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Playing with various deep learning tools and network architectures
Sneak preview for H2O's Deep Water project:
https://github.com/h2oai/h2o-3/blob/master/examples/deeplearning/notebooks/deeplearning_benchmark_mnist.ipynb
To try this script yourself on Ubuntu 16 with CUDA8, CUDNN5, simply pip install this package: https://slack-files.com/T0329MHH6-F2TN01TUN-b0577b68da
You will also want to install the matching mxnet egg: https://slack-files.com/T0329MHH6-F2PU85GEN-2f4fee68e2
More instructions and links provided here: https://github.com/h2oai/deepwater
Please note that this is not a final version for benchmark purposes, but rather a community preview among friends.
g2.2xlarge: 47s
p2.2xlarge: TBD
2x 10-core Xeon with GTX1080: 26s
Cheers,
Arno
Assuming accurate model creation and sufficient benchmarking there should be no accuracy delta between different libraries in creating different models. The results you've presented are all far below the threshold of showing any real difference between models, and I'd assert that they're well within noise parameters.
I'd suggest either removing them or confirming them, but given the size of the differences you're reporting on the chance of them being statistically significant is very close to zero.
Hi, I found your analysis very interesting. May I suggest to complete the section "GPU vs CPU" using Theano backend? I heard that for CPU, Theano is faster than Tensorflow.
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