tprof
A targeting profiler.
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Requirements
Python 3.12 to 3.15 supported.
Installation
Install with pip:
python -m pip install tprof
Usage
tprof measures the time spent in specified target functions when running a script or module. Unlike a full program profiler, it only tracks the specified functions using sys.monitoring (new in Python 3.12), reducing overhead and helping you focus on the bits you’re changing. Timing is done in C to further reduce overhead.
tprof supports usage as a CLI and with a Python API.
CLI
Specify one or more target functions with -t, then what to run: a script file by filename, or a module with -m then its name. Any unrecognized arguments are passed to the script or module.
Use the format <module>:<function> to specify target functions. When using -m with a module, you can skip the <module> part and it will be inferred from the module name.
$ tprof -t lib:maths ./example.py
...
🎯 tprof results:
function calls total median ± σ min … max
lib:maths() 2 610ms 305ms ± 2ms 304ms … 307msFull help:
usage: tprof [-h] -t target [-x | --baseline path] [--json path]
(-m module | script) ...
positional arguments:
script Python script to run
args Arguments to pass to the script or module
options:
-h, --help show this help message and exit
-t target Target callable to profile (format: module:function).
-x, --compare Compare performance of targets, with the first as baseline.
--baseline path Compare against statistics from a previous run's --json
file.
--json path Write statistics as JSON to this file, or '-' for stdout.
-m module Run library module as a script (like python -m)Comparison mode
Pass -x (--compare) to compare the performance of multiple target functions, with the first as the baseline, in an extra “delta” column. For example, given this code:
def before():
total = 0
for i in range(100_000):
total += i
return total
def after():
return sum(range(100_000))
for _ in range(100):
before()
after()…you can run tprof like this to compare the two functions:
$ tprof -x -t before -t after -m example
🎯 tprof results:
function calls total median ± σ min … max delta
example:before() 100 227ms 2ms ± 34μs 2ms … 2ms -
example:after() 100 86ms 856μs ± 15μs 835μs … 910μs -62.27%JSON output
Pass --json <path> to also write the statistics to the given file as JSON, or - to write them to standard output. The file contains a functions list with the name, call count, and total, minimum, maximum, median, and standard deviation of times, in nanoseconds, per target:
{
"version": 1,
"label": null,
"functions": [
{
"name": "lib:maths",
"calls": 2,
"total_ns": 610622917,
"min_ns": 304285875,
"max_ns": 306337042,
"median_ns": 305311458.5,
"stdev_ns": 1450393.5
}
]
}Baseline comparison mode
Pass --baseline <path> with the --json output of a previous run to compare against that run, in an extra “delta” column that compares each function’s median against its median in the baseline run. Use this to check the effect of a change to the profiled code:
$ tprof -t lib:maths --json before.json ./example.py
...
$ tprof -t lib:maths --baseline before.json ./example.py
...
🎯 tprof results:
function calls total median ± σ min … max delta
lib:maths() 2 592ms 296ms ± 2ms 294ms … 297ms -3.11%API
tprof(*targets, label=None, compare=False, json_path=None, baseline_path=None)
Use this context manager / decorator within your code to perform profiling in a specific block. The report is printed when the block ends, each time it ends.
Each item in targets may be a callable to profile, or a string reference to one that will be resolved with pkgutil.resolve_name().
label is an optional string to add to the report heading to distinguish multiple reports.
Set compare to True to enable comparison mode, as documented above in the CLI section.
Set json_path to a file path to also write the statistics as JSON, as documented above in the CLI section, or - for standard output.
Set baseline_path to the path of a previous run’s JSON statistics to enable baseline comparison mode, as documented above in the CLI section. It cannot be combined with compare.
The context manager yields a list of FunctionStats, populated with one entry per target when the profiled block ends, for programmatic access to the results. Each FunctionStats has these attributes: name, calls, total_ns, min_ns, max_ns, median_ns, and stdev_ns.
For example, given this code:
from lib import maths
from tprof import tprof
print("Doing the maths…")
with tprof(maths):
maths()
print("The maths has been done!")…running it would produce output like:
$ python example.py
Doing the maths…
🎯 tprof results:
function calls total median ± σ min … max
lib:maths() 1 305ms 305ms 305ms … 305ms
The maths has been done!Another example using comparison mode:
from tprof import tprof
def before():
total = 0
for i in range(100_000):
total += i
return total
def after():
return sum(range(100_000))
with tprof(before, after, compare=True):
for _ in range(100):
before()
after()…which produces output like:
$ python example.py
🎯 tprof results:
function calls total median ± σ min … max delta
__main__:before() 100 227ms 2ms ± 83μs 2ms … 3ms -
__main__:after() 100 85ms 853μs ± 22μs 835μs … 1ms -62.35%Use the yielded list to access the results programmatically. It contains one FunctionStats per target, in the same order as passed to tprof(), so with a single target you can unpack it directly:
from lib import maths
from tprof import tprof
with tprof(maths) as results:
maths()
(function_stats,) = results # unpack the single result for maths()
print(f"{function_stats.name} took {function_stats.median_ns}ns")History
When optimizing Python code, I found I was using this workflow:
Profile the whole program with a tool like cProfile or py-spy to find slow functions.
Pick a function to optimize.
Make a change.
Re-profile the whole program to see if the changes helped.
This works fined but profiling the whole program again adds overhead, and picking out the one function’s stats from the report is extra work. When I saw that Python 3.12’s sys.monitoring allows tracking specific functions with low overhead, I created tprof to streamline this workflow, allowing the final step to re-profile just the target function.
It also seemed a natural extension that tprof could compare multiple functions, supporting a nice microbenchmarking workflow.
1 maintainer
Metadata
1 maintainer
Release files for tprof 1.4.0
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Built distributions (wheels)
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