yt-dlp can be installed using official releases or via package manager.

Unix-like operating system

curl -L https://github.com/yt-dlp/yt-dlp/releases/latest/download/yt-dlp -o ~/.local/bin/yt-dlp
chmod a+rx ~/.local/bin/yt-dlp  # Make executable

To update yt-dlp in Unix-like operating system

yt-dlp -U

Homebrew MacOS

brew install yt-dlp

Ubuntu

sudo add-apt-repository ppa:tomtomtom/yt-dlp # Add ppa repo to apt
sudo apt update # Update package list
sudo apt install yt-dlp # Install yt-dlp

Snap

sudo snap install --edge yt-dlp

Windows operating system

yt-dlp is also available for Windows operating system by using, winget:

winget install yt-dlp

Microsoft Windows binary package

The binary package for Microsoft Windows binary package can be downloaded from yt-dlp GitHub release page

Note that yt-dlp requires ffmpeg windows binaries which can be obtained from gyan.dev’s Codex FFMPEG Build

Please refer to this post for more information on the tips and tricks on using yt-dlp.

The post yt-dlp – a verstatile video downloader tool appeared first on mypapit gnu/linux .

https://gist.github.com/mypapit/01770a1fa47826db1f50caf45f6a9035.js

Hello everybody. I’m sharing a handy script I use to semi-automatically check my GPU temperature and turn on the fan if things get a bit too warm.

I run this on my dedicated SoHo PC server, which hosts a modest LLM and a Minecraft server (yes, both at the same time).

Feel free to drop any suggestions or ideas in the comments. I’d love to hear how you’d improve it!

p/s: I’ve used GWE and CoolerControl, but it doesn’t fit my use case.

The post Semi-automatic bash script for triggering NVIDIA GPU fan in Linux appeared first on mypapit gnu/linux .

This is a guide to install Tensorflow with RTX50 (Blackwell) card GPU acceleration with the python wheel.

First you need to download this python wheel. It is compiled for Ubuntu 24.04 or WSL2 with Ubuntu 24.04.

https://github.com/mypapit/tensorflowRTX50/releases

Then you may need to create a python virtual environment for the wheel.

sudo apt install python3-pip
sudo apt install python3-venv

# replace tensorflow220 with your preferred env name
python3 -m venv tensorflow220

Then you need to activate the environment.

source tensorflow220/bin/activate

Then you may install the tensorflow wheel. To make it easier, you can also install it alongside other requirements.

pip install tensorflow-2.20.0dev0+selfbuild-cp312-cp312-linux_x86_64.whl seaborn pandas matplotlib opencv-python pillow imutils pydot graphviz librosa

Then, you must install CUDA 12.8.1 and CUDNN 9.8.0, please refer to these links:

• https://developer.nvidia.com/cuda-12-8-1-download-archive
• https://developer.nvidia.com/cudnn-9-8-0-download-archive?target_os=Linux&target_arch=x86_64

Follow the instructions on the NVIDIA websites to install both cuda-12.8.1 and CUDNN 9.8.0

Install Nvidia Linux Driver (not required for WSL2)

Then you must install the latest NVIDIA Linux driver.

At the time of the writing, the latest driver is 570.169 (June 17, 2025)

The NVIDIA Linux driver is not required for Ubuntu 24.04 under WSL2. For that, you need to install Microsoft Windows NVIDIA driver available from NVIDIA App.

About the python wheel file:

The python wheel file is compiled in Ubuntu 24.04 with llvm and CUDA Toolkit 12.8.1. It supports compute_86, compute_89 and compute_120 cuda devices, which correspond to NVIDIA GPU card with Turing, Ada and Blackwell architectures (or in layman terms: RTX 30, RTX 40 and RTX 50) series.

The python wheel file also comes with AVX, AVX2 and FMA support for both Intel and AMD cpu acceleration.

The Tensorflow version installed is the Tensorflow 2.20dev edition, nightly from :

https://github.com/tensorflow/tensorflow

Finally you can test the Tensorflow binaries with the following command.

The post Install Tensorflow with RTX 50 series GPU acceleration with Python wheel for Ubuntu 24.04 (and Windows WSL2) appeared first on mypapit gnu/linux .

This handy script is useful for webserver traffic analysis task. It can be use to detect abusive IP address or spambots which accesses the webserver.

ystem administrators can utilize the generated list of IP addresses to enhance webserver security by blocking those IPs deemed potentially harmful or suspicious.

The post Bash code for analyzing web server traffic to determine which ip accessed the web server the most appeared first on mypapit gnu/linux .

So most implementation would settle with Apache HTTPD server or install it behind nginx reverse proxy.

However the problem is that the OJS3 behave badly when placed behind nginx reverse proxy, especially when the reverse proxy is using HTTPS / TLS. This messed up the based URL in the OJS3, subsequently causing some resources from the website to be unavailable.

To solve this, you only need to add a single line in the Apache HTTPD site configuration file.

        SetEnvIf X-Forwarded-Proto "https" HTTPS=on

A full blown example is included via gist

The post Configuration to run OJS 3 smoothly behind nginx reverse proxy appeared first on mypapit gnu/linux .