JupyterNotebook - C/C++, Javascript, HTML, LaTex, Shell scripts in Jupyter Notebook Also run them on remote computer

Overview

JupyterNotebook

Read, write and execute C, C++, Javascript, Shell scripts, HTML, LaTex in jupyter notebook, And also execute them on remote computer

Open In Colab

Requirements

Files

Run code on remote computer

In order to run our code from local computer to remote computer we need to use ssh.
As we, have a jupyter lab/notebook is running we can simply use ssh local port forwoarding to tunnel our ipython files with kernel that we are running.

  • 1st we have to run ipython kernel in terminal
%  ipython kernel 
NOTE: When using the `ipython kernel` entry point, Ctrl-C will not work.

To exit, you will have to explicitly quit this process, by either sending
"quit" from a client, or using Ctrl-\ in UNIX-like environments.

To read more about this, see https://github.com/ipython/ipython/issues/2049


To connect another client to this kernel, use:
    --existing kernel-1839.json

As we get kernel-wxyz.json. we have to read it so we can get which port our jupyter is running.

  • For getting kernel-wxyz.json we can run jupyter --runtime --dir

*Remember in order to execute bash command in Jupyter notebook you have to add "!" before your command.

e.g. !jupyter --runtime --dir

%   jupyter --runtime --dir
/Users/mithunparab/Library/Jupyter/runtime
 %  cd /Users/mithunparab/Library/Jupyter/runtime
 %  ls
.
.
kernel-1839.json
.
.
.
 %  cat kernel-1839.json
{
  "shell_port": 50170,
  "iopub_port": 50174,
  "stdin_port": 50171,
  "control_port": 50172,
  "hb_port": 50176,
  "ip": "127.0.0.1",
  "key": "6a45fe25-2wegc5erw3uro4fw8rw3",
  "transport": "tcp",
  "signature_scheme": "hmac-sha256",
  "kernel_name": ""
}                    
  • After we get the ports, we can do local ssh port forwording

Note: Try to use key based authentication for ssh for security and avoid repeatability of password.

% ssh [email protected] -f -N -L 50170:127.0.0.1:50170
% ssh [email protected] -f -N -L 50174:127.0.0.1:50174
% ssh [email protected] -f -N -L 50171:127.0.0.1:50171
% ssh [email protected] -f -N -L 50172:127.0.0.1:50172
  • copy kernel-wxyz.json to remote computer
% rsync -av [email protected]:.ipython/profile_default/security/kernel-1839.json ~/.ipython/profile_default/security/kernel-1839.json
  • That's it now you can start ipkernel on your remote computer with aboved kernel
% ipython3 console --existing kernel-1839.json

Note:

In Jupyter notebook, LaTex syntax can be execucate using magic tag %%latex
In order to convert yout LaTex to PDF you need to install nbconvert and follow this link for using latex tool of your choice
%%latex supports in Jupyter Notebook but may not work in Google colab

To propose and implement a multi-class classification approach to disaster assessment from the given data set of post-earthquake satellite imagery.

To propose and implement a multi-class classification approach to disaster assessment from the given data set of post-earthquake satellite imagery.

Kunal Wadhwa 2 Jan 05, 2022
An open source Python package for plasma science that is under development

PlasmaPy PlasmaPy is an open source, community-developed Python 3.7+ package for plasma science. PlasmaPy intends to be for plasma science what Astrop

PlasmaPy 444 Jan 07, 2023
What can linearized neural networks actually say about generalization?

What can linearized neural networks actually say about generalization? This is the source code to reproduce the experiments of the NeurIPS 2021 paper

gortizji 11 Dec 09, 2022
😊 Python module for face feature changing

PyWarping Python module for face feature changing Installation pip install pywarping If you get an error: No such file or directory: 'cmake': 'cmake',

Dopevog 10 Sep 10, 2021
Simulation code and tutorial for BBHnet training data

Simulation Dataset for BBHnet NOTE: OLD README, UPDATE IN PROGRESS We generate simulation dataset to train BBHnet, our deep learning framework for det

0 May 31, 2022
The devkit of the nuPlan dataset.

The devkit of the nuPlan dataset.

Motional 264 Jan 03, 2023
DrWhy is the collection of tools for eXplainable AI (XAI). It's based on shared principles and simple grammar for exploration, explanation and visualisation of predictive models.

Responsible Machine Learning With Great Power Comes Great Responsibility. Voltaire (well, maybe) How to develop machine learning models in a responsib

Model Oriented 590 Dec 26, 2022
CaFM-pytorch ICCV ACCEPT Introduction of dataset VSD4K

CaFM-pytorch ICCV ACCEPT Introduction of dataset VSD4K Our dataset VSD4K includes 6 popular categories: game, sport, dance, vlog, interview and city.

96 Jul 05, 2022
InsCLR: Improving Instance Retrieval with Self-Supervision

InsCLR: Improving Instance Retrieval with Self-Supervision This is an official PyTorch implementation of the InsCLR paper. Download Dataset Dataset Im

Zelu Deng 25 Aug 30, 2022
Official implementation of "OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association" in PyTorch.

openpifpaf Continuously tested on Linux, MacOS and Windows: New 2021 paper: OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Te

VITA lab at EPFL 50 Dec 29, 2022
Prevent `CUDA error: out of memory` in just 1 line of code.

🐨 Koila Koila solves CUDA error: out of memory error painlessly. Fix it with just one line of code, and forget it. 🚀 Features 🙅 Prevents CUDA error

RenChu Wang 1.7k Jan 02, 2023
An open-source outlier detection package by Getcontact Data Team

pyfbad The pyfbad library supports anomaly detection projects. An end-to-end anomaly detection application can be written using the source codes of th

Teknasyon Tech 41 Dec 27, 2022
Official implementation of "SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers"

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers Figure 1: Performance of SegFormer-B0 to SegFormer-B5. Project page

NVIDIA Research Projects 1.4k Dec 31, 2022
Code for the paper "Curriculum Dropout", ICCV 2017

Curriculum Dropout Dropout is a very effective way of regularizing neural networks. Stochastically "dropping out" units with a certain probability dis

Pietro Morerio 21 Jan 02, 2022
Make a Turtlebot3 follow a figure 8 trajectory and create a robot arm and make it follow a trajectory

HW2 - ME 495 Overview Part 1: Makes the robot move in a figure 8 shape. The robot starts moving when launched on a real turtlebot3 and can be paused a

Devesh Bhura 0 Oct 21, 2022
TensorFlow implementation of "A Simple Baseline for Bayesian Uncertainty in Deep Learning"

TensorFlow implementation of "A Simple Baseline for Bayesian Uncertainty in Deep Learning"

YeongHyeon Park 7 Aug 28, 2022
A tutorial on DataFrames.jl prepared for JuliaCon2021

JuliaCon2021 DataFrames.jl Tutorial This is a tutorial on DataFrames.jl prepared for JuliaCon2021. A video recording of the tutorial is available here

Bogumił Kamiński 106 Jan 09, 2023
Controlling the MicriSpotAI robot from scratch

Project-MicroSpot-AI Controlling the MicriSpotAI robot from scratch Colaborators Alexander Dennis Components from MicroSpot The MicriSpotAI has the fo

Dennis Núñez-Fernández 5 Oct 20, 2022
Pretrained Cost Model for Distributed Constraint Optimization Problems

Pretrained Cost Model for Distributed Constraint Optimization Problems Requirements PyTorch 1.9.0 PyTorch Geometric 1.7.1 Directory structure baseline

2 Aug 28, 2022
Self-Learned Video Rain Streak Removal: When Cyclic Consistency Meets Temporal Correspondence

In this paper, we address the problem of rain streaks removal in video by developing a self-learned rain streak removal method, which does not require any clean groundtruth images in the training pro

Yang Wenhan 44 Dec 06, 2022