Open In Colab

Lesson 14: I/O

Pragmatic AI Labs

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This notebook was produced by Pragmatic AI Labs. You can continue learning about these topics by:

14.1 Reading and Writing Files and Serializing Data in Python

Working with Files

Writing to a file with ‘context’

with open("food.txt", "w") as workfile:
    workfile.write("whey protein\n")
    workfile.write("cliff bar")
!cat food.txt

whey protein
cliff bar

Reading a file with ‘context’

with open("food.txt", "r") as workfile:
    #print(workfile.readlines())
    print(workfile.read())


whey protein
cliff bar

Serialization Techniques

Ingest
import pandas as pd

df = pd.read_csv(
    "https://raw.githubusercontent.com/noahgift/food/master/data/features.en.openfoodfacts.org.products.csv")
df.drop(["Unnamed: 0", "exceeded", "g_sum", "energy_100g"], axis=1, inplace=True) #drop two rows we don't need
df = df.drop(df.index[[1,11877]]) #drop outlier
df.rename(index=str, columns={"reconstructed_energy": "energy_100g"}, inplace=True)
df.head()
fat_100g carbohydrates_100g sugars_100g proteins_100g salt_100g energy_100g product
0 28.57 64.29 14.29 3.57 0.00000 2267.85 Banana Chips Sweetened (Whole)
2 57.14 17.86 3.57 17.86 1.22428 2835.70 Organic Salted Nut Mix
3 18.75 57.81 15.62 14.06 0.13970 1953.04 Organic Muesli
4 36.67 36.67 3.33 16.67 1.60782 2336.91 Zen Party Mix
5 18.18 60.00 21.82 14.55 0.02286 1976.37 Cinnamon Nut Granola

Serialize a Python Dictionary to Pickle

temp = df.head(1).to_dict('index')
mydict = list(temp.values())[0]
mydict

{'carbohydrates_100g': 64.29,
 'energy_100g': 2267.85,
 'fat_100g': 28.57,
 'product': 'Banana Chips Sweetened (Whole)',
 'proteins_100g': 3.57,
 'salt_100g': 0.0,
 'sugars_100g': 14.29}

import pickle

pickle.dump(mydict, open('mydictionary.pickle', 'wb'))

!ls -l mydictionary.pickle

-rw-r--r-- 1 root root 225 Feb 14 19:30 mydictionary.pickle


res = pickle.load(open('mydictionary.pickle', "rb"))

res

{'carbohydrates_100g': 64.29,
 'energy_100g': 2267.85,
 'fat_100g': 28.57,
 'product': 'Banana Chips Sweetened (Whole)',
 'proteins_100g': 3.57,
 'salt_100g': 0.0,
 'sugars_100g': 14.29}

Serialize a Python Dictionary to JSON

import json
with open('data.json', 'w') as outfile:
    json.dump(res, outfile)

!cat data.json

{"fat_100g": 28.57, "carbohydrates_100g": 64.29, "sugars_100g": 14.29, "proteins_100g": 3.57, "salt_100g": 0.0, "energy_100g": 2267.85, "product": "Banana Chips Sweetened (Whole)"}

with open('data.json', 'rb') as outfile:
    res2 = json.load(outfile)

res2

{'carbohydrates_100g': 64.29,
 'energy_100g': 2267.85,
 'fat_100g': 28.57,
 'product': 'Banana Chips Sweetened (Whole)',
 'proteins_100g': 3.57,
 'salt_100g': 0.0,
 'sugars_100g': 14.29}

Save to Yaml

import yaml

with open("data.yaml", "w") as yamlfile:                                               
    yaml.safe_dump(res2, yamlfile, default_flow_style=False)

!cat data.yaml

carbohydrates_100g: 64.29
energy_100g: 2267.85
fat_100g: 28.57
product: Banana Chips Sweetened (Whole)
proteins_100g: 3.57
salt_100g: 0.0
sugars_100g: 14.29

Load Yaml

with open("data.yaml", "rb") as yamlfile:                                               
    res3 = yaml.safe_load(yamlfile) 

type(res3)

dict

res3

{'carbohydrates_100g': 64.29,
 'energy_100g': 2267.85,
 'fat_100g': 28.57,
 'product': 'Banana Chips Sweetened (Whole)',
 'proteins_100g': 3.57,
 'salt_100g': 0.0,
 'sugars_100g': 14.29}

14.2 Reading and Writing Files and Serializing Data with Pandas

Use Pandas DataFrames

Creating Pandas DataFrames

Creating DataFrames CSV file
  • Can be local
  • Can be hosted on a website
df = pd.read_csv(
    "https://raw.githubusercontent.com/noahgift/food/master/data/features.en.openfoodfacts.org.products.csv")
df.drop(["Unnamed: 0", "exceeded", "g_sum", "energy_100g"], axis=1, inplace=True) #drop two rows we don't need
df = df.drop(df.index[[1,11877]]) #drop outlier
df.rename(index=str, columns={"reconstructed_energy": "energy_100g"}, inplace=True)
df.head()
fat_100g carbohydrates_100g sugars_100g proteins_100g salt_100g energy_100g product
0 28.57 64.29 14.29 3.57 0.00000 2267.85 Banana Chips Sweetened (Whole)
2 57.14 17.86 3.57 17.86 1.22428 2835.70 Organic Salted Nut Mix
3 18.75 57.81 15.62 14.06 0.13970 1953.04 Organic Muesli
4 36.67 36.67 3.33 16.67 1.60782 2336.91 Zen Party Mix
5 18.18 60.00 21.82 14.55 0.02286 1976.37 Cinnamon Nut Granola 
df.median()

fat_100g                 3.170
carbohydrates_100g      22.390
sugars_100g              5.880
proteins_100g            4.000
salt_100g                0.635
energy_100g           1121.540
dtype: float64
List to Pandas DataFrame

Convert a column to a list to a Pandas DataFrame

products = df['product'].tolist()
products_df = pd.DataFrame(products)
products_df.columns = ["product"]
products_df.head(3)
product
0 Banana Chips Sweetened (Whole)
1 Organic Salted Nut Mix
2 Organic Muesli 
products_df.describe()
product
count 44976
unique 30720
top Ice Cream
freq 122

Exporting Pandas DataFrames

Pandas DataFrame to CSV

Write out DataFrame using to_csv

df.head().to_csv("small_food_records.csv")
!cat small_food_records.csv

,fat_100g,carbohydrates_100g,sugars_100g,proteins_100g,salt_100g,energy_100g,product
0,28.57,64.29,14.29,3.57,0.0,2267.85,Banana Chips Sweetened (Whole)
2,57.14,17.86,3.57,17.86,1.22428,2835.7,Organic Salted Nut Mix
3,18.75,57.81,15.62,14.06,0.1397,1953.04,Organic Muesli
4,36.67,36.67,3.33,16.67,1.60782,2336.91,Zen Party Mix
5,18.18,60.0,21.82,14.55,0.02286,1976.37,Cinnamon Nut Granola

Using Pandas on Ray

More info on Pandas: https://rise.cs.berkeley.edu/blog/pandas-on-ray/

*Note: Pandas is small data…data science. You may need to use Spark on Pandas on Ray

Using Pandas on Dask

Dask natively scales Python

https://dask.org/

Using Google Sheets with Pandas DataFrames

Reference: Official Google Colab Documentation on IO

Install Google Spreadsheet Library

!pip install --upgrade -q gspread

Authenticate to API

from google.colab import auth
auth.authenticate_user()

import gspread
from oauth2client.client import GoogleCredentials

gc = gspread.authorize(GoogleCredentials.get_application_default())

Create a Spreadsheet and Put Items in It

Note, could use existing spreadsheet

sh = gc.create('pragmaticai-test')
worksheet = gc.open('pragmaticai-test').sheet1
cell_list = worksheet.range('A1:A10')

for cell in cell_list:
  cell.value = products.pop()
worksheet.update_cells(cell_list)

{'spreadsheetId': '1JUh0In3pmh6J7K5KQtGU1_6S9s3sB3cCa01meRIrYmU',
 'updatedCells': 10,
 'updatedColumns': 1,
 'updatedRange': 'Sheet1!A1:A10',
 'updatedRows': 10}

Convert Spreadsheet Data to Pandas DataFrame

worksheet = gc.open('pragmaticai-test').sheet1
rows = worksheet.get_all_values()
import pandas as pd
df = pd.DataFrame.from_records(rows)
print(df.median())
df

#df.head()

Series([], dtype: float64)
0
0 100% Juice Reconstituted Lemon Juice With Adde...
1 Cranberry Grape, 100% Juice Blend
2 Cranberry Apple Flavored Juice Blended With On...
3 100% Juice Blend
4 100% Juice, Prune Juice
5 Prune Juice From Concentrate With Added Pulp
6 Prune Juice From Concentrate
7 100% Juice Grape Juice
8 White Grape Juice
9 100% Apple Juice From Concentrate
10 Whey Protein

14.3 Reading and Writing using web resources

Using Python Requests

import requests
url = """https://wikimedia.org/api/rest_v1/metrics/pageviews/per-article/en.wikipedia/all-access/user/LeBron_James/daily/2015070100/2017070500"""
result = requests.get(url)
result.json()["items"][0]


{'access': 'all-access',
 'agent': 'user',
 'article': 'LeBron_James',
 'granularity': 'daily',
 'project': 'en.wikipedia',
 'timestamp': '2015070100',
 'views': 18390}

Using Boto

import boto3
resource = boto3.resource("s3")
resource.meta.client.download_file('testntest', 'nba_2017_endorsement_full_stats.csv',
'/tmp/nba_2017_endorsement_full_stats.csv')

Using Github Files

Using Kaggle Files

Use kaggle by mounting Google Drive with credentials

14.4 Using Function based concurrency

Multiprocessing

Mapping processes to Functions

Processes are forked and run truly parallel (unlike threads)

from multiprocessing import Pool
import datetime
import time
import random

def fight_club(x):
  
    sleep_time = random.randrange(0,3)
    time.sleep(sleep_time)
    timestamp = datetime.datetime.now()
    print(f"Calculating punch with attack strength {x} to the {x} power: @timestamp {timestamp} with sleep {sleep_time}")
    return x**x

if __name__ == '__main__':
    p = Pool(5)
    print(p.map(fight_club, [1, 2, 3]))

Calculating punch with attack strength 1 to the 1 power: @timestamp 2019-02-14 19:50:11.489381 with sleep 1
Calculating punch with attack strength 3 to the 3 power: @timestamp 2019-02-14 19:50:11.489381 with sleep 1
Calculating punch with attack strength 2 to the 2 power: @timestamp 2019-02-14 19:50:12.490516 with sleep 2
[1, 4, 27]

Process Pool Joined on Queue (Threadlike behavior)

Mimicks Threading interface, but with actual multi-core functionality

from multiprocessing import Process, Queue

def f(q):
    q.put(["armbar", "kimura",  "Mata Leão"])

if __name__ == '__main__':
    q = Queue()
    p = Process(target=f, args=(q,))
    p.start()
    print(f"Grabbing some attacks: {q.get()}")    
    p.join()

Grabbing some attacks: ['armbar', 'kimura', 'Mata Leão']

Async IO

Async IO in Python Examples

More info here: https://docs.python.org/3/library/asyncio.html

Using Python3 Async

import asyncio

def send_async_firehose_events(count=100):
    """Async sends events to firehose"""

    start = time.time() 
    client = firehose_client()
    extra_msg = {"aws_service": "firehose"}
    loop = asyncio.get_event_loop()
    tasks = []
    LOG.info(f"sending aysnc events TOTAL {count}",extra=extra_msg)
    num = 0
    for _ in range(count):
        tasks.append(asyncio.ensure_future(put_record(gen_uuid_events(), client)))
        LOG.info(f"sending aysnc events: COUNT {num}/{count}")
        num +=1
    loop.run_until_complete(asyncio.wait(tasks))
    loop.close()
    end = time.time()  
    LOG.info("Total time: {}".format(end - start))

Using trollius library with Python 2: DEPRECATED

"""Generates an Async MetaData call.  Note, this isn't available in Boto3
In [56]: res = all_metadata_async()
In [57]: res
Out[57]: 
[('ami-manifest-path', <Response [200]>),
 ('instance-type', <Response [200]>),
 ('instance-id', <Response [200]>),
 ('iam', <Response [200]>),
 ('local-hostname', <Response [200]>),
 ('network', <Response [200]>),
 ('hostname', <Response [200]>),
 ('ami-id', <Response [200]>),
 ('instance-action', <Response [200]>),
 ('profile', <Response [200]>),
 ('reservation-id', <Response [200]>),
 ('security-groups', <Response [200]>),
 ('metrics', <Response [200]>),
 ('mac', <Response [200]>),
 ('public-ipv4', <Response [200]>),
 ('services', <Response [200]>),
 ('local-ipv4', <Response [200]>),
 ('placement', <Response [200]>),
 ('ami-launch-index', <Response [200]>),
 ('public-hostname', <Response [200]>),
 ('public-keys', <Response [200]>),
 ('block-device-mapping', <Response [200]>)]
"""

import requests
import trollius

def get_metadata_api_urls():
    """Retrieves the api endpoints for metadata"""

    full_urls = {}
    metadata_url = "http://169.254.169.254/latest/meta-data/"
    resp = requests.get(metadata_url)
    urls = resp.content.split()
    for url in urls:
        stripped_url = url.rstrip("/")
        full_urls[stripped_url]=(os.path.join(metadata_url, url))
    return full_urls

def _get(key_url):
    key,url = key_url
    return key, requests.get(url)

def _do_calls(urls):
    loop = trollius.get_event_loop()
    futures = []
    for url in urls:
        futures.append(loop.run_in_executor(None, _get, url))
    return futures

@trollius.coroutine
def call():
    results = []
    futures = _do_calls(get_metadata_api_urls().items())
    for future in futures:
        result = yield trollius.From(future)
        results.append(result)
    raise trollius.Return(results)

def all_metadata_async():
    """Retrieves all available metadata for an instance async"""

    loop = trollius.get_event_loop()
    res = loop.run_until_complete(call())

Serverless or FaaS (Functions as a service)

AWS Lambda

AWS Lambda and Chalice Example

Standalone Lambda with Chalice: http://chalice.readthedocs.io/en/latest/

@app.lambda_function()
def send_message(event, context):
    """Send a message to a channel"""

    slack_client = SlackClient(SLACK_TOKEN)
    res = slack_client.api_call(
      "chat.postMessage",
      channel="#general",
      text=event
    )
    return res

Fn Project

Fn Project

Fn Project


fn init --runtime python --trigger http pythonfn



import fdk
import json


def handler(ctx, data=None, loop=None):
    name = "World"
    if data and len(data) > 0:
        body = json.loads(data)
        name = body.get("name")
    return {"message": "Hello {0}".format(name)}



if __name__ == "__main__":
    fdk.handle(handler)

Large Scale Concurrency Solutions

Larger Scale Concurrency

alt text

alt text

High Level Concurrency Overview for Machine Learning and HPC (High Performance Computing)

Diagram of Python Performance Problems

63,000X Speedup for Matrix Multiply from Standard Python

Source: Dave Patterson, UC Berkeley

Numba

Numba

  • open source JIT (Just in Time Compiler)
  • translates a subset of Python and Numpy code into fast machine code
  • Can approach speed of C
  • Can also parallize: “true threads” and “GPU”
Install Numba
!pip3 install numba

Requirement already satisfied: numba in /usr/local/lib/python3.6/dist-packages (0.40.1)
Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from numba) (1.14.6)
Requirement already satisfied: llvmlite>=0.25.0dev0 in /usr/local/lib/python3.6/dist-packages (from numba) (0.27.0)
Use Numba
from numba import (cuda, vectorize)
import numba
import pandas as pd
import numpy as np

def real_estate_df():
    """30 Years of Housing Prices"""

    df = pd.read_csv("https://raw.githubusercontent.com/noahgift/real_estate_ml/master/data/Zip_Zhvi_SingleFamilyResidence.csv")
    df.rename(columns={"RegionName":"ZipCode"}, inplace=True)
    df["ZipCode"]=df["ZipCode"].map(lambda x: "{:.0f}".format(x))
    df["RegionID"]=df["RegionID"].map(lambda x: "{:.0f}".format(x))
    return df

def numerical_real_estate_array(df):
    """Converts df to numpy numerical array"""

    columns_to_drop = ['RegionID', 'ZipCode', 'City', 'State', 'Metro', 'CountyName']
    df_numerical = df.dropna()
    df_numerical = df_numerical.drop(columns_to_drop, axis=1)
    return df_numerical.values

def real_estate_array():
    """Returns Real Estate Array"""

    df = real_estate_df()
    rea = numerical_real_estate_array(df)
    return np.float32(rea)
  
rea = real_estate_array()
Use Numba decorator
import numba

@numba.jit(nopython=True)
def expmean_jit(rea):
    """Perform multiple mean calculations"""

    val = rea.mean() ** 2
    return val
  
expmean_jit(rea)

44968886272.0
Multi-threaded numba

True multi-threaded code (Warning will use all cores on anymachine that runs it)

@numba.jit(parallel=True)
def add_sum_threaded(rea):
    """Use all the cores"""

    x,_ = rea.shape
    total = 0
    for _ in numba.prange(x):
        total += rea.sum()  
        print(total)
        
add_sum_threaded(rea)

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GPU

Heavily used in Deep Learning

Use GPU
@vectorize(['float32(float32, float32)'], target='cuda')
def add_ufunc(x, y):
    return x + y
  
def cuda_operation():
    """Performs Vectorized Operations on GPU"""

    x = real_estate_array()
    y = real_estate_array()

    print("Moving calculations to GPU memory")
    x_device = cuda.to_device(x)
    y_device = cuda.to_device(y)
    out_device = cuda.device_array(
        shape=(x_device.shape[0],x_device.shape[1]), dtype=np.float32)
    print(x_device)
    print(x_device.shape)
    print(x_device.dtype)

    print("Calculating on GPU")
    add_ufunc(x_device,y_device, out=out_device)

    out_host = out_device.copy_to_host()
    print(f"Calculations from GPU {out_host}")
    
cuda_operation()

TPU

Tensor Processing Unit

  • “Google’s custom-developed application-specific integrated circuits (ASICs) used to accelerate machine learning workloads”
  • Available both in colab notebooks and on Google Cloud