VizTracer

This repository publishes the Chalk-maintained chalk-viztracer distribution. It preserves VizTracer's viztracer Python package and command-line interfaces while carrying patches that have not yet shipped in an upstream release.

VizTracer is a low-overhead logging/debugging/profiling tool that can trace and visualize your python code execution.

The front-end UI is powered by Perfetto. Use "AWSD" to zoom/navigate. More help can be found in "Support - Controls".

Highlights

Detailed function entry/exit information on timeline with source code
Super easy to use, no source code change for most features, no package dependency
Low overhead, probably the fastest tracer in the market
Supports threading, multiprocessing, subprocess, async and PyTorch
Powerful front-end, able to render GB-level trace smoothly
Works on Linux/MacOS/Windows

Install

The preferred way to install VizTracer is via pip

pip install chalk-viztracer

Basic Usage

Command Line

# Instead of "python3 my_script.py arg1 arg2"
viztracer my_script.py arg1 arg2

A result.json file will be generated, which you can open with vizviewer

# You can display all the files in a directory and open them in browser too
vizviewer ./
# For very large trace files, try external trace processor
vizviewer --use_external_processor result.json

vizviewer will host an HTTP server on http://localhost:9001. You can also open your browser and use that address.

If you do not want vizviewer to open the webbrowser automatically, you can use

vizviewer --server_only result.json

If you just need to bring up the trace report once, and do not want the persistent server, use

vizviewer --once result.json

vizviewer result.json

A VS Code Extension is available to make your life even easier.

Add --open to open the reports right after tracing

viztracer --open my_script.py arg1 arg2
viztracer -o result.html --open my_script.py arg1 arg2

modules and console scripts(like flask) are supported as well

viztracer -m your_module

viztracer flask run

Inline

You can also manually start/stop VizTracer in your script as well.

from viztracer import VizTracer

tracer = VizTracer()
tracer.start()
# Something happens here
tracer.stop()
tracer.save()  # also takes output_file as an optional argument

Or, you can do it with with statement

with VizTracer(output_file="optional.json") as tracer:
    # Something happens here

Jupyter

If you are using Jupyter, you can use viztracer cell magics.

# You need to load the extension first
%load_ext viztracer

%%viztracer
# Your code after

A VizTracer Report button will appear after the cell and you can click it to view the results

PyTorch

VizTracer can log native calls and GPU events of PyTorch (based on torch.profiler) with --log_torch.

with VizTracer(log_torch=True) as tracer:
    # Your torch code

viztracer --log_torch your_model.py

Advanced Usage

Trace Filter

VizTracer can filter out the data you don't want to reduce overhead and keep info of a longer time period before you dump the log.

Extra Logs without Code Change

VizTracer can log extra information without changing your source code

Add Custom Event

VizTracer supports inserting custom events while the program is running. This works like a print debug, but you can know when this print happens while looking at trace data.

Misc

Multi Thread Support

For Python3.12+, VizTracer supports Python-level multi-thread tracing without the need to do any modification to your code.

For versions before 3.12, VizTracer supports python native threading module. Just start VizTracer before you create threads and it will just work.

For other multi-thread scenarios, you can use enable_thread_tracing() to notice VizTracer about the thread to trace it.

Refer to multi thread docs for details

Multi Process Support

VizTracer supports subprocess, multiprocessing, os.fork(), concurrent.futures, and loky out of the box.

For more general multi-process cases, VizTracer can support with some extra steps.

Refer to multi process docs for details

Async Support

VizTracer supports asyncio natively, but could enhance the report by using --log_async.

Refer to async docs for details

Flamegraph

Perfetto supports native flamegraph, just select slices on the UI and choose "Slice Flamegraph".

Remote attach

VizTracer supports remote attach to an arbitrary Python process to trace it, as long as viztracer is importable

Refer to remote attach docs

JSON alternative

VizTracer needs to dump the internal data to json format. It is recommended for the users to install orjson, which is much faster than the builtin json library. VizTracer will try to import orjson and fall back to the builtin json library if orjson does not exist.

Performance

VizTracer puts in a lot of effort to achieve low overhead. The actual performance impact largely depends on your application. For typical codebases, the overhead is expected to be below 1x. If your code has infrequent function calls, the overhead could be minimal.

Detailed explanation

The overhead introduced by VizTracer is basically a fixed amount of time during function entry and exit, so the more time spent on function entries and exits, the more overhead will be observed. A pure recursive fib function could suffer 3x-4x overhead on Python3.11+ (when the Python call is optimized, before that Python call was slower so the overhead ratio would be less).

In the real life scenario, your code should not spend too much time on function calls (they don't really do anything useful), so the overhead would be much smaller.

Many techniques are applied to minimize the overall overhead during code execution to reduce the inevitable skew introduced by VizTracer (the report saving part is not as critical). For example, VizTracer tries to use the CPU timestamp counter instead of a syscall to get the time when available. On Python 3.12+, VizTracer uses sys.monitoring which has less overhead than sys.setprofile. All of the efforts made it observably faster than cProfile, the Python stdlib profiler.

However, VizTracer is a tracer, which means it has to record every single function entry and exit, so it can't be as fast as the sampling profilers - they are not the same thing. With the extra overhead, VizTracer provides a lot more information than normal sampling profilers.

Documentation

For full documentation, please see https://viztracer.readthedocs.io/en/stable

Bugs/Requests

Please send bug reports and feature requests through github issue tracker. VizTracer is currently under development now and it's open to any constructive suggestions.

License

Distributed under the terms of the Apache 2.0 license.

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Source Distribution

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SHA256	`ea7330b9470480a15ab8144cebc5d828081c9b396ee156b336a91c49b701e430`
MD5	`953ca2d96f7b6179d4508d47fb1713d4`
BLAKE2b-256	`fc61712f32dc359a381323e9a2648464a208be0cc02d89d9a770c95a50c56151`

chalk-viztracer 1.1.2

VizTracer

Highlights

Install

Basic Usage

Command Line

Inline

Jupyter

PyTorch

Advanced Usage

Trace Filter

Extra Logs without Code Change

Add Custom Event

Misc

Multi Thread Support

Multi Process Support

Async Support

Flamegraph

Remote attach

JSON alternative

Performance

Documentation

Bugs/Requests

License

Project links

Key dates

Owner

Credits

GitHub Statistics

License expression

Requires

Provides Extra

Classifiers

Download files

Source Distribution

Built Distributions

File details

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File hashes

Provenance

Algorithm	Hash digest
SHA256	`f53cfcee714b92aca81c09f2f501bdb9d9d38d7c62d53ccbd8c78fdbd8a4582d`
MD5	`a35c877e5dba26ff687cbef627cbe5e3`
BLAKE2b-256	`7c8ddbe90d9ef9007a1632b18eb3b98889678d4f84456acfbf1106cbe498dc01`

Algorithm	Hash digest
SHA256	`979b3cf889188d95ec15b788bb6a4770930a24a1780b554eceff054109412759`
MD5	`378d33e8182d4114253586503a41950f`
BLAKE2b-256	`5318f47507a3adf59b19b7d531eec68b5685b879876be47cffc21fddcc41d24c`

Algorithm	Hash digest
SHA256	`79b1632788b66eb83ab6ee4fc2710ac6a72e47c13eaf1ac077ad74dc673c7105`
MD5	`cfa15f5614edd41879306c8ee1defab9`
BLAKE2b-256	`3f789b74f0270460bc1582b4c925bff2e22fb99e952c3cdf9b8aaa51c14a87d8`

Algorithm	Hash digest
SHA256	`cb2c2accba659a2e3a2399d51d2a5dd5f5874cfe5bc0c52025fd3414cf6d5cd6`
MD5	`b94addf5fe0e0413e8b69c887502ec6c`
BLAKE2b-256	`facab59a6881319263d9346b7cf17b83ad27659255660241b932d3c9b6b66ed3`

Algorithm	Hash digest
SHA256	`464e00cb4b0717a718197d73ccf82f93b0d8474354f3d4d8ddd843a164d7e788`
MD5	`9ef33006567c4f3a6cbd9e0f2f39719e`
BLAKE2b-256	`c30d45bf3ceaad9bb465bb111ea694f6503c0114c124deba33569e5e63b6cd49`

Algorithm	Hash digest
SHA256	`1ff603bde69ea5d479455bd0125d0dd08b65dd4b39fc48825ecbfdae53b928dd`
MD5	`85cd10116971c8f0462ee03bfcc67a27`
BLAKE2b-256	`ff27a24389fab7680e1f7b21d63ae2c5f0b1ac5b8b0aced5869e9388723f7cf5`

Algorithm	Hash digest
SHA256	`beda02501424bb4fb2b2d224ad494fd1c58f5a29e06205854a83e17ea33aa011`
MD5	`c5c549ab6655e353ed2d71ad03661b6a`
BLAKE2b-256	`ba0002068a801b27df3b2105d5ea514167beada669f2fabd24420ceed53c364e`

Algorithm	Hash digest
SHA256	`5f50369fdd805b2c47f18f29cf88f40c831cb984e80ba64e92064a60cb60cddf`
MD5	`9a8579d20ff9ffed2220bfd4b2795446`
BLAKE2b-256	`6e0d529c349a48e83e472c6efa5819532a2511e3e54dbdadc9f6ef808c71a0d9`

Algorithm	Hash digest
SHA256	`ddc339c7782f9605080c1674640f9bb111b82aa50fc1fc565184466f2d04d6d4`
MD5	`2404001414f6fbd1e6174dcfef9549cb`
BLAKE2b-256	`699969549340a79e9ffd4d592c0055064348562c668ab4f640d55e8fa09ce878`

Algorithm	Hash digest
SHA256	`788f00d999b591e88113de9b31f59c561644b5338327887931e4d30ba3571dab`
MD5	`757475f2fdf59cb8ebe6d25e80d19b91`
BLAKE2b-256	`753bb3865665e57b081555d4278c90d2a490383f62afbfb65f7b7e51f6171d55`