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.
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-namespaceFor detailed setup instructions, see our Installation Guide.
Contact us (email, Slack) to try out Kubetorch on our fully managed serverless platform.
- Documentation - API Reference, concepts, and guides
- Examples - Real-world usage patterns and tutorials
- Join our Slack - Connect with the community and get support
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