Your code will be run inside a Docker container based on the defined Docker image.

The Docker image should preferably contain all dependencies you need, to ensure your runs can get to work as quickly as possible.


You can run dependency installation commands as part of your command but it will result in slower computation time as then each execution starts by dependency setup, which is sub-optimal but nevertheless a good staring point.

You can find Docker images for the most popular machine learning libraries on Docker Hub.

You can also create and host your images on Docker Hub or any other Docker repository.

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

tensorflow/tensorflow:<VERSION>-gpu                                  # e.g. 2.6.1-gpu, for GPU support
tensorflow/tensorflow:<VERSION>                                      # e.g. 2.6.1, for CPU only
pytorchlightning/pytorch_lightning:latest-py<version>-torch<version> # e.g. py3.6-torch1.6
pytorch/pytorch:<VERSION>                                            # e.g. 1.3-cuda10.1-cudnn7-runtime
python:<VERSION>                                                     # e.g. 3.8.0
r-base:<VERSION>                                                     # e.g. 3.6.1
julia:<VERSION>                                                      # e.g. 1.3.0
valohai/fbprophet                                                    # Valohai hosted image with Prophet
valohai/sklearn:1.0                                                  # Valohai hosted image with sklean
valohai/xgboost:1.4.2                                                 # Valohai hosted image with xgboost