TFCC

TFCC is a C++ deep learning inference framework.

TFCC provides the following toolkits that faciliate your development and deployment of your trained DL models:

Porject	Source	Description
TFCC	./tfcc	The core of deep learning inference library. It provides friendly interfaces for model deployment, as well as the implementation of diverse operations in both MKL and CUDA environments.
TFCC Code Generator	./tfcc_code_generator	An automatic generator that can optimize the structure of your high-level models (tensorflows, pytorch, etc.) and generate the TFCC model.
TFCC Runtime	./tfcc_runtime	An runtime to load TFCC model and inference.

BUILD

Run

./build.sh ${INSTALL_PREFIX_PATH}

Quick Start

1. Convert Model

   The script generator.py can convert onnx model or tensorflow model to tfcc model. The docs Convert ONNX Model and Convert TF Model show the details.

2. Load Model

   There is a simple way to load a model as following code:

   // load tfcc model to a string.
   std::string modelData = load_data_from_file(path);
   tfcc::runtime::Model model(modelData);

3. Inference

   Finally run the model

   tfcc::runtime::data::Inputs inputs;
   tfcc::runtime::data::Outputs outputs;
   
   // set inputs
   auto item = inputs.add_items();
   item->set_name("The input name");
   item->set_dtype(tfcc::runtime::common::FLOAT);
   std::vector<float> data = {1.0, 2.0};
   item->set_data(data.data(), data.size() * sizeof(float));
   
   model.run(inputs, outputs);

   Complete code