Configure SRX interfaces with Scrapli

Last update: Jan 07, 2022

Overview

Configure SRX interfaces with Scrapli

Overview

This example will show how to configure interfaces on Juniper's SRX firewalls.

In addition to the Python script, this project also ships with additional tools to help you along your way. You will find a Dockerfile for running the project in an isolated environment, and an Invoke tasks.py file for those of us that hate typing out everything all the time.

⚙️ `How it works`

The configuration is pushed to the device using the NETCONF API on board.

Let's take a second to review the documentation in the files/docs/ directory.

Name	Description
app_async.py	Configure interface `ge-0/0/1` on our firewalls with `asyncio`

📝 `Dependencies`

Refer to the Poetry Lock file located at poetry.lock for detailed descriptions on each package installed.

🚀 `Executing the script`

This project provides two unique methods of executing the playbook:

Executing with Docker | Execute with Docker

Executing with Python | Execute with Python

Owner

Calvin Remsburg

check out the Stars tab for some much needed organization

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Configure SRX interfaces with Scrapli

Related tags

Overview

Configure SRX interfaces with Scrapli

Overview

⚙️ `How it works`

📝 `Dependencies`

🚀 `Executing the script`

Owner

Calvin Remsburg

SPTAG: A library for fast approximate nearest neighbor search

Unofficial pytorch implementation of paper "One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing"

A pytorch implementation of Paper "Improved Training of Wasserstein GANs"

Official code for paper "Optimization for Oriented Object Detection via Representation Invariance Loss".

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An implementation for the loss function proposed in Decoupled Contrastive Loss paper.

Python scripts for performing stereo depth estimation using the MobileStereoNet model in Tensorflow Lite.

Configure SRX interfaces with Scrapli

Related tags

Overview

Configure SRX interfaces with Scrapli

Overview

⚙️ How it works

📝 Dependencies

🚀 Executing the script

Owner

Calvin Remsburg

SPTAG: A library for fast approximate nearest neighbor search

Unofficial pytorch implementation of paper "One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing"

A pytorch implementation of Paper "Improved Training of Wasserstein GANs"

Official code for paper "Optimization for Oriented Object Detection via Representation Invariance Loss".

Computational Pathology Toolbox developed by TIA Centre, University of Warwick.

Implementation for On Provable Benefits of Depth in Training Graph Convolutional Networks

Official implementation of deep Gaussian process (DGP)-based multi-speaker speech synthesis with PyTorch.

FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks

ManipulaTHOR, a framework that facilitates visual manipulation of objects using a robotic arm

An NLP library with Awesome pre-trained Transformer models and easy-to-use interface, supporting wide-range of NLP tasks from research to industrial applications.

Source code of the paper "Deep Learning of Latent Variable Models for Industrial Process Monitoring".

Machine Learning Models were applied to predict the mass of the brain based on gender, age ranges, and head size.

PyTorch implementation of Graph Convolutional Networks in Feature Space for Image Deblurring and Super-resolution, IJCNN 2021.

HuSpaCy: industrial-strength Hungarian natural language processing

Official Pytorch implementation of the paper "Action-Conditioned 3D Human Motion Synthesis with Transformer VAE", ICCV 2021

[CVPR 2022 Oral] EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation

This is the repository of our article published on MDPI Entropy "Feature Selection for Recommender Systems with Quantum Computing".

ICLR 2021, Fair Mixup: Fairness via Interpolation

An implementation for the loss function proposed in Decoupled Contrastive Loss paper.

Python scripts for performing stereo depth estimation using the MobileStereoNet model in Tensorflow Lite.

⚙️ `How it works`

📝 `Dependencies`

🚀 `Executing the script`