NVIDIA DGX-1 SupercomputerCommunity-Based Deep Learning Benchmark

Community-Based Deep Learning Benchmark

We have an NVIDIA DGX-1 available for testingand would like to organise a Community-Driven Benchmark

We plan to have the benchmark set-up by mid-March 2017.If you are interested in running your tests, contact us asap

Please check our Blogpost for info:bit.ly/DGX1-Benchmark

IMPORTANT:

Benchmark we’ve already done onNVIDIA GTX1080 - TITAN X Maxwell - TITAN X Pascal - K40

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GPU vs Framework vs Network - K40This Benchmark compares the efficiency of various GPUs when training different networks using different frameworks.

This is a common approach that allows us to decide on which framework to use for a given GPU and a given architecture

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GPU vs Framework vs Network - TITAN X MaxwellThis Benchmark compares the efficiency of various GPUs when training different networks using different frameworks.

This is a common approach that allows us to decide on which framework to use for a given GPU and a given architecture

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GPU vs Framework vs Network - GTX 1080This Benchmark compares the efficiency of various GPUs when training different networks using different frameworks.

This is a common approach that allows us to decide on which framework to use for a given GPU and a given architecture

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GPU vs Framework vs Network - TITAN X PascalThis Benchmark compares the efficiency of various GPUs when training different networks using different frameworks.

This is a common approach that allows us to decide on which framework to use for a given GPU and a given architecture

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Minibatch Efficiency for TensorFlowThis Benchmark compares the mini-batch efficiency for each GPU when training different architectures.

As a rule of thumb, larger minibatch size means more efficient training, however be prepare for a few surprise

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Minibatch Efficiency for TensorFlowThis Benchmark compares the mini-batch efficiency for each GPU when training different architectures.

As a rule of thumb, larger minibatch size means more efficient training, however be prepare for a few surprise

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Minibatch Efficiency for TensorFlowThis Benchmark compares the mini-batch efficiency for each GPU when training different architectures.

As a rule of thumb, larger minibatch size means more efficient training, however be prepare for a few surprise

Plea

se c

heck

our

Blo

gpos

t for

info

: bit

.ly/D

GX

1-B

ench

mar

k

Minibatch Efficiency for TensorFlowThis Benchmark compares the mini-batch efficiency for each GPU when training different architectures.

As a rule of thumb, larger minibatch size means more efficient training, however be prepare for a few surprise

Plea

se c

heck

our

Blo

gpos

t for

info

: bit

.ly/D

GX

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mar

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Contact me for any other question