jainxy

# Execute Colab notebook on local Jupyter Runtime - https://research.google.com/colaboratory/local-runtimes.html

# Mount Google drive to collab notebooks to use files and folders into the notebook
# Ref : https://towardsdatascience.com/getting-started-with-google-colab-f2fff97f594c

from google.colab import drive
drive.mount('/content/gdrive')

jainxy

-> Markdown => Simple Markup language
-> Colab's MD => Rendered via marked.js and similar to those in Jupyter and Github's version
-> Difference: Supports Latex equations(MathJax) like Jupyter but not HTML tags
->             Syntax Highlighting in the code blocks
****************HEADINGS**************
# This is equivalent to an <h1> tag

##### This is equivalent to an <h5> tag

_A text in italics_ ; __A text in bold__ ; ~~strike-through text~~

jainxy

# Ref - https://colab.research.google.com/notebooks/widgets.ipynb
# Build and control layout dynamically through code

from google.colab import widgets

# ----GRIDS-----
grid = widgets.Grid(2, 2, header_row=True, header_column=True)  # Grid object

# Grid.output_to(indices) to direct output stream to that place in the grid.
with grid.output_to(1, 1):

jainxy

# Ref -> https://colab.research.google.com/notebooks/io.ipynb#scrollTo=BaCkyg5CV5jF
# ---------- UPLOAD FROM LOCAL FILE SYSTEM --------------------
# files.upload returns a dictionary of the files which were uploaded. The dictionary is keyed by the file name, the value is the data which was uploaded.

# files.download will invoke a browser download of the file to the user's local computer.

jainxy

# load the iris dataset 
from sklearn.datasets import load_iris 
iris = load_iris() 
  
# store the feature matrix (X) and response vector (y) 
X = iris.data 
y = iris.target 
  
# splitting X and y into training and testing sets 

jainxy

jainxy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

jainxy

References

• Bash Scripting: Everything you need to know about Bash-shell programming

Notes

Introduction

• Bash (AKA Bourne Again Shell) is a type of interpreter that processes shell commands.
• A shell interpreter takes commands in plain text format and calls Operating System services to do something.
• Bash is the improved version of Sh (Bourne Shell).
• A shell scripting is writing a program for the shell to execute and a shell script is a file or program that shell will execute.
• Terminal is the interface to the shell interpreter.
• A shell script is a fully-fledged programming language in itself. It can define variables, functions and we can do conditional execution of shell commands as well.

jainxy

 === Use conda environment to run a python script ===
SHELL=/bin/bash
CONDA_PREFIX=/home/code/packages/miniconda3
CONDA_INIT="/home/code/packages/miniconda3/etc/conda/activate.d/proj4-activate.sh"
PYTHON=/home/code/packages/miniconda3/bin/python
12 13 * * * . $CONDA_INIT ; $PYTHON <python file>

 === Use date utility for naming a log file ===
DATEVAR="date +%Y%m%d_%H%M"
17 * * * * echo "logs_$($DATEVAR).txt"

jainxy

=======================
FLASK module ->
from flask import Flask, g, request, redirect, url_for
=======================