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test: Add asv benchmark jobs to CI #234

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73 changes: 73 additions & 0 deletions .github/workflows/benchmarks.yml
Original file line number Diff line number Diff line change
@@ -0,0 +1,73 @@
name: Benchmark

on:
pull_request:
types: [opened, reopened, synchronize, labeled]
workflow_dispatch:

jobs:
benchmark:
if: ${{ contains( github.event.pull_request.labels.*.name, 'run-benchmark') && github.event_name == 'pull_request' || github.event_name == 'workflow_dispatch' }}
name: Linux
runs-on: ubuntu-20.04
env:
ASV_DIR: "./asv_bench"

steps:
# We need the full repo to avoid this issue
# https://github.com/actions/checkout/issues/23
- uses: actions/checkout@v2
with:
fetch-depth: 0

- name: Setup Miniconda
uses: conda-incubator/setup-miniconda@v2
with:
installer-url: https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh

- name: Setup some dependencies
shell: bash -l {0}
run: |
pip install asv
sudo apt-get update -y

- name: Run benchmarks
shell: bash -l {0}
id: benchmark
env:
OPENBLAS_NUM_THREADS: 1
MKL_NUM_THREADS: 1
OMP_NUM_THREADS: 1
ASV_FACTOR: 1.5
ASV_SKIP_SLOW: 1
run: |
set -x
# ID this runner
asv machine --yes
echo "Baseline: ${{ github.event.pull_request.base.sha }} (${{ github.event.pull_request.base.label }})"
echo "Contender: ${GITHUB_SHA} (${{ github.event.pull_request.head.label }})"
# Use mamba for env creation
# export CONDA_EXE=$(which mamba)
export CONDA_EXE=$(which conda)
# Run benchmarks for current commit against base
ASV_OPTIONS="--split --show-stderr --factor $ASV_FACTOR"
asv continuous $ASV_OPTIONS ${{ github.event.pull_request.base.sha }} ${GITHUB_SHA} \
| sed "/Traceback \|failed$\|PERFORMANCE DECREASED/ s/^/::error::/" \
| tee benchmarks.log
# Report and export results for subsequent steps
if grep "Traceback \|failed\|PERFORMANCE DECREASED" benchmarks.log > /dev/null ; then
exit 1
fi
working-directory: ${{ env.ASV_DIR }}

- name: Add instructions to artifact
if: always()
run: |
cp benchmarks/README_CI.md benchmarks.log .asv/results/
working-directory: ${{ env.ASV_DIR }}

- uses: actions/upload-artifact@v2
if: always()
with:
name: asv-benchmark-results-${{ runner.os }}
path: ${{ env.ASV_DIR }}/.asv/results
149 changes: 149 additions & 0 deletions asv_bench/asv.conf.json
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{
// The version of the config file format. Do not change, unless
// you know what you are doing.
"version": 1,

// The name of the project being benchmarked
"project": "ruptures",

// The project's homepage
"project_url": "https://github.com/deepcharles/ruptures",

// The URL or local path of the source code repository for the
// project being benchmarked
"repo": "..",

// List of branches to benchmark. If not provided, defaults to "master"
// (for git) or "default" (for mercurial).
"branches": ["main"], // for git
// "branches": ["default"], // for mercurial

// The DVCS being used. If not set, it will be automatically
// determined from "repo" by looking at the protocol in the URL
// (if remote), or by looking for special directories, such as
// ".git" (if local).
"dvcs": "git",

// The tool to use to create environments. May be "conda",
// "virtualenv" or other value depending on the plugins in use.
// If missing or the empty string, the tool will be automatically
// determined by looking for tools on the PATH environment
// variable.
"environment_type": "conda",

// timeout in seconds for installing any dependencies in environment
// defaults to 10 min
"install_timeout": 600,

// the base URL to show a commit for the project.
"show_commit_url": "https://github.com/deepcharles/ruptures/commit/",

// The Pythons you'd like to test against. If not provided, defaults
// to the current version of Python used to run `asv`.
"pythons": ["3.10"],

// The matrix of dependencies to test. Each key is the name of a
// package (in PyPI) and the values are version numbers. An empty
// list or empty string indicates to just test against the default
// (latest) version. null indicates that the package is to not be
// installed. If the package to be tested is only available from
// PyPi, and the 'environment_type' is conda, then you can preface
// the package name by 'pip+', and the package will be installed via
// pip (with all the conda available packages installed first,
// followed by the pip installed packages).
//
// "matrix": {
// "numpy": ["1.6", "1.7"],
// "six": ["", null], // test with and without six installed
// "pip+emcee": [""], // emcee is only available for install with pip.
// },
"matrix": {
"setuptools": [""],
"setuptools_scm[toml]": [""],
"Cython": [""],
"numpy": [""],
"scipy": [""],
},
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All the required installs needs to be here as well.



// Combinations of libraries/python versions can be excluded/included
// from the set to test. Each entry is a dictionary containing additional
// key-value pairs to include/exclude.
//
// An exclude entry excludes entries where all values match. The
// values are regexps that should match the whole string.
//
// An include entry adds an environment. Only the packages listed
// are installed. The 'python' key is required. The exclude rules
// do not apply to includes.
//
// In addition to package names, the following keys are available:
//
// - python
// Python version, as in the *pythons* variable above.
// - environment_type
// Environment type, as above.
// - sys_platform
// Platform, as in sys.platform. Possible values for the common
// cases: 'linux2', 'win32', 'cygwin', 'darwin'.
//
// "exclude": [
// {"python": "3.2", "sys_platform": "win32"}, // skip py3.2 on windows
// {"environment_type": "conda", "six": null}, // don't run without six on conda
// ],
//
// "include": [
// // additional env for python2.7
// {"python": "2.7", "numpy": "1.8"},
// // additional env if run on windows+conda
// {"platform": "win32", "environment_type": "conda", "python": "2.7", "libpython": ""},
// ],

// The directory (relative to the current directory) that benchmarks are
// stored in. If not provided, defaults to "benchmarks"
"benchmark_dir": "benchmarks",

// The directory (relative to the current directory) to cache the Python
// environments in. If not provided, defaults to "env"
"env_dir": ".asv/env",

// The directory (relative to the current directory) that raw benchmark
// results are stored in. If not provided, defaults to "results".
"results_dir": ".asv/results",

// The directory (relative to the current directory) that the html tree
// should be written to. If not provided, defaults to "html".
"html_dir": ".asv/html",

// The number of characters to retain in the commit hashes.
// "hash_length": 8,

// `asv` will cache wheels of the recent builds in each
// environment, making them faster to install next time. This is
// number of builds to keep, per environment.
// "wheel_cache_size": 0

// The commits after which the regression search in `asv publish`
// should start looking for regressions. Dictionary whose keys are
// regexps matching to benchmark names, and values corresponding to
// the commit (exclusive) after which to start looking for
// regressions. The default is to start from the first commit
// with results. If the commit is `null`, regression detection is
// skipped for the matching benchmark.
//
// "regressions_first_commits": {
// "some_benchmark": "352cdf", // Consider regressions only after this commit
// "another_benchmark": null, // Skip regression detection altogether
// }

// The thresholds for relative change in results, after which `asv
// publish` starts reporting regressions. Dictionary of the same
// form as in ``regressions_first_commits``, with values
// indicating the thresholds. If multiple entries match, the
// maximum is taken. If no entry matches, the default is 5%.
//
// "regressions_thresholds": {
// "some_benchmark": 0.01, // Threshold of 1%
// "another_benchmark": 0.5, // Threshold of 50%
// }
}
124 changes: 124 additions & 0 deletions asv_bench/benchmarks/README_CI.md
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# Benchmark CI

<!-- Author: @jaimergp -->
<!-- Last updated: 2021.07.06 -->
<!-- Describes the work done as part of https://github.com/scikit-image/scikit-image/pull/5424 -->

## How it works

The `asv` suite can be run for any PR on GitHub Actions (check workflow `.github/workflows/benchmarks.yml`) by adding a `run-benchmark` label to said PR. This will trigger a job that will run the benchmarking suite for the current PR head (merged commit) against the PR base (usually `main`).

We use `asv continuous` to run the job, which runs a relative performance measurement. This means that there's no state to be saved and that regressions are only caught in terms of performance ratio (absolute numbers are available but they are not useful since we do not use stable hardware over time). `asv continuous` will:

* Compile `scikit-image` for _both_ commits. We use `ccache` to speed up the process, and `mamba` is used to create the build environments.
* Run the benchmark suite for both commits, _twice_ (since `processes=2` by default).
* Generate a report table with performance ratios:
* `ratio=1.0` -> performance didn't change.
* `ratio<1.0` -> PR made it slower.
* `ratio>1.0` -> PR made it faster.

Due to the sensitivity of the test, we cannot guarantee that false positives are not produced. In practice, values between `(0.7, 1.5)` are to be considered part of the measurement noise. When in doubt, running the benchmark suite one more time will provide more information about the test being a false positive or not.

## Running the benchmarks on GitHub Actions

1. On a PR, add the label `run-benchmark`.
2. The CI job will be started. Checks will appear in the usual dashboard panel above the comment box.
3. If more commits are added, the label checks will be grouped with the last commit checks _before_ you added the label.
4. Alternatively, you can always go to the `Actions` tab in the repo and [filter for `workflow:Benchmark`](https://github.com/scikit-image/scikit-image/actions?query=workflow%3ABenchmark). Your username will be assigned to the `actor` field, so you can also filter the results with that if you need it.

## The artifacts

The CI job will also generate an artifact. This is the `.asv/results` directory compressed in a zip file. Its contents include:

* `fv-xxxxx-xx/`. A directory for the machine that ran the suite. It contains three files:
* `<baseline>.json`, `<contender>.json`: the benchmark results for each commit, with stats.
* `machine.json`: details about the hardware.
* `benchmarks.json`: metadata about the current benchmark suite.
* `benchmarks.log`: the CI logs for this run.
* This README.

## Re-running the analysis

Although the CI logs should be enough to get an idea of what happened (check the table at the end), one can use `asv` to run the analysis routines again.

1. Uncompress the artifact contents in the repo, under `.asv/results`. This is, you should see `.asv/results/benchmarks.log`, not `.asv/results/something_else/benchmarks.log`. Write down the machine directory name for later.
2. Run `asv show` to see your available results. You will see something like this:

```
$> asv show

Commits with results:

Machine : Jaimes-MBP
Environment: conda-py3.9-cython-numpy1.20-scipy

00875e67

Machine : fv-az95-499
Environment: conda-py3.7-cython-numpy1.17-pooch-scipy

8db28f02
3a305096
```

3. We are interested in the commits for `fv-az95-499` (the CI machine for this run). We can compare them with `asv compare` and some extra options. `--sort ratio` will show largest ratios first, instead of alphabetical order. `--split` will produce three tables: improved, worsened, no changes. `--factor 1.5` tells `asv` to only complain if deviations are above a 1.5 ratio. `-m` is used to indicate the machine ID (use the one you wrote down in step 1). Finally, specify your commit hashes: baseline first, then contender!

```
$> asv compare --sort ratio --split --factor 1.5 -m fv-az95-499 8db28f02 3a305096

Benchmarks that have stayed the same:

before after ratio
[8db28f02] [3a305096]
<ci-benchmark-check~9^2>
n/a n/a n/a benchmark_restoration.RollingBall.time_rollingball_ndim
1.23±0.04ms 1.37±0.1ms 1.12 benchmark_transform_warp.WarpSuite.time_to_float64(<class 'numpy.float64'>, 128, 3)
5.07±0.1μs 5.59±0.4μs 1.10 benchmark_transform_warp.ResizeLocalMeanSuite.time_resize_local_mean(<class 'numpy.float32'>, (192, 192, 192), (192, 192, 192))
1.23±0.02ms 1.33±0.1ms 1.08 benchmark_transform_warp.WarpSuite.time_same_type(<class 'numpy.float32'>, 128, 3)
9.45±0.2ms 10.1±0.5ms 1.07 benchmark_rank.Rank3DSuite.time_3d_filters('majority', (32, 32, 32))
23.0±0.9ms 24.6±1ms 1.07 benchmark_interpolation.InterpolationResize.time_resize((80, 80, 80), 0, 'symmetric', <class 'numpy.float64'>, True)
38.7±1ms 41.1±1ms 1.06 benchmark_transform_warp.ResizeLocalMeanSuite.time_resize_local_mean(<class 'numpy.float32'>, (2048, 2048), (192, 192, 192))
4.97±0.2μs 5.24±0.2μs 1.05 benchmark_transform_warp.ResizeLocalMeanSuite.time_resize_local_mean(<class 'numpy.float32'>, (2048, 2048), (2048, 2048))
4.21±0.2ms 4.42±0.3ms 1.05 benchmark_rank.Rank3DSuite.time_3d_filters('gradient', (32, 32, 32))

...
```

If you want more details on a specific test, you can use `asv show`. Use `-b pattern` to filter which tests to show, and then specify a commit hash to inspect:

```
$> asv show -b time_to_float64 8db28f02

Commit: 8db28f02 <ci-benchmark-check~9^2>

benchmark_transform_warp.WarpSuite.time_to_float64 [fv-az95-499/conda-py3.7-cython-numpy1.17-pooch-scipy]
ok
=============== ============= ========== ============= ========== ============ ========== ============ ========== ============
-- N / order
--------------- --------------------------------------------------------------------------------------------------------------
dtype_in 128 / 0 128 / 1 128 / 3 1024 / 0 1024 / 1 1024 / 3 4096 / 0 4096 / 1 4096 / 3
=============== ============= ========== ============= ========== ============ ========== ============ ========== ============
numpy.uint8 2.56±0.09ms 523±30μs 1.28±0.05ms 130±3ms 28.7±2ms 81.9±3ms 2.42±0.01s 659±5ms 1.48±0.01s
numpy.uint16 2.48±0.03ms 530±10μs 1.28±0.02ms 130±1ms 30.4±0.7ms 81.1±2ms 2.44±0s 653±3ms 1.47±0.02s
numpy.float32 2.59±0.1ms 518±20μs 1.27±0.01ms 127±3ms 26.6±1ms 74.8±2ms 2.50±0.01s 546±10ms 1.33±0.02s
numpy.float64 2.48±0.04ms 513±50μs 1.23±0.04ms 134±3ms 30.7±2ms 85.4±2ms 2.55±0.01s 632±4ms 1.45±0.01s
=============== ============= ========== ============= ========== ============ ========== ============ ========== ============
started: 2021-07-06 06:14:36, duration: 1.99m
```

## Other details

### Skipping slow or demanding tests

To minimize the time required to run the full suite, we trimmed the parameter matrix in some cases and, in others, directly skipped tests that ran for too long or require too much memory. Unlike `pytest`, `asv` does not have a notion of marks. However, you can `raise NotImplementedError` in the setup step to skip a test. In that vein, a new private function is defined at `benchmarks.__init__`: `_skip_slow`. This will check if the `ASV_SKIP_SLOW` environment variable has been defined. If set to `1`, it will raise `NotImplementedError` and skip the test. To implement this behavior in other tests, you can add the following attribute:

```python
from . import _skip_slow # this function is defined in benchmarks.__init__


def time_something_slow():
pass


time_something.setup = _skip_slow
```
30 changes: 30 additions & 0 deletions asv_bench/benchmarks/__init__.py
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import itertools
import os

import numpy as np


def parameterized(names, params):
def decorator(func):
func.param_names = names
func.params = params
return func

return decorator


def _skip_slow():
"""Use this function to skip slow or highly demanding tests.

Use it as a `Class.setup` method or a `function.setup` attribute.

Examples
--------
>>> from . import _skip_slow
>>> def time_something_slow():
... pass
...
>>> time_something.setup = _skip_slow
"""
if os.environ.get("ASV_SKIP_SLOW", "0") == "1":
raise NotImplementedError("Skipping this test...")
15 changes: 15 additions & 0 deletions asv_bench/benchmarks/ruptures.py
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import ruptures as rpt

from . import parameterized


class Ruptures:
def setup(self, *args, **kwargs):
n_samples, dim, sigma = 1000, 3, 4
n_bkps = 4 # number of breakpoints
signal, bkps = rpt.pw_constant(n_samples, dim, n_bkps, noise_std=sigma)
self.signal = signal

@parameterized(["algo"], [("Binseg", "BottomUp", "KernelCPD", "Pelt", "Window")])
def time_algos(self, algo):
getattr(rpt, algo)().fit_predict(self.signal, pen=10)