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Deep Learning Benchmarks
- jimfleming 11y agoIt's nice to see Leaf coming along so well. Part of me would love to be able to build models in rust. For more benchmarks (including updated TensorFlow performance with cudnn v4) see https://github.com/soumith/convnet-benchmarks https://github.com/soumith/convnet-benchmarks
- narrator 11y agoToo bad Tensorflow Cuda only works with the top of the line NVIDIA cards that cost over 1k.
- jimfleming 11y agoCan you expand on that? As far as I'm aware that's not true (anymore). It runs quite fine on AWS which uses older NVIDIA cards and I know several people use it on older-gen GPU-enabled MBPs. EDIT: clarification
- narrator 11y agohttps://www.tensorflow.org/versions/r0.7/get_started/os_setup.html#optional-install-cuda-gpus-on-linux https://www.tensorflow.org/versions/r0.7/get_started/os_setu... I guess the cards listed there are not an all inclusive list?
- vrv 11y agoYeah, those are just cards we know that work: we don't have all possible GPUs to test our 20+ changes a day on, so we can't formally guarantee it will work on older cards, but in general we try our best to keep it all working for older and even newer cards.
- jimfleming 11y agoFrom that page: > Supported cards include but are not limited to[...]
- barneso 11y agoIn my experience, the architecture supports cards with shader model >= 3.0. Occasionally a commit will break the support (eg https://bitbucket.org/eigen/eigen/commits/a19653b8035d8ace53af184b02acb1ee9ab417c6 https://bitbucket.org/eigen/eigen/commits/a19653b8035d8ace53... was required earlier this year) but this is a function of the speed of development and usually straightforward to fix.
- rough-sea 11y agoNot true, works on GTX 750ti out of the box which is currently around $130
- reality_hacker 11y agoJust curious, does this benchmark include models quality somewhere?
- mjhirn 11y agoYou mean, comparing the performance after training a model?
- reality_hacker 11y agoPerformance in terms of model accuracy. How accurate will be those models.
- hobofan 11y agoNo accuracy is not included, since that is something that should be constant for each model across frameworks. These benchmarks aim to highlight the performance differences in terms of speed/memory usage across frameworks and machine configurations. There is also the practical hurdle that training imagenet models to maximum accuracy takes 1 week+.
- reality_hacker 11y agoSo, are you saying that output of all frameworks are exactly and always the same? Sorry, if I am asking stupid questions.
- hobofan 11y agoNot exactly, no. But not even the output of the same framework will always be the same since you are usually randomly initalizing the weights in a network and randomly picking the samples used in SGD[1] (the seed for the RNG could of course be a fixed one to mitigate this somewhat). But in the end, if you are using the same model, the same solver and the same RNG, yes the output of all frameworks should be the same. In practice this also mostly holds true, since the stochastic processes involved are geared towards finding a good local minimum, which is the same given a model and a dataset. [1]: https://en.wikipedia.org/wiki/Stochastic_gradient_descent https://en.wikipedia.org/wiki/Stochastic_gradient_descent
- mjhirn 11y agoI just patched the link on the landing page to the repo [1] and updated benchmarks for Leaf 0.2 + cuDNN 4 for Overfeat and VGG. But I couldn't get Torch and Tensorflow running with cuDNN 4, yet. [1]: https://github.com/autumnai/deep-learning-benchmarks https://github.com/autumnai/deep-learning-benchmarks
- vrv 11y agoFeel free to ping us (TensorFlow) on github issues to get installation issues resolved -- on cudnn r4 we're doing much better, and we're soon to check in a series of changes to get us roughly on par with Torch on cudnn r4. We'd love to get a more up-to-date representation of the state of our own progress :)
- mjhirn 11y agoGreat, I will let you know. Looking forward to the benches.
- Houshalter 11y agoFor benchmarks of classification results of different algorithms and methods, there is this: https://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html https://rodrigob.github.io/are_we_there_yet/build/classifica...
- viksit 11y agoCurious - no Theano benchmarks?
- mjhirn 11y agoWe would love to include those, but we didn't find the implementation for the tested models for Theano, yet. Do you have link? You can also submit your own Benchmarks via PR, if you'd like to.
- viksit 11y agoAh. I'd look at keras examples - they have alexnet and VGG right now. Although, an exact implementation may be hard to find - the best you can do is an "alexnet~ish" implementation keeping some keras graph limitations on convs. I haven't run any in a while so I don't have the data myself, unfortunately. Great work btw. What was the motivation to build leaf?
- mjhirn 11y agoGreat input, thank you so much for the links. I will try to get them to work and publish the results. Same with Keras and LSTMs, very curious to see those.
- viksit 11y agoOh actually: https://github.com/uoguelph-mlrg/theano_alexnet https://github.com/uoguelph-mlrg/theano_alexnet and VGG: https://gist.github.com/baraldilorenzo/07d7802847aaad0a35d3 https://gist.github.com/baraldilorenzo/07d7802847aaad0a35d3 For Googlenet, sadly not yet: https://github.com/fchollet/keras/issues/302 https://github.com/fchollet/keras/issues/302 I'd also try some non conv, RNN/LSTM stuff btw. Those are of special interest to me, but also, keras has some great models there.
- waleedka 11y agoI hope the TensorFlow team is working on improving its performance. Anyone know if they're working on that?
- vrv 11y agoIndeed we are. Here's a recent commit from today https://github.com/tensorflow/tensorflow/commit/d6f3ebfdfc1d5b5df1f6ae73466abe2ec5721b5b https://github.com/tensorflow/tensorflow/commit/d6f3ebfdfc1d... :)