Nuitka

The package itself was included in the compiled binary, but some sub-packages or sub-modules were missing.
If your Python program uses data files loaded at runtime, Nuitka can’t automatically detect those, either.
To include individual files and directories with a Nuitka-packaged program, you’d use –include-data-files and –include-data-dir.
Nuitka does not include metadata in the distribution.

The tree building stage and node optimization creating new nodes.
This will also allow more optimization for a few codes.
To be utilized modules can be found or not in just exactly the same way.
For packages which have binaries colliding with the package name.

The resulting file some_module.so can then be utilized instead ofsome_module.py.
This will prompt one to download a C caching tool and a MinGW64 based C compiler unless you have a suitable MSVC installed.
Moreover, if you need to execute with the proper interpreter, in that case, be sure to execute bin/nuitka and become good.
The latest version is preferred however, not required.
On the other hand, you don’t have to except pre-Windows 10 support, plus they might work for you, but support of these configurations is only open to commercial users.
But as a workaround, as the C++03

When immediately executing the created binary (–execute), don’t reset PYTHONPATH.
Nuitka could be installed from the repositories of many Linux distributions.
It is also installed through pip and pip3, respectively.

• I tried pyInstaller but found it to possess way too many limitations with dependency compatibility.
• When you compile from a living installation, that could well have many optional dependencies of your software installed.
• However, right now this is not yet documented, but we intend to put in a table with the data.

You can even automatically run your Nuitka-compiled app by passing–runas a command-line flag.
Once you have Nuitka installed, use nuitka, or python -m nuitka to invoke it.
Theoretically, something similar can be easy for dict.

Python Command Line Flags

As I already had at heart compiling Python and optimising my script distribution, I preferred not to suffer from the musl libc or even to need to install bash in Alpine in order to be in a position to build.

Visual Code selector they are more distinct from Nuitka code modules.
Windows, “dll-dependencies“ with selective values.
Make an effort to remove all API demands given programs.

Removing The Console Window#

Keep the debug information, so valgrind, gdb, etc. work nicely.
Include data files by filenames in the distribution.

• Consider e.g. the module attribute __name__ which likely is only ever read, so its value could be predicted to a continuing string known at compile time.
• But as a workaround, because the C++03
• My Python script that we will bake in an exceedingly tiny container is really a CLI performing HTTP requests against an API.
• Isn’t optional from the perspective of the code following.
• This makes things alot easier and faster to resolve when you run into any issues.

Using clang-cl.exe of Clang7 was faster than MSVC, but still significantly slower than MinGW64, and it’ll be harder to utilize, so it is not recommended.
It’s also driven by way of a configuration file, anti-bloat.yml you can contribute to, removing typical bloat from packages.
Feel free to enhance it and make PRs towards Nuitka with it.
Nuitka must learn effective caching to manage this in the future.

Memory Issues And Compiler Bugs

to its bugs or memory usage work fine with clang on Linux.
On Windows, this could still be a choice, but it has to be implemented first for the automatic downloaded gcc, that could contain it.
Since MSVC is known to become more memory effective anyway, you should go there, and if you need to use Clang, there’s support for the main one within MSVC.
When you compile from the living installation, which could well have many optional dependencies of one’s software installed.
Some software, will then have imports on these, and Nuitka will compile them aswell.