Tracking metadata and injecting parameters
protoNN uses Python type hints to specify which parameters need to be tracked or modified
Transparent and elastic scheduling of DNN training jobs on modern HPC systems
- Monitoring and visualizing model parameters and computational performance statistics.
- Perform semi-automatic hyperparameter tuning/optimization and architecture search using evolutionary algorithms.
- A user-defined interactive interface to drive the framework/ design process, not bound to any particular framework.
- Scaling the functionality and performance of the model as the resources increase.