Cerebro is a data system for optimized deep learning model selection. It uses a novel parallel execution strategy called Model Hopper Parallelism (MOP) to execute end-to-end deep learning model selection workloads in a more resource-efficient manner. Detailed technical information about Cerebro can be found in our Technical Report.

The best way to install the Cerebro is via pip.

pip install -U cerebro-dl

Alternatively, you can git clone and run the provided Makefile script

git clone https://github.com/ADALabUCSD/cerebro-system.git && cd cerebro-system && make

You MUST be running on Python >= 3.6 with Tensorflow >= 2.2 and Apache Spark >= 2.4

Detailed documentation about the system can be found here.

This project was/is supported in part by a Hellman Fellowship, the NIDDK of the NIH under award number R01DK114945, and an NSF CAREER Award.

We used the following projects when building Cerebro.

• Horovod: Cerebro's Apache Spark implementation uses code from the Horovod's implementation for Apache Spark.
• Petastorm: We use Petastorm to read Apache Parquet data from remote storage (e.g., HDFS)

If you use this software for research, plase cite the following papers:

@inproceedings{nakandala2019cerebro,
  title={Cerebro: Efficient and Reproducible Model Selection on Deep Learning Systems},
  author={Nakandala, Supun and Zhang, Yuhao and Kumar, Arun},
  booktitle={Proceedings of the 3rd International Workshop on Data Management for End-to-End Machine Learning},
  pages={1--4},
  year={2019}
}