To create an experiment, you first need to specify few options including cluster, resource, image, start command, volume, environment variables and termination protection.
By now you should have your own custom cluster added to Vessl. Here, you can choose between your custom cluster or Vessl's managed cluster. As noted previously, your custom cluster could be either on-premise or on-cloud. Vessl's managed cluster is on the cloud vendor server.
Vessl's Managed Cluster
If you wish to use Vessl's managed cluster, you should specify the type of the resource that the pod will use. Select the Resource under the dropdown menu.
For the Custom Cluster, you should specify the processor type and resource requirements. The experiment job will be automatically assigned to an available node according to the given resource requirements.
You can choose Docker image that the experiment container will use. There are two types of images: the Python image and the public image.
Python Images are pre-pulled images provided by Vessl. You can find the available image tags in Vessl's Amazon ECR Public Gallery. These images include the frequently used PyTorch and Tensorflow images. Some of them are pushed by Vessl. You can find detailed information in the Readme file in Docker Hub. Clicks PACKAGES to list and view the installed pip packages.