cicdw

- import os
...
+ import prefect
+ from prefect import Flow, Parameter, task
+ from prefect.client import Secret
+ from prefect.schedules import CronSchedule

+ @task
def get_standup_date():

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import datetime
from google.cloud.firestore import Client
import os
import random
import requests


def get_standup_date():
    """
    Returns the current date, formatted, which maps 

cicdw

Test

$$ \langle X, X + Y \rangle \leq 2 $$

cicdw

%%timeit -n 100
_, _ = dask.compute(results['w_overlay'], 
                    results['predict_another_thing'])
# 100 loops, best of 3: 1.61 ms per loop

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@delayed
def predict_another_thing(x, y, z):
    # model scoring code goes here
    pass


@delayed
def complicated_feature_b(x, y):
    # lets imagine this feature is *very* expensive to create
    # and takes a full minute to process

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from dask import delayed


# won't be evaluated until we call .compute()
fast_predict_over_time = delayed(fast_predict_over_time)

## using the same numpy arrays from above...

%%timeit -n 100
_ = fast_predict_over_time(x, y, z, False, res).compute()

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res = np.zeros((n, 15))

%%timeit -n 100
_ = fast_predict_over_time(x, y, z, False, res) # 100 loops, best of 3: 575 µs per loop

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from numba import guvectorize


@guvectorize('i8, f8, f8, b1, f8[:], f8[:]',
             '(), (), (), (), (s) -> (s)')
def fast_predict_over_time(x, y, z, overlay, _, out):
    adj = 1.5 if overlay else 1.0
    for t in range(len(out)):
        out[t] = adj * (t * x ** 2 + y - 2 * z - 2 * t)

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from numba import jit


@jit
def jitted_func(x, y, z, overlay=False):
    "Predicts a quantity at times = 0, 1, ... 14"
    out = np.zeros((x.shape[0], 15))
    for t in range(15):
        out[:, t] = t * x ** 2 + y - 2 * z - 2 * t
    adj = 1.5 if overlay else 1.0

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def predict_over_time(x, y, z, overlay=False):
    "Predicts a quantity at times = 0, 1, ... 14"
    out = np.zeros((x.shape[0], 15))
    for t in range(15):
        out[:, t] = t * x ** 2 + y - 2 * z - 2 * t
    adj = 1.5 if overlay else 1.0
    return adj * out