Introduction to Arista PyeAPI

Introduction to Arista PyeAPI

The Python Client for eAPI (pyeapi) is a Python library that simplifies working with Arista eAPI, removing the need to deal with the specifics of its implementation. It's straightforward to configure and use. In this blog post, we'll look at how to install pyeapi and go through some examples.

If you're familiar with Arista's eAPI, you know that you can browse to the device's IP in a browser, run commands, and get the output directly. You can also achieve the same result using Python, but it typically requires understanding which libraries to use and how to construct the REST API requests.

However, pyeapi simplifies all of this. You don't need to worry about what's happening behind the scenes. Below is a screenshot of running show vlan command via the REST API, and in the following examples, we'll see how to get the same output using pyeapi.

Introduction to Arista PyeAPI

PyeAPI Installation

To install pyeapi, you can use pip, which is the standard package manager for Python. It's a good practice to use a virtual environment (venv) to keep your dependencies isolated and avoid conflicts with other projects. First, create and activate a virtual environment. Once your virtual Continue reading

Sandia To Push Both HPC And AI With Cerebras “Kingfisher” Cluster

Lawrence Livermore National Laboratory, Sandia National Laboratories, and Los Alamos National Laboratory are known by the shorthand “Tri-Labs” in the HPC community, but these HPC centers perhaps could be called “Try-Labs” because they historically have tried just about any new architecture to see what promise it might hold in advancing the missions of the US Department of Energy. …

Sandia To Push Both HPC And AI With Cerebras “Kingfisher” Cluster was written by Timothy Prickett Morgan at The Next Platform.

N4N003: What’s a VLAN?

Today we explore Virtual Local Area Networks (VLANs). This topic was prompted by a question from college student Douglas. We’ll explain the fundamental concepts of VLANs, such as their role in segmenting and managing network traffic, and the technical details for implementation. We’ll also address key topics including VLAN tags, access and trunk ports, and... Read more »

What’s new in Cloudflare: Account Owned Tokens and Zaraz Automated Actions

In October 2024, we started publishing roundup blog posts to share the latest features and updates from our teams. Today, we are announcing general availability for Account Owned Tokens, which allow organizations to improve access control for their Cloudflare services. Additionally, we are launching Zaraz Automated Actions, which is a new feature designed to streamline event tracking and tool integration when setting up third-party tools. By automating common actions like pageviews, custom events, and e-commerce tracking, it removes the need for manual configurations.

Improving access control for Cloudflare services with Account Owned Tokens

Cloudflare is critical infrastructure for the Internet, and we understand that many of the organizations that build on Cloudflare rely on apps and integrations outside the platform to make their lives easier. In order to allow access to Cloudflare resources, these apps and integrations interact with Cloudflare via our API, enabled by access tokens and API keys. Today, the API Access Tokens and API keys on the Cloudflare platform are owned by individual users, which can lead to some difficulty representing services, and adds an additional dependency on managing users alongside token permissions.

What’s new about Account Owned Tokens

First, a little explanation because the terms can Continue reading

AI for Network Engineers: Convolutional Neural Network

 Introduction


The previous chapter explained how Feed-forward Neural Networks (FNNs) can be used for multi-class classification of 28 x 28 pixel handwritten digits from the MNIST dataset. While FNNs work well for this type of task, they have significant limitations when dealing with larger, high-resolution color images.

In neural network terminology, each RGB value of an image is treated as an input feature. For instance, a high-resolution 600 dpi RGB color image with dimensions 3.937 x 3.937 inches contains approximately 5.58 million pixels, resulting in roughly 17 million RGB values.

If we use a fully connected FNN for training, all these 17 million input values are fed into every neuron in the first hidden layer. Each neuron must compute a weighted sum based on these 17 million inputs. The memory required for storing the weights depends on the numerical precision format used. For example, using the 16-bit floating-point (FP16) format, each weight requires 2 bytes. Thus, the memory requirement per neuron would be approximately 32 MB. If the first hidden layer has 10,000 neurons, the total memory required for storing the weights in this layer would be around 316 GB.

In contrast, Convolutional Neural Networks (CNNs) use Continue reading

D2DO257: Love is in the Firmware

With the current cultural emphasis on AI and how that is changing our world, we often forget the human element of individuals and teams when building an effective software development team. On today’s Day Two DevOps, we explore the psychology behind software teams, psychological safety for those teams and how the advent of AI plays... Read more »

PP039: Securing Active Directory from a Pen Tester’s Perspective

Microsoft’s Active Directory and Entra ID are valuable targets for attackers because they store critical identity information. On today’s Packet Protector, we talk with penetration tester and security consultant Eric Kuehn about how he approaches compromising AD/Entra ID, common problems he sees during client engagements, quick wins for administrators and security pros to fortify their... Read more »

Worldwide deployment of real-time flow analytics

Industry standard sFlow telemetry is widely supported by network equipment vendors and network management platforms. However, the advent of real-time sFlow analytics has opened up a range of new applications for sFlow. The map above shows the proportion of sFlow-RT instances running in each of the over 70 countries in which it is deployed.

The following use cases are driving current deployments:

Addressing the challenge of operating AI / ML clusters is the emerging application for sFlow visibility. High speed (400/800G) data center switches needed to handle machine learning traffic flows include sFlow agents and real-time analytics are essential to optimize the network so that expensive GPU and compute resources are fully utilized, see Leveraging open technologies to monitor packet drops in AI cluster fabrics.

If you would like to see how real-time network analytics can transform network operations, Getting Started describes how to download and configure sFlow-RT analytics software for use in your network, or how to try it out using an emulator, or pre-captured data.

NB503: Apple Buys Stake in Satellite Telecom Company; Arista Enjoys AI Revenue Bump

Take a Network Break! This week we cover a serious Cisco vulnerability, SonicWall offering a firewall warranty, and a security advisory from ID provider Okta. Apple buys a stake in a satellite telecommunications company, Lumen and Google team up on a joint networking/AI deal, and we check on financial results from Arista Networks, Extreme Networks,... Read more »
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