On-Prem VMs as Compute Targets

The [documentation](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-attach-compute-targets#vm) says that we can attach remote VMs such as _"an Azure VM, **a remote server in your organization, or on-premises**. Specifically, given the IP address and credentials (user name and password, or SSH key), you can use any accessible VM for remote runs."_

However, in both the studio as well as the provided code snippet, a resource ID is required.

```python
from azureml.core.compute import RemoteCompute, ComputeTarget

# Create the compute config
compute_target_name = "attach-dsvm"

attach_config = RemoteCompute.attach_configuration(resource_id='',
ssh_port=22,
username='',
password="")

# Attach the compute
compute = ComputeTarget.attach(ws, compute_target_name, attach_config)

compute.wait_for_completion(show_output=True)
```

As such, I was wondering if it's possible to do as the documentation suggests and attach our SSH-accessible servers to AML.