module external.onnxruntime_perf_list

Short summary

module pymlbenchmark.external.onnxruntime_perf_list

Returns predefined tests.

source on GitHub

Functions

function	truncated documentation
onnxruntime_perf_binary_classifiers	Returns a list of benchmarks for binary classifier. It compares onnxruntime predictions against scikit-learn. …
onnxruntime_perf_regressors	Returns a list of benchmarks for binary classifier. It compares onnxruntime predictions against scikit-learn. …
run_onnxruntime_test	Runs a benchmark for onnxruntime.

Documentation

Returns predefined tests.

source on GitHub

pymlbenchmark.external.onnxruntime_perf_list.onnxruntime_perf_binary_classifiers(bincl=None, N_fit=100000)

Returns a list of benchmarks for binary classifier. It compares onnxruntime predictions against scikit-learn.

Parameters:

• bincl – test class to use, by default, it is OnnxRuntimeBenchPerfTestBinaryClassification

• N_fit – number of rows needed to train a model

source on GitHub

pymlbenchmark.external.onnxruntime_perf_list.onnxruntime_perf_regressors(regcl=None, N_fit=100000)

Returns a list of benchmarks for binary classifier. It compares onnxruntime predictions against scikit-learn.

Parameters:

• regcl – test class to use, by default, it is OnnxRuntimeBenchPerfTestRegression

• N_fit – number of rows needed to train a model

source on GitHub

pymlbenchmark.external.onnxruntime_perf_list.run_onnxruntime_test(folder, name, repeat=100, verbose=True, stop_if_error=True, validate=True, N=None, dim=None, N_fit=100000, fLOG=None, kwbefore=None)

Runs a benchmark for onnxruntime.

Parameters:

• folder – where to dump the results

• name – name of the test (one in the list returned by onnxruntime_perf_binary_classifiers)

• repeat – number of times to repeat predictions

• verbose – print progress with tqdm

• stop_if_error – by default, it stops when method validate fails, if False, the function stores the exception

• validate – validate the outputs against the baseline

• N – overwrites N parameter

• dim – overwrites dims parameter

• N_fit – number of rows needed to train a model

• kwbefore – additional arguments before training

• fLOG – logging function

Returns:

two dataframes, one for the results, the other one for the context (see machine_information)

source on GitHub