This allows for Pythonic way of working with BenchResult
as if it was a dictionary.
```
In [1]: import array, numpy as np, cuda.bench
In [2]: r = cuda.bench.BenchResult("temp_data/axes_run1.json")
In [3]: list(r)
Out[3]:
['simple',
'single_float64_axis',
'copy_sweep_grid_shape',
'copy_type_sweep',
'copy_type_conversion_sweep',
'copy_type_and_block_size_sweep']
In [4]: r["simple"].centers(lambda t: np.percentile(t, [25,75]))
Out[4]: {'Device=0': array([0.00100966, 0.00101299])}
In [5]: r.centers(lambda t: np.percentile(t, [25,75]))["simple"]
Out[5]: {'Device=0': array([0.00100966, 0.00101299])}
In [6]: len(r)
Out[6]: 6
In [7]: "fake" in r
Out[7]: False
```
Add arbitrary BenchResult metadata and explicit parse control, replacing
the previous code/elapsed fields. Make BenchResult subscriptable by
subbenchmark name and make SubBenchResult list-like over its states.
Extend SubBenchState parsing to expose summaries by tag, read paired
sample frequency data, return None for unavailable sample/frequency
files, and validate matching sample/frequency lengths.
Harden parsing for NVBench JSON output with no-axis benchmarks, null
axis_values, skipped states with null summaries, float axis input_string
lookups, and recorded sidecar binary paths.
Expand BenchResult tests to cover metadata, parse=False, sequence-style
access, frequency-aware centers, missing binary data, skipped states,
and mismatched sample/frequency counts.
Example usage:
```
import array, numpy as np, cuda.bench
r = cuda.bench.BenchResult("perf_data/axes_run1.json")
r["copy_sweep_grid_shape"].centers_with_frequencies(
lambda t, f: np.median(np.asarray(t)*np.asarray(f)))
```
* Correct Python API signature of State.get_axis_values_as_strings
The C++ API has default boolean argument color, but Python API
declared no arguments.
Closes#345
* Also exercise invocation of get_axis_values_as_string with keyword argument value
* Remove use of cuda.core.experimental
Fixed relative text alignment in docstrings to fix autodoc warnigns
Renamed cuda.bench.test_cpp_exception and cuda.bench.test_py_exception functions
to start with underscore, signaling that these functions are internal and should
not be documented
Account for test_cpp_exceptions -> _test_cpp_exception, same for *_py_*
Make sure to reset __module__ of reexported symbols to be cuda.bench
Add nvbench.State methods to get Python dictionary representing
axis values of benchmark configuration state represents.
get_axis_values_as_string gives a string of space-separated
name=values pairs.
Add comments stating the need to keep implementation and Python stub
file in sync to both files. In the stub file to comment documents
use of mypy's stubgen to generate stubs and calls to compare that against
current stubs. It also calls out the need to keep docstrings and
doctring examples in sync with implementation.
Removed use of __all__ per PR feedback. Emit warnings.warn if
version information could not be retrieved from the package metadata,
e.g., package has been renamed by source code was not updated.
Introduce get_int64_or_default method, and counterparts for
float64 and string.
Provided names for Python arguments.
Tried generating Python stubs automatically with
```
stubgen -m cuda.nvbench._nvbench
```
Gave up on this, since it does not include doc-strings.
It would be nice to compare auto-generated _nvbench.pyi with
__init__.pyi for discrepancies though.
state.add_summary(column_name: str, value: Union[int, float, str])
This is used in examples/axes.py to map integral value from Int64Axis
to string description.
make batch/sync arguments of State.exec keyword-only
Provide default column_name value for State.addElementCount method,
so that it can be called state.addElementCount(count), or as
state.addElementCount(count, column_name="Descriptive Name")
