deepmark
THE Deep Learning Benchmarks
See: https://github.com/soumith/convnet-benchmarks/issues/101
Come back here on June 15th, 2016.
A bit delayed due to y'know -- a lot of co-ordination among groups.
Networks
Images
Video
Audio
Text
Platform
- Initially multi-GPU with (1 to 4 titan-X cards)
- However, multi-machine, custom hardware, other GPU cards such as AMD, CPUs etc. can and should be accommodated, we will work this out after the initial push.
Metrics
- Round-trip time for 1 epoch of training (will define an epoch size separately for each network)
- Maximum batch-size that fits (to show and focus on the extra memory consumption that the framework uses)
Frameworks
Everyone who wants to join-in, but I thought an initial set that is important to cover would be:
- Caffe
- Chainer
- CNTK
- MXNet
- Neon
- Theano
- TensorFlow
- Torch
Scripts format
Guarantees
- I will personally to the best of my abilities make sure that the benchmarking is fair and unbiased. The hope is that the community at large will watch these and point-out / fix mistakes.
Governance
- The benchmarks will be placed at https://github.com/DeepMark/deepmark and other key community members / organizations who want ownership will be welcome to join in proposing new benchmarks that get relevant as the field progresses.