+++++ Usage +++++ Installation ============ Command to install pyperformance:: python3 -m pip install pyperformance The command installs a new ``pyperformance`` program. If needed, ``pyperf`` and ``six`` dependencies are installed automatically. pyperformance works on Python 3.6 and newer, but it may work on Python 3.4 and 3.5. At runtime, Python development files (header files) may be needed to install some dependencies like ``dulwich_log`` or ``psutil``, to build their C extension. Commands on Fedora to install dependencies: * Python 3: ``sudo dnf install python3-devel`` * PyPy: ``sudo dnf install pypy-devel`` Windows notes ------------- On Windows, to allow pyperformance to build dependencies from source like ``greenlet``, ``dulwich`` or ``psutil``, if you want to use a ``python.exe`` built from source, you should not use the ``python.exe`` directly. Instead, you must run the little-known command ``PC\layout`` to create a filesystem layout that resembles an installed Python:: .\python.bat -m PC.layout --preset-default --copy installed -v (Use the ``--help`` flag for more info about ``PC\layout``.) Now you can use the "installed" Python executable:: installed\python.exe -m pip install pyperformance installed\python.exe -m pyperformance run ... Using an *actually* installed Python executable (e.g. via ``py``) works fine too. Run benchmarks ============== Commands to compare Python 3.6 and Python 3.7 performance:: pyperformance run --python=python3.6 -o py36.json pyperformance run --python=python3.7 -o py38.json pyperformance compare py36.json py38.json Note: ``python3 -m pyperformance ...`` syntax works as well (ex: ``python3 -m pyperformance run -o py38.json``), but requires to install pyperformance on each tested Python version. JSON files are produced by the pyperf module and so can be analyzed using pyperf commands:: python3 -m pyperf show py36.json python3 -m pyperf check py36.json python3 -m pyperf metadata py36.json python3 -m pyperf stats py36.json python3 -m pyperf hist py36.json python3 -m pyperf dump py36.json (...) It's also possible to use pyperf to compare results of two JSON files:: python3 -m pyperf compare_to py36.json py38.json --table Basic commands -------------- pyperformance actions:: run Run benchmarks on the running python show Display a benchmark file compare Compare two benchmark files list List benchmarks of the running Python list_groups List benchmark groups of the running Python venv Actions on the virtual environment Common options -------------- Options available to all commands:: -h, --help show this help message and exit run --- Run benchmarks on the running python. Usage:: pyperformance run [-h] [-r] [-f] [--debug-single-value] [-v] [-m] [--affinity CPU_LIST] [-o FILENAME] [--append FILENAME] [--manifest MANIFEST] [-b BM_LIST] [--inherit-environ VAR_LIST] [-p PYTHON] options:: -h, --help show this help message and exit -r, --rigorous Spend longer running tests to get more accurate results -f, --fast Get rough answers quickly --debug-single-value Debug: fastest mode, only compute a single value -v, --verbose Print more output -m, --track-memory Track memory usage. This only works on Linux. --affinity CPU_LIST Specify CPU affinity for benchmark runs. This way, benchmarks can be forced to run on a given CPU to minimize run to run variation. -o FILENAME, --output FILENAME Run the benchmarks on only one interpreter and write benchmark into FILENAME. Provide only baseline_python, not changed_python. --append FILENAME Add runs to an existing file, or create it if it doesn't exist --manifest MANIFEST benchmark manifest file to use -b BM_LIST, --benchmarks BM_LIST Comma-separated list of benchmarks to run. Can contain both positive and negative arguments: --benchmarks=run_this,also_this,-not_this. If there are no positive arguments, we'll run all benchmarks except the negative arguments. Otherwise we run only the positive arguments. --inherit-environ VAR_LIST Comma-separated list of environment variable names that are inherited from the parent environment when running benchmarking subprocesses. -p PYTHON, --python PYTHON Python executable (default: use running Python) --same-loops SAME_LOOPS Use the same number of loops as a previous run (i.e., don't recalibrate). Should be a path to a .json file from a previous run. show ---- Display a benchmark file. Usage:: show FILENAME positional arguments:: FILENAME compare ------- Compare two benchmark files. Usage:: pyperformance compare [-h] [-v] [-O STYLE] [--csv CSV_FILE] [--inherit-environ VAR_LIST] [-p PYTHON] baseline_file.json changed_file.json positional arguments:: baseline_file.json changed_file.json options:: -v, --verbose Print more output -O STYLE, --output_style STYLE What style the benchmark output should take. Valid options are 'normal' and 'table'. Default is normal. --csv CSV_FILE Name of a file the results will be written to, as a three-column CSV file containing minimum runtimes for each benchmark. --inherit-environ VAR_LIST Comma-separated list of environment variable names that are inherited from the parent environment when running benchmarking subprocesses. -p PYTHON, --python PYTHON Python executable (default: use running Python) list ---- List benchmarks of the running Python. Usage:: pyperformance list [-h] [--manifest MANIFEST] [-b BM_LIST] [--inherit-environ VAR_LIST] [-p PYTHON] options:: --manifest MANIFEST benchmark manifest file to use -b BM_LIST, --benchmarks BM_LIST Comma-separated list of benchmarks to run. Can contain both positive and negative arguments: --benchmarks=run_this,also_this,-not_this. If there are no positive arguments, we'll run all benchmarks except the negative arguments. Otherwise we run only the positive arguments. --inherit-environ VAR_LIST Comma-separated list of environment variable names that are inherited from the parent environment when running benchmarking subprocesses. -p PYTHON, --python PYTHON Python executable (default: use running Python) Use ``python3 -m pyperformance list -b all`` to list all benchmarks. list_groups ----------- List benchmark groups of the running Python. Usage:: pyperformance list_groups [-h] [--manifest MANIFEST] [--inherit-environ VAR_LIST] [-p PYTHON] options:: --manifest MANIFEST benchmark manifest file to use --inherit-environ VAR_LIST Comma-separated list of environment variable names that are inherited from the parent environment when running benchmarking subprocesses. -p PYTHON, --python PYTHON Python executable (default: use running Python) venv ---- Actions on the virtual environment. Actions:: show Display the path to the virtual environment and its status (created or not) create Create the virtual environment recreate Force the recreation of the the virtual environment remove Remove the virtual environment Common options:: --venv VENV Path to the virtual environment --inherit-environ VAR_LIST Comma-separated list of environment variable names that are inherited from the parent environment when running benchmarking subprocesses. -p PYTHON, --python PYTHON Python executable (default: use running Python) venv show ~~~~~~~~~ Display the path to the virtual environment and its status (created or not). Usage:: pyperformance venv show [-h] [--venv VENV] [--inherit-environ VAR_LIST] [-p PYTHON] venv create ~~~~~~~~~~~ Create the virtual environment. Usage:: pyperformance venv create [-h] [--venv VENV] [--manifest MANIFEST] [-b BM_LIST] [--inherit-environ VAR_LIST] [-p PYTHON] options:: --manifest MANIFEST benchmark manifest file to use -b BM_LIST, --benchmarks BM_LIST Comma-separated list of benchmarks to run. Can contain both positive and negative arguments: --benchmarks=run_this,also_this,-not_this. If there are no positive arguments, we'll run all benchmarks except the negative arguments. Otherwise we run only the positive arguments. venv recreate ~~~~~~~~~~~~~ Force the recreation of the the virtual environment. Usage:: pyperformance venv recreate [-h] [--venv VENV] [--manifest MANIFEST] [-b BM_LIST] [--inherit-environ VAR_LIST] [-p PYTHON] options:: --manifest MANIFEST benchmark manifest file to use -b BM_LIST, --benchmarks BM_LIST Comma-separated list of benchmarks to run. Can contain both positive and negative arguments: --benchmarks=run_this,also_this,-not_this. If there are no positive arguments, we'll run all benchmarks except the negative arguments. Otherwise we run only the positive arguments. venv remove ~~~~~~~~~~~ Remove the virtual environment. Usage:: pyperformance venv remove [-h] [--venv VENV] [--inherit-environ VAR_LIST] [-p PYTHON] Compile Python to run benchmarks ================================ pyperformance actions:: compile Compile and install CPython and run benchmarks on installed Python compile_all Compile and install CPython and run benchmarks on installed Python on all branches and revisions of CONFIG_FILE upload Upload JSON results to a Codespeed website All these commands require a configuration file. Simple configuration usable for ``compile`` (but not for ``compile_all`` nor ``upload``), ``doc/benchmark.conf``: .. literalinclude:: benchmark.conf :language: ini Configuration file sample with comments, ``doc/benchmark.conf.sample``: .. literalinclude:: benchmark.conf.sample :language: ini .. _cmd-compile: compile ------- Compile Python, install Python and run benchmarks on the installed Python. Usage:: pyperformance compile [-h] [--patch PATCH] [-U] [-T] [--inherit-environ VAR_LIST] [-p PYTHON] config_file revision [branch] positional arguments:: config_file Configuration filename revision Python benchmarked revision branch Git branch options:: --patch PATCH Patch file -U, --no-update Don't update the Git repository -T, --no-tune Don't run 'pyperf system tune' to tune the system for benchmarks --inherit-environ VAR_LIST Comma-separated list of environment variable names that are inherited from the parent environment when running benchmarking subprocesses. -p PYTHON, --python PYTHON Python executable (default: use running Python) Notes: * PGO is broken on Ubuntu 14.04 LTS with GCC 4.8.4-2ubuntu1~14.04: ``Modules/socketmodule.c:7743:1: internal compiler error: in edge_badness, at ipa-inline.c:895`` compile_all ----------- Compile all branches and revisions of CONFIG_FILE. Usage:: pyperformance compile_all [-h] [--inherit-environ VAR_LIST] [-p PYTHON] config_file positional arguments:: config_file Configuration filename options:: --inherit-environ VAR_LIST Comma-separated list of environment variable names that are inherited from the parent environment when running benchmarking subprocesses. -p PYTHON, --python PYTHON Python executable (default: use running Python) upload ------ Upload results from a JSON file to a Codespeed website. Usage:: pyperformance upload [-h] [--inherit-environ VAR_LIST] [-p PYTHON] config_file json_file positional arguments:: config_file Configuration filename json_file JSON filename options:: --inherit-environ VAR_LIST Comma-separated list of environment variable names that are inherited from the parent environment when running benchmarking subprocesses. -p PYTHON, --python PYTHON Python executable (default: use running Python) How to get stable benchmarks ============================ * Run ``python3 -m pyperf system tune`` command * Compile Python using LTO (Link Time Optimization) and PGO (profile guided optimizations): use the :ref:`pyperformance compile ` command with uses LTO and PGO by default * See advices of the pyperf documentation: `How to get reproductible benchmark results `_. pyperformance virtual environment ================================= To run benchmarks, pyperformance first creates a virtual environment. It installs requirements with fixed versions to get a reproductible environment. The system Python has unknown module installed with unknown versions, and can have ``.pth`` files run at Python startup which can modify Python behaviour or at least slow down Python startup. What is the goal of pyperformance ================================= A benchmark is always written for a specific purpose. Depending how the benchmark is written and how the benchmark is run, the result can be different and so have a different meaning. The pyperformance benchmark suite has multiple goals: * Help to detect performance regression in a Python implementation * Validate that an optimization change makes Python faster and don't performance regressions, or only minor regressions * Compare two implementations of Python, for example CPython and PyPy * Showcase of Python performance which ideally would be representative of performances of applications running on production Don't disable GC nor ASLR ------------------------- The pyperf module and pyperformance benchmarks are designed to produce reproductible results, but not at the price of running benchmarks in a special mode which would not be used to run applications in production. For these reasons, the Python garbage collector, Python randomized hash function and system ASLR (Address Space Layout Randomization) are **not disabled**. Benchmarks don't call ``gc.collect()`` neither since CPython implements it with `stop-the-world `_ and so applications don't call it to not kill performances. Include outliers and spikes --------------------------- Moreover, while the pyperf documentation explains how to reduce the random noise of the system and other applications, some benchmarks use the system and so can get different timing depending on the system workload, depending on I/O performances, etc. Outliers and temporary spikes in results are **not automatically removed**: values are summarized by computing the average (arithmetic mean) and standard deviation which "contains" these spikes, instead of using median and the median absolute deviation for example which to ignore outliers. It is deliberate choice since applications running in production are impacted by such temporary slowdown caused by various things like a garbage collection or a JIT compilation. Warmups and steady state ------------------------ A borderline issue are the benchmarks "warmups". The first values of each worker process are always slower: 10% slower in the best case, it can be 1000% slower or more on PyPy. Right now (2017-04-14), pyperformance ignore first values considered as warmup until a benchmark reachs its "steady state". The "steady state" can include temporary spikes every 5 values (ex: caused by the garbage collector), and it can still imply further JIT compiler optimizations but with a "low" impact on the average pyperformance. To be clear "warmup" and "steady state" are a work-in-progress and a very complex topic, especially on PyPy and its JIT compiler. Notes ===== Tool for comparing the performance of two Python implementations. pyperformance will run Student's two-tailed T test on the benchmark results at the 95% confidence level to indicate whether the observed difference is statistically significant. Omitting the ``-b`` option will result in the default group of benchmarks being run Omitting ``-b`` is the same as specifying `-b default`. To run every benchmark pyperformance knows about, use ``-b all``. To see a full list of all available benchmarks, use `--help`. Negative benchmarks specifications are also supported: `-b -2to3` will run every benchmark in the default group except for 2to3 (this is the same as `-b default,-2to3`). `-b all,-django` will run all benchmarks except the Django templates benchmark. Negative groups (e.g., `-b -default`) are not supported. Positive benchmarks are parsed before the negative benchmarks are subtracted. If ``--track_memory`` is passed, pyperformance will continuously sample the benchmark's memory usage. This currently only works on Linux 2.6.16 and higher or Windows with PyWin32. Because ``--track_memory`` introduces performance jitter while collecting memory measurements, only memory usage is reported in the final report.