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

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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

Benchmarks for Object Detection in Aerial Images

Official implementation of the paper WAV2CLIP: LEARNING ROBUST AUDIO REPRESENTATIONS FROM CLIP

ECCV18 Workshops - Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution. The training codes are in BasicSR.

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Code for ACM MM 2020 paper "NOH-NMS: Improving Pedestrian Detection by Nearby Objects Hallucination"

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Autotype on websites that have copy-paste disabled like Moodle, HackerEarth contest etc.

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PyTorch implementation of "Contrast to Divide: self-supervised pre-training for learning with noisy labels"

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⚙️ `How it works`

📝 `Dependencies`

🚀 `Executing the script`