Docker Images

You can use any Docker images to package your dependencies. So Valohai platform is capable to run any kind of code from C to Python as long as it runs inside a predefined Docker container.

We recommend hosting your images on Docker Hub but you can use any Docker repository.

Here are the most common Docker images currently used on the platform:

  • tensorflow/tensorflow:1.8.0-devel
  • tensorflow/tensorflow:1.8.0-devel-py3
  • tensorflow/tensorflow:1.8.0-devel-gpu
  • tensorflow/tensorflow:1.8.0-devel-gpu-py3
  • tensorflow/tensorflow:1.7.0-devel-gpu-py3
  • tensorflow/tensorflow:1.7.0-devel-gpu
  • tensorflow/tensorflow:1.7.0-devel-py3
  • tensorflow/tensorflow:1.7.0-devel
  • tensorflow/tensorflow:1.6.0-devel-gpu-py3
  • tensorflow/tensorflow:1.6.0-devel-gpu
  • tensorflow/tensorflow:1.6.0-devel-py3
  • tensorflow/tensorflow:1.6.0-devel
  • valohai/keras:2.1.4-theano1.0.1-python3.6-cuda9.0-cudnn7-devel-ubuntu16.04
  • valohai/keras:2.1.3-tensorflow1.4.0-python3.5-cuda8.0-cudnn6-devel-ubuntu14.04
  • valohai/keras:2.0.0-tensorflow1.0.1-python3.6-cuda8.0-cudnn5-devel-ubuntu16.04
  • valohai/keras:2.0.0-theano0.9.0rc4-python3.6-cuda8.0-cudnn5-devel-ubuntu16.04
  • valohai/keras:2.0.0-theano0.8.2-python3.6-cuda8.0-cudnn5-devel-ubuntu16.04
  • valohai/darknet:62b781a-cuda8.0-cudnn5-devel-ubuntu16.04
  • valohai/darknet:b61bcf5-cuda8.0-cudnn5-devel-ubuntu16.04
  • r-base:3.4.2
  • python:3.6.5
  • python:3.6
  • python:3
  • python:2.7.14
  • python:2.7
  • python:2

Tip

Using these images will result in faster executions since they’re cached on workers.