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)

kracekumar

krace@hotbox /m/u/code> cat json_pg.py 
import psycopg2


def run(stmt):
     cur =  psycopg2.connect(database='test', user='postgres', password='password', host='localhost').cursor()
     cur.execute(stmt)
     result = cur.fetchall()
     print(list(result))

kracekumar

krace@hotbox /m/u/code> cat json_pg.py 
import psycopg2


def run(stmt):
     cur =  psycopg2.connect(database='test', user='postgres', password='password', host='localhost').cursor()
     cur.execute(stmt)
     result = cur.fetchall()
     print(list(result))