kracekumar

"""
Datastructures
1. Tuple
2. List
3. Set
4. Dictionary
5. Class
"""
import datetime

kracekumar

Rust is a multi-paradigm, general-purpose programming language designed for performance and safety, 
especially safe concurrency.
Rust is syntactically similar to C++, but can guarantee memory safety by using a borrow checker to validate references.
Rust achieves memory safety without garbage collection, and reference counting is optional.
Rust has been called a systems programming language and in addition to high-level features 
such as functional programming it also offers mechanisms for low-level memory management.

First appearing in 2010, Rust was designed by Graydon Hoare at Mozilla Research, with contributions from 
Dave Herman, Brendan Eich, and others.
The designers refined the language while writing the Servo experimental browser engine 

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Python is an interpreted high-level general-purpose programming language. 
Its design philosophy emphasizes code readability with its use of significant indentation. 
Its language constructs as well as its object-oriented approach aim to help programmers write clear, 
logical code for small and large-scale projects.

Python is dynamically-typed and garbage-collected. 
It supports multiple programming paradigms, including structured (particularly, procedural), 
object-oriented and functional programming. It is often described as a "batteries included" language 
due to its comprehensive standard library.

kracekumar

[tool.poetry]
name = "flask-sqlalchemy"
version = "0.0.1"
description = ""
authors = ["kracekumar <>"]
license = "MIT"
readme = ""
repository = ""

[tool.poetry.dependencies]

kracekumar

import tweepy
import json
from sqlite_utils import Database

def authenticate():
    # twitter API credentials
    cred = json.load(open('.cred.json', 'r'))
    auth = tweepy.OAuthHandler(consumer_key=cred['api_key'], consumer_secret=cred['api_secret_key'])
    auth.set_access_token(key=cred['access_token'], secret=cred['access_token_secret'])
    api = tweepy.API(auth)

kracekumar

{
  "cropHintsAnnotation": {
    "cropHints": [
      {
        "boundingPoly": {
          "vertices": [
            {
              "x": 89
            },
            {

kracekumar

import torch
import torch.nn.functional as F
import torchvision
import torchvision.models as models
import mlflow
import mlflow.pytorch
import numpy as np


class Model(mlflow.pyfunc.PythonModel):

kracekumar

kracekumar

kracekumar

annotation=> explain (analyze, timing off) select label, value from data limit 10000;
                                           QUERY PLAN                                           
------------------------------------------------------------------------------------------------
 Limit  (cost=0.00..322.22 rows=10000 width=8) (actual rows=10000 loops=1)
   ->  Seq Scan on data  (cost=0.00..33860.40 rows=1050840 width=8) (actual rows=10000 loops=1)
 Total runtime: 5.994 ms
(3 rows)

Time: 6.801 ms