As of January 2024, the linuxserver/unifi-controller
Docker image has been deprecated and is no longer receiving updates. This means it may no longer be secure or compatible with newer versions of UniFi devices. Users are now advised to switch to the linuxserver/unifi-network-application
image, which is actively maintained and provides the latest version of the UniFi Network Application.
In this blog post, we will cover how to run the new image in Docker and how to migrate from the old image to the new one without losing your existing configuration.
As always, if you find this post helpful, press the ‘clap’ button on the left. It means a lot to me and helps me know you enjoy this type of content.
In my homelab, I have a USW Lite 8 PoE, U6-Pro, and U6 Mesh. I had been running the older linuxserver/unifi-controller
Docker image since I first set them up. My controller was on version 8.0.34, if I remember correctly.
Recently, I moved to a new host, so I decided it was the right time to migrate to the new linuxserver/unifi-network-application
image. While checking their GitHub page, I found this note.
You Continue reading
Every recruiter and hiring manager wants people with five years of experience, but you cannot get experience without being hired into a position. How can you break this conundrum? Daniel Dib joins Tom and Russ to talk about how folks just coming into IT, or even those with lots of experience who are trying to shift their focus, can gain experience.
During the last three weeks, we were busy squashing bugs (device configuration fixes, other bug fixes). Some were recent; others were ancient pests uncovered by better integration tests. The end result: netlab release 1.9.4.
netlab release 1.9.4 passed hundreds of integration tests and should be a better choice than the previous 1.9 releases. To upgrade, execute pip3 install --upgrade networklab
.
We still missed a few quirks :( Release 1.9.4-post1 addresses those (and, unfortunately, I’m pretty sure there will be more).
sudo tcpdump -i any -s 0 -w sflow.pcap udp port 6343Run the command above on the system you are using to collect sFlow data (if you aren't yet collecting sFlow, see Agents for suggested configuration settings). Type Control-C to end the capture after 5 to 10 minutes. Copy the resulting sflow.pcap file to your laptop.
docker run --rm -it -v $PWD/sflow.pcap:/sflow.pcap sflow/sflowtool \ -r /sflow.pcap -P 1Either compile the latest version of sflowtool or, as shown above, use Docker to run the pre-built sflow/sflowtool image. The -P (Playback) option replays the trace in real-time and displays the contents of each sFlow message. Running sflowtool using Docker provides additional examples, including converting the sFlow messages into JSON format for processing by a Python script.
docker run --rm -it -v $PWD/sflow.pcap:/sflow.pcap sflow/sflowtool \ -r /sflow.pcap -f 192.168.4.198/6343 -P 1The -f (forwarding) option takes an IP address and UDP port number as arguments, in this Continue reading
Welcome back to another post on local LLMs. In this post, we’ll look at setting up a fully local coding assistant inside VSCode using the Continue extension and Ollama. Let’s get started.
As always, if you find this post helpful, press the ‘clap’ button on the left. It means a lot to me and helps me know you enjoy this type of content.
We’ve covered Ollama and Local LLMs in previous blog posts (linked below), but here’s a quick summary.
Ollama is a tool that lets you run large language models (LLMs) directly on your local machine. Local LLMs are language models that run on your computer instead of relying on cloud-based services like ChatGPT. This means you can use them without sending your data to external servers, which is great for privacy. They also work offline, so you’re not dependent on an Internet connection.
That said, it’s important to note that local models, especially on smaller setups, won’t match the speed or performance of cloud-based models like ChatGPT. These cloud models are powered by massive infrastructure, so they’re faster and often more accurate. However, the trade-off is privacy and offline access, which local LLMs provide.
When I first started using local LLMs with Ollama, I quickly realised it relies on a command-line interface to interact with the models. It also comes with an API, but let’s be honest, most of us, myself included, prefer a GUI, much like the one ChatGPT provides. There are plenty of options available, but I decided to try Open Web GUI. In this blog post, we’ll explore what Open-WebGUI is and how simple it is to set up a web-based interface for your local LLMs.
As always, if you find this post helpful, press the ‘clap’ button on the left. It means a lot to me and helps me know you enjoy this type of content.
Ollama is a tool for running local LLMs, offering privacy and control over your data. Out of the box, it lets you interact with models via the terminal or through its API. Installing Ollama is straightforward, and if you’d like a detailed guide, check out my other blog post which is linked below.
This blog post assumes you already have Ollama set up and running. For reference, I’m running this on my MacBook (M3 Pro with 18GB of RAM).
Open Continue reading
I’m going to start by saying I’m totally new to LLMs and running them locally, so I’m not going to pretend like I know what I am doing. I’ve been learning about Ollama for some time now and thought I would share it with my readers as always. This is such an interesting topic and I’m ready to go into the rabbit hole.
As always, if you find the content useful, don’t forget to press the ‘clap’ button to your left. This is one way for me to know that you like this type of content, which means a lot to me. So, let's get started.
LLMs, or Large Language Models, are a type of artificial intelligence designed to process and generate natural language. They are trained on vast amounts of text data, enabling them to understand context, identify patterns, and produce human-like responses. These models can perform various tasks such as answering questions, translating languages, summarising text, generating creative content, and assisting with coding. LLMs have gained significant attention in recent years due to their impressive performance and versatility.
I got this question from Paul:
Have you ever seen a BGP peer in the “Connect” state? In 20 years, I have never been able to see or reproduce this state, nor any mention in a debug/log. I am starting to believe that all the documentation is BS, and this does not exist.
The BGP Finite State Machine (FSM) (at least the one defined in RFC 4271 and amended in RFC 9687) is “a bit” hard to grasp but the basics haven’t changed from the ancient days of RFC 1771:
This should summarise it all.
Colab Notebook – https://colab.research.google.com/drive/1WV6J8IqEfYbn__H2g9-hOoHqfx-YD5iA?usp=sharing
Dalton Ortega, Cisco Modeling Labs Product Manager, sent me the following email as a response to my Configuring IP Addresses Won't Make You an Expert blog post:
First, your statement on Autonetkit is indeed correct. We had removed that from the product due to lack of popularity. That being said, in our roadmap we are looking at methods to reintroduce on-the-fly configuration as well as enhancing our sample labs library to make getting started with CML easier.
Secondly, CML can be run in full IaC mode because of the API-first build. In fact, many of our customers are using CML as an automated test/validation bed for their CI/CD pipelines. Tools like Ansible and Terraform are available to facilitate this inside CML too. For more details, read:
Cloudflare’s network spans more than 330 cities in over 120 countries, where we interconnect with over 13,000 network providers in order to provide a broad range of services to millions of customers. The breadth of both our network and our customer base provides us with a unique perspective on Internet resilience, enabling us to observe the impact of Internet disruptions at both a local and national level, as well as at a network level.
As we have noted in the past, this post is intended as a summary overview of observed and confirmed disruptions, and is not an exhaustive or complete list of issues that have occurred during the quarter. A larger list of detected traffic anomalies is available in the Cloudflare Radar Outage Center.
In the third quarter we covered quite a few government-directed Internet shutdowns, including many intended to prevent cheating on exams. In the fourth quarter, however, we only observed a single government-directed shutdown, this one related to protests. Terrestrial cable cuts impacted connectivity in two African countries. As we have seen multiple times before, both unexpected power outages and rolling power outages following military action resulted in Internet disruptions. Violent storms and an earthquake Continue reading