Metadata-Version: 2.1
Name: edgebenchmark
Version: 0.0.4
Summary: Tool to benchmark speed of machine learning models on real mobile devices
Home-page: https://github.com/bisonai/edgebenchmark
Author: Martin Kersner
Author-email: martin@bisonai.com
License: UNKNOWN
Description: # Edge Benchmark
        
        Measure the speed of your machine learning models on real devices!
        
        ## Installation
        
        ```bash
        pip install edgebenchmark
        ```
        
        ## First Use
        
        Before you can use Edge Benchmark, register at [http://52.231.69.96/app/#/register](http://52.231.69.96/app/#/register) and generate your secret token in profile section.
        
        Then, run following command
        
        ```bash
        edgebenchmark configure
        ```
        
        and insert your secret token when you see prompt as shown below.
        
        ```bash
        Edge Benchmark Token [None]:
        ```
        
        Your secret token is saved at `~/.edgebenchmark/token`.
        
        ## Edge Benchmark Usage
        
        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`, `ncnn` and `tflite`.
        
        ```bash
        edgebenchmark --help
        ```
        
        ```bash
        Usage: edgebenchmark [OPTIONS] COMMAND [ARGS]...
        
        Options:
          --help  Show this message and exit.
        
        Commands:
          configure
          ncnn
          tflite
        ```
        
        `configure` command is explained in the [First use](https://github.com/bisonai/edgebenchmark#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.
        Below, you can see all options for `tflite` command.
        
        ```
        edgebenchmark tflite --help
        Usage: edgebenchmark tflite [OPTIONS]
        
        Options:
          --features FEATURES
          -d, --devices TEXT              [required]
          --model_path MODEL_PATH         [required]
          --num_threads INTEGER
          --warmup_runs INTEGER
          --num_runs INTEGER
          --run_delay FLOAT
          --use_nnapi / --no-use_nnapi
          --use_legacy_nnapi / --no-use_legacy_nnapi
          --use_gpu / --no-use_gpu
          --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()
        
        benchmark.num_threads = 2
        benchmark.warmup_runs = 10
        benchmark.num_runs = 13
        benchmark.run_delay = 3.3
        benchmark.use_nnapi = False
        benchmark.use_legacy_nnapi = False
        benchmark.use_gpu = False
        benchmark.features = "{}"
        benchmark.devices = ["SamsungGalaxyNote3"]
        
        model_path = "model.tflite"
        benchmark.run(model_path)
        ```
        
        
        ## Licence
        [Apache License 2.0](https://github.com/bisonai/edgebenchmark/blob/master/LICENSE)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
