测试步骤和方法
使用https://zhuanlan.zhihu.com/p/363319763 中的：
- "PyTorch resnet50 benchmark步骤"
- "LibTorch resnet50 benchmark步骤"
注意：MLab HomePod 1.0中的PyTorch版本为1.8.1
测试模型：resnet50
环境
宿主机OS：Ubuntu 20.04
软件环境：MLab HomePod 1.0
CPU：Intel(R) Core(TM) i9-9820X CPU @ 3.30GHz
GPU：NVIDIA GTX 2080ti
GPU驱动：NVIDIA-SMI 450.102.04 Driver Version: 450.102.04 CUDA Version: 11.0
| 测试场景 |
CPU利用率 |
内存(GB) |
GPU利用率 |
显存(GB) |
线程数 |
| C++ libtorch |
~105% |
~4.1 |
100% |
~3.7 |
25 |
| PyTorch |
~132% |
~3.9 |
~95% |
~9 |
32 |
input shape = 224x224
| 测试场景 |
forward time(ms) |
| C++ libtorch |
3.950 |
| PyTorch |
6.4478 |
input shape = 640x640
| 测试场景 |
forward time(ms) |
| C++ libtorch |
11.242 |
| PyTorch |
10.4656 |
input shape = 1280x720
| 测试场景 |
forward time(ms) |
| C++ libtorch |
24.678 |
| PyTorch |
20.8056 |
input shape = 1280x1280
| 测试场景 |
forward time(ms) |
| C++ libtorch |
38.094 |
| PyTorch |
37.6037 |
测试步骤和方法
使用https://zhuanlan.zhihu.com/p/363319763 中的：
注意：MLab HomePod 1.0中的PyTorch版本为1.8.1
测试模型：resnet50
环境
宿主机OS：Ubuntu 20.04
软件环境：MLab HomePod 1.0
CPU：Intel(R) Core(TM) i9-9820X CPU @ 3.30GHz
GPU：NVIDIA GTX 2080ti
GPU驱动：NVIDIA-SMI 450.102.04 Driver Version: 450.102.04 CUDA Version: 11.0
input shape = 224x224
input shape = 640x640
input shape = 1280x720
input shape = 1280x1280