app-mlperf-inference-ctuning-cpp-tflite
Automatically generated README for this automation recipe: app-mlperf-inference-ctuning-cpp-tflite
Category: Modular MLPerf inference benchmark pipeline
License: Apache 2.0
- CM meta description for this script: _cm.json
- Output cached? False
Reuse this script in your project
Install MLCommons CM automation meta-framework
Pull CM repository with this automation recipe (CM script)
cm pull repo mlcommons@cm4mlops
Print CM help from the command line
cmr "app mlperf inference tflite-cpp" --help
Run this script
Run this script via CLI
cm run script --tags=app,mlperf,inference,tflite-cpp[,variations] [--input_flags]
Run this script via CLI (alternative)
cmr "app mlperf inference tflite-cpp [variations]" [--input_flags]
Run this script from Python
import cmind
r = cmind.access({'action':'run'
'automation':'script',
'tags':'app,mlperf,inference,tflite-cpp'
'out':'con',
...
(other input keys for this script)
...
})
if r['return']>0:
print (r['error'])
Run this script via Docker (beta)
cm docker script "app mlperf inference tflite-cpp[variations]" [--input_flags]
Variations
-
No group (any combination of variations can be selected)
Click here to expand this section.
_armnn- ENV variables:
- CM_MLPERF_TFLITE_USE_ARMNN:
yes - CM_TMP_LINK_LIBS:
tensorflowlite,armnn
- CM_MLPERF_TFLITE_USE_ARMNN:
- ENV variables:
-
Group "backend"
Click here to expand this section.
_tf- ENV variables:
- CM_MLPERF_BACKEND:
tf
- CM_MLPERF_BACKEND:
- ENV variables:
_tflite(default)- ENV variables:
- CM_MLPERF_BACKEND:
tflite - CM_MLPERF_BACKEND_VERSION:
master - CM_TMP_LINK_LIBS:
tensorflowlite - CM_TMP_SRC_FOLDER:
src
- CM_MLPERF_BACKEND:
- ENV variables:
-
Group "device"
Click here to expand this section.
_cpu(default)- ENV variables:
- CM_MLPERF_DEVICE:
cpu
- CM_MLPERF_DEVICE:
- ENV variables:
_gpu- ENV variables:
- CM_MLPERF_DEVICE:
gpu - CM_MLPERF_DEVICE_LIB_NAMESPEC:
cudart
- CM_MLPERF_DEVICE:
- ENV variables:
-
Group "loadgen-scenario"
Click here to expand this section.
_singlestream(default)- ENV variables:
- CM_MLPERF_LOADGEN_SCENARIO:
SingleStream
- CM_MLPERF_LOADGEN_SCENARIO:
- ENV variables:
-
Group "model"
Click here to expand this section.
_efficientnet- ENV variables:
- CM_MODEL:
efficientnet
- CM_MODEL:
- ENV variables:
_mobilenet- ENV variables:
- CM_MODEL:
mobilenet
- CM_MODEL:
- ENV variables:
_resnet50(default)- ENV variables:
- CM_MODEL:
resnet50
- CM_MODEL:
- ENV variables:
-
Group "optimization-target"
Click here to expand this section.
_use-neon- ENV variables:
- CM_MLPERF_SUT_NAME_RUN_CONFIG_SUFFIX1:
using_neon - CM_MLPERF_TFLITE_USE_NEON:
1
- CM_MLPERF_SUT_NAME_RUN_CONFIG_SUFFIX1:
- ENV variables:
_use-opencl- ENV variables:
- CM_MLPERF_SUT_NAME_RUN_CONFIG_SUFFIX1:
using_opencl - CM_MLPERF_TFLITE_USE_OPENCL:
1
- CM_MLPERF_SUT_NAME_RUN_CONFIG_SUFFIX1:
- ENV variables:
-
Group "precision"
Click here to expand this section.
_fp32(default)- ENV variables:
- CM_MLPERF_MODEL_PRECISION:
float32
- CM_MLPERF_MODEL_PRECISION:
- ENV variables:
_int8- ENV variables:
- CM_DATASET_COMPRESSED:
on - CM_MLPERF_MODEL_PRECISION:
int8
- CM_DATASET_COMPRESSED:
- ENV variables:
_uint8- ENV variables:
- CM_DATASET_COMPRESSED:
on - CM_MLPERF_MODEL_PRECISION:
uint8
- CM_DATASET_COMPRESSED:
- ENV variables:
Default variations
_cpu,_fp32,_resnet50,_singlestream,_tflite
Script flags mapped to environment
--compressed_dataset=value→CM_DATASET_COMPRESSED=value--count=value→CM_MLPERF_LOADGEN_QUERY_COUNT=value--mlperf_conf=value→CM_MLPERF_CONF=value--mode=value→CM_MLPERF_LOADGEN_MODE=value--output_dir=value→CM_MLPERF_OUTPUT_DIR=value--performance_sample_count=value→CM_MLPERF_LOADGEN_PERFORMANCE_SAMPLE_COUNT=value--scenario=value→CM_MLPERF_LOADGEN_SCENARIO=value--user_conf=value→CM_MLPERF_USER_CONF=value--verbose=value→CM_VERBOSE=value
Default environment
These keys can be updated via --env.KEY=VALUE or env dictionary in @input.json or using script flags.
- CM_DATASET_COMPRESSED:
off - CM_DATASET_INPUT_SQUARE_SIDE:
224 - CM_FAST_COMPILATION:
yes - CM_LOADGEN_BUFFER_SIZE:
1024 - CM_MLPERF_LOADGEN_MODE:
accuracy - CM_MLPERF_LOADGEN_SCENARIO:
SingleStream - CM_MLPERF_LOADGEN_TRIGGER_COLD_RUN:
0 - CM_MLPERF_OUTPUT_DIR:
. - CM_MLPERF_SUT_NAME_IMPLEMENTATION_PREFIX:
tflite_cpp - CM_MLPERF_TFLITE_USE_NEON:
0 - CM_MLPERF_TFLITE_USE_OPENCL:
0 - CM_ML_MODEL_GIVEN_CHANNEL_MEANS:
123.68 116.78 103.94 - CM_ML_MODEL_NORMALIZE_DATA:
0 - CM_ML_MODEL_SUBTRACT_MEANS:
1 - CM_VERBOSE:
0
Script output
cmr "app mlperf inference tflite-cpp [variations]" [--input_flags] -j