提交 e9d17c26 编写于 作者: G guoqi

update the hyper link of resnet50 benchmark

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......@@ -16,7 +16,7 @@ For details about the MindSpore pre-trained model, see [Model Zoo](https://gitee
| | | | | Ascend: 16 * Ascend 910 </br> 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](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Classification/RN50v1.5#nvidia-dgx-2-16x-v100-32g).
2. For details about other open source frameworks, see [ResNet-50 v1.5 for TensorFlow](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Classification/ConvNets/resnet50v1.5).
### BERT
......@@ -26,4 +26,4 @@ For details about the MindSpore pre-trained model, see [Model Zoo](https://gitee
| | | | | Ascend: 8 * Ascend 910 </br> 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](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/LanguageModeling/BERT).
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2. For details about other open source frameworks, see [BERT For TensorFlow](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/LanguageModeling/BERT).
......@@ -15,7 +15,7 @@
| | | | | Ascend: 16 * Ascend 910 </br> 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/RN50v1.5#nvidia-dgx-2-16x-v100-32g)
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
......@@ -25,4 +25,4 @@
| | | | | Ascend: 8 * Ascend 910 </br> 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)
\ No newline at end of file
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)
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