Benchmark of opensource Platforms
Machine:
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Server: Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz, 2 Sockets, 20 Cores per socket
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CPU environment System: Ubuntu 16.04.3 LTS, Docker 17.05.0-ce, build 89658be
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GPU environment System: Ubuntu 16.04.3 LTS, NVIDIA-Docker 17.05.0-ce, build 89658be NVIDIA Docker image: nvidia/cuda:8.0-cudnn5-devel-ubuntu16.04
PaddlePaddle: 0.11.0(Fluid)
- paddlepaddle/paddle:latest
TensorFlow: 1.4.0
- tensorflow/tensorflow:latest
Benchmark Model
selected models PaddlePaddle Fluid vs TensorFlow
We selected some classic models, compare the performance and speed with TensorFlow.
train cost | train accuracy | test accuracy | samples/sec | train cost | train accuracy | test accuracy | samples/sec | |
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MNIST CNN | ||||||||
VGG-19 | ||||||||
RESNET-101 | ||||||||
Stacked LSTM |
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TBD add charts compare here
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VGG-19 input image size - 3 * 224 * 224, Time: images/second
BatchSize | 64 | 128 | 256 |
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PaddlePaddle Fluid | |||
TensorFlow |
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TBD add charts compare here
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RESNET-101
BatchSize | 64 | 128 | 256 |
---|---|---|---|
PaddlePaddle Fluid | |||
TensorFlow |
- TBD
add charts here
- Stacked LSTM
BatchSize | 64 | 128 | 256 |
---|---|---|---|
PaddlePaddle Fluid | |||
TensorFlow |
- TBD
add charts here
PaddlePaddle books Fluid vs Paddle 0.10.0
To validate the Fluid performance on general models, we choose the models in book chapter, compare the performance and speed with Paddle 0.10.0.
train cost | train accuracy | test accuracy | samples/sec | train cost | train accuracy | test accuracy | samples/sec | |
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01.fit_a_line | ||||||||
02.recognize_digits | ||||||||
03.image_classification | ||||||||
04.word2vec | ||||||||
05.recommender_system | ||||||||
06.understand_sentiment | ||||||||
07.label_semantic_roles | ||||||||
08.machine_translation |