asv_spyglass 0.1.0

File oriented ASV benchmark comparer

About

asv output file comparer, for comparing across different environments or runs.

For other functionality, refer to the asv package or consider writing an extension.

Basic usage

Comparing two benchmark results

This is agnostic to the environment, however the benchmarks.json is required. The practical usage of this command is to compare asv runs from builds which are not handled by the asv environment management machinery.

➜ asv-spyglass compare tests/data/d6b286b8-virtualenv-py3.12-numpy.json tests/data/d6b286b8-rattler-py3.12-numpy.json tests/data/d6b286b8_asv_samples_benchmarks.json


| Change   | Before      | After       |   Ratio | Benchmark (Parameter)                                                                                                               |
|----------|-------------|-------------|---------|-------------------------------------------------------------------------------------------------------------------------------------|
| -        | 157±3ns     | 137±3ns     |    0.87 | benchmarks.TimeSuiteDecoratorSingle.time_keys(10) [rgx1gen11/virtualenv-py3.12-numpy -> rgx1gen11/rattler-py3.12-numpy]             |
| -        | 643±2ns     | 543±2ns     |    0.84 | benchmarks.TimeSuiteDecoratorSingle.time_keys(100) [rgx1gen11/virtualenv-py3.12-numpy -> rgx1gen11/rattler-py3.12-numpy]            |
|          | 1.17±0μs    | 1.07±0μs    |    0.91 | benchmarks.TimeSuiteDecoratorSingle.time_keys(200) [rgx1gen11/virtualenv-py3.12-numpy -> rgx1gen11/rattler-py3.12-numpy]            |
| +        | 167±3ns     | 187±3ns     |    1.12 | benchmarks.TimeSuiteDecoratorSingle.time_values(10) [rgx1gen11/virtualenv-py3.12-numpy -> rgx1gen11/rattler-py3.12-numpy]           |
| +        | 685±4ns     | 785±4ns     |    1.15 | benchmarks.TimeSuiteDecoratorSingle.time_values(100) [rgx1gen11/virtualenv-py3.12-numpy -> rgx1gen11/rattler-py3.12-numpy]          |
| +        | 1.26±0μs    | 1.46±0μs    |    1.16 | benchmarks.TimeSuiteDecoratorSingle.time_values(200) [rgx1gen11/virtualenv-py3.12-numpy -> rgx1gen11/rattler-py3.12-numpy]          |
| +        | 1.17±0.01μs | 1.37±0.01μs |    1.17 | benchmarks.TimeSuiteMultiDecorator.time_ranges(10, 'arange') [rgx1gen11/virtualenv-py3.12-numpy -> rgx1gen11/rattler-py3.12-numpy]  |
|          | 211±0.9ns   | 231±0.9ns   |    1.09 | benchmarks.TimeSuiteMultiDecorator.time_ranges(10, 'range') [rgx1gen11/virtualenv-py3.12-numpy -> rgx1gen11/rattler-py3.12-numpy]   |
| +        | 3.43±0.02μs | 3.83±0.02μs |    1.12 | benchmarks.TimeSuiteMultiDecorator.time_ranges(100, 'arange') [rgx1gen11/virtualenv-py3.12-numpy -> rgx1gen11/rattler-py3.12-numpy] |
| +        | 551±1ns     | 651±1ns     |    1.18 | benchmarks.TimeSuiteMultiDecorator.time_ranges(100, 'range') [rgx1gen11/virtualenv-py3.12-numpy -> rgx1gen11/rattler-py3.12-numpy]  |
|          | 1.14±0μs    | 1.04±0μs    |    0.91 | benchmarks.time_ranges_multi(10, 'arange') [rgx1gen11/virtualenv-py3.12-numpy -> rgx1gen11/rattler-py3.12-numpy]                    |
| -        | 196±1ns     | 176±1ns     |    0.9  | benchmarks.time_ranges_multi(10, 'range') [rgx1gen11/virtualenv-py3.12-numpy -> rgx1gen11/rattler-py3.12-numpy]                     |
|          | 3.39±0.03μs | 3.09±0.03μs |    0.91 | benchmarks.time_ranges_multi(100, 'arange') [rgx1gen11/virtualenv-py3.12-numpy -> rgx1gen11/rattler-py3.12-numpy]                   |
| -        | 532±1ns     | 432±1ns     |    0.81 | benchmarks.time_ranges_multi(100, 'range') [rgx1gen11/virtualenv-py3.12-numpy -> rgx1gen11/rattler-py3.12-numpy]                    |
|          | 1.18±0μs    | 1.08±0μs    |    0.91 | benchmarks.time_sort(10) [rgx1gen11/virtualenv-py3.12-numpy -> rgx1gen11/rattler-py3.12-numpy]                                      |
| -        | 1.83±0.01μs | 1.63±0.01μs |    0.89 | benchmarks.time_sort(100) [rgx1gen11/virtualenv-py3.12-numpy -> rgx1gen11/rattler-py3.12-numpy]                                     |

Consuming a single result file

Can be useful for exporting to other dashboards, or internally for further inspection.

➜ asv-spyglass to-df tests/data/d6b286b8-rattler-py3.12-numpy.json tests/data/d6b286b8_asv_samples_benchmarks.json
shape: (16, 17)
| benchmark_base                 | name                           | result    | units   | machine   | env                  | version                       | ci_99_a   | ci_99_b   | q_25      | q_75      | number | repeat | samples | param_size | param_n | param_func_name |
|--------------------------------|--------------------------------|-----------|---------|-----------|----------------------|-------------------------------|-----------|-----------|-----------|-----------|--------|--------|---------|------------|---------|-----------------|
| benchmarks.TimeSuiteDecoratorS | benchmarks.TimeSuiteDecoratorS | 1.3738e-7 | seconds | rgx1gen11 | rattler-py3.12-numpy | 64746c9051ff76aa879b428c27b42 | 1.3444e-7 | 1.4947e-7 | 1.3621e-7 | 1.4310e-7 | 67364  | 10     | null    | 10         | null    | null            |
| ingle.time_keys                | ingle.time_keys(10)            |           |         |           |                      | 47e8ed976c44a40579ae9...      |           |           |           |           |        |        |         |            |         |                 |
| benchmarks.TimeSuiteDecoratorS | benchmarks.TimeSuiteDecoratorS | 5.4292e-7 | seconds | rgx1gen11 | rattler-py3.12-numpy | 64746c9051ff76aa879b428c27b42 | 5.3813e-7 | 5.4586e-7 | 5.4190e-7 | 5.4495e-7 | 16815  | 10     | null    | 100        | null    | null            |
| ingle.time_keys                | ingle.time_keys(100)           |           |         |           |                      | 47e8ed976c44a40579ae9...      |           |           |           |           |        |        |         |            |         |                 |
| benchmarks.TimeSuiteDecoratorS | benchmarks.TimeSuiteDecoratorS | 0.000001  | seconds | rgx1gen11 | rattler-py3.12-numpy | 64746c9051ff76aa879b428c27b42 | 0.000001  | 0.000001  | 0.000001  | 0.000001  | 8960   | 10     | null    | 200        | null    | null            |
| ingle.time_keys                | ingle.time_keys(200)           |           |         |           |                      | 47e8ed976c44a40579ae9...      |           |           |           |           |        |        |         |            |         |                 |
| benchmarks.TimeSuiteDecoratorS | benchmarks.TimeSuiteDecoratorS | 1.8705e-7 | seconds | rgx1gen11 | rattler-py3.12-numpy | ab162b6142a1390a0e2a667ed8d2d | 1.8023e-7 | 1.9282e-7 | 1.8595e-7 | 1.9121e-7 | 63961  | 10     | null    | 10         | null    | null            |
| ingle.time_values              | ingle.time_values(10...        |           |         |           |                      | 3285f77152d9caa559b4f...      |           |           |           |           |        |        |         |            |         |                 |
| benchmarks.TimeSuiteDecoratorS | benchmarks.TimeSuiteDecoratorS | 7.8471e-7 | seconds | rgx1gen11 | rattler-py3.12-numpy | ab162b6142a1390a0e2a667ed8d2d | 7.7445e-7 | 7.9307e-7 | 7.8003e-7 | 7.8758e-7 | 15516  | 10     | null    | 100        | null    | null            |
| ingle.time_values              | ingle.time_values(10...        |           |         |           |                      | 3285f77152d9caa559b4f...      |           |           |           |           |        |        |         |            |         |                 |
| ...                            | ...                            | ...       | ...     | ...       | ...                  | ...                           | ...       | ...       | ...       | ...       | ...    | ...    | ...     | ...        | ...     | ...             |
| benchmarks.time_ranges_multi   | benchmarks.time_ranges_multi(1 | 0.000001  | seconds | rgx1gen11 | rattler-py3.12-numpy | f9ae8b134446c273c0d3eb1e90246 | 0.000001  | 0.000001  | 0.000001  | 0.000001  | 9631   | 10     | null    | null       | 10      | 'arange'        |
|                                | 0, 'arange')                   |           |         |           |                      | ae0d6f99389d06119dfe4...      |           |           |           |           |        |        |         |            |         |                 |
| benchmarks.time_ranges_multi   | benchmarks.time_ranges_multi(1 | 4.3222e-7 | seconds | rgx1gen11 | rattler-py3.12-numpy | f9ae8b134446c273c0d3eb1e90246 | 4.3157e-7 | 4.3557e-7 | 4.3176e-7 | 4.3420e-7 | 20588  | 10     | null    | null       | 100     | 'range'         |
|                                | 00, 'range')                   |           |         |           |                      | ae0d6f99389d06119dfe4...      |           |           |           |           |        |        |         |            |         |                 |
| benchmarks.time_ranges_multi   | benchmarks.time_ranges_multi(1 | 0.000003  | seconds | rgx1gen11 | rattler-py3.12-numpy | f9ae8b134446c273c0d3eb1e90246 | 0.000003  | 0.000003  | 0.000003  | 0.000003  | 3042   | 10     | null    | null       | 100     | 'arange'        |
|                                | 00, 'arange')                  |           |         |           |                      | ae0d6f99389d06119dfe4...      |           |           |           |           |        |        |         |            |         |                 |
| benchmarks.time_sort           | benchmarks.time_sort(10)       | 0.000001  | seconds | rgx1gen11 | rattler-py3.12-numpy | 60785bf757da0254d857b696482db | 0.000001  | 0.000001  | 0.000001  | 0.000001  | 9345   | 10     | null    | null       | 10      | null            |
|                                |                                |           |         |           |                      | 7a25509a5b28a2c9d2a54...      |           |           |           |           |        |        |         |            |         |                 |
| benchmarks.time_sort           | benchmarks.time_sort(100)      | 0.000002  | seconds | rgx1gen11 | rattler-py3.12-numpy | 60785bf757da0254d857b696482db | 0.000002  | 0.000002  | 0.000002  | 0.000002  | 5828   | 10     | null    | null       | 100     | null            |
|                                |                                |           |         |           |                      | 7a25509a5b28a2c9d2a54...      |           |           |           |           |        |        |         |            |         |                 |

Advanced usage

Benchmarking across arbitrary environments

Consider the following situation:

pixi shell & pdm install -G:all # To start with the right setup for asv_spyglass
# Somewhere else..
gh repo clone airspeed-velocity/asv_samples
cd asv_samples
git checkout decorator-params
# Generate the config
python scripts/gen_asv_conf.py asv.conf.base.json

Now assuming there are two environments which are present, and both have the project to be tested installed. For this we will use micromamba.

micromamba create -p $(pwd)/.tmp_1 -c conda-forge "python==3.8" pip asv numpy
$(pwd)/.tmp_1/bin/pip install .
micromamba create -p $(pwd)/.tmp_2 -c conda-forge "python==3.12" pip asv numpy
$(pwd)/.tmp_2/bin/pip install .

Activating the environment is not necessary in this instance, but for more complex workflows where the installation can be more convoluted, feel free to work within the environment. Now we can run asv.

➜ asv run -E existing:$(pwd)/.tmp_2/bin/python --record-samples --bench 'multi' --set-commit-hash "HEAD"
· Discovering benchmarks
· Running 1 total benchmarks (1 commits * 1 environments * 1 benchmarks)
[ 0.00%] · For asv_samples commit d6b286b8 <decorator-params>:
[ 0.00%] ·· Building for existing-py_home_rgoswami_Git_Github_Quansight_asvWork_asv_samples_.tmp_2_bin_python
[ 0.00%] ·· Benchmarking existing-py_home_rgoswami_Git_Github_Quansight_asvWork_asv_samples_.tmp_2_bin_python
[50.00%] ··· Running (benchmarks.time_ranges_multi--).
[100.00%] ··· benchmarks.time_ranges_multi                                                                                                                                                                         ok
[100.00%] ··· ===== =========== =============
              --            func_name
              ----- -------------------------
                n      range        arange
              ===== =========== =============
                10    197±1ns      1.12±0μs
               100   535±0.8ns   3.30±0.03μs
              ===== =========== =============

➜ asv run -E existing:$(pwd)/.tmp_1/bin/python --record-samples --bench 'multi' --set-commit-hash "HEAD"
· Discovering benchmarks
· Running 1 total benchmarks (1 commits * 1 environments * 1 benchmarks)
[ 0.00%] · For asv_samples commit d6b286b8 <decorator-params>:
[ 0.00%] ·· Building for existing-py_home_rgoswami_Git_Github_Quansight_asvWork_asv_samples_.tmp_1_bin_python
[ 0.00%] ·· Benchmarking existing-py_home_rgoswami_Git_Github_Quansight_asvWork_asv_samples_.tmp_1_bin_python
[50.00%] ··· Running (benchmarks.time_ranges_multi--).
[100.00%] ··· benchmarks.time_ranges_multi                                                                                                                                                                         ok
[100.00%] ··· ===== ========= =============
              --           func_name
              ----- -----------------------
                n     range       arange
              ===== ========= =============
                10   324±2ns     1.09±0μs
               100   729±4ns   3.25±0.03μs
              ===== ========= =============

Bear in mind that --dry-run or -n or --python=same will skip writing the results file, and therefore are not going to be relevant here.

With the results files in place, it is now trivial to compare the results across environments.

asv-spyglass compare .asv/results/rgx1gen11/*.tmp_1* .asv/results/rgx1gen11/*.tmp_2* .asv/results/benchmarks.json
| Change   | Before      | After       |   Ratio | Benchmark (Parameter)                                                                                                                                                                                                                          |
|----------|-------------|-------------|---------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|          | 1.09±0μs    | 1.12±0μs    |    1.03 | benchmarks.time_ranges_multi(10, 'arange') [rgx1gen11/existing-py_home_asv_samples_.tmp_1_bin_python -> rgx1gen11/existing-py_home_asv_samples_.tmp_2_bin_python]  |
| -        | 324±2ns     | 197±1ns     |    0.61 | benchmarks.time_ranges_multi(10, 'range') [rgx1gen11/existing-py_home_asv_samples_.tmp_1_bin_python -> rgx1gen11/existing-py_home_asv_samples_.tmp_2_bin_python]   |
|          | 3.25±0.03μs | 3.30±0.03μs |    1.02 | benchmarks.time_ranges_multi(100, 'arange') [rgx1gen11/existing-py_home_asv_samples_.tmp_1_bin_python -> rgx1gen11/existing-py_home_asv_samples_.tmp_2_bin_python] |
| -        | 729±4ns     | 535±0.8ns   |    0.73 | benchmarks.time_ranges_multi(100, 'range') [rgx1gen11/existing-py_home_asv_samples_.tmp_1_bin_python -> rgx1gen11/existing-py_home_asv_samples_.tmp_2_bin_python]  |

Contributions

All contributions are welcome, this includes code and documentation contributions but also questions or other clarifications. Note that we expect all contributors to follow our Code of Conduct.

Developing locally

Testing

Since the output of these are mostly text oriented, and the inputs are json, these are handled via a mixture of reading known data and using golden master testing aka approval testing. Thus pytest with pytest-datadir and ApprovalTests.Python is used.

Linting and Formatting

A pre-commit job is setup on CI to enforce consistent styles, so it is best to set it up locally as well (using pipx for isolation):

# Run before commiting
pipx run pre-commit run --all-files
# Or install the git hook to enforce this
pipx run pre-commit install

History

Why another CLI instead of being in asv?

I didn't want to handle the argparse oriented CLI in asv. That being said this will be under the airspeed-velocity organization..