View collect_tweet.py
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) |
View response.json
{ | |
"cropHintsAnnotation": { | |
"cropHints": [ | |
{ | |
"boundingPoly": { | |
"vertices": [ | |
{ | |
"x": 89 | |
}, | |
{ |
View custom_predict_mnist_cnn.py
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): |
View Analysis.ipynb
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View lmdb.ipynb
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View psql.sql
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 |
View fizzbuzz.py
# 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 |
View strace.txt
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 |
View read.py
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: |
View sqlalchemy_row_to_json.py
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) |
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