Ubuntu System Monitors

A suite of extensions for Ubuntu's top bar that show CPU, GPU, RAM and disk usage in real time

Python Ubuntu GNOME Shell

Four lightweight monitors that integrate into Ubuntu's top bar (GNOME Shell) to keep an eye on your system at a glance, without opening external applications:

Before you start

All four monitors are cloned and installed the same way. Before installing the dependencies, make sure you don't have any venv or conda environment active:

	
		
			< >
			Input
		
		
			Bash
			
		
	
	
		if [ -n "$VIRTUAL_ENV" ]; then
    deactivate
fi
if command -v conda &>/dev/null; then
    conda deactivate
fi
	
	Copied

And to make any of them start with the system, run inside its folder:

	
		
			< >
			Input
		
		
			Bash
			
		
	
	
		./add_to_startup.sh
	
	Copied

CPU Monitor

Processor usage in real time: frequency, per-core temperature, load and thermal throttling detection.

	
		
			< >
			Input
		
		
			Bash
			
		
	
	
		git clone https://github.com/maximofn/cpu_monitor.git
	
	Copied

Dependencies:

	
		
			< >
			Input
		
		
			Bash
			
		
	
	
		sudo apt install lm-sensors
sudo sensors-detect
sudo apt install psensor
	
	Copied

Answer yes to every sensors-detect question.

GPU Monitor

NVIDIA graphics card usage in real time from Ubuntu's top bar.

	
		
			< >
			Input
		
		
			Bash
			
		
	
	
		git clone https://github.com/maximofn/gpu_monitor.git
	
	Copied

Dependencies:

	
		
			< >
			Input
		
		
			Bash
			
		
	
	
		sudo apt-get install python3-gi python3-gi-cairo gir1.2-gtk-3.0
sudo apt-get install gir1.2-appindicator3-0.1
pip3 install nvidia-ml-py3
pip3 install pynvml
	
	Copied

RAM Monitor

System memory in real time from Ubuntu's top bar.

	
		
			< >
			Input
		
		
			Bash
			
		
	
	
		git clone https://github.com/maximofn/ram_monitor.git
	
	Copied

Dependencies (same as CPU Monitor):

	
		
			< >
			Input
		
		
			Bash
			
		
	
	
		sudo apt install lm-sensors
sudo sensors-detect
sudo apt install psensor
	
	Copied

Disk Monitor

Disk usage in real time from Ubuntu's top bar.

	
		
			< >
			Input
		
		
			Bash
			
		
	
	
		git clone https://github.com/maximofn/disk_monitor.git
	
	Copied

Dependencies (same as CPU Monitor):

	
		
			< >
			Input
		
		
			Bash
			
		
	
	
		sudo apt install lm-sensors
sudo sensors-detect
sudo apt install psensor
	
	Copied

Support and related projects

If you liked them, consider starring the repositories, but if you really liked them, consider buying me a coffee.

Continue reading

RL environments for LLMs: how they're built, scored and scaled

Six frameworks build the same reinforcement learning environment: eleven components, four reward patterns, server or in-process, and how to scale to thousands.

NapMem: an agent's memory isn't retrieved, it's navigated

An agent's memory shouldn't be a chunk of context it's handed, but a place it moves through: NapMem's pyramid and how it...

Quarq Agent: 98.2% on LongMemEval with an agent that remembers

How an open-source agent scores 98.2% on LongMemEval: layered memory, three models, HyDE, two-pass retrieval and rules f...

Last posts -->

Have you seen these projects?

Gymnasia

LangGraph Deep Researcher

Tau

View all projects -->

>_ Available for projects

Do you have an AI project?

Let's talk.

maximofn@gmail.com

Machine Learning and AI specialist. I develop solutions with generative AI, intelligent agents and custom models.

Write me LinkedIn

Do you want to watch any talk?

The rice journey: building an agent in a gym and nutrition app

Interactive 3D talk on the Gymnasia agent: where data lives, from plate to data, tools, duplicates, health limits and how to test it all.

Best practices building agents with Claude Code

Technical talk: skills, subagents, slash commands and MCPs in Claude Code

Last talks -->

Do you want to improve with these tips?

Best practices building agents with Claude Code

Technical talk: skills, subagents, slash commands and MCPs in Claude Code

o1 prompt engineering

Create better prompts for o1 following an example

Memory profiler

See the memory usage of a script

Last tips -->

Use this locally

Hugging Face spaces allow us to run models with very simple demos, but what if the demo breaks? Or if the user deletes it? That's why I've created docker containers with some interesting spaces, to be able to use them locally, whatever happens. In fact, if you click on any project view button, it may take you to a space that doesn't work.