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import argparse
import os.path as osp

import torch
import torch.optim as optim

from torch_geometric.datasets import FB15k_237
from torch_geometric.nn import ComplEx, DistMult, RotatE, TransE

model_map = {

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# Server 1: 
ssh <UPI>@foscsmlprd01.its.auckland.ac.nz

# Server 2: 
ssh <UPI>@foscsmlprd02.its.auckland.ac.nz

# Pssword: <UoA_PASSWORD>:<6_DIGITS_2FACTOR> eg, Pass1234:123456

# For Linux user, using GUI to tansfer files:
# Open `Files` browser, press ctrl+L

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# Use `nvtop` or `nvidia-smi` to check avalible GPUs. Only allows the Python code to access a specific GPU:
# CUDA_VISIBLE_DEVICES=<GPU_INDEX>

# Redirect standard output to `main.log` and then redirect standard error to standard output:
# > main.log 2>&1

# Allows code to run in the bakcgounrd:
# &
CUDA_VISIBLE_DEVICES=0 python main.py > main.log 2>&1 &

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import torch

device = torch.device('cuda:0') if torch.cuda.is_available() else 'cpu'

print(f'Using: {device}')
print('GPU Memory Usage:')
print('Allocated:', round(torch.cuda.memory_allocated(0) / 1024**3, 1), 'GB')
print('Cached:   ', round(torch.cuda.memory_reserved(0) / 1024**3, 1), 'GB')

# Run a trivial task

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# Instructions for connecting Jupyter Lab with the remove server

# SSH with port forward:
# Replace <PORT> with the port number you selected in the above step
# Replace <REMOTE_USER> with the remote server username
# Replace <REMOTE_HOST> with your remote server address
ssh -L 8080:localhost:<PORT> <REMOTE_USER>@<REMOTE_HOST>
####################################################################

# Replace <PORT> with your selected port number, e.g., 8080

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# These parameters are only apply in your current terminal.
# Run them again once you start a new terminal!
export XDG_CACHE_HOME=/data/<username>/.cache
export PIP_CACHE_DIR=/data/<username>/.cache

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#!/bin/bash
echo 0 | sudo tee /sys/module/hid_apple/parameters/fnmode

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#!/bin/bash

# To access banana

DATA=("apple" "banana" "cherry")
IDX=1

echo ${DATA[$IDX]}

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# Predefined methods
class Stack:
    def __init__(self):
        self.__items = []
        
    def is_empty(self):
        return self.__items == []
    
    def push(self, item):
        self.__items.append(item)

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How to install OpenCV 4.2.0 with CUDA 10.0 in Ubuntu distro 18.04

First of all install update and upgrade your system:

    $ sudo apt update
    $ sudo apt upgrade

Then, install required libraries: