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program:image-classification-tflite-loadgen (v1.1.1)
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License: BSD 3-clause (code) and CC BY-SA 4.0 (data)
Creation date: 2019-07-09
Source: GitHub
cID: b0ac08fe1d3c2615:45ee0d34a73b7a2c

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Description  

This portable workflow is our attempt to provide a common CLI with Python JSON API and a JSON meta description to automatically detect or install required components (models, data sets, libraries, frameworks, tools), and then build, run, validate, benchmark and auto-tune the associated method (program) across diverse models, datasets, compilers, platforms and environments. Our on-going project is to make the onboarding process as simple as possible via this platform. Please check this CK white paper and don't hesitate to contact us if you have suggestions or feedback!
  • Automation framework: CK
  • Development repository: ck-mlperf
  • Source: GitHub
  • Available command lines:
    • ck run program:image-classification-tflite-loadgen --cmd_key=default (META)
  • Support for host OS: any
  • Support for target OS: android, linux
  • Tags: image-classification,tflite,loadgen,lang-cpp
  • Template: Image Classification via TFLite (C++)
  • How to get the stable version via the client:
    pip install cbench
    cb download program:image-classification-tflite-loadgen --version=1.1.1 --all
    ck run program:image-classification-tflite-loadgen
  • How to get the development version:
    pip install ck
    ck pull repo:ck-mlperf
    ck run program:image-classification-tflite-loadgen
  • CLI and Python API: module:program

    • Dependencies    

    ReadMe  

    MLPerf Inference - Image Classification - TFLite with LoadGen

    Running this program is similar to running ck-tensorflow:program:image-classification-tflite, as described in the MLPerf Inference repo.

    Detect LoadGen Config file

    $ ck detect soft --tags=config,loadgen,image-classification-tflite

    Run once

    firefly $ ck benchmark program:image-classification-tflite-loadgen \
--speed --repetitions=1 \
--env.CK_VERBOSE=1 \
--env.CK_LOADGEN_SCENARIO=SingleStream \
--env.CK_LOADGEN_MODE=PerformanceOnly \
--env.CK_LOADGEN_DATASET_SIZE=1024 \
--env.CK_LOADGEN_BUFFER_SIZE=1024 \
--dep_add_tags.weights=model,tflite,resnet \
--dep_add_tags.library=tflite,v1.15 \
--dep_add_tags.compiler=gcc,v7 \
--dep_add_tags.images=side.224,preprocessed \
--dep_add_tags.loadgen-config-file=image-classification-tflite \
--dep_add_tags.python=v3 \
--skip_print_timers
...
------------------------------------------------------------
|            LATENCIES (in nanoseconds and fps)            |
------------------------------------------------------------
Number of queries run: 1024
Min latency:                      397952762ns  (2.51286 fps)
Median latency:                   426440993ns  (2.34499 fps)
Average latency:                  433287227ns  (2.30794 fps)
90 percentile latency:            460194271ns  (2.173 fps)
Max latency:                      679467557ns  (1.47174 fps)
------------------------------------------------------------

    Explore different models

    See run.sh scripts we used to generate the MLPerf Inference v0.5 results for more details:

    $ ck list ck-mlperf:script:mlperf-inference-v0.5.*.image-classification
mlperf-inference-v0.5.closed.image-classification
mlperf-inference-v0.5.open.image-classification

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