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
This is my codes that can visualize the psnr image in testing videos.

CVPR2018-Baseline-PSNRplot This is my codes that can visualize the psnr image in testing videos. Future Frame Prediction for Anomaly Detection – A New

Wenhao Yang 12 May 29, 2021
BisQue is a web-based platform designed to provide researchers with organizational and quantitative analysis tools for 5D image data. Users can extend BisQue by implementing containerized ML workflows.

Overview BisQue is a web-based platform specifically designed to provide researchers with organizational and quantitative analysis tools for up to 5D

Vision Research Lab @ UCSB 26 Nov 29, 2022
Learning RAW-to-sRGB Mappings with Inaccurately Aligned Supervision (ICCV 2021)

Learning RAW-to-sRGB Mappings with Inaccurately Aligned Supervision (ICCV 2021) PyTorch implementation of Learning RAW-to-sRGB Mappings with Inaccurat

Zhilu Zhang 53 Dec 20, 2022
Garbage Detection system which will detect objects based on whether it is plastic waste or plastics or just garbage.

Garbage Detection using Yolov5 on Jetson Nano 2gb Developer Kit. Garbage detection system which will detect objects based on whether it is plastic was

Rishikesh A. Bondade 2 May 13, 2022
PixelPick This is an official implementation of the paper "All you need are a few pixels: semantic segmentation with PixelPick."

PixelPick This is an official implementation of the paper "All you need are a few pixels: semantic segmentation with PixelPick." [Project page] [Paper

Gyungin Shin 59 Sep 25, 2022
General Assembly Capstone: NBA Game Predictor

Project 6: Predicting NBA Games Problem Statement Can I predict the results of NBA games from the back-half of a season from the opening half of the s

Adam Muhammad Klesc 1 Jan 14, 2022
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models

PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models Code accompanying CVPR'20 paper of the same title. Paper lin

Alex Damian 7k Dec 30, 2022
A Keras implementation of YOLOv3 (Tensorflow backend)

keras-yolo3 Introduction A Keras implementation of YOLOv3 (Tensorflow backend) inspired by allanzelener/YAD2K. Quick Start Download YOLOv3 weights fro

7.1k Jan 03, 2023
Scalable machine learning based time series forecasting

mlforecast Scalable machine learning based time series forecasting. Install PyPI pip install mlforecast Optional dependencies If you want more functio

Nixtla 145 Dec 24, 2022
Diverse Object-Scene Compositions For Zero-Shot Action Recognition

Diverse Object-Scene Compositions For Zero-Shot Action Recognition This repository contains the source code for the use of object-scene compositions f

7 Sep 21, 2022
Video lie detector using xgboost - A video lie detector using OpenFace and xgboost

video_lie_detector_using_xgboost a video lie detector using OpenFace and xgboost

2 Jan 11, 2022
Scalable Optical Flow-based Image Montaging and Alignment

SOFIMA SOFIMA (Scalable Optical Flow-based Image Montaging and Alignment) is a tool for stitching, aligning and warping large 2d, 3d and 4d microscopy

Google Research 16 Dec 21, 2022
PyTorch code for EMNLP 2021 paper: Don't be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System

PyTorch code for EMNLP 2021 paper: Don't be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System

Libo Qin 25 Sep 06, 2022
Implementation of CVPR 2021 paper "Spatially-invariant Style-codes Controlled Makeup Transfer"

SCGAN Implementation of CVPR 2021 paper "Spatially-invariant Style-codes Controlled Makeup Transfer" Prepare The pre-trained model is avaiable at http

118 Dec 12, 2022
Text mining project; Using distilBERT to predict authors in the classification task authorship attribution.

DistilBERT-Text-mining-authorship-attribution Dataset used: https://www.kaggle.com/azimulh/tweets-data-for-authorship-attribution-modelling/version/2

1 Jan 13, 2022
TensorFlow Metal Backend on Apple Silicon Experiments (just for fun)

tf-metal-experiments TensorFlow Metal Backend on Apple Silicon Experiments (just for fun) Setup This is tested on M1 series Apple Silicon SOC only. Te

Timothy Liu 161 Jan 03, 2023
Transformer part of 12th place solution in Riiid! Answer Correctness Prediction

kaggle_riiid Transformer part of 12th place solution in Riiid! Answer Correctness Prediction. Please see here for more information. Execution You need

Sakami Kosuke 2 Apr 23, 2022
This is an open source python repository for various python tests

Welcome to Py-tests This is an open source python repository for various python tests. This is in response to the hacktoberfest2021 challenge. It is a

Yada Martins Tisan 3 Oct 31, 2021
This repository contains an implementation of the Permutohedral Attention Module in Pytorch

Permutohedral_attention_module This repository contains an implementation of the Permutohedral Attention Module

Samuel JOUTARD 26 Nov 27, 2022
This is a re-implementation of TransGAN: Two Pure Transformers Can Make One Strong GAN (CVPR 2021) in PyTorch.

TransGAN: Two Transformers Can Make One Strong GAN [YouTube Video] Paper Authors: Yifan Jiang, Shiyu Chang, Zhangyang Wang CVPR 2021 This is re-implem

Ahmet Sarigun 79 Jan 05, 2023