3.9.2. TVM Runtime Introduction

TVM is an open deep learning compiler stack for CPUs, GPUs, and specialized accelerators. It aims to close the gap between the productivity-focused deep learning frameworks, and the performance- or efficiency-oriented hardware backends. TVM provides the compilation of deep learning models in Keras, MXNet, PyTorch, Tensorflow, CoreML, DarkNet into minimum deployable modules on diverse hardware backends.

Processor SDK Linux has integrated TVM runtime <https://docs.tvm.ai/dev/runtime.html> for deep learning inference at the edge. Currently, TVM runtime is available on ARM cores for all Sitara devices (AM3/AM4/AM5/AM6).

To run deep learning inference on Sitara devices, a network model needs to be compiled by TVM compiler. Build and Install TVM Compiler

Follow instructions at https://docs.tvm.ai/install/from_source.html to build and install TVM compiler from source. Please note that USE_LLVM needs to be set to ON in config.cmake. Running Inference on the Target

  • Start RPC on the target

python3 -m tvm.exec.rpc_server --host --port=9090
python3 deploy_tfmodel_sitara.py '<remote target ip address>', e.g. python3 deploy_tfmodel_sitara.py ''

Please note that this script worked with commit id 76c239269935288e51fbce14f135d75ad9742b2a, but may not work with latest code. One can modify the script accordingly or follow this example in order to work with newer code.