Introduction to Docker images

Valohai utilizes Docker images to define your runtime environment. This means that the platform is capable of running any code from C to Python as long as it can run inside a Docker container.

You can use any Docker image available online. After getting initial versions working, it makes sense to package your dependencies by building your own images.

We recommend hosting your images on Docker Hub as it’s the most straight forward but you can use any Docker registry. You can configure authenticated access under organization settings.

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/prophet                                                    # Valohai hosted image with Prophet
valohai/xgboost:1.4.2                                                # Valohai hosted image with xgboost
valohai/sklearn:1.0
valohai/sklearn:0.24.2

Which images to use depend on your specific use-case, but it usually makes sense to:

• start with as minimal image as possible

• use a specific image tag (the :<VERSION> part) so everything stays reproducible