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Renate: Automatic Neural Networks Retraining and Continual Learning in Python#

Renate is a Python package for automatic retraining of neural networks models. It uses advanced Continual Learning and Lifelong Learning algorithms to achieve this purpose. The implementation is based on PyTorch and Lightning for deep learning, and Syne Tune for hyperparameter optimization.

Who needs Renate?#

In many applications data is made available over time and retraining from scratch for every new batch of data is prohibitively expensive. In these cases, we would like to use the new batch of data provided to update our previous model with limited costs. Unfortunately, since data in different chunks is not sampled according to the same distribution, just fine-tuning the old model creates problems like catastrophic forgetting. The algorithms in Renate help mitigating the negative impact of forgetting and increase the model performance overall.

Renate vs Model Fine-Tuning.

Renate’s update mechanisms improve over naive fine-tuning approaches. [1]#

Renate also offers hyperparameter optimization (HPO), a functionality that can heavily impact the performance of the model when continuously updated. To do so, Renate employs Syne Tune under the hood, and can offer advanced HPO methods such multi-fidelity algorithms (ASHA) and transfer learning algorithms (useful for speeding up the retuning).

Impact of HPO on Renate's Updating Algorithms.

Renate will benefit from hyperparameter tuning compared to Renate with default settings. [2]#

Key features#

  • Easy to scale and run in the cloud

  • Designed for real-world retraining pipelines

  • Advanced HPO functionalities available out-of-the-box

  • Open for experimentation


Cite Renate#

  title           = {Renate: A Library for Real-World Continual Learning},
  author          = {Martin Wistuba and
                     Martin Ferianc and
                     Lukas Balles and
                     Cedric Archambeau and
                     Giovanni Zappella},
  year            = {2023},
  eprint          = {2304.12067},
  archivePrefix   = {arXiv},
  primaryClass    = {cs.LG}

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Indices and tables#