prettytimer 0.1.dev5

a sssuper simple yet useful tool for you to collct the running time of the codeblock

PrettyTimer

PrettyTimer is a simple yet useful tool for you to collect the running time of the code blocks.

If you want to evaluate the running time of your code blocks, for example, in a machine learning project, the program has several stages: data load, model forward, gradient backward, etc. Even you will statistic running time more than ten code blocks. The general way is to implement it like this:

import time

start_time_load_data = time.time()
time.sleep(3)
end_time_load_data = time.time()
print(end_time_load_data - start_time_load_data)

start_time_forward = time.time()
time.sleep(3)
end_time_forward = time.time()
print(end_time_forward - start_time_forward)

start_time_backward = time.time()
time.sleep(3)
end_time_backward = time.time()
print(end_time_backward - start_time_backward)

start_time_clloct = time.time()
time.sleep(3)
end_time_clloct = time.time()
print(end_time_clloct - start_time_clloct)

PrettyTimer will reproduce it efficiently and elegantly as follows:

import time
from prettytimer import PrettyTimer

timer = PrettyTimer()

timer.start('load_data')
time.sleep(3)
timer.end('load_data')

timer.start('forward')
time.sleep(3)
timer.end('forward')

timer.start('backward')
time.sleep(3)
timer.end('backward')

timer.start('collect')
time.sleep(3)
timer.end('collect')

timer.collect()

The collected information is a Markdown table style which can be copied and pasted to your Markdown document.

Moreover, Prettytimer provides an ETA (Estimated Time of Arrival) method:

timer.eta(NAME, ITER, TOTAL)

Where NANE is a unique identifier to a timer, ITER is the current iteration and TOTAL is the total iteration of your training schedule.

That's all! Pretty concise, right?

Maybe there is a more graceful way to implement this, if yes, please tell me.

Installation

Install via pip:

pip install -U prettytimer

Install latest development version:

pip install -U [e-mail]:kinredon/prettytimer.git