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

kracekumar

# Fizz Buzz in Tensorflow!
# see http://joelgrus.com/2016/05/23/fizz-buzz-in-tensorflow/

import time
import sys
import numpy as np
import tensorflow as tf
from utils import binary_encode, fizz_buzz_encode, fizz_buzz
#from models import gen, gen_all

kracekumar

open("/tmp/empty.json", O_RDONLY|O_CLOEXEC) = 12
fstat(12, {st_mode=S_IFREG|0640, st_size=3, ...}) = 0
ioctl(12, TCGETS, 0x3fffed9c8200)       = -1 ENOTTY (Inappropriate ioctl for device)
lseek(12, 0, SEEK_CUR)                  = 0
ioctl(12, TCGETS, 0x3fffed9c8170)       = -1 ENOTTY (Inappropriate ioctl for device)
lseek(12, 0, SEEK_CUR)                  = 0
lseek(12, 0, SEEK_CUR)                  = 0
fstat(12, {st_mode=S_IFREG|0640, st_size=3, ...}) = 0
read(12, "{}\n", 4)                     = 3

kracekumar

def read_data(files, total_records):
    records = []
    while len(records) < total_records:
        # Rotate the files queue
        files.rotate(1)
        with open(files[0]) as handle:
            data = json.load(handle)
            for record in data:
                records.append(record)
            If len(records) >= total_records:

kracekumar

from sqlalchemy import func, select, Column, ForeignKey, Integer, String, create_engine
from sqlalchemy.orm import sessionmaker, relationship
from sqlalchemy.sql.expression import literal_column
from sqlalchemy.ext.declarative import declarative_base

Base = declarative_base()

engine = create_engine('postgresql+psycopg2://postgres:password@localhost/test', echo=True)