improving speed of Python module import
Question:
The question of how to speed up importing of Python modules has been asked previously (Speeding up the python "import" loader and Python — Speed Up Imports?) but without specific examples and has not yielded accepted solutions. I will therefore take up the issue again here, but this time with a specific example.
I have a Python script that loads a 3-D image stack from disk, smooths it, and displays it as a movie. I call this script from the system command prompt when I want to quickly view my data. I’m OK with the 700 ms it takes to smooth the data as this is comparable to MATLAB. However, it takes an additional 650 ms to import the modules. So from the user’s perspective the Python code runs at half the speed.
This is the series of modules I’m importing:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import scipy.ndimage
import scipy.signal
import sys
import os
Of course, not all modules are equally slow to import. The chief culprits are:
matplotlib.pyplot [300ms]
numpy [110ms]
scipy.signal [200ms]
I have experimented with using from, but this isn’t any faster. Since Matplotlib is the main culprit and it’s got a reputation for slow screen updates, I looked for alternatives. One is PyQtGraph, but that takes 550 ms to import.
I am aware of one obvious solution, which is to call my function from an interactive Python session rather than the system command prompt. This is fine but it’s too MATLAB-like, I’d prefer the elegance of having my function available from the system prompt.
I’m new to Python and I’m not sure how to proceed at this point. Since I’m new, I’d appreciate links on how to implement proposed solutions. Ideally, I’m looking for a simple solution (aren’t we all!) because the code needs to be portable between multiple Mac and Linux machines.
Answers:
you could build a simple server/client, the server running continuously making and updating the plot, and the client just communicating the next file to process.
I wrote a simple server/client example based on the basic example from the socket module docs: http://docs.python.org/2/library/socket.html#example
here is server.py:
# expensive imports
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import scipy.ndimage
import scipy.signal
import sys
import os
# Echo server program
import socket
HOST = '' # Symbolic name meaning all available interfaces
PORT = 50007 # Arbitrary non-privileged port
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.bind((HOST, PORT))
s.listen(1)
while 1:
conn, addr = s.accept()
print 'Connected by', addr
data = conn.recv(1024)
if not data: break
conn.sendall("PLOTTING:" + data)
# update plot
conn.close()
and client.py:
# Echo client program
import socket
import sys
HOST = '' # The remote host
PORT = 50007 # The same port as used by the server
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect((HOST, PORT))
s.sendall(sys.argv[1])
data = s.recv(1024)
s.close()
print 'Received', repr(data)
you just run the server:
python server.py
which does the imports, then the client just sends via the socket the filename of the new file to plot:
python client.py mytextfile.txt
then the server updates the plot.
On my machine running your imports take 0.6 seconds, while running client.py 0.03 seconds.
1.35 seconds isn’t long, but I suppose if you’re used to half that for a “quick check” then perhaps it seems so.
Andrea suggests a simple client/server setup, but it seems to me that you could just as easily call a very slight modification of your script and keep it’s console window open while you work:
- Call the script, which does the imports then waits for input
- Minimize the console window, switch to your work, whatever: *Do work*
- Select the console again
- Provide the script with some sort of input
- Receive the results with no import overhead
- Switch away from the script again while it happily awaits input
I assume your script is identical every time, ie you don’t need to give it image stack location or any particular commands each time (but these are easy to do as well!).
Example RAAC’s_Script.py:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import scipy.ndimage
import scipy.signal
import sys
import os
print('********* RAAC's Script Now Running *********')
while True: # Loops forever
# Display a message and wait for user to enter text followed by enter key.
# In this case, we're not expecting any text at all and if there is any it's ignored
input('Press Enter to test image stack...')
'''
*
*
**RAAC's Code Goes Here** (Make sure it's indented/inside the while loop!)
*
*
'''
To end the script, close the console window or press ctrl+c.
I’ve made this as simple as possible, but it would require very little extra to handle things like quitting nicely, doing slightly different things based on input, etc.
You can import your modules manually instead, using imp. See documentation here.
For example, import numpy as np could probably be written as
import imp
np = imp.load_module("numpy",None,"/usr/lib/python2.7/dist-packages/numpy",('','',5))
This will spare python from browsing your entire sys.path to find the desired packages.
See also:
Not an actual answer to the question, but a hint on how to profile the import speed with Python 3.7 and tuna (a small project of mine):
python3 -X importtime -c "import scipy" 2> scipy.log
tuna scipy.log
You can use lazy imports, but it depends on your use case.
If it’s an application, you can run necessary modules for GUI, then after window is loaded, you can import all your modules.
If it’s a module and user do not use all the dependencies, you can import inside function.
[warning]
It’s against pep8 i think and it’s not recomennded at some places, but all the reason behind this is mostly readability (i may be wrong though…) and some builders (e.g. pyinstaller) bundling (which can be solved with adding missing dependencies param to spec)
If you use lazy imports, use comments so user knows that there are extra dependencies.
Example:
import numpy as np
# Lazy imports
# import matplotlib.pyplot as plt
def plot():
import matplotlib.pyplot as plt
# Your function here
# This will be imported during runtime
For some specific libraries i think it’s necessity.
You can also create some let’s call it api in __init__.py
For example on scikit learn. If you import sklearn and then call some model, it’s not found and raise error. You need to be more specific then and import directly submodule. Though it can be unconvenient for users, it’s imho good practice and can reduce import times significantly.
Usually 10% of imported libraries cost 90% of import time. Very simple tool for analysis is line_profiler
import line_profiler
import atexit
profile = line_profiler.LineProfiler()
atexit.register(profile.print_stats)
@profile
def profiled_function():
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
profiled_function()
This give results
Line # Hits Time Per Hit % Time Line Contents
==============================================================
20 @profile
21 def profiled_function():
22
23 1 2351852.0 2351852.0 6.5 import numpy as np
24 1 6545679.0 6545679.0 18.0 import pandas as pd
25 1 27485437.0 27485437.0 75.5 import matplotlib.pyplot as plt
75% of three libraries imports time is matplotlib (this does not mean that it’s bad written, it just needs a lot of stuff for grafic output)
Note:
If you import library in one module, other imports cost nothing, it’s globally shared…
Another note:
If using imports directly from python (e.g pathlib, subprocess etc.) do not use lazy load, python modules import times are close to zero and don’t need to be optimized from my experience…
I have done just a basic test below, but it shows that runpy can be used to solve this issue when you need to have a whole Python script to be faster (you don’t want to put any logic in test_server.py).
test_server.py
import socket
import time
import runpy
import matplotlib.pyplot
HOST = 'localhost'
PORT = 50007
serversocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
try:
serversocket.bind((HOST, PORT))
except:
print("Server is already running")
exit(1)
# Start server with maximum 100 connections
serversocket.listen(100)
while True:
connection, address = serversocket.accept()
buf = connection.recv(64)
if len(buf) > 0:
buf_str = str(buf.decode("utf-8"))
now = time.time()
runpy.run_path(path_name=buf_str)
after = time.time()
duration = after - now
print("I received " + buf_str + " script and it took " + str(duration) + " seconds to execute it")
test_client.py
import socket
import sys
HOST = 'localhost'
PORT = 50007
clientsocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
clientsocket.connect((HOST, PORT))
message = sys.argv[1].encode()
clientsocket.send(message)
test_lag.py
import matplotlib.pyplot
Testing:
$ python3 test_client.py test_lag.py
I received test_lag.py script and it took 0.0002799034118652344 seconds to execute it
$ time python3 test_lag.py
real 0m0.624s
user 0m1.307s
sys 0m0.180s
Based on this, module is pre-loaded for fast usage.
The question of how to speed up importing of Python modules has been asked previously (Speeding up the python "import" loader and Python — Speed Up Imports?) but without specific examples and has not yielded accepted solutions. I will therefore take up the issue again here, but this time with a specific example.
I have a Python script that loads a 3-D image stack from disk, smooths it, and displays it as a movie. I call this script from the system command prompt when I want to quickly view my data. I’m OK with the 700 ms it takes to smooth the data as this is comparable to MATLAB. However, it takes an additional 650 ms to import the modules. So from the user’s perspective the Python code runs at half the speed.
This is the series of modules I’m importing:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import scipy.ndimage
import scipy.signal
import sys
import os
Of course, not all modules are equally slow to import. The chief culprits are:
matplotlib.pyplot [300ms]
numpy [110ms]
scipy.signal [200ms]
I have experimented with using from, but this isn’t any faster. Since Matplotlib is the main culprit and it’s got a reputation for slow screen updates, I looked for alternatives. One is PyQtGraph, but that takes 550 ms to import.
I am aware of one obvious solution, which is to call my function from an interactive Python session rather than the system command prompt. This is fine but it’s too MATLAB-like, I’d prefer the elegance of having my function available from the system prompt.
I’m new to Python and I’m not sure how to proceed at this point. Since I’m new, I’d appreciate links on how to implement proposed solutions. Ideally, I’m looking for a simple solution (aren’t we all!) because the code needs to be portable between multiple Mac and Linux machines.
you could build a simple server/client, the server running continuously making and updating the plot, and the client just communicating the next file to process.
I wrote a simple server/client example based on the basic example from the socket module docs: http://docs.python.org/2/library/socket.html#example
here is server.py:
# expensive imports
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import scipy.ndimage
import scipy.signal
import sys
import os
# Echo server program
import socket
HOST = '' # Symbolic name meaning all available interfaces
PORT = 50007 # Arbitrary non-privileged port
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.bind((HOST, PORT))
s.listen(1)
while 1:
conn, addr = s.accept()
print 'Connected by', addr
data = conn.recv(1024)
if not data: break
conn.sendall("PLOTTING:" + data)
# update plot
conn.close()
and client.py:
# Echo client program
import socket
import sys
HOST = '' # The remote host
PORT = 50007 # The same port as used by the server
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect((HOST, PORT))
s.sendall(sys.argv[1])
data = s.recv(1024)
s.close()
print 'Received', repr(data)
you just run the server:
python server.py
which does the imports, then the client just sends via the socket the filename of the new file to plot:
python client.py mytextfile.txt
then the server updates the plot.
On my machine running your imports take 0.6 seconds, while running client.py 0.03 seconds.
1.35 seconds isn’t long, but I suppose if you’re used to half that for a “quick check” then perhaps it seems so.
Andrea suggests a simple client/server setup, but it seems to me that you could just as easily call a very slight modification of your script and keep it’s console window open while you work:
- Call the script, which does the imports then waits for input
- Minimize the console window, switch to your work, whatever: *Do work*
- Select the console again
- Provide the script with some sort of input
- Receive the results with no import overhead
- Switch away from the script again while it happily awaits input
I assume your script is identical every time, ie you don’t need to give it image stack location or any particular commands each time (but these are easy to do as well!).
Example RAAC’s_Script.py:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import scipy.ndimage
import scipy.signal
import sys
import os
print('********* RAAC's Script Now Running *********')
while True: # Loops forever
# Display a message and wait for user to enter text followed by enter key.
# In this case, we're not expecting any text at all and if there is any it's ignored
input('Press Enter to test image stack...')
'''
*
*
**RAAC's Code Goes Here** (Make sure it's indented/inside the while loop!)
*
*
'''
To end the script, close the console window or press ctrl+c.
I’ve made this as simple as possible, but it would require very little extra to handle things like quitting nicely, doing slightly different things based on input, etc.
You can import your modules manually instead, using imp. See documentation here.
For example, import numpy as np could probably be written as
import imp
np = imp.load_module("numpy",None,"/usr/lib/python2.7/dist-packages/numpy",('','',5))
This will spare python from browsing your entire sys.path to find the desired packages.
See also:
Not an actual answer to the question, but a hint on how to profile the import speed with Python 3.7 and tuna (a small project of mine):
python3 -X importtime -c "import scipy" 2> scipy.log
tuna scipy.log
You can use lazy imports, but it depends on your use case.
If it’s an application, you can run necessary modules for GUI, then after window is loaded, you can import all your modules.
If it’s a module and user do not use all the dependencies, you can import inside function.
[warning]
It’s against pep8 i think and it’s not recomennded at some places, but all the reason behind this is mostly readability (i may be wrong though…) and some builders (e.g. pyinstaller) bundling (which can be solved with adding missing dependencies param to spec)
If you use lazy imports, use comments so user knows that there are extra dependencies.
Example:
import numpy as np
# Lazy imports
# import matplotlib.pyplot as plt
def plot():
import matplotlib.pyplot as plt
# Your function here
# This will be imported during runtime
For some specific libraries i think it’s necessity.
You can also create some let’s call it api in __init__.py
For example on scikit learn. If you import sklearn and then call some model, it’s not found and raise error. You need to be more specific then and import directly submodule. Though it can be unconvenient for users, it’s imho good practice and can reduce import times significantly.
Usually 10% of imported libraries cost 90% of import time. Very simple tool for analysis is line_profiler
import line_profiler
import atexit
profile = line_profiler.LineProfiler()
atexit.register(profile.print_stats)
@profile
def profiled_function():
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
profiled_function()
This give results
Line # Hits Time Per Hit % Time Line Contents
==============================================================
20 @profile
21 def profiled_function():
22
23 1 2351852.0 2351852.0 6.5 import numpy as np
24 1 6545679.0 6545679.0 18.0 import pandas as pd
25 1 27485437.0 27485437.0 75.5 import matplotlib.pyplot as plt
75% of three libraries imports time is matplotlib (this does not mean that it’s bad written, it just needs a lot of stuff for grafic output)
Note:
If you import library in one module, other imports cost nothing, it’s globally shared…
Another note:
If using imports directly from python (e.g pathlib, subprocess etc.) do not use lazy load, python modules import times are close to zero and don’t need to be optimized from my experience…
I have done just a basic test below, but it shows that runpy can be used to solve this issue when you need to have a whole Python script to be faster (you don’t want to put any logic in test_server.py).
test_server.py
import socket
import time
import runpy
import matplotlib.pyplot
HOST = 'localhost'
PORT = 50007
serversocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
try:
serversocket.bind((HOST, PORT))
except:
print("Server is already running")
exit(1)
# Start server with maximum 100 connections
serversocket.listen(100)
while True:
connection, address = serversocket.accept()
buf = connection.recv(64)
if len(buf) > 0:
buf_str = str(buf.decode("utf-8"))
now = time.time()
runpy.run_path(path_name=buf_str)
after = time.time()
duration = after - now
print("I received " + buf_str + " script and it took " + str(duration) + " seconds to execute it")
test_client.py
import socket
import sys
HOST = 'localhost'
PORT = 50007
clientsocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
clientsocket.connect((HOST, PORT))
message = sys.argv[1].encode()
clientsocket.send(message)
test_lag.py
import matplotlib.pyplot
Testing:
$ python3 test_client.py test_lag.py
I received test_lag.py script and it took 0.0002799034118652344 seconds to execute it
$ time python3 test_lag.py
real 0m0.624s
user 0m1.307s
sys 0m0.180s
Based on this, module is pre-loaded for fast usage.