Benchmarks

Linux Ascend Model Training Intermediae Expert

This document describes the MindSpore benchmarks. For details about the MindSpore networks, see Model Zoo.

Training Performance

ResNet

Network Network Type Dataset MindSpore Version Resource                 Precision Batch Size Throughput Speedup
ResNet-50 v1.5 CNN ImageNet2012 0.5.0-beta Ascend: 1 * Ascend 910
CPU: 24 Cores
Mixed 256 2115 images/sec -
Ascend: 8 * Ascend 910
CPU: 192 Cores
Mixed 256 16600 images/sec 0.98
Ascend: 16 * Ascend 910
CPU: 384 Cores
Mixed 256 32768 images/sec 0.96
  1. The preceding performance is obtained based on ModelArts, the HUAWEI CLOUD AI development platform. It is the average performance obtained by the Ascend 910 AI processor during the overall training process.

  2. For details about other open source frameworks, see ResNet-50 v1.5 for TensorFlow.

BERT

Network Network Type Dataset MindSpore Version Resource                 Precision Batch Size Throughput Speedup
BERT-Large Attention zhwiki 0.5.0-beta Ascend: 1 * Ascend 910
CPU: 24 Cores
Mixed 96 269 sentences/sec -
Ascend: 8 * Ascend 910
CPU: 192 Cores
Mixed 96 2069 sentences/sec 0.96
  1. The preceding performance is obtained based on ModelArts, the HUAWEI CLOUD AI development platform. The network contains 24 hidden layers, the sequence length is 128 tokens, and the vocabulary contains 21128 tokens.

  2. For details about other open source frameworks, see BERT For TensorFlow.

Wide & Deep (data parallel)

Network Network Type Dataset MindSpore Version Resource                 Precision Batch Size Throughput Speedup
Wide & Deep Recommend Criteo 0.6.0-beta Ascend: 1 * Ascend 910
CPU: 24 Cores
Mixed 16000 796892 samples/sec -
Ascend: 8 * Ascend 910
CPU: 192 Cores
Mixed 16000*8 4872849 samples/sec 0.76
  1. The preceding performance is obtained based on Atlas 800, and the model is data parallel.

  2. For details about other open source frameworks, see Wide & Deep For TensorFlow.

Wide & Deep (Host-Device model parallel)

Network Network Type Dataset MindSpore Version Resource                 Precision Batch Size Throughput Speedup
Wide & Deep Recommend Criteo 0.6.0-beta Ascend: 1 * Ascend 910
CPU: 24 Cores
Mixed 1000 68715 samples/sec -
Ascend: 8 * Ascend 910
CPU: 192 Cores
Mixed 8000*8 283830 samples/sec 0.51
Ascend: 16 * Ascend 910
CPU: 384 Cores
Mixed 8000*16 377848 samples/sec 0.34
Ascend: 32 * Ascend 910
CPU: 768 Cores
Mixed 8000*32 433423 samples/sec 0.20
  1. The preceding performance is obtained based on Atlas 800, and the model is model parallel.

  2. For details about other open source frameworks, see Wide & Deep For TensorFlow.