To create an experiment, first specify a few options such as cluster, resource, image, and start command. Here is an explanation of the config options.

Cluster & Resource (Required)

You can run your experiment on either VESSL’s managed cluster or your custom cluster. Start by selecting a cluster.

Once you selected VESSL’s managed cluster, you can view a list of available resources under the dropdown menu.

You also have an option to use spot instances.

Check out the full list of resource types and corresponding prices:

Distribution Mode (Optional)

You have an option to use multi-node distributed training. The default option is single-node training.

Image (Required)

Select the Docker image that the experiment container will use. You can either use a managed image provided by VESSL or your own custom image.

Managed images are pre-pulled images provided by VESSL. You can find the available image tags at VESSL’s Amazon ECR Public Gallery.

Start Command (Required)

Specify the start command in the experiment container. Write a running script with command-line arguments just as you are using a terminal. You can put multiple commands by using the && command or a new line separation.

Volume (Optional)

You can mount the project, dataset, and files to the experiment container.

Learn more about volume mount on the following page:

Hyperparameters

You can set hyperparameters as key-value pairs. The given hyperparameters are automatically added to the container as environment variables with the given key and value. A typical experiment will include hyperparameters like learning_rate and optimizer.

You can also use them at runtime by appending them to the start command as follows.

python main.py  \
  --learning-rate $learning_rate  \
  --optimizer $optimizer

Termination Protection

Checking the termination protection option puts experiments in idle once it completes running, so you to access the container of a finished experiment.