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docs: add Multi-AIPC PXE netboot deployment guide #111

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87 changes: 87 additions & 0 deletions source/_static/basic-example-pxe-architecture.svg
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1 change: 1 addition & 0 deletions source/index.rst
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Expand Up @@ -23,6 +23,7 @@ AUP Learning Cloud is a tailored JupyterHub deployment designed to provide an in
installation/single-node
installation/customizing-deployment
installation/multi-node
installation/multi-node/multi-aipc-hardware-deployment

.. toctree::
:maxdepth: 2
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16 changes: 14 additions & 2 deletions source/installation/multi-node.md
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@@ -1,6 +1,15 @@
# Multi-Node Cluster Deployment

This guide covers the current Ansible + Helm workflow for deploying AUP Learning Cloud on a multi-node K3s cluster.
This guide covers the product-supported Ansible + Helm workflow for deploying
AUP Learning Cloud on a multi-node K3s cluster. It is the software deployment
runbook for operators who already have the target machines, network, storage,
and image path planned.

::::{seealso}
For a concrete, runnable example that deploys this software stack across three
AIPCs using PXE netboot (diskless agents, NFS root, and automatic K3s join), see
[Basic Example Multi-AIPC PXE Netboot Deployment Guide](multi-node/multi-aipc-hardware-deployment.md).
::::

Unlike the single-node path, multi-node deployment is not driven by `./auplc-installer install`. The main flow is:

Expand Down Expand Up @@ -346,11 +355,13 @@ helm upgrade --install jupyterhub ./chart \

### High Availability Scope

This guide covers the base multi-node chart deployment. Choices such as:
This guide covers the base product deployment path with a single K3s
control-plane node. Choices such as:

- external database backends
- multiple Hub replicas
- dedicated load balancers
- multiple K3s control-plane nodes
- production TLS and certificate rotation

should be treated as explicit operator decisions layered on top of this base flow.
Expand Down Expand Up @@ -430,4 +441,5 @@ sudo ansible-playbook playbooks/pb-k3s-reset.yml --limit <node_name>

- The sample multi-node values file is a starting point, not a promise that every advanced topology is turnkey.
- The most important cluster-specific alignment is between real node labels and `custom.accelerators.*.nodeSelector`.
- This runbook focuses on AMD GPU-backed worker nodes. NPU enablement is not part of the current product deployment scope.
- If you want the simplest local install, use the single-node installer flow instead of this guide.
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