Home > Storage > PowerFlex > White Papers > Dell Validated Design for Virtual GPU with VMware and NVIDIA on PowerFlex > Test setup
Train the model using mixed precision or FP32 by performing the following steps to build the test environment:
cd DeepLearningExamples/TensorFlow/Classification/ConvNets
Use this link to download the images. A log in is required to download all these images.
mkdir train && mv ILSVRC2012_img_train.tar train/ && cd train
tar -xvf ILSVRC2012_img_train.tar && rm -f ILSVRC2012_img_train.tar
find . -name “*.tar” | while read NAME ; do mkdir -p “${NAME%.tar}”; tar -xvf “${NAME}” -C “${NAME%.tar}”; rm -f “${NAME}”; done
cd mkdir val && mv ILSVRC2012_img_val.tar val/ && cd val && tar -xvf ILSVRC2012_img_val.tar
docker build . -t nvidia_rn50
nvidia-docker run --rm -it -v /home/core/data/install --ipc=host nvidia_rn50
bash ./utils/dali_index.sh /data/install /data/install/index
Index files can be created, reused, and then stored in a persistent location.
After completing these steps, the test set up is ready. We can perform training and performance tests to validate against the ImageNet 1K repository.