A Python interface for running ML workloads on Kubernetes

Kubetorch enables you to run any Python code on Kubernetes at any scale by specifying required resources, distribution, and scaling directly in code. It provides caching and hot redeployment for 1-2 second iteration cycles, handles hardware faults and preemptions programmatically, and orchestrates complex, heterogeneous workloads with built-in observability and fault tolerance.

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!"

• 100x faster iteration from 10+ minutes to 1-3 seconds for complex ML applications like RL and distributed training
• 50%+ compute cost savings through intelligent resource allocation, bin-packing, and dynamic scaling
• 95% fewer production faults with built-in fault handling with programmatic error recovery and resource adjustment

pip install "kubetorch[client]"

# Option 1: Install directly from OCI registry
helm upgrade --install kubetorch oci://ghcr.io/run-house/charts/kubetorch \
  --version 0.2.2 -n kubetorch --create-namespace

# Option 2: Download chart locally first
helm pull oci://ghcr.io/run-house/charts/kubetorch --version 0.2.2 --untar
helm upgrade --install kubetorch ./kubetorch -n kubetorch --create-namespace

For detailed setup instructions, see our Installation Guide.

Contact us (email, Slack) to try out Kubetorch on our fully managed cloud platform.

• Documentation - API Reference, concepts, and guides
• Examples - Real-world usage patterns and tutorials
• Join our Slack - Connect with the community and get support

Apache 2.0 License

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