Custom Benchmarks¶

pyperformance includes its own set of benchmarks (see Benchmarks). However, it also supports using custom benchmarks.

Using Custom Benchmarks¶

To use custom benchmarks, you will need to use the --manifest CLI option and provide the path to the manifest file describing those benchmarks.

The pyperformance File Formats¶

pyperformance uses two file formats to identify benchmarks:

manifest - a set of benchmarks
metadata - a single benchmark

For each benchmark, there are two required files and several optional ones. Those files are expected to be in a specific directory structure (unless customized in the metadata).

The structure (see below) is such that it’s easy to maintain a benchmark (or set of benchmarks) on GitHub and distribute it on PyPI. It also simplifies publishing a Python project’s benchmarks. The alternative is pointing people at a repo.

Benchmarks can inherit metadata from other metadata files. This is useful for keeping common metadata for a set of benchmarks (e.g. “version”) in one file. Likewise, benchmarks for a Python project can inherit metadata from the project’s pyproject.toml.

Sometimes a benchmark will have one or more variants that run using the same script. Variants like this are supported by pyperformance without requiring much extra effort.

Benchmark Directory Structure¶

Normally a benchmark is structured like this:

bm_NAME/
   data/  # if needed
   requirements.txt  # lock file, if any
   pyproject.toml
   run_benchmark.py

(Note the “bm_” prefix on the directory name.)

“pyproject.toml” holds the metadata. “run_benchmark.py” holds the actual benchmark code. Both are necessary.

pyperformance treats the metadata file as the fundamental source of information about a benchmark. A manifest for a set of benchmarks is effectively a mapping of names to metadata files. So a metadata file is essential. It can be located anywhere on disk. However, if it isn’t located in the structure described above then the metadata must identify where to find the other files.

Other than that, only a benchmark script (e.g. “run_benchmark.py” above) is required. All other files are optional.

When a benchmark has variants, each has its own metadata file next to the normal “pyproject.toml”, named “bm_NAME.toml”. (Note the “bm_” prefix.) The format of variant metadata files is exactly the same. pyperformance treats them the same, except that the sibling “pyproject.toml” is inherited by default.

Manifest Files¶

A manifest file identifies a set of benchmarks, as well as (optionally) how they should be grouped. pyperformance uses the manifest to determine which benchmarks are available to run (and thus which to run by default).

A manifest normally looks like this:

[benchmarks]

name        metafile
bench1      somedir/bm_bench1/pyproject.toml
bench2      somedir/pyproject.toml
bench3      ../anotherdir

The “benchmarks” section is a table with rows of tab-separated-values. The “name” value is how pyperformance will identify the benchmark. The “metafile” value is where pyperformance will look for the benchmark’s metadata. If a metafile is a directory then it looks for “pyproject.toml” in that directory.

Benchmark Groups¶

The other sections in the manifest file relate to grouping:

[benchmarks]

name        metafile
bench1      somedir/bm_bench1
bench2      somedir/bm_bench2
bench3      anotherdir/mybench.toml

[groups]
tag1
tag2

[group default]
bench2
bench3

[group tricky]
bench2

The “groups” section specifies available groups that may be identified by benchmark tags (see about tags in the metadata section below). Any other group sections in the manifest are automatically added to the list of available groups.

If no “default” group is specified then one is automatically added with all benchmarks from the “benchmarks” section in it. If there is no “groups” section and no individual group sections (other than “default”) then the set of all tags of the known benchmarks is treated as “groups”. A group named “all” as also automatically added which has all known benchmarks in it.

Benchmarks can be excluded from a group by using a - (minus) prefix. Any benchmark alraedy in the list (at that point) that matches will be dropped from the list. If the first entry in the section is an exclusion then all known benchmarks are first added to the list before the exclusion is applied.

For example:

[benchmarks]

name        metafile
bench1      somedir/bm_bench1
bench2      somedir/bm_bench2
bench3      anotherdir/mybench.toml

[group default]
-bench1

This means by default only “bench2” and “bench3” are run.

Merging Manifests¶

To combine manifests, use the [includes] section in the manifest:

[includes]
project1/benchmarks/MANIFEST
project2/benchmarks/MANIFEST
<default>

Note that <default> is the same as including the manifest file for the default pyperformance benchmarks.

A Local Benchmark Suite¶

Often a project will have more than one benchmark that it will treat as a suite. pyperformance handles this without any extra work.

In the dirctory holding the manifest file put all the benchmarks. Then put <local> in the “metafile” column, like this:

[benchmarks]

name        metafile
bench1      <local>
bench2      <local>
bench3      <local>
bench4      <local>
bench5      <local>

It will look for DIR/bm_NAME/pyproject.toml.

If there are also variants, identify the main benchmark in the “metafile” value, like this:

[benchmarks]

name        metafile
bench1      <local>
bench2      <local>
bench3      <local>
variant1    <local:bench3>
variant2    <local:bench3>

pyperformance will look for DIR/bm_BASE/bm_NAME.toml, where “BASE” is the part after “local:”.

A Project’s Benchmark Suite¶

A Python project can identify its benchmark suite by putting the path to the manifest file in the project’s top-level pyproject.toml. Additional manifests can be identified as well:

[tool.pyperformance]
manifest = "..."
manifests = ["...", "..."]

(Reminder: that is the pyproject.toml, not the manifest file.)

Benchmark Metadata Files¶

A benchmark’s metadata file (usually pyproject.toml) follows the format specified in PEP 621 and PEP 518. So there are two supported sections in the file: “project” and “tool.pyperformance”.

A typical metadata file will look something like this:

[project]
version = "0.9.1"
dependencies = ["pyperf"]
dynamic = ["name"]

[tool.pyperformance]
name = "my_benchmark"

A highly detailed one might look like this:

[project]
name = "pyperformance_bm_json_dumps"
version = "0.9.1"
description = "A benchmark for json.dumps()"
requires-python = ">=3.8"
dependencies = ["pyperf"]
urls = {repository = "https://github.com/python/pyperformance"}
dynamic = ["version"]

[tool.pyperformance]
name = "json_dumps"
tags = "serialize"
runscript = "bench.py"
datadir = ".data-files/extras"
extra_opts = ["--special"]

Inheritance¶

For one benchmark to inherit from another (or from common metadata), the “inherits” field is available:

[project]
dependencies = ["pyperf"]
dynamic = ["name", "version"]

[tool.pyperformance]
name = "my_benchmark"
inherits = "../common.toml"

All values in either section of the inherited metadata are treated as defaults, on top of which the current metadata is applied. In the above example, for instance, a value for “version” in common.toml would be used here.

If the “inherits” value is a directory (even for “..”) then “base.toml” in that directory will be inherited.

For variants, the base pyproject.toml is the default value for “inherits”.

Inferred Values¶

In some situations, omitted values will be inferred from other available data (even for required fields).

project.name <= tool.pyperformance.name
project.* <= inherited metadata (except for “name” and “dynamic”)
tool.pyperformance.name <= metadata filename
tool.pyperformance.* <= inherited metadata (except for “name” and “inherits”)

When the name is inferred from the filename for a regularly structured benchmark, the “bm_” prefix is removed from the benchmark’s directory. If it is a variant that prefix is removed from the metadata filename, as well as the .toml suffix.

The `[project]` Section¶

field	type	R	T	B	D
project.name	str	X	X
project.version	ver	X		X	X
project.dependencies	[str]			X
project.dynamic	[str]

“R”: required “T”: inferred from the tool section “B”: inferred from the inherited metadata “D”: for default benchmarks, inferred from pyperformance

“dynamic” is required by PEP 621 for when a field will be filled in dynamically by the tool. This is especially important for required fields.

All other PEP 621 fields are optional (e.g. requires-python = ">=3.8", {repository = "https://github.com/..."}).

The `[tool.pyperformance]` Section¶

field	type	R	B	F
tool.name	str	X		X
tool.tags	[str]		X
tool.extra_opts	[str]		X
tool.inherits	file
tool.runscript	file		X
tool.datadir	file		X

“R”: required “B”: inferred from the inherited metadata “F”: inferred from filename

tags: optional list of names to group benchmarks
extra_opts: optional list of args to pass to tool.runscript
runscript: the benchmark script to use instead of run_benchmark.py.

Custom Benchmarks¶

Using Custom Benchmarks¶

The pyperformance File Formats¶

Benchmark Directory Structure¶

Manifest Files¶

Benchmark Groups¶

Merging Manifests¶

A Local Benchmark Suite¶

A Project’s Benchmark Suite¶

Benchmark Metadata Files¶

Inheritance¶

Inferred Values¶

The `[project]` Section¶

The `[tool.pyperformance]` Section¶

Python Performance Benchmark Suite

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Related Topics

Custom Benchmarks¶

Using Custom Benchmarks¶

The pyperformance File Formats¶

Benchmark Directory Structure¶

Manifest Files¶

Benchmark Groups¶

Merging Manifests¶

A Local Benchmark Suite¶

A Project’s Benchmark Suite¶

Benchmark Metadata Files¶

Inheritance¶

Inferred Values¶

The [project] Section¶

The [tool.pyperformance] Section¶

The `[project]` Section¶

The `[tool.pyperformance]` Section¶