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asv benchmarks for imports and tools modules (#184)
Co-authored-by: Will Graham <[email protected]>
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@@ -124,3 +124,9 @@ pip-wheel-metadata/ | |
mprofile*.dat | ||
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*.DS_Store | ||
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# asv | ||
.asv | ||
benchmarks/results | ||
benchmarks/html | ||
benchmarks/env |
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# Benchmarks | ||
`detect_and_classify.py` contains a simple script that runs | ||
detection and classification with the small test dataset. | ||
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## Memory | ||
[memory_profiler](https://github.com/pythonprofilers/memory_profiler) | ||
can be used to profile memory useage. Install, and then run | ||
`mprof run --include-children --multiprocess detect_and_classify.py`. It is **very** | ||
important to use these two flags to capture memory usage by the additional | ||
processes that cellfinder_core uses. | ||
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To show the results of the latest profile run, run `mprof plot`. | ||
# Benchmarking with asv | ||
[Install asv](https://asv.readthedocs.io/en/stable/installing.html) by running: | ||
``` | ||
pip install asv | ||
``` | ||
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`asv` works roughly as follows: | ||
1. It creates a virtual environment (as defined in the config) | ||
2. It installs the software package version of a specific commit (or of a local commit) | ||
3. It times the benchmarking tests and saves the results to json files | ||
4. The json files are 'published' into an html dir | ||
5. The html dir can be visualised in a static website | ||
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## Running benchmarks | ||
To run benchmarks on a specific commit: | ||
``` | ||
$ asv run 88fbbc33^! | ||
``` | ||
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To run them up to a specific commit: | ||
``` | ||
$ asv run 88fbbc33 | ||
``` | ||
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To run them on a range of commits: | ||
``` | ||
$ asv run 827f322b..729abcf3 | ||
``` | ||
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To collate the benchmarks' results into a viewable website: | ||
``` | ||
$ asv publish | ||
``` | ||
This will create a tree of files in the `html` directory, but this cannot be viewed directly from the local filesystem, so we need to put them in a static site. `asv publish` also detects statistically significant decreases of performance, the results can be inspected in the 'Regression' tab of the static site. | ||
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To visualise the results in a static site: | ||
``` | ||
$ asv preview | ||
``` |
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{ | ||
// The version of the config file format. Do not change, unless | ||
// you know what you are doing. | ||
"version": 1, | ||
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// The name of the project being benchmarked | ||
"project": "cellfinder-core", | ||
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// The project's homepage | ||
"project_url": "https://brainglobe.info/documentation/cellfinder/index.html", | ||
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// The URL or local path of the source code repository for the | ||
// project being benchmarked | ||
// To use the upstream repository: uncomment the 1st line (and comment the 2nd) | ||
// To use the local repository: comment the 1st line (and uncomment the 2nd) | ||
//"repo": "https://github.com/brainglobe/cellfinder-core.git", | ||
"repo": "..", | ||
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// The Python project's subdirectory in your repo. If missing or | ||
// the empty string, the project is assumed to be located at the root | ||
// of the repository (where setup.py is located) | ||
// "repo_subdir": "", | ||
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// Customizable commands for building, installing, and | ||
// uninstalling the project. See asv.conf.json documentation. | ||
// | ||
"install_command": ["in-dir={env_dir} python -mpip install {wheel_file}"], | ||
"uninstall_command": ["return-code=any python -mpip uninstall -y {project}"], | ||
"build_command": [ | ||
"python -m pip install build", | ||
"python -m build", | ||
"PIP_NO_BUILD_ISOLATION=false python -mpip wheel --no-deps --no-index -w {build_cache_dir} {build_dir}" | ||
], | ||
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// 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 | ||
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// 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", | ||
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// 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", | ||
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// timeout in seconds for installing any dependencies in environment | ||
// defaults to 10 min | ||
//"install_timeout": 600, | ||
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// the base URL to show a commit for the project. | ||
"show_commit_url": "http://github.com/brainglobe/cellfinder-core/commit/", | ||
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// 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"], // same as pyproject.toml? ["3.8", "3.9", "3.10"] | ||
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// The list of conda channel names to be searched for benchmark | ||
// dependency packages in the specified order | ||
"conda_channels": ["conda-forge", "defaults"], | ||
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// A conda environment file that is used for environment creation. | ||
// "conda_environment_file": "environment.yml", | ||
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// The matrix of dependencies to test. Each key of the "req" | ||
// requirements dictionary 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). | ||
// | ||
// The ``@env`` and ``@env_nobuild`` keys contain the matrix of | ||
// environment variables to pass to build and benchmark commands. | ||
// An environment will be created for every combination of the | ||
// cartesian product of the "@env" variables in this matrix. | ||
// Variables in "@env_nobuild" will be passed to every environment | ||
// during the benchmark phase, but will not trigger creation of | ||
// new environments. A value of ``null`` means that the variable | ||
// will not be set for the current combination. | ||
// | ||
"matrix": { | ||
"req": {}, | ||
// "napari": ["", null], // test with and without | ||
// // "six": ["", null], // test with and without six installed | ||
// // "pip+emcee": [""] // emcee is only available for install with pip. | ||
// }, | ||
// "env": {"ENV_VAR_1": ["val1", "val2"]}, | ||
// "env_nobuild": {"ENV_VAR_2": ["val3", null]}, | ||
}, | ||
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// 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'. | ||
// - req | ||
// Required packages | ||
// - env | ||
// Environment variables | ||
// - env_nobuild | ||
// Non-build environment variables | ||
// | ||
// "exclude": [ | ||
// {"python": "3.2", "sys_platform": "win32"}, // skip py3.2 on windows | ||
// {"environment_type": "conda", "req": {"six": null}}, // don't run without six on conda | ||
// {"env": {"ENV_VAR_1": "val2"}}, // skip val2 for ENV_VAR_1 | ||
// ], | ||
// | ||
// "include": [ | ||
// // additional env for python2.7 | ||
// {"python": "2.7", "req": {"numpy": "1.8"}, "env_nobuild": {"FOO": "123"}}, | ||
// // additional env if run on windows+conda | ||
// {"platform": "win32", "environment_type": "conda", "python": "2.7", "req": {"libpython": ""}}, | ||
// ], | ||
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// The directory (relative to the current directory) that benchmarks are | ||
// stored in. If not provided, defaults to "benchmarks" | ||
"benchmark_dir": "benchmarks", | ||
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// The directory (relative to the current directory) to cache the Python | ||
// environments in. If not provided, defaults to "env" | ||
"env_dir": "env", | ||
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// The directory (relative to the current directory) that raw benchmark | ||
// results are stored in. If not provided, defaults to "results". | ||
"results_dir": "results", | ||
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// The directory (relative to the current directory) that the html tree | ||
// should be written to. If not provided, defaults to "html". | ||
"html_dir": "html", | ||
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// The number of characters to retain in the commit hashes. | ||
// "hash_length": 8, | ||
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// `asv` will cache results of the recent builds in each | ||
// environment, making them faster to install next time. This is | ||
// the number of builds to keep, per environment. | ||
"build_cache_size": 2, | ||
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// 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 | ||
// }, | ||
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// 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% | ||
// }, | ||
} |
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# ------------------------------------ | ||
# Runtime benchmarks | ||
# ------------------------------------ | ||
def timeraw_import_main(): | ||
return """ | ||
from cellfinder_core.main import main | ||
""" | ||
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def timeraw_import_io_dask(): | ||
return """ | ||
from cellfinder_core.tools.IO import read_with_dask | ||
""" | ||
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def timeraw_import_io_tiff_meta(): | ||
return """ | ||
from cellfinder_core.tools.IO import get_tiff_meta | ||
""" | ||
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def timeraw_import_prep_tensorflow(): | ||
return """ | ||
from cellfinder_core.tools.prep import prep_tensorflow | ||
""" | ||
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def timeraw_import_prep_models(): | ||
return """ | ||
from cellfinder_core.tools.prep import prep_models | ||
""" | ||
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def timeraw_import_prep_classification(): | ||
return """ | ||
from cellfinder_core.tools.prep import prep_classification | ||
""" | ||
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def timeraw_import_prep_training(): | ||
return """ | ||
from cellfinder_core.tools.prep import prep_training | ||
""" |
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from pathlib import Path | ||
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from cellfinder_core.tools.IO import get_tiff_meta, read_with_dask | ||
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CELLFINDER_CORE_PATH = Path(__file__).parents[3] | ||
TESTS_DATA_INTEGRATION_PATH = ( | ||
Path(CELLFINDER_CORE_PATH) / "tests" / "data" / "integration" | ||
) | ||
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class Read: | ||
# ------------------------------------ | ||
# Data | ||
# ------------------------------ | ||
detection_crop_planes_ch0 = TESTS_DATA_INTEGRATION_PATH / Path( | ||
"detection", "crop_planes", "ch0" | ||
) | ||
detection_crop_planes_ch1 = TESTS_DATA_INTEGRATION_PATH / Path( | ||
"detection", "crop_planes", "ch1" | ||
) | ||
cells_tif_files = list( | ||
Path(TESTS_DATA_INTEGRATION_PATH, "training", "cells").glob("*.tif") | ||
) | ||
non_cells_tif_files = list( | ||
Path(TESTS_DATA_INTEGRATION_PATH, "training", "non_cells").glob( | ||
"*.tif" | ||
) | ||
) | ||
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# --------------------------------------------- | ||
# Setup function | ||
# -------------------------------------------- | ||
def setup(self, subdir): | ||
self.data_dir = str(subdir) | ||
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# --------------------------------------------- | ||
# Reading 3d arrays with dask | ||
# -------------------------------------------- | ||
def time_read_with_dask(self, subdir): | ||
read_with_dask(self.data_dir) | ||
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# parameters to sweep across | ||
time_read_with_dask.param_names = [ | ||
"tests_data_integration_subdir", | ||
] | ||
time_read_with_dask.params = ( | ||
[detection_crop_planes_ch0, detection_crop_planes_ch1], | ||
) | ||
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# ----------------------------------------------- | ||
# Reading metadata from tif files | ||
# ------------------------------------------------- | ||
def time_get_tiff_meta( | ||
self, | ||
subdir, | ||
): | ||
get_tiff_meta(self.data_dir) | ||
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# parameters to sweep across | ||
time_get_tiff_meta.param_names = [ | ||
"tests_data_integration_tiffile", | ||
] | ||
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time_get_tiff_meta.params = cells_tif_files + non_cells_tif_files |
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