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