SrivastavaKshitij

This document is about running jupyter notebook on a server and accessing it on a local machine. 

On the remote machine, start jupyter notebook
remote_user@remote_host$ ipython notebook --no-browser --port=8889

when the notebook will start, it will generate a token.


On the local machine, start SSH tunnel

SrivastavaKshitij

This document shows step by step procedure on how to SSH without using password everytime

Type the following commands:

1. cd .ssh
2. vim config

when you open config file, type the following:

host snake

SrivastavaKshitij

Running the program on server remotely

• to be run sequentially

description	command
run python script	python xx.py
run the following command : this pauses the process	CTRL + Z ( this works for ubuntu server)
put the process in background	bg

SrivastavaKshitij

set nu
set nocompatible              " required
filetype off                  " required

" set the runtime path to include Vundle and initialize
set rtp+=~/.vim/bundle/Vundle.vim
call vundle#begin()

" alternatively, pass a path where Vundle should install plugins
"call vundle#begin('~/some/path/here')

SrivastavaKshitij

import numpy as np
def pf(output,target,metric=None):
    TP = np.count_nonzero(data*target)
    TN = np.count_nonzero((data - 1) * (target - 1))
    FP = np.count_nonzero(data * (target - 1))
    FN = np.count_nonzero((data - 1) * target)
    precision = TP / (TP + FP)
    recall = TP / (TP + FN)
    F1 = 2 * precision * recall / (precision + recall)
    accuracy = (TP+TN)/(TP+TN+FP+FN)

SrivastavaKshitij

import torch
def pf1(output,target,metric=None):
    d = output.data
    t = target.data
    TP = torch.nonzero(d*t).size(0)
    TN = torch.nonzero((d - 1) * (t - 1)).size(0)
    FP = torch.nonzero(d * (t - 1)).size(0)
    FN = torch.nonzero((d - 1) * t).size(0)
    precision = TP / (TP + FP)
    recall = TP / (TP + FN)

SrivastavaKshitij

import torch 
from torch import nn
class autoencoder(nn.Module):
    def __init__(self,downsizing_factor=None,in_channels=1):
        self.downsize = downsizing_factor
        self.in_channels = in_channels
        super(autoencoder,self).__init__()
        
        conv_modules=[]
        self.in_channels = self.in_channels

SrivastavaKshitij

import torch 
from torch import nn
class autoencoder(nn.Module):
    def __init__(self,downsizing_factor=None,in_channels=1):
        self.downsize = downsizing_factor
        self.in_channels = in_channels
        super(autoencoder,self).__init__()
        
        conv_modules=[]
        self.in_channels = self.in_channels

SrivastavaKshitij

set tabstop=8
set expandtab
set softtabstop=4
set shiftwidth=4
filetype plugin indent on
set number
set backspace=indent,start
set hlsearch

SrivastavaKshitij

# Use C-o like we do in screen as action key
#unbind C-b
set -g prefix C-a
#bind-key C-b last-window
bind-key a send-prefix
bind-key C-n next-window
bind-key C-p previous-window

# Terminal emulator window title
set -g set-titles on