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https://github.com/olk/mnist-performance

performance test of MNIST hand writings usign MXNet + TF
https://github.com/olk/mnist-performance

classification gluon horovod keras mirrored-strategy mnist model-parallelism multi-gpu multi-gpu-training mxnet python tensorflow

Last synced: 7 days ago
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performance test of MNIST hand writings usign MXNet + TF

Host: GitHub
URL: https://github.com/olk/mnist-performance
Owner: olk
License: gpl-3.0
Created: 2019-12-30T23:09:29.000Z (about 5 years ago)
Default Branch: master
Last Pushed: 2020-01-31T16:41:24.000Z (about 5 years ago)
Last Synced: 2024-12-04T22:10:28.672Z (2 months ago)
Topics: classification, gluon, horovod, keras, mirrored-strategy, mnist, model-parallelism, multi-gpu, multi-gpu-training, mxnet, python, tensorflow
Language: Python
Size: 22.5 KB
Stars: 0
Watchers: 2
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        # MNIST performance tests

The projects tests the performance of MXNet and Tensorflow using MNIST data.

## project structure

    ├── LICENSE

    ├── README.md                  <- Top-level README for developers using this project

    ├── mnist_mx_gluon.py          <- MXNet + Gluon

    ├── mnist_mx_gluon_mgpu.py     <- MXNet + Gluon + multi-gpu

    ├── mnist_mx_gluon_hvd.py      <- MXNet + Gluon + Horovod

    ├── mnist_mx_keras.py          <- MXNet + Keras

    ├── mnist_mx_keras_mgpu.py     <- MXNet + Keras + multi-gpu (multi_gpu_model())

    ├── mnist_mx_sym.py            <- MXNet + symbol/module API

    ├── mnist_mx_sym_mgpu.py       <- MXNet + symbol/module API + multi-gpu

    ├── mnist_tf_keras.py          <- Tensorflow + Keras

    └── mnist_tf_keras_mirrored.py <- Tensorflow + Keras + multi-gpu (MirroredStrategy())

### MXNet Info

    Version      : 1.5.1

    Directory    : /home/graemer/Projekte/MXNet/apache-mxnet-src-1.5.1-incubating/python/mxnet

    Num GPUs     : 2

### System Info

    system       : Linux

    node         : e5lx

    release      : 5.4.3-arch1-1

    version      : #1 SMP PREEMPT Fri, 13 Dec 2019 09:39:02 +0000

### Hardware Info

    Architektur:                     x86_64

    CPU Operationsmodus:             32-bit, 64-bit

    Byte-Reihenfolge:                Little Endian

    Adressgrößen:                    46 bits physical, 48 bits virtual

    CPU(s):                          32

    Liste der Online-CPU(s):         0-31

    Thread(s) pro Kern:              2

    Kern(e) pro Socket:              8

    Sockel:                          2

    NUMA-Knoten:                     2

    Anbieterkennung:                 GenuineIntel

    Prozessorfamilie:                6

    Modell:                          79

    Modellname:                      Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz

    Stepping:                        1

    CPU MHz:                         1197.887

    Maximale Taktfrequenz der CPU:   3000,0000

    Minimale Taktfrequenz der CPU:   1200,0000

    BogoMIPS:                        4192.42

    Virtualisierung:                 VT-x

    L1d Cache:                       512 KiB

    L1i Cache:                       512 KiB

    L2 Cache:                        4 MiB

    L3 Cache:                        40 MiB

    NUMA-Knoten0 CPU(s):             0-7,16-23

    NUMA-Knoten1 CPU(s):             8-15,24-31

## test results

multigpu [[Horovod]] (2 GPUs): `horovodrun -np 2 -H localhost:2 python mnist_mx_gluon_hvd.py`

 | framework                 | duration | accuracy |

 |---------------------------|----------|----------|

 | mnist_mx_gluon.py         | 14.2s    | 0.9926   |

 | mnist_mx_keras.py         | 16.4s    | 0.9931   |

 | mnist_mx_sym.py           | 16.8s    | 0.9924   |

 | mnist_tf_keras.py         | 17.6s    | 0.9932   |

 | mnist_mx_gluon_ds.py      | 17.6s    | 0.9902   |

 |                           |          |          |

 | mnist_mx_gluon_mgpu.py    | 7.2s     | 0.9926   |

 | mnist_mx_gluon_hvd.py     | 9.0s     | 0.9963   |

 | mnist_mx_sym_mgpu.py      | 10.9s    | 0.9923   |

 | mnist_mx_keras_mgpu.py    | 11.4s    | 0.9927   |

 | mnist_tf_keras_mgpu.py    | 12.7s    | 0.9921   |

 | mnist_mx_gluon_ds_mgpu.py | 15.2s    | 0.9906   |