Simple way to measure cell execution time in ipython notebook

问题

I would like to get the time spent on the cell execution in addition to the original output from cell.

To this end, I tried %%timeit -r1 -n1 but it doesn't expose the variable defined within cell.

%%time works for cell which only contains 1 statement.

In[1]: %%time
       1
CPU times: user 4 µs, sys: 0 ns, total: 4 µs
Wall time: 5.96 µs
Out[1]: 1

In[2]: %%time
       # Notice there is no out result in this case.
       x = 1
       x
CPU times: user 3 µs, sys: 0 ns, total: 3 µs
Wall time: 5.96 µs

What's the best way to do it?

Update

I have been using Execute Time in Nbextension for quite some time now. It is great.

回答1:

Use cell magic and this project on github by Phillip Cloud:

Load it by putting this at the top of your notebook or put it in your config file if you always want to load it by default:

%install_ext https://raw.github.com/cpcloud/ipython-autotime/master/autotime.py
%load_ext autotime

If loaded, every output of subsequent cell execution will include the time in min and sec it took to execute it.

回答2:

The only way I found to overcome this problem is by executing the last statement with print.

Do not forget that cell magic starts with %% and line magic starts with %.

%%time
clf = tree.DecisionTreeRegressor().fit(X_train, y_train)
res = clf.predict(X_test)
print(res)

Notice that any changes performed inside the cell are not taken into consideration in the next cells, something that is counter intuitive when there is a pipeline:

回答3:

%time and %timeit now come part of ipython's built-in magic commands

回答4:

An easier way is to use ExecuteTime plugin in jupyter_contrib_nbextensions package.

pip install jupyter_contrib_nbextensions
jupyter contrib nbextension install --user
jupyter nbextension enable execute_time/ExecuteTime

回答5:

I simply added %%time at the beginning of the cell and got the time. You may use the same on Jupyter Spark cluster/ Virtual environment using the same. Just add %%time at the top of the cell and you will get the output. On spark cluster using Jupyter, I added to the top of the cell and I got output like below:-

[1]  %%time
     import pandas as pd
     from pyspark.ml import Pipeline
     from pyspark.ml.classification import LogisticRegression
     import numpy as np
     .... code ....

Output :-

CPU times: user 59.8 s, sys: 4.97 s, total: 1min 4s
Wall time: 1min 18s

回答6:

This is not exactly beautiful but without extra software

class timeit():
    from datetime import datetime
    def __enter__(self):
        self.tic = self.datetime.now()
    def __exit__(self, *args, **kwargs):
        print('runtime: {}'.format(self.datetime.now() - self.tic))

Then you can run it like:

with timeit():
    # your code, e.g., 
    print(sum(range(int(1e7))))

% 49999995000000
% runtime: 0:00:00.338492

回答7:

Sometimes the formatting is different in a cell when using print(res), but jupyter/ipython comes with a display. See an example of the formatting difference using pandas below.

%%time
import pandas as pd 
from IPython.display import display

df = pd.DataFrame({"col0":{"a":0,"b":0}
              ,"col1":{"a":1,"b":1}
              ,"col2":{"a":2,"b":2}
             })

#compare the following
print(df)
display(df)

The display statement can preserve the formatting.

回答8:

import time
start = time.time()
"the code you want to test stays here"
end = time.time()
print(end - start)

回答9:

You can use timeit magic function for that.

%timeit CODE_LINE

Or on the cell

%%timeit 

SOME_CELL_CODE

Check more IPython magic functions at https://nbviewer.jupyter.org/github/ipython/ipython/blob/1.x/examples/notebooks/Cell%20Magics.ipynb

回答10:

you may also want to look in to python's profiling magic command %prunwhich gives something like -

def sum_of_lists(N):
    total = 0
    for i in range(5):
        L = [j ^ (j >> i) for j in range(N)]
        total += sum(L)
    return total

then

%prun sum_of_lists(1000000)

will return

14 function calls in 0.714 seconds  

Ordered by: internal time      

ncalls  tottime  percall  cumtime  percall filename:lineno(function)
    5    0.599    0.120    0.599    0.120 <ipython-input-19>:4(<listcomp>)
    5    0.064    0.013    0.064    0.013 {built-in method sum}
    1    0.036    0.036    0.699    0.699 <ipython-input-19>:1(sum_of_lists)
    1    0.014    0.014    0.714    0.714 <string>:1(<module>)
    1    0.000    0.000    0.714    0.714 {built-in method exec}

I find it useful when working with large chunks of code.

回答11:

When in trouble what means what:

?%timeit or ??timeit

To get the details:

Usage, in line mode:
  %timeit [-n<N> -r<R> [-t|-c] -q -p<P> -o] statement
or in cell mode:
  %%timeit [-n<N> -r<R> [-t|-c] -q -p<P> -o] setup_code
  code
  code...

Time execution of a Python statement or expression using the timeit
module.  This function can be used both as a line and cell magic:

- In line mode you can time a single-line statement (though multiple
  ones can be chained with using semicolons).

- In cell mode, the statement in the first line is used as setup code
  (executed but not timed) and the body of the cell is timed.  The cell
  body has access to any variables created in the setup code.

来源：https://stackoverflow.com/questions/32565829/simple-way-to-measure-cell-execution-time-in-ipython-notebook

标签

python

ipython

ipython-notebook

jupyter