AVD Quickstart Containerlab

Overview

AVD Quickstart Containerlab

WARNING This repository is still under construction. It's fully functional, but has number of limitations. For example:

  • README is still work-in-progress
  • Lab configuration and adresses are hardcoded and have to be redefined in many different files if you setup is different. That will be simplified before the final release.
  • Some workflow and code optimization required.

Overview

This repository helps to build your own AVD test lab based on Containerlab in minutes. The main target is to provide an easy way to build the environment to learn and test AVD automation. The lab can be used together with CVP VM, but it's not mandatory.

WARNING: if CVP VM is part of the lab, make sure that it's reachable and credentials configured on CVP are matching the lab.

Release Notes:

  • 0.1
    • initial release with many shortcuts
  • 0.2
    • Fix bugs.
    • Improve lab topology.
    • Improve lab workflow.
    • Add EVPN AA scenario.

Lab Prerequisites

The lab requires a single Linux host (Ubuntu server recommended) with Docker and Containerlab installed. It's possible to run Containerlab on MacOS, but that was not tested. Dedicated Linux machine is currently the preferred option.

To test AVD with CVP, KVM can be installed on the same host. To install KVM, check this guide or any other resource available on internet. Once KVM is installed, you can use one of the following repositories to install CVP:

It is definitely possible to run CVP on a dedicated host and a different hypervisor as long as it can be reached by cLab devices.

NOTE: to use CVP VM with container lab it's not required to recompile Linux core. That's only required if you plan to use vEOS on KVM for you lab setup.

The lab setup diagram:

lab diagram

How To Use The Lab

  1. Clone this repository to your lab host: git clone https://github.com/arista-netdevops-community/avd-quickstart-containerlab.git
  2. It is recommended to remove git remote as changes are not supposed to be pushed to the origin: git remote remove origin
  3. Change to the lab directory: cd avd-quickstart-containerlab
  4. Before running the lab it is recommended to create a dedicated git branch for you lab experiments to keep original branch clean.
  5. Check makefile help for the list of commands available: make help
[email protected]:~/avd-quickstart-containerlab$ make help
avd_build_cvp                  build configs and configure switches via eAPI
avd_build_eapi                 build configs and configure switches via eAPI
build                          Build docker image
clab_deploy                    Deploy ceos lab
clab_destroy                   Destroy ceos lab
clab_graph                     Build lab graph
help                           Display help message
inventory_evpn_aa              onboard devices to CVP
inventory_evpn_mlag            onboard devices to CVP
onboard                        onboard devices to CVP
rm                             Remove all containerlab directories
run                            run docker image. This requires cLab "custom_mgmt" to be present
  1. If you don't have cEOS image on your host yet, download it from arista.com and import. Make sure that image name is matching the parameters defined in CSVs_EVPN_AA/clab.yml or CSVs_EVPN_MLAG/clab.yml
  2. Use make build to build avd-quickstart:latest container image. If that was done earlier and the image already exists, you can skip this step.
  3. Run make inventory_evpn_aa or make inventory_evpn_mlag to build the inventory for EVPN AA or MLAG scenario. Ideally AVD inventroy must be a different repository, but for simplicity script will generate inventory in the current directory.
  4. Review the inventory generated by avd-quickstart. You can optionally git commit the changes.
  5. Run make clab_deploy to build the containerlab. Wait until the deployment will finish.
  6. Execute make run to run avd-quickstart container.
  7. If CVP VM is used in the lab, onboard cLab switches with make onboard. Once the script behind this shortcut wil finish, devices will appear in the CVP inventory.
  8. To execute Ansible AVD playbook, use make avd_build_eapi or make avd_build_cvp shortcuts. That will execute playbook/fabric-deploy-eapi.yml or playbook/fabric-deploy-cvp.yml.
  9. Run make avd_validate to execute AVD state validation playbook playbooks/validate-states.yml.
  10. Run make avd_snapshot if you want to collect a network snapshot with playbooks/snapshot.yml.
  11. Connect to hosts and switches and run some pings, show commands, etc. To connect to a lab device, you can type it's hostname in the container:

connect to a device from the container

NOTE: device hostnames are currently hardcoded inside the avd-quickstart container. If you have customized the inventory, ssh to the device manually. That will be improved in the coming versions.

You can optionally git commit the changes and start playing with the lab. Use CSVs to add some VLANs, etc. for example. Re-generate the inventory and check how the AVD repository data changes.

How To Destroy The Lab

  1. Exit the avd-quickstart container by typing exit
  2. Execute make clab_destroy to destroy the containerlab.
  3. Execute make rm to delete the generated AVD inventory.
Owner
Carl Buchmann
Systems Engineer @ Arista Networks Passionate about designing networks and automating them!
Carl Buchmann
the code of the paper: Recurrent Multi-view Alignment Network for Unsupervised Surface Registration (CVPR 2021)

RMA-Net This repo is the implementation of the paper: Recurrent Multi-view Alignment Network for Unsupervised Surface Registration (CVPR 2021). Paper

Wanquan Feng 205 Nov 09, 2022
LeafSnap replicated using deep neural networks to test accuracy compared to traditional computer vision methods.

Deep-Leafsnap Convolutional Neural Networks have become largely popular in image tasks such as image classification recently largely due to to Krizhev

Sujith Vishwajith 48 Nov 27, 2022
Really awesome semantic segmentation

really-awesome-semantic-segmentation A list of all papers on Semantic Segmentation and the datasets they use. This site is maintained by Holger Caesar

Holger Caesar 400 Nov 28, 2022
Team Enigma at ArgMining 2021 Shared Task: Leveraging Pretrained Language Models for Key Point Matching

Team Enigma at ArgMining 2021 Shared Task: Leveraging Pretrained Language Models for Key Point Matching This is our attempt of the shared task on Quan

Manav Nitin Kapadnis 12 Jul 08, 2022
U^2-Net - Portrait matting This repository explores possibilities of using the original u^2-net model for portrait matting.

U^2-Net - Portrait matting This repository explores possibilities of using the original u^2-net model for portrait matting.

Dennis Bappert 104 Nov 25, 2022
Multi-task Multi-agent Soft Actor Critic for SMAC

Multi-task Multi-agent Soft Actor Critic for SMAC Overview The CARE formulti-task: Multi-Task Reinforcement Learning with Context-based Representation

RuanJingqing 8 Sep 30, 2022
Collect super-resolution related papers, data, repositories

Collect super-resolution related papers, data, repositories

WangChaofeng 1.7k Jan 03, 2023
Implementation of the paper "Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning"

Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning This is the implementation of the paper "Self-Promoted Prototype Refinement

Kai Zhu 78 Dec 02, 2022
IMBENS: class-imbalanced ensemble learning in Python.

IMBENS: class-imbalanced ensemble learning in Python. Links: [Documentation] [Gallery] [PyPI] [Changelog] [Source] [Download] [知乎/Zhihu] [中文README] [a

Zhining Liu 176 Jan 04, 2023
Deep Unsupervised 3D SfM Face Reconstruction Based on Massive Landmark Bundle Adjustment.

(ACMMM 2021 Oral) SfM Face Reconstruction Based on Massive Landmark Bundle Adjustment This repository shows two tasks: Face landmark detection and Fac

BoomStar 51 Dec 13, 2022
An Extendible (General) Continual Learning Framework based on Pytorch - official codebase of Dark Experience for General Continual Learning

Mammoth - An Extendible (General) Continual Learning Framework for Pytorch NEWS STAY TUNED: We are working on an update of this repository to include

AImageLab 277 Dec 28, 2022
SwinTrack: A Simple and Strong Baseline for Transformer Tracking

SwinTrack This is the official repo for SwinTrack. A Simple and Strong Baseline Prerequisites Environment conda (recommended) conda create -y -n SwinT

LitingLin 196 Jan 04, 2023
NDE: Climate Modeling with Neural Diffusion Equation, ICDM'21

Climate Modeling with Neural Diffusion Equation Introduction This is the repository of our accepted ICDM 2021 paper "Climate Modeling with Neural Diff

Jeehyun Hwang 5 Dec 18, 2022
PyTorch implementation for the visual prior component (i.e. perception module) of the Visually Grounded Physics Learner [Li et al., 2020].

VGPL-Visual-Prior PyTorch implementation for the visual prior component (i.e. perception module) of the Visually Grounded Physics Learner (VGPL). Give

Toru 8 Dec 29, 2022
JittorVis - Visual understanding of deep learning models

JittorVis: Visual understanding of deep learning model JittorVis is an open-source library for understanding the inner workings of Jittor models by vi

thu-vis 182 Jan 06, 2023
A set of examples around hub for creating and processing datasets

Examples for Hub - Dataset Format for AI A repository showcasing examples of using Hub Uploading Dataset Places365 Colab Tutorials Notebook Link Getti

Activeloop 11 Dec 14, 2022
TransCD: Scene Change Detection via Transformer-based Architecture

TransCD: Scene Change Detection via Transformer-based Architecture

wangzhixue 29 Dec 11, 2022
A program to recognize fruits on pictures or videos using yolov5

Yolov5 Fruits Detector Requirements Either Linux or Windows. We recommend Linux for better performance. Python 3.6+ and PyTorch 1.7+. Installation To

Fateme Zamanian 30 Jan 06, 2023
Breaking the Dilemma of Medical Image-to-image Translation

Breaking the Dilemma of Medical Image-to-image Translation Supervised Pix2Pix and unsupervised Cycle-consistency are two modes that dominate the field

Kid Liet 86 Dec 21, 2022
A pytorch implementation of Paper "Improved Training of Wasserstein GANs"

WGAN-GP An pytorch implementation of Paper "Improved Training of Wasserstein GANs". Prerequisites Python, NumPy, SciPy, Matplotlib A recent NVIDIA GPU

Marvin Cao 1.4k Dec 14, 2022