kubetorch

📦Kubetorch🔥

A Fast, Pythonic, "Serverless" Interface for Running ML Workloads on Kubernetes

Kubetorch lets you programmatically build, iterate, and deploy ML applications on Kubernetes at any scale - directly from Python.

It brings your cluster's compute power into your local development environment, enabling extremely fast iteration (1-2 seconds). Logs, exceptions, and hardware faults are automatically propagated back to you in real-time.

Since Kubetorch has no local runtime or code serialization, you can access large-scale cluster compute from any Python environment - your IDE, notebooks, CI pipelines, or production code - just like you would use a local process pool.

Hello World

import kubetorch as kt

def hello_world():
    return "Hello from Kubetorch!"

if __name__ == "__main__":
    # Define your compute
    compute = kt.Compute(cpus=".1")

    # Send local function to freshly launched remote compute
    remote_hello = kt.fn(hello_world).to(compute)

    # Runs remotely on your Kubernetes cluster
    result = remote_hello()
    print(result)  # "Hello from Kubetorch!"