Update README.md

README.md

@@ -1,3 +1,71 @@
+<div align="center">
+
 # Nebula
 
-The control plane for GPUaaS.
+**The control plane for GPUaaS**
+
+[![Go Reference](https://pkg.go.dev/badge/github.com/InftyAI/Nebula.svg)](https://pkg.go.dev/github.com/InftyAI/Nebula)
+[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](LICENSE)
+![Go Version](https://img.shields.io/badge/go-1.24-00ADD8?logo=go&logoColor=white)
+
+Run GPU workloads on any NeoCloud or hyperscaler — AWS, Modal, and more — through one Kubernetes API.
+
+</div>
+
+Nebula turns external GPU capacity into ordinary Pods: ask for an accelerator, and
+it picks a provider, provisions the instance, and reclaims it when you're done — no
+per-cloud glue. The API follows a Karpenter-style split:
+
+- **NodePool** — policy: which providers are allowed, how to pick between them
+  (cost / availability), failover behaviour, and the GPU shape.
+- **NodeClaim** — one provisioned instance and its lifecycle, held by a finalizer
+  so a paid instance is never leaked.
+
+## How it works
+
+1. **Opt in.** You label a Pod to request an accelerator.
+2. **Gate.** A mutating webhook injects a scheduling gate (and a toleration for the
+   virtual node) at Pod CREATE, so the Pod sits `SchedulingGated`.
+3. **Place.** The placement controller picks a provider from the matching NodePool,
+   stamps its `nodeSelector`, and lifts the gate — or leaves the Pod gated if no
+   provider can serve it.
+4. **Provision.** The Pod binds to that provider's virtual node; a per-provider
+   virtual kubelet spins up the real instance and reports status (phase, endpoint)
+   back onto the Pod.
+5. **Reclaim.** A NodeClaim tracks the instance and guarantees teardown via a
+   finalizer — even if the Pod is force-deleted while the virtual kubelet is down.
+
+## Opting a workload in
+
+Three labels on the **Pod template** (not the Deployment metadata) are all it
+takes; Nebula fills in the rest:
+
+```yaml
+metadata:
+  labels:
+    nebula.inftyai.com/enabled: "true"          # opt in
+    nebula.inftyai.com/nodepool: aws            # which NodePool to place against
+    nebula.inftyai.com/accelerator-type: h100   # GPU type (case-insensitive)
+spec:
+  containers:
+  - name: workload
+    image: nvidia/cuda:12.4.1-base-ubuntu22.04
+    resources:
+      limits:
+        nvidia.com/gpu: "8"                     # GPU count
+```
+
+The accelerator **type** rides on the label and is matched case-insensitively
+against the provider catalog (`pkg/provider/catalog/data`); the **count** rides on
+the standard `nvidia.com/gpu` resource limit, so scheduling and provisioning read
+the same number. Do not set `nodeName` or a provider `nodeSelector` yourself — the
+placement controller owns those.
+
+> `kubectl logs`/`exec` do not work against a virtual node (it doesn't serve the
+> kubelet API); read workload output on the provider side.
+
+## Getting started
+
+See [docs/deploy.md](docs/deploy.md) to install and [config/samples](config/samples)
+for example NodePools and a runnable workload. Design details live in
+[docs/architecture.md](docs/architecture.md).

config/samples/deployment.yaml

@@ -37,8 +37,8 @@ spec:
       labels:
         app: gpu-workload-sample
         nebula.inftyai.com/enabled: "true"
-        nebula.inftyai.com/nodepool: aws
-        nebula.inftyai.com/accelerator-type: l4
+        nebula.inftyai.com/nodepool: neocloud
+        nebula.inftyai.com/accelerator-type: a100-40gb
     spec:
       # Do NOT set nodeName or a provider nodeSelector yourself — the placement
       # controller fills the nodeSelector in when it ungates the Pod. Setting