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Playing with various deep learning tools and network architectures
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Ceci n'est pas un benchmark

Deep learning has been extremely successful on a few classes of data/machine learning problems such as involving images, speech and text (supervised learning) and games (reinforcement learning).

In this repo we'll look at the performance of the most commonly used deep learning tools with high-level API from R/Python (keras on tensorflow and theano backends, mxnet, neon etc.) running on EC2 machines with GPUs (P2 instances with NVIDIA Tesla K80 GPUs) using the most common network architectures on basic datasets of the classes mentioned above.

This author's interest is mainly in "traditional" machine learning problems such as fraud detection, credit scoring or churn, and it seems that on that kind of data/problems deep learning is not as successful and it provides lower accuracy than random forests or gradient boosting machines. Unfortunately most of the hype surrounding deep learning and "artificial intelligence" does not acknowledge this reality.

Conv-nets (CNN)

Image recognition on the MNIST dataset. 2x conv+pooling, dropout, 2x fully connected layers, trained with SGD for 10 epochs. Code and detailed results here.

By Tools

On p2.xlarge (1 GPU):

Tool Time (s) vs Best vs TF GPU (%) CPUs CPU1 (%) Error rate
neon 25 0.7x 57 1 100 0.9%
Keras (tensorflow) 37 1.5x 66 4 18 0.8%
R Keras (TF) 38 1.5x 63 4 22 1.0%
Keras (CNTK) 58 2.3x 1.6x 80 1 100 0.8%
Keras (theano) 130 5x 3.5x 97 1 100 0.9%
mxnet 50 2x 1.3x 94 4 34 1.0%

On GTX 1080 Ti:

Tool Time (s) vs Best vs K80 Error rate
Keras (CNTK) 23 0.4x 0.9%
mxnet 25 1.1x 0.5x 0.8%
Keras (tensorflow) 27 1.2x 0.7x 0.8%
Keras (theano) 73 3.2x 0.6x 0.9%

Let's say that GTX 1080 Ti is ~2x faster than K80.

On p3.2xlarge (Tesla V100):

Tool Time (s) Error rate
Keras (tensorflow) 20 0.7%

1.3x faster than GTX 1080 Ti, 1.8x faster than K80 (keras TF)

By GPU number/types

Tensorflow backend:

Instance GPUs GPU Time (s) GPU (%) GPUs
p2.xlarge 1 Tesla K80 37 66 1
p2.8xlarge 8 Tesla K80 36 67 (1) 1/8
g2.2xlarge 1 GRID K520 56 70 1

P2 GPU is 1.5x faster than G2.

Tensorflow uses only 1 GPU even on the multi-GPU server (maybe because of small dataset/model).

mxnet can be set to use multiple GPUs, but then it runs slower (probably because of small dataset/model).

Theano obtained multi-GPU support only recently and that feature has not been added to Keras yet.

Neon disabled multi-GPU support (except on their cloud hosted version).


Tensorflow backend:

Device Time (s) vs GPU
GPU (P2) 37 1x
CPU 4 cores 326 9x
CPU 32 cores 130 3.5x

"If it's not running on the GPU, it's crap" - Scott Le Grand [ref]

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