Research map / Architectures and efficiency / RNNs and normalization
PyTorch: An Imperative Style, High-Performance Deep Learning Library
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use PyTorch instead of MXNet | Single-machine eager-mode training · AlexNet | throughput: 1547 ± 316 vs 1554 ± 22 | No clear effect |
| Use PyTorch instead of MXNet | Single-machine eager-mode training · VGG-19 | throughput: 113 ± 1 -> 119 ± 1 | Helped |
| Use PyTorch instead of Chainer | Single-machine eager-mode training · ResNet-50 | throughput: 219 ± 1 -> 212 ± 2 | Hurt |
| Use PyTorch instead of PaddlePaddle | Single-machine eager-mode training · MobileNet | throughput: 557 ± 24 -> 463 ± 17 | Hurt |
| Use PyTorch instead of TensorFlow | Single-machine eager-mode training · GNMTv2 | throughput: 9631 ± 1.3% -> 15512 ± 4.8% | Helped |
| Use PyTorch instead of TensorFlow | Single-machine eager-mode training · NCF | throughput: 4.8e6 ± 2.9% -> 5.4e6 ± 3.4% | Helped |
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