructions come with no warranty and could break at any time. They assume some level of familiarity with C/C++/Fortran program compilation, Makefile syntax, and Unix shells. As usual, sudo at your own risk.

I will try to address compilers and OpenMP support at the same time. I am going to assume that you are starting from nothing. Feel free to skip steps you've already taken, though you might find a fresh start helpful.

I've tested these instructions on a machine running Big Sur, and at least one person has tested them on a machine running Monterey. I would be glad to hear from others.

1. Download an R binary from CRAN here and install. Be sure to select the binary built for Apple silicon.

2. Run

The following works by subclassing the relevant class, and duplicating code with the relevant changes. We also have to subclass the Client class, to overwrite the place where the gateway/websocket class is used. This results in a lot of duplicated code, however it does work, and requires neither dirty monkey-patching nor editing the library source code.

However, it does come with many of the same problems as editing the library source code - mainly that as the library is updated, this code will become out of date (if you're using the archived and obsolete version of the library, you have bigger problems instead).

Your setuptools version is likely to be yanked:

https://pypi.org/project/setuptools/60.3.0/

Not sure how to fix that without a working pip though.

A "multiarch" Python interpreter built on MacOS is intended to target MacOS-on-Intel and MacOS-on-Apple's-arm64.

There is absolutely no binary compatibility with Linux-on-Apple's-arm64, or with Linux-on-aarch64. You can't run MacOS executables on Linux, no matter if the architecture matches or not.

Since you mention executing the same files (with proper performance) from within Git Bash, I'm going to make an assumption here. Correct me if I'm wrong on this, and I'll delete the answer and look for another possibility.

This would be explained (and expected) if your files are stored on /mnt/c (a.k.a. C:, or /C under Git Bash) or any other Windows drive, as they would likely need to be to be accessed by Git Bash.

WSL2 uses the 9P protocol to access Windows drives, and it is currently known to be very slow when compared to:

• Native NTFS (obviously)
• The ext4 filesystem on the virtual disk used by WSL2
• And even the performance of WSL1 with Windows drives

I've seen a git clone of a large repo (the WSL2 Linux kernel Github) take 8 minutes on WSL2 on a Windows drive, but only seconds on the root filesystem.

Two possibilities:

• If possible (and it is for most Node projects), convert your WSL to version 1 with wsl --set-version 1. I always recommend making a backup with wsl --export first.

  And since you are making a backup anyway, you may as well just create a copy of the instance by wsl --importing your backup as --version 1 (as the last argument). WSL1 and WSL2 both have their uses, and you may find it helpful to keep both around.

  See this answer for more details on the exact syntax..

• Or just move the project over to somewhere under the WSL root, such as /home/username/src/.

Does it make sense to iterate through layers like this and keep adding files (to some target, does not matter for now) and deleting the added files in case they are found with a .wh prefix? Or am I totally off and is there a much better way?

There is a much better way, you do not want to reimplement (with worse performances) what Docker already does. The main reason is that Docker uses a mount filesystem called overlay2 by default that allows the creation of images and containers leveraging the concepts of a Union Filesystem: lowerdir, upperdir, workdir and mergeddir.

What you might not expect is that you can reproduce an image or container building process using the mount command available in almost any Unix-like machine.

I found a very interesting article that explains how the overlay storage system works and how Docker internally uses it, I highly recommend the reading.

Actually, if you have read the article, the solution is there: you can mount the image data you have by docker inspecting its LowerDir, UpperDir, WorkDir and by setting the merged dir to a custom path. To make the process simpler, you can run a script like: