- Build: Construct models from scratch.
- Approximation: Create approximate versions of the models.
- Fine-tuning: Optimize pre-trained models.
- Querying: Retrieve information about the models.
- TensorFlow
- Horovod for distributed training
- Bespoke (a custom module for machine learning workflows)
- NNCompress (a custom module for neural network compression)
Run the script from the command line, providing the necessary arguments:
python runner.py --config path/to/config.yaml --mode [mode] --source_dir path/to/source --target_dir path/to/target--config: Path to the configuration file.--mode: Operation mode.--source_dir: Working directory path.--target_dir: Result directory path.- Additional arguments are available for specific operations.
To contribute to the development of this runner script, you can extend its capabilities or improve the existing code to increase efficiency and performance.
This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2021-0-00907, Development of Adaptive and Lightweight Edge-Collaborative Analysis Technology for Enabling Proactively Immediate Response and Rapid Learning).