pangyuteng

Using SSH through airplane WiFi that blocks port 22 (via GCP and Ubuntu)

SUMMARY

Open WIFI often allow only traffic via http and https ports. This gist contains steps to allow you to ssh to a machine by first creating a VM, switching the ssh port to listen to port 80. So now, you can ssh (port 80) into the VM, in order to ssh (port 22) into your desired machine.

original gist by guillochon, this Fork contains reformatted steps, and I switched OS from CentOs to Ubuntu... just as an exercise to get famaliarized with GCP.

https://gist.github.com/guillochon/eeaa54b328952d260472c14c559f698a

Estimated time to complete the below steps is 5 to 10 minutes (assuming the internet connection is good, and GCP is operating smoothly).

rdp with reverse tunnel

TARGET (within VPN) -- INTERMEDIATE -- LAPTOP

If you want to connect to TARGET and are not able to setup vpn from LAPTOP to TARGET. You can try RDP with the below setup via an intermediate server (in the cloud, or local server at home).

run below in TARGET:

** PLEASE NOTE ***
there are many other solutions for computing SUV, see below link for more info.
https://qibawiki.rsna.org/index.php/Standardized_Uptake_Value_(SUV)

This gist contains code to perform the below with python (3.7).

+ download data from TCIA via tcia-rest api. (download.py, tciaclient.py)

new pc setup ( work windows OS)

just install the below only (work machine, win os) for git bash

wsltty (to use as main terminal, because copy-pasting works in terminal).
- you'll first need to install wsl and docker-desktop

	class ConcatSpatialCoordinate(Layer):
	def __init__(self, **kwargs):
	"""Concatenates the (x, y) coordinate normalised to 0-1 to each spatial location in the image.
	Allows a network to learn spatial bias. Has been explored in at least one paper,
	"An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution"
	https://arxiv.org/abs/1807.03247

	Improves performance where spatial bias is appropriate.

	Works with dynamic shapes.

	FROM ubuntu:18.04

	RUN apt-get update && apt-get install -yq \
	autotools-dev \
	build-essential \
	ca-certificates \
	vim curl wget \
	cmake g++

	WORKDIR /opt/sources

	# sample code to augment image,mask and keypoints with albumentations

	import numpy as np
	import matplotlib.pyplot as plt
	%matplotlib inline
	import albumentations as A

	image = np.random.rand(256,256)
	image[64:128,64:128]+=0.5
	mask = np.zeros((256,256))


	FROM nvidia/cuda:10.0-cudnn7-devel-ubuntu18.04

	# install python 3.7 and pip
	RUN apt-get update \
	&& apt-get install -y --no-install-recommends \
	build-essential \
	software-properties-common \
	pkg-config \
	rsync \