domarps

print("Hello World!")

let airports: [String : String] = [
    "SFO" : "San Francisco",
    "BOS" :  "Boston"
]

for (code, name) in airports {
    print("\(code) : \(name)")
}

domarps

# model settings
model = dict(
    type='FasterRCNN',
    pretrained='modelzoo://resnet101',
    backbone=dict(
        type='ResNet',
        depth=101,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,

domarps

model {
  faster_rcnn {
    num_classes: 6
    image_resizer {
      keep_aspect_ratio_resizer {
        min_dimension: 500
        max_dimension: 500
      }
    }
    feature_extractor {

domarps

'''
Requirements:
  pip3 install elasticsearch
  pip3 install certifi
'''
 
from elasticsearch import Elasticsearch
import certifi
import re

domarps

import re

def extract_url(cid, id32):
    chop_id = '/'.join(re.findall('..', '{:0>10}'.format(cid))[0:4])
    size = '240'
    return (str(cid), 'https://t3.ftcdn.net/jpg/{}/{}_F_{}_{}_NW.jpg'.format(chop_id, size, cid, id32))

def extract_tags(rdd_record):
    j = json.loads(rdd_record)
    tags = [x.split('^')[0] for x in j['k']['eksrg']]

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# Copy files from one directory to another directory in the same S3 bucket

all_tsvs = set([line.rstrip('\n') for line in open('all_files.log')])
processed_tsvs = set([line.rstrip('\n') for line in open('processed.log')])

to_do_tsvs = [elem.split('s3://psriniva/')[-1] for elem in list(all_tsvs - processed_tsvs)]

to_do_tsvs[-1]

import boto3

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sketchstyle-barebones

Steps to install caffe with extra layer

1. Install NCCL

git clone https://github.com/NVIDIA/nccl.git /home/ubuntu/nccl --recursive
make CUDA_HOME=/usr/local/cuda -j4
make install

domarps

from random import choice
import string
def compress_string(s):
  if len(s) < 3:
    return 0
  
  max_len = len(s) - 2
  str_list = []
  for num in range(len(s), 2, -1):
    for i in range(1, len(s) - num):

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class Solution:
    def helper(self, candidates, target, curr_comb, result, curr_id, curr_sum):
        if curr_sum >= target:
            if curr_sum == target:
                result.append(curr_comb)
            return
        
        for i in range(curr_id, len(candidates)):
            curr_comb.append(candidates[i])
            self.helper(candidates, target, curr_comb, result, i, curr_sum + candidates[i])

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def maximum_under_budget_1d(array, budget):
    st, max_sum, max_len = 0, 0, 0
    #print(array)
    for ed in range(len(array)):
        max_sum += array[ed]
        while max_sum > budget and st <= ed:
                max_sum -= array[st]
                st += 1
        max_len = max(max_len, ed - st + 1)
    return max_len