A python library that automates the conversion of Temporal Convolutional Network (TCN) models to a streaming format without affecting model accuracy, significantly reducing deployment time and computational resources.
This library is based on the research outlined in the following paper:
git clone [email protected]:ahmad-mirsalari/TCN_Pre_release.git
cd StreamEaseYou can install the required dependencies by running:
pip install -r requirements.txtRun the setup_env.sh script to add the project root to your PYTHONPATH. This will ensure that Python can locate the streamease package.
chmod +x setup_env.sh
./setup_env.shYou will be prompted to add the PYTHONPATH to your .bashrc to make it permanent for future sessions.
- streamease: Main package containing modules for streaming inference, buffering, and quantization.
- examples: Contains example scripts demonstrating how to use the toolkit.
- utils: Contains helper functions to facilitate running the streaming network, with support for ONNX Runtime and GreenWaves Technologies' nntool for optimized model execution.
- setup_env.sh: Script for setting up dependencies and environment variables.
- requirements.txt: List of required dependencies
- Extend the library by adding additional networks in different applications
StreamEase is released under Apache 2.0, see the LICENSE file in the root of this repository for details.
This work was supported by the APROPOS project (g.a. no. 956090), founded by the European Union’s Horizon 2020 research and innovation program.
- Seyed Ahmad Mirsalari, University of Bologna,E-mail