This Docker image is based on the latest tensorflow/tensorflow image with python and gpu support. The tensorflow/benchmarks repository is cloned and used as an entrypoint for the container. This allows some image classification models to be executed within the container with GPUs by passing the corresponding arguments to the docker run command.

For further informations please refer to official TensorFlow Guide.

To run ResNet50 with synthetic data and a single GPU use:

docker run --runtime=nvidia --rm cemizm/tf-benchmark-gpu --model resnet50 --num_gpus=1

Frequently used flags:

• model to use for benchmarks. Examples: alexnet, resnet50, resnet152, inception3, vgg16. default: trivial
• num_gpus number of gpus to use. default: all available gpus
• variable_update method for managing variables: parameter_server, replicated, distributed_replicated, independent. default: parameter_server
• batch_size for each GPU. default: 32

For a list of all available flags use:

docker run --runtime=nvidia --rm cemizm/tf-benchmark-gpu --help

Official TensorFlow results for P100 and K80 can be found in the TensorFlow Guide linked above. Here are some results of consumer hardware in workstation environments.