module cli.asv_bench
#
Short summary#
module mlprodict.cli.asv_bench
Command line about validation of prediction runtime.
Functions#
function |
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Creates an asv benchmark in a folder but does not run it. |
Documentation#
Command line about validation of prediction runtime.
- mlprodict.cli.asv_bench.asv_bench(location='asvsklonnx', opset_min=-1, opset_max=None, runtime='scikit-learn, python_compiled', models=None, skip_models=None, extended_list=True, dims='1, 10, 100, 1000, 10000', n_features='4, 20', dtype=None, verbose=1, fLOG=<built-in function print>, clean=True, flat=False, conf_params=None, build=None, add_pyspy=False, env=None, matrix=None)#
Creates an asv benchmark in a folder but does not run it.
- Parameters:
location – location of the benchmark
n_features – number of features to try
dims – number of observations to try
verbose – integer from 0 (None) to 2 (full verbose)
opset_min – tries every conversion from this minimum opset, -1 to get the current opset defined by module onnx
opset_max – tries every conversion up to maximum opset, -1 to get the current opset defined by module onnx
runtime – runtime to check, scikit-learn, python, python_compiled compiles the graph structure and is more efficient when the number of observations is small, onnxruntime1 to check onnxruntime, onnxruntime2 to check every ONNX node independently with onnxruntime, many runtime can be checked at the same time if the value is a comma separated list
models – list of models to test or empty string to test them all
skip_models – models to skip
extended_list – extends the list of scikit-learn converters with converters implemented in this module
dtype – ‘32’ or ‘64’ or None for both, limits the test to one specific number types
fLOG – logging function
clean – clean the folder first, otherwise overwrites the content
conf_params – to overwrite some of the configuration parameters, format
name,value;name2,value2
flat – one folder for all files or subfolders
build – location of the outputs (env, html, results)
add_pyspy – add an extra folder with code to profile each configuration
env – default environment or
same
to use the current onematrix – specifies versions for a module as a json string, example:
{'onnxruntime': ['1.1.1', '1.1.2']}
, if a package name starts with ‘~’, the package is removed
- Returns:
created files
Automatically creates an asv benchmark
The command creates a benchmark based on asv module. It does not run it.
Example:
python -m mlprodict asv_bench --models LogisticRegression,LinearRegression
<<<
python -m mlprodict asv_bench --help
>>>
usage: asv_bench [-h] [-l LOCATION] [-o OPSET_MIN] [-op OPSET_MAX] [-r RUNTIME] [-m MODELS] [-s SKIP_MODELS] [-e EXTENDED_LIST] [-d DIMS] [-n N_FEATURES] [-dt DTYPE] [-v VERBOSE] [-c CLEAN] [-f FLAT] [-co CONF_PARAMS] [-b BUILD] [-a ADD_PYSPY] [--env ENV] [-ma MATRIX] Creates an `asv` benchmark in a folder but does not run it. optional arguments: -h, --help show this help message and exit -l LOCATION, --location LOCATION location of the benchmark (default: asvsklonnx) -o OPSET_MIN, --opset_min OPSET_MIN tries every conversion from this minimum opset, `-1` to get the current opset defined by module onnx (default: -1) -op OPSET_MAX, --opset_max OPSET_MAX tries every conversion up to maximum opset, `-1` to get the current opset defined by module onnx (default: ) -r RUNTIME, --runtime RUNTIME runtime to check, *scikit-learn*, *python*, *python_compiled* compiles the graph structure and is more efficient when the number of observations is small, *onnxruntime1* to check `onnxruntime`, *onnxruntime2* to check every ONNX node independently with onnxruntime, many runtime can be checked at the same time if the value is a comma separated list (default: scikit-learn,python_compiled) -m MODELS, --models MODELS list of models to test or empty string to test them all (default: ) -s SKIP_MODELS, --skip_models SKIP_MODELS models to skip (default: ) -e EXTENDED_LIST, --extended_list EXTENDED_LIST extends the list of :epkg:`scikit-learn` converters with converters implemented in this module (default: True) -d DIMS, --dims DIMS number of observations to try (default: 1,10,100,1000,10000) -n N_FEATURES, --n_features N_FEATURES number of features to try (default: 4,20) -dt DTYPE, --dtype DTYPE '32' or '64' or None for both, limits the test to one specific number types (default: ) -v VERBOSE, --verbose VERBOSE integer from 0 (None) to 2 (full verbose) (default: 1) -c CLEAN, --clean CLEAN clean the folder first, otherwise overwrites the content (default: True) -f FLAT, --flat FLAT one folder for all files or subfolders (default: False) -co CONF_PARAMS, --conf_params CONF_PARAMS to overwrite some of the configuration parameters, format ``name,value;name2,value2`` (default: ) -b BUILD, --build BUILD location of the outputs (env, html, results) (default: ) -a ADD_PYSPY, --add_pyspy ADD_PYSPY add an extra folder with code to profile each configuration (default: False) --env ENV default environment or ``same`` to use the current one (default: ) -ma MATRIX, --matrix MATRIX specifies versions for a module as a json string, example: ``{'onnxruntime': ['1.1.1', '1.1.2']}``, if a package name starts with `'~'`, the package is removed (default: )