User guide

Installation

pip install pyinstrument

Pyinstrument supports Python 3.8+.

Profile a Python script

Call Pyinstrument directly from the command line. Instead of writing python3 script.py, type pyinstrument script.py. Your script will run as normal, and at the end (or when you press ^C), Pyinstrument will output a colored summary showing where most of the time was spent.

Here are the options you can use:

Usage: pyinstrument [options] scriptfile [arg] ...

Options:
  --version             show program's version number and exit
  -h, --help            show this help message and exit
  --load=FILENAME       instead of running a script, load a profile session
                        from a pyisession file
  --load-prev=IDENTIFIER
                        instead of running a script, load a previous profile
                        session as specified by an identifier
  -m MODULE             run library module as a script, like 'python -m
                        module'
  -c PROGRAM            program passed in as string, like 'python -c "..."'
  --from-path           (POSIX only) instead of the working directory, look
                        for scriptfile in the PATH environment variable
  -o OUTFILE, --outfile=OUTFILE
                        save to <outfile>
  -r RENDERER, --renderer=RENDERER
                        how the report should be rendered. One of: 'text',
                        'html', 'json', 'speedscope', 'pyisession', 'pstats',
                        or python import path to a renderer class. Defaults to
                        the appropriate format for the extension if OUTFILE is
                        given, otherwise, defaults to 'text'.
  -p RENDER_OPTION, --render-option=RENDER_OPTION
                        options to pass to the renderer, in the format
                        'flag_name' or 'option_name=option_value'. For
                        example, to set the option 'time', pass '-p
                        time=percent_of_total'. To pass multiple options, use
                        the -p option multiple times. You can set processor
                        options using dot-syntax, like '-p
                        processor_options.filter_threshold=0'. option_value is
                        parsed as a JSON value or a string.
  -t, --timeline        render as a timeline - preserve ordering and don't
                        condense repeated calls
  --target-description=TARGET_DESCRIPTION
                        description text to display in the report. The
                        placeholder '{args}' may be used to include the CLI
                        arguments passed to the target script, including the
                        script name. Default: 'Program: {args}'
  --hide=EXPR           glob-style pattern matching the file paths whose
                        frames to hide. Defaults to hiding non-application
                        code
  --hide-regex=REGEX    regex matching the file paths whose frames to hide.
                        Useful if --hide doesn't give enough control.
  --show=EXPR           glob-style pattern matching the file paths whose
                        frames to show, regardless of --hide or --hide-regex.
                        For example, use --show '*/<library>/*' to show frames
                        within a library that would otherwise be hidden.
  --show-regex=REGEX    regex matching the file paths whose frames to always
                        show. Useful if --show doesn't give enough control.
  --show-all            show everything
  --unicode             (text renderer only) force unicode text output
  --no-unicode          (text renderer only) force ascii text output
  --color               (text renderer only) force ansi color text output
  --no-color            (text renderer only) force no color text output
  -i INTERVAL, --interval=INTERVAL
                        Minimum time, in seconds, between each stack sample.
                        Smaller values allow resolving shorter duration
                        function calls but incur a greater runtime and memory
                        consumption overhead. For longer running scripts,
                        setting a larger interval reduces the memory
                        consumption required to store the stack samples.
  --use-timing-thread   Use a separate thread to time the interval between
                        stack samples. This can reduce the overhead of
                        sampling on some systems.

Protip: -r html will give you a interactive profile report as HTML - you can really explore this way!

Profile a Python CLI command

For profiling an installed Python script via the “console_script” entry point, call Pyinstrument directly from the command line with the --from-path flag. Instead of writing cli-script, type pyinstrument --from-path cli-script. Your script will run as normal, and at the end (or when you press ^C), Pyinstrument will output a colored summary showing where most of the time was spent.

Profile a specific chunk of code

Pyinstrument also has a Python API. You can use a with-block, like this:

import pyinstrument

with pyinstrument.profile():
    # code you want to profile

Or you can decorate a function/method, like this:

import pyinstrument

@pyinstrument.profile()
def my_function():
    # code you want to profile

There’s also a lower-level API called Profiler, that’s more flexible:

from pyinstrument import Profiler

profiler = Profiler()
profiler.start()

# code you want to profile

profiler.stop()
profiler.print()

If you get “No samples were recorded.” because your code executed in under 1ms, hooray! If you still want to instrument the code, set an interval value smaller than the default 0.001 (1 millisecond) like this:

pyinstrument.profile(interval=0.0001)
# or,
profiler = Profiler(interval=0.0001)
...

Experiment with the interval value to see different depths, but keep in mind that smaller intervals could affect the performance overhead of profiling.

Protip: To explore the profile in a web browser, use profiler.open_in_browser(). To save this HTML for later, use profiler.output_html().

Profile code in Jupyter/IPython

Via IPython magics, you can profile a line or a cell in IPython or Jupyter.

Example:

%load_ext pyinstrument

%%pyinstrument
import time

def a():
    b()
    c()
def b():
    d()
def c():
    d()
def d():
    e()
def e():
    time.sleep(1)
a()

To customize options, see %%pyinstrument??.

Profile a web request in Django

To profile Django web requests, add pyinstrument.middleware.ProfilerMiddleware to MIDDLEWARE in your settings.py.

Profile specific request

Once installed, add ?profile to the end of a request URL to activate the profiler. Your request will run as normal, but instead of getting the response, you’ll get pyinstrument’s analysis of the request in a web page.

Save all requests to a directory

If you’re writing an API, it’s not easy to change the URL when you want to profile something. In this case, add PYINSTRUMENT_PROFILE_DIR = 'profiles' to your settings.py. Pyinstrument will profile every request and save the HTML output to the folder profiles in your working directory.

Custom file name by string

You can further customize the filename by adding PYINSTRUMENT_FILENAME to settings.py, default value is "{total_time:.3f}s {path} {timestamp:.0f}.{ext}".

Custom file name by callback function

For more control you can provide a callback function by adding PYINSTRUMENT_FILENAME_CALLBACK to settings.py, that returns a filename as a string.

def get_pyinstrument_filename(request, session, renderer):
    path = request.get_full_path().replace("/", "_")[:100]
    ext = renderer.output_file_extension
    filename = f"{request.method}_{session.duration}{path}.{ext}"
    return filename

PYINSTRUMENT_FILENAME_CALLBACK = get_pyinstrument_filename

(This callback takes precedence over PYINSTRUMENT_FILENAME).

Control shown profiling page

If you want to show the profiling page depending on the request you can define PYINSTRUMENT_SHOW_CALLBACK as dotted path to a function used for determining whether the page should show or not. You can provide your own function callback(request) which returns True or False in your settings.py.

def custom_show_pyinstrument(request):
    return request.user.is_superuser


PYINSTRUMENT_SHOW_CALLBACK = "%s.custom_show_pyinstrument" % __name__

You can configure the profile output type using setting’s variable PYINSTRUMENT_PROFILE_DIR_RENDERER. Default value is pyinstrument.renderers.HTMLRenderer. The supported renderers are pyinstrument.renderers.JSONRenderer, pyinstrument.renderers.HTMLRenderer, pyinstrument.renderers.SpeedscopeRenderer.

Set a custom interval

You can configure the sampling interval using setting’s variable PYINSTRUMENT_INTERVAL. Default value is 0.001.

Profile a web request in Flask

A simple setup to profile a Flask application is the following:

from flask import Flask, g, make_response, request
from pyinstrument import Profiler

app = Flask(__name__)

@app.before_request
def before_request():
    if "profile" in request.args:
        g.profiler = Profiler()
        g.profiler.start()


@app.after_request
def after_request(response):
    if not hasattr(g, "profiler"):
        return response
    g.profiler.stop()
    output_html = g.profiler.output_html()
    return make_response(output_html)

This will check for the ?profile query param on each request and if found, it starts profiling. After each request where the profiler was running it creates the html output and returns that instead of the actual response.

Profile a web request in FastAPI

To profile call stacks in FastAPI, you can write a middleware extension for pyinstrument.

Caution

Only async path operation functions are profiled with this approach. Routes that are defined without async def are executed in a separate execution thread, and therefore not profiled by this approach. See issue #257 and FastAPI Concurrency and async / await for more information.

Create an async function and decorate with app.middleware('http') where app is the name of your FastAPI application instance.

Make sure you configure a setting to only make this available when required.

from fastapi import Request
from fastapi.responses import HTMLResponse
from pyinstrument import Profiler


PROFILING = True  # Set this from a settings model

if PROFILING:
    @app.middleware("http")
    async def profile_request(request: Request, call_next):
        profiling = request.query_params.get("profile", False)
        if profiling:
            profiler = Profiler()
            profiler.start()
            await call_next(request)
            profiler.stop()
            return HTMLResponse(profiler.output_html())
        else:
            return await call_next(request)

To invoke, make any request to your application with the GET parameter profile=1 and it will print the HTML result from pyinstrument.

Profile a web request in Falcon

For profile call stacks in Falcon, you can write a middleware extension using pyinstrument.

Create a middleware class and start the profiler at process_request and stop it at process_response. The middleware can be added to the app.

Make sure you configure a setting to only make this available when required.

from pyinstrument import Profiler
import falcon

class ProfilerMiddleware:
    def __init__(self, interval=0.01):
        self.profiler = Profiler(interval=interval)

    def process_request(self, req, resp):
        self.profiler.start()

    def process_response(self, req, resp, resource, req_succeeded):
        self.profiler.stop()
        self.profiler.open_in_browser()

PROFILING = True  # Set this from a settings model

app = falcon.App()
if PROFILING:
    app.add_middleware(ProfilerMiddleware())

To invoke, make any request to your application and it launch a new window printing the HTML result from pyinstrument.

Profile a web request in Litestar

Minimal application setup allowing request profiling.

The middleware overrides the response to return a profiling report in HTML format.

from __future__ import annotations

from asyncio import sleep

from litestar import Litestar, get
from litestar.middleware import MiddlewareProtocol
from litestar.types import ASGIApp, Message, Receive, Scope, Send

from pyinstrument import Profiler


class ProfilingMiddleware(MiddlewareProtocol):
    def __init__(self, app: ASGIApp) -> None:
        super().__init__(app)  # type: ignore
        self.app = app

    async def __call__(self, scope: Scope, receive: Receive, send: Send) -> None:
        profiler = Profiler(interval=0.001, async_mode="enabled")
        profiler.start()
        profile_html: str | None = None

        async def send_wrapper(message: Message) -> None:
            if message["type"] == "http.response.start":
                profiler.stop()
                nonlocal profile_html
                profile_html = profiler.output_html()
                message["headers"] = [
                    (b"content-type", b"text/html; charset=utf-8"),
                    (b"content-length", str(len(profile_html)).encode()),
                ]
            elif message["type"] == "http.response.body":
                assert profile_html is not None
                message["body"] = profile_html.encode()
            await send(message)

        await self.app(scope, receive, send_wrapper)


@get("/")
async def index() -> str:
    await sleep(1)
    return "Hello, world!"


app = Litestar(
    route_handlers=[index],
    middleware=[ProfilingMiddleware],
)

To invoke, make any request to your application and it will return the HTML result from pyinstrument instead of your application’s response.

Profile a web request in aiohttp.web

You can use a simple middleware to profile aiohttp web server requests with Pyinstrument:

from aiohttp import web
from pyinstrument import Profiler

@web.middleware
async def profiler_middleware(request, handler):
    with Profiler() as p:
        await handler(request)
    return web.Response(text=p.output_html(), content_type="text/html")

app = web.Application(middlewares=(profiler_middleware,))

Pyinstrument’s HTML output will be returned as response, showing the profiling result of each request.

Make use of aiohttp.web development CLI feature to isolate configurations and make sure profiling is only enabled when needed:

...

def dev_app(argv):
    app = web.Application(middlewares=(profiler_middleware,))
    app.add_routes(routes)
    return app # for development

if __name__ == '__main__':
    app = web.Application()
    app.add_routes(routes)
    web.run_app(...) # for deployment

python3 -m aiohttp.web app:dev_app # develop with profiling and debug enabled
python3 ./app.py # run app without profiling

Profile Pytest tests

Pyinstrument can be invoked via the command-line to run pytest, giving you a consolidated report for the test suite.

pyinstrument -m pytest [pytest-args...]

Or, to instrument specific tests, create and auto-use fixture in conftest.py in your test folder:

from pathlib import Path
import pytest
from pyinstrument import Profiler

TESTS_ROOT = Path.cwd()

@pytest.fixture(autouse=True)
def auto_profile(request):
    PROFILE_ROOT = (TESTS_ROOT / ".profiles")
    # Turn profiling on
    profiler = Profiler()
    profiler.start()

    yield  # Run test

    profiler.stop()
    PROFILE_ROOT.mkdir(exist_ok=True)
    results_file = PROFILE_ROOT / f"{request.node.name}.html"
    profiler.write_html(results_file)

This will generate a HTML file for each test node in your test suite inside the .profiles directory.

Profile something else?

I’d love to have more ways to profile using Pyinstrument - e.g. other web frameworks. PRs are encouraged!