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d7d3b411
编写于
1月 11, 2018
作者:
K
Kavya Srinet
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Merge branch 'develop' of
https://github.com/PaddlePaddle/Paddle
into doc
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# Cluster Training Benchmark
## Setup
-
Platform
-
Kubernetes: v1.6.2
-
Linux Kernel: v3.10.0
-
Resource
-
CPU: 10 Cores per Pod
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Memory: 5GB per Pod
-
Docker Image
We use different base Docker Image to run the benchmark on Kubernetes:
-
PaddlePaddle v2: paddlepaddle/paddle:0.11.0
-
PaddlePaddle Fluid: paddlepaddle/paddle:[commit-id]
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TensorFlow: tensorflow/tensorflow:1.5.0-rc0
-
Model
vgg16 is used in this benchmark.
## Cases
-
Variable
-
Batch Size of training data.
-
PServer count of the training job.
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The number of trainers.
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Invariant
-
The resource of trainer/pserver Pod.
### Measure the Performance for Different Batch Size
-
PServer Count: 40
-
Trainer Count: 100
-
Metrics: mini-batch / sec
| Batch Size | 32 | 64 | 128 | 256 |
| -- | -- | -- | -- | -- |
| PaddlePaddle Fluid | - | - | - | - |
| PaddlePaddle v2 | - | - | - | - |
| TensorFlow | - | - | - | - |
### Measure the Performance for Different PServer Count
-
Trainer Count: 100
-
Batch Size: 64
-
Metrics: mini-batch / sec
| PServer Count | 10 | 20 | 40 | 60 |
| -- | -- | -- | -- | -- |
| PaddlePaddle Fluid | - | - | - | - |
| PaddlePaddle v2 | - | - | - | - |
| TensorFlow | - | - | - | - |
### Measure Parallel Efficiency By Increasing Trainer Count
-
PServer Count: 20
-
Batch Size: 64
-
Metrics:
$S =
\d
iv(T1, TN)$
which S is the ratio of T1 over TN, training time of 1 and N trainers.
The parallel efficiency is:
$E =
\d
iv(S, N)$
| Trainer Counter | 1 | 10 | 20 | 30 | 40 | 50 | 60 | 70 | 80 | 90 | 100 |
| -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- |
| PaddlePaddle Fluid | - | - | - | - | - | - | - | - | - | - | - |
| PaddlePaddle v2 | - | - | - | - | - | - | - | - | - | - | - | - |
| TensorFlow | - | - | - | - | - | - | - | - | - | - | - | - | - |
## Reproduce the benchmark
TODO
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