# CNTK Examples: Image/Classification/VGG
## Python
### VGG16_ImageNet_Distributed.py
This is the VGG model that contains 16 layers, which was referred as `ConvNet configuration D` in the [original paper](https://arxiv.org/pdf/1409.1556v6.pdf).
Run the example from the current folder using:
`python VGG16_ImageNet_Distributed.py`
To run it in a distributed manner, please check [here](https://github.com/Microsoft/CNTK/wiki/Multiple-GPUs-and-machines#32-python). For example, the command for distributed training on the same machine (with multiple GPUs) with Windows is:
`mpiexec -n <#workers> python VGG16_ImageNet_Distributed.py`
### VGG19_ImageNet_Distributed.py
This is the VGG model that contains 19 layers, which was referred as `ConvNet configuration E` in the [original paper](https://arxiv.org/pdf/1409.1556v6.pdf).
Run the example from the current folder using:
`python VGG19_ImageNet_Distributed.py`
To run it in a distributed manner, please check [here](https://github.com/Microsoft/CNTK/wiki/Multiple-GPUs-and-machines#32-python). For example, the command for distributed training on the same machine (with multiple GPUs) with Windows is:
`mpiexec -n <#workers> python VGG19_ImageNet_Distributed.py`