ased key, create the service connector as follows:zenml service-connector register <SERVICE_CONNECTOR_NAME> --type=hyperai --auth-method=rsa-key --base64_ssh_key=<BASE64_SSH_KEY> --hostnames=<INSTANCE_1>,<INSTANCE_2>,..,<INSTANCE_N> --username=<INSTANCE_USERNAME>

Hostnames are either DNS resolvable names or IP addresses.

For example, if you have two servers - one at 1.2.3.4 and another at 4.3.2.1, you could provide them as --hostnames=1.2.3.4,4.3.2.1.

Optionally, it is possible to provide a passphrase for the key (--ssh_passphrase).

Following registering the service connector, we can register the orchestrator and use it in our active stack:

zenml orchestrator register <ORCHESTRATOR_NAME> --flavor=hyperai

# Register and activate a stack with the new orchestrator

zenml stack register <STACK_NAME> -o <ORCHESTRATOR_NAME> ... --set

You can now run any ZenML pipeline using the HyperAI orchestrator:

python file_that_runs_a_zenml_pipeline.py

Enabling CUDA for GPU-backed hardware

Note that if you wish to use this orchestrator to run steps on a GPU, you will need to follow the instructions on this page to ensure that it works. It requires adding some extra settings customization and is essential to enable CUDA for the GPU to give its full acceleration.

PreviousSkypilot VM Orchestrator

NextDevelop a custom orchestrator

Last updated 15 days ago