qbit-/nnsc_2022_mobile

Materials for the Winter term course "Neural network speedup and compression"

NNSC_2022_mobile

Running PyTorch models on mobile phone

Brief description

• This code provides scripts to profile neural networks on Android devices
• Compression may be done using MusCo toolkit.

Repository structure

• ./device_profiling helper code to profile models on phones
• ./doc Markdown documentation

Dependencies

• see env_docker.yaml. You can install requirements with conda:

conda create -n nnsc_2022_mobile --file env_docker.yaml

• you can also use a docker image qbit271/mmsc_2022_mobile or build your own here

Benchmarking on mobile phones

1. You need to first download or compile an appropriate PyTorch benchmarking binary, instructions can be found here. Otherwise, you can download the one we prebuilt for you from https://github.com/qbit-/NNSC_2022_mobile/tree/main/bin. For the prebuilt binary to work with your models, you need to use Pytorch 1.7.1 to build your models.

2. Copy the benchmark binary to /data/local/tmp/speed_benchmark_torch on the device. Check that the device is accessible:

adb devices

copy the profiler to the device and make it executable

adb push speed_benchmark_torch-$ANDROID_ABI /data/local/tmp/speed_benchmark_torch
adb shell chmod +x /data/local/tmp/speed_benchmark_torch

3. Check that you can execute the benchmarking binary using ADB:

adb shell /data/local/tmp/speed_benchmark_torch --help

Now you can use functions in ./device_profiling to automate profiling. See the example