Cloud Quick Start

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Runhouse lets you quickly and easily deploy your Python code as production-grade applications on your own infra.

This tutorial demonstrates how to

  • Start a cloud VM with the Runhouse API server running on it

  • Send a locally defined function onto the VM to serve it as a service.

Installing Runhouse

The Runhouse base package can be installed with:

!pip install runhouse

To use Runhouse to launch on-demand clusters, please instead run the following command. This additionally installs SkyPilot, which is used for launching fresh VMs through your cloud provider.

!pip install "runhouse[sky]"

Local Python Function

First, let’s define the function that we want to be run on our remote compute. This is just a regular Python function; no decorators, wrappers, or configs are necessary.

def get_pid(a = 0): import os return os.getpid()

Runhouse Cluster

In Runhouse, a “cluster” is a unit of compute, somewhere you can send code, data, or requests to execute. We define a Runhouse cluster using the rh.cluster factory function.

This requires having access to a cluster or a cloud provider account. If you do not have access to a cluster, you can try the local version of this tutorial, which sets up and deploys the Python function to a local server, rather than a remote cluster.

To use a cluster that’s already running:

cluster = rh.cluster( name="rh-cluster", host="example-cluster", # hostname or ip address, ssh_creds={"ssh_user": "ubuntu", "ssh_private_key": "~/.ssh/id_rsa"}, # credentials for ssh-ing into the cluster )

If you do not have a cluster up, but have cloud credentials (e.g. aws, gcp, azure) for launching clusters, you can set up and launch an on-demand cluster with rh.ondemand_cluster. This uses SkyPilot under the hood, so run sky check on CLI first to set up the cloud credentials locally.

cluster = rh.ondemand_cluster( name="rh-cluster", instance_type="CPU:2+", provider="aws" ) cluster.up_if_not() # terminate this cluster with `cluster.teardown()` in Python, or `sky down rh-cluster` in CLI


For the function, simply wrap it in rh.function, then send it to the cluster with .to. This sets up the function on the cluster as a proper service, by syncing over the code and setting up and specified dependencies. Furthermore, it runs through SSH, and no additional auth, port, or manual setup is necessary.

Modules, or classes, are also supported. For finer control of where the function/module runs, you will also be able to specify the environment (a list of package requirements, a Conda env, or Runhouse env) where it runs. These are covered in more detail in the API tutorials.

remote_fn = rh.function(get_pid).to(cluster)
INFO | 2024-02-26 21:01:50.579156 | Writing out function to /Users/caroline/Documents/runhouse/notebooks/docs/ Please make sure the function does not rely on any local variables, including imports (which should be moved inside the function body).
INFO | 2024-02-26 21:01:50.584346 | Copying package from file:///Users/caroline/Documents/runhouse/notebooks to: rh-cluster
INFO | 2024-02-26 21:01:54.745264 | Calling base_env.install
Installing Package: notebooks with method reqs.
reqs path: notebooks/requirements.txt
notebooks/requirements.txt not found, skipping
INFO | 2024-02-26 21:01:56.116714 | Time to call base_env.install: 1.37 seconds
INFO | 2024-02-26 21:02:04.892297 | Sending module get_pid to rh-cluster

The function we defined above, get_pid, now exists remotely on the cluster, and can be called remotely using remote_fn. You can call this remote function just as you would any other Python function, with remote_fn(), and it runs on the cluster and returns the result to our local environment.

Below, we run both the local and remote versions of this function, which give different results and confirms that the functions are indeed being run on different processes.

print(f"Local PID {get_pid()}") print(f"Remote PID {remote_fn()}")
INFO | 2024-02-26 21:02:43.117612 | Calling
INFO | 2024-02-26 21:02:44.228964 | Time to call 1.11 seconds
Local PID 27818
Remote PID 33366

If you launched an on-demand cluster, you can terminate it by calling cluster.teardown().


Dive Deeper

What we just did, running a locally defined function on remote compute, is just the tip of the iceberg of what’s possible with Runhouse. With a large suite of even more abstractions and features, Runhouse lets you quickly and seamlessly integrate between local and remote environments.

To learn more, please take a look at our other tutorials, or at the API reference