Happy Holidays and All the Best in 2025!

Another year is almost gone, and it’s time for my traditional “I will disappear until mid-January” retreat (also, don’t expect me to read my email until I’m back).

I hope you’ll also be able to disconnect from the crazy pace of the networking world, forget the “AI will make networking engineers obsolete” shenanigans (hint: SDN did not), and focus on your loved ones. I would also like to wish you all the best in 2025!

I will probably get bored sometime in late December, so expect a few new netlab features in early January.

AI for Network Engineers: Recurrent Neural Neural Network (RNN)

 Introduction

So far, this book has introduced two neural network architectures. The first one, the Feed-Forward Neural Network (FNN), works well for simple tasks, such as recognizing handwritten digits in small-sized images. The second one, the Convolutional Neural Network (CNN), is designed for processing larger images. CNNs can identify objects in images even when the location or orientation of the object changes.

This chapter introduces the Recurrent Neural Network (RNN). Unlike FNNs and CNNs, an RNN’s inputs include not only the current data but also all the inputs it has processed previously. In other words, an RNN preserves and uses historical data. This is achieved by feeding the output of the previous time step back into the hidden layer along with the current input vector.

Although RNNs can be used for predicting sequential data of variable lengths, such as sales figures or a patient’s historical health records, this chapter focuses on how RNNs can perform character-based text autocompletion. The upcoming chapters will explore word-based text prediction.


Text Datasets

For training the RNN model, we typically use text datasets like IMDB Reviews or the Wikipedia Text Corpus. However, in this chapter, we simplify the process by using a tailored dataset containing Continue reading

From Python to Go 007. (Data)Classes, Structs, and Custom Data Types.

Hello my friend,

Today we are going to talk about the last data type, which in generally exists in Python and Go (Golang), and which we need dearly for all meaningful applications including network and IT infrastructure automation. We are talking about structured, typed data, which is represented in Python in form of objects and classes and in Go (Golang) in form of structs. These structures are truly powerful and once you figure out how to use them, I’m quite confident you will be using it everywhere, where you can.

You Talk So Much About Go (Golang), But You Offer Python In Trainings. Why?

This question I’ve been asked rather frequently recently. Indeed, why do we in each blog post talk about Network Automation Trainings, which gravitate around Python/Ansible duet. The answer is straightforward: whilst Go (Golang) is very powerful as we showing it in these blogs, its usage in many cases is justified only in very high-scale environment. For majority of networks and IT systems, Python is great. It is suffice to say that entire OpenStack is built in Python. And ourselves we use it extensively in many customers’ systems. Go (Golang) is useful as extension of automation skills, Continue reading

Worth Reading: Hard Truths about AI-assisted Coding

Addy Osmani published an excellent overview of the challenges of AI-assisted coding. They apply equally well to the “AI will generate device configurations for me” or “AI will troubleshoot my network” ideas (ignoring for the moment the impact of the orders-of-magnitude smaller training set), so it’s definitely worth reading.

I particularly liked the “‌AI is like having a very eager junior developer on your team” take, as well as the description of the “70% problem” (AI will get you 70% there, but the last 30% will be frustrating) – a phenomenon perfectly illustrated by the following diagram by Forrest Brazeal:

Read more …

N4N007: Performance vs. Cost

When building your network and buying equipment, is performance or cost more important? On today’s episode, we discuss the balance between performance and cost in selecting networking equipment. We also cover other considerations, including why you should understand client and network needs, the fluid nature of pricing, and the necessity of creative problem-solving. Bonus material: ... Read more »

HN761: Who Are You Building Automation For? An AutoCon2 Roundtable

Today’s Heavy Networking comes to you from the AutoCon2 tech event being held in Westminster, Colorado. This episode was recorded in conference room on site at AutoCon2 in November, 2024. The format? Roundtable. Four network automators have raised their hand and brought topics they want to discuss. Those topics include: Crafting tools to “listen” to... Read more »

Use Disaggregated BGP Prefixes to Influence Inbound Internet Traffic

As much as I love explaining how to use BGP in an optimal way, sometimes we have to do what we know is bad to get the job done. For example, if you have to deal with clueless ISPs who cannot figure out how to use BGP communities, you might be forced to use the Big Hammer of disaggregated prefixes. You can practice how that works in the next BGP lab exercise.

Click here to start the lab in your browser using GitHub Codespaces (or set up your own lab infrastructure). After starting the lab environment, change the directory to policy/b-disaggregate and execute netlab up.

How Long Before Broadcom Makes More AI Compute Engines Than Nvidia?

Chip maker and enterprise software player Broadcom announced its financial results for the final quarter of its fiscal 2024 today, which ended on November third, and all we kept thinking about as chief executive officer Hock Tan went over the numbers was the question in the title above. …

How Long Before Broadcom Makes More AI Compute Engines Than Nvidia? was written by Timothy Prickett Morgan at The Next Platform.

N4N006: Packet Analysis Basics

Packet analysis can be your friend for troubleshooting network problems. In this episode, hosts Ethan Banks and Holly Metlitzky explore packet analysis, They discuss tools such as Wireshark and Tcpdump, explain their functionalities, and talk about the importance of filtering data for effective analysis. Listeners are encouraged to engage with Wireshark and other tools  themselves.... Read more »

Getting Started with Infrahub

Getting Started with Infrahub

If you're in the Network Automation space or attended one of the last two Autocon events, you might have come across a new tool called 'Infrahub' from OpsMill. I've been keeping an eye on it and experimenting with the product for some time now. In this blog post, we'll cover how to install Infrahub, what it is, and walk through a simple example to get you started. Let's dive in.

Infrahub Installation

Installing Infrahub is straightforward if you're familiar with Docker and have it installed. For this example, I'm using an Ubuntu 22.04 server with Docker and Docker Compose already set up. Here's all I had to do.

  1. Clone the Infrahub repository
  2. Run a single docker command to bring up the services
suresh@infrahub:~$ git clone https://github.com/opsmill/infrahub.git
Cloning into 'infrahub'...
remote: Enumerating objects: 95389, done.
remote: Counting objects: 100% (5707/5707), done.
remote: Compressing objects: 100% (2801/2801), done.
remote: Total 95389 (delta 3698), reused 4482 (delta 2877), pack-reused 89682
Receiving objects: 100% (95389/95389), 136.18 MiB | 40.26 MiB/s, done.
Resolving deltas: 100% (69451/69451), done.
suresh@infrahub:~$ cd infrahub/
suresh@infrahub:~/infrahub$ docker-compose up -d
[+] Running 70/7
 ✔ message-queue 10 layers [⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿]      		0B/0B      Pulled
 ✔ task-manager 13 layers  Continue reading
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