include_dir | logical evolution of the include_str macro | Dataset library

Extension is from setuptools which has somewhat limited support for Cython: it automatically invokes cythonize for *.pyx-files but for more options one should use cythonize directly. That means the following for your setup.py:

I actually attempted this earlier today and these are the steps I used:

• In the site.cfg file, put

Q : " What is going wrong? "

A :
There is not a single thing that we can say that it "goes wrong", the code-execution eco-system is so multi-layered, that it is not as trivial as we might wish to enjoy & there are several (different, some hidden) places, where configurations decide, how many CPU-cores will actually bear the overall processing-load.

Situation is also version-dependent & configuration-specific ( both Scikit, Numpy, Scipy have mutual dependencies & underlying dependencies on respective compilation options for numerical packages used )

Given a documented feature of interpretation of negative numbers in top-level n_jobs parameter in RandomizedSearchCV(...) methods, submit the very same task, yet configured so that it has got explicit amount of permitted (top-level) n_jobs = CPU_cores_allowed_to_load and observe, when & how many cores do actually get loaded during the whole flow of processing.

Results:
if and only if that very number of "permitted" CPU-cores was loaded, the top-level call did correctly "propagate" the parameter settings to each & every method or procedure used alongside the flow of processing

In case your observation proves the settings were not "obeyed", we can only review the whole scope of all source-code verticals to decide, who is to be blamed for such dis-obedience of not keeping the work compliant with the top-level set ceiling for the n_jobs. While O/S tools for CPU-core affinity mappings may give us some chances to "externally" restrict the number of such cores used, some other adverse effects ( the add-on management costs being the least performance-punishing ones ) will arise - thermal-management introduced CPU-core "hopping", being the disallowed by affinity maps, will on contemporary processors cause a more and more reduced clock-frequency (as cores get indeed hot in numerically intensive processing), thus prolonging the overall task processing times, as there are "cooler" (thus faster) CPU-cores in the system (those, that were prevented from being used by the affinity-mapping), yet these are very the same CPU-cores, that the affinity-mappings disallowed from being used for temporally placing our task processing (while the hot ones, from which the flow of the processing was reallocated due to reached thermal-ceilings, got some time to cold down and re-gain the chances to run at not decreased CPU-clock-rates)

Top-level call might have set an n_jobs-parameter, yet any lower-level component might have "obeyed" that one value ( without knowing, how many other, concurrently working peers did the same - as in joblib.Parallel() and similar constructors do, not mentioning the other, inherently deployed, GIL-evading multithreading libraries - as that happen to lack any mutual coordination so as to keep the top-level set n_jobs-ceiling )

I just accidentally found the solution to the above problem. The problem is the package setuptools (which in my case is the version 60.9.1)! Indeed, by executing python setup.py build_ext --inplace --compiler=mingw32, the latter will call the class Mingw32CCompiler into setuptools/_distutils/cygwinccompiler.py which contains these two lines:

you should install libcurl4-openssl-dev in Ubuntu, try the following codes in Ubuntu terminal:

In your yaws.conf file, your keyfile parameter in the block refers to a file with a .key suffix. According to the Erlang ssl module man page, that file should instead be in PEM format (i.e., a .pem file).

• The ssl man page says if you leave out the keyfile parameter, it defaults to the same as certfile, so you could try dropping keyfile from your yaws.conf file to see if that helps.
• If that doesn't work, you likely need to convert the .key file to a .pem file; this answer describes how to do it.

Because static binaries are different on every system, I need to compile my libraries on the corresponding platform. In the end, I used the CIBW_BEFORE_ALL variable to execute the build commands for my libraries.

Electron already includes ffmpeg (unlike stock Node.js) leaving you no other choice but to link with the included version - otherwise you will get symbol clashes and weird behavior - which is your case - because some symbols will be resolved to your version, others to the built-in version.

Maybe there is a possible workaround which is to build ffmpeg statically into your addon.

The problem mainly comes from the lack of SIMD instructions (due to both the bound-checking and the inefficient default compiler flags) compared to Numpy (which use OpenBLAS on most platforms by default).

To fix that, you should first add the following line in the beginning of the hello.pix file: