Edge Benchmark

Measure the speed of your machine learning models on real devices!

Installation

pip install edgebenchmark

First Use

Before you can use Edge Benchmark, sign up and generate your secret token in Profile section.

Then, run following command

edgebenchmark configure

and insert your secret token when you see prompt as shown below.

Edge Benchmark Token [None]:

Your secret token is saved at ~/.edgebenchmark/token.

Edge Benchmark

Edge Benchmark can be either used directly from command line with edgebenchmark command, or from Python script.

Edge Benchmark CLI

Edge Benchmark CLI tool offers several commands: configure and tflite.

edgebenchmark --help

Usage: edgebenchmark [OPTIONS] COMMAND [ARGS]...

Options:
  --help  Show this message and exit.

Commands:
  configure
  devices
  tflite

configure command is explained in the First use section.

tflite command is for benchmarking the speed of TensorFlow Lite models. You can setup many parameters to control the benchmarking process and also select devices (--devices) which you want to benchmark with.

We support several different TFLite version. All supported version can be shown by executing the help command below.

edgebenchmark tflite --help

Usage: edgebenchmark tflite [OPTIONS] COMMAND [ARGS]...

Options:
  --help  Show this message and exit.

Commands:
  1.14.0
  1.15.0
  1.15.2
  1.15.3
  1.15.4
  2.0.0
  2.0.1
  2.0.2
  2.0.3
  2.1.0
  2.1.1
  2.1.2
  2.2.0
  2.2.1
  2.3.0
  2.4.0
  2.4.1

Each TfLite version has a little bit different parameter set. For example, if you want to see supported parameters for TFLite version 1.14.0, run the command below.

edgebenchmark tflite 1.14.0 --help

Usage: edgebenchmark tflite 1.14.0 [OPTIONS]

Options:
  --features FEATURES
  -d, --device TEXT
  --model_path MODEL_PATH   [required]
  --allow_fp16 BOOLEAN
  --input_layer_shape TEXT
  --input_layer TEXT
  --warmup_runs INTEGER
  --output_prefix TEXT
  --benchmark_name TEXT
  --num_threads INTEGER
  --run_delay INTEGER
  --num_runs INTEGER
  --help                    Show this message and exit.

Edge Benchmark Python Package

If you prefer to benchmark your machine learning models directly from Python, you can use our Python package edgebenchmark.

from edgebenchmark import TFLiteBenchmark

benchmark = TFLiteBenchmark("1.14.0")

benchmark.num_threads = 2
benchmark.warmup_runs = 10
benchmark.num_runs = 20
benchmark.devices = ["LGG6"]
benchmark.features = {"num_params": 14000}
benchmark.run("model.tflite")

Currently available devices

If you want to select specific devices for benchmarking, but you are not sure what devices we currently support, you can use devices command as shown below.

edgebenchmark devices

This command will return you a set of available devices which you can then specify for benchmarking using --device or -d parameter.

OnePlus6t
SamsungGalaxy3

FAQ

Here, you can find the most common questions and their answers. If you would like to ask any question, feel free to open a new issue or send us email to contact@bisonai.com.

How to select multiple devices?

Devices should be selected with -d od --device parameter. For example, following script will benchmark model.tflite model on OnePlus6t and SonyXperiaZ5.

edgebenchmark tflite 1.14.0 \
  --model_path model.tflite \
  -d OnePlus6t \
  -d SonyXperiaZ5

How to send extra features to Edge Benchmark?

If you want to attach aditional information to your benchmark, you can use --features pararemeter as shown below.

edgebenchmark tflite \
  --model_path model.tflite \
  -d OnePlus6t \
  --features "{'num_params': 14000}"

Licence

Apache License 2.0