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

GitHub Repository

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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

Code needed to reproduce the examples found in "The Temporal Robustness of Stochastic Signals"

Open-CyKG: An Open Cyber Threat Intelligence Knowledge Graph

Real-time analysis of intracranial neurophysiology recordings.

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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

Code needed to reproduce the examples found in "The Temporal Robustness of Stochastic Signals"

Open-CyKG: An Open Cyber Threat Intelligence Knowledge Graph

Real-time analysis of intracranial neurophysiology recordings.

JupyterLite demo deployed to GitHub Pages 🚀

modelvshuman is a Python library to benchmark the gap between human and machine vision

Time-stretch audio clips quickly with PyTorch (CUDA supported)! Additional utilities for searching efficient transformations are included.

Standalone pre-training recipe with JAX+Flax

Attention-based Transformation from Latent Features to Point Clouds (AAAI 2022)

Flexible-Modal Face Anti-Spoofing: A Benchmark

A Blender python script for getting asset browser custom preview images for objects and collections.

MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python

Cancer Drug Response Prediction via a Hybrid Graph Convolutional Network

Pytorch implementation for reproducing StackGAN_v2 results in the paper StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks

Reviving Iterative Training with Mask Guidance for Interactive Segmentation

Cross-modal Retrieval using Transformer Encoder Reasoning Networks (TERN). With use of Metric Learning and FAISS for fast similarity search on GPU

🧠 A PyTorch implementation of 'Deep CORAL: Correlation Alignment for Deep Domain Adaptation.', ECCV 2016

Python interface for the DIGIT tactile sensor

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Code For TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations (EMNLP2021)

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⚙️ `How it works`

📝 `Dependencies`

🚀 `Executing the script`