# 基准性能 本文介绍MindSpore的基准性能。MindSpore网络定义可参考[ModelZoo](https://gitee.com/mindspore/models/blob/r2.0.0-alpha/README_CN.md#)。 ## 训练性能 ### 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. 以上数据基于华为云AI开发平台ModelArts测试获得,是训练过程整体下沉至Ascend 910 AI处理器执行所得的平均性能。 2. 业界其他开源框架数据可参考:[ResNet-50 v1.5 for TensorFlow](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Classification/ConvNets/resnet50v1.5)。 ### 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. 以上数据基于华为云AI开发平台ModelArts测试获得,其中网络包含24个隐藏层,句长为128个token,字典表包含21128个token。 2. 业界其他开源框架数据可参考:[BERT For TensorFlow](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/LanguageModeling/BERT)。 ### Wide & Deep (数据并行) | 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. 以上数据基于Atlas 800测试获得,且网络模型为数据并行。 2. 业界其他开源框架数据可参考:[Wide & Deep For TensorFlow](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Recommendation/WideAndDeep)。 ### Wide & Deep (Host-Device混合计算模型并行) | 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 | 8000 | 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. 以上数据基于Atlas 800测试获得,且网络模型为模型并行。 2. 业界其他开源框架数据可参考:[Wide & Deep For TensorFlow](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Recommendation/WideAndDeep)。