The deep learning metaframework
Deep500: A Deep Learning Meta-Framework and HPC Benchmarking Library
(or: 500 ways to train deep neural networks)
Deep500 is a library that can be used to customize and measure anything with deep neural networks, using a clean, high-performant, and simple interface. Deep500 includes four levels of abstraction: (L0) Operators (layers); (L1) Network Evaluation; (L2) Training; and (L3) Distributed Training.
Using Deep500, you automatically gain:
- Operator validation, including gradient checking for backpropagation
- Statistically-accurate performance benchmarks and plots
- High-performance integration with popular deep learning frameworks (see Supported Frameworks below)
- Running your operator/framework/optimizer/communicator/... with real workloads, alongside existing environments
- and much more...
pip install deep500
See the tutorials.
- Python 3.5 or later
- Protobuf (
sudo apt-get install protobuf-compiler libprotoc-dev)
- For plotted metrics: matplotlib
- For distributed optimization:
- Any MPI implementation (OpenMPI, MPICH, MVAPICH etc.)
- mpi4py Python package
Deep500 is an open-source, community driven project. We are happy to accept Pull Requests with your contributions!
Deep500 is published under the New BSD license, see LICENSE.
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