populate quickstart

Signed-off-by: Avi Deitcher <avi@deitcher.net>

Author: deitch

docs/quickstart/index.md

@@ -5,4 +5,141 @@ title: Quick Start
 sidebar_position: 1
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-Welcome to the quickstart guide.
+Welcome to the Clowder quickstart guide.
+
+## Requirements
+
+What do you need to run Clowder?
+
+Clowder runs on top of Kubernetes, so you will need a Kubernetes cluster. You can use any Kubernetes provider, such as:
+
+- [Official Kubernetes](https://kubernetes.io/)
+- [Minikube](https://minikube.sigs.k8s.io/docs/)
+- [Kind](https://kind.sigs.k8s.io/)
+- [K3s](https://k3s.io/)
+- [OpenShift](https://www.openshift.com/)
+
+As well as managed Kubernetes services, such as:
+
+- [Amazon EKS](https://aws.amazon.com/eks/)
+- [Google GKE](https://cloud.google.com/kubernetes-engine)
+- [Azure AKS](https://azure.microsoft.com/en-us/services/kubernetes-service/)
+
+Because Clowder runs inference on AI models, you will want hardware to run your model. That can be both a CPU and a GPU as well
+as dedicated inference processors.
+
+If you want to run on an [0xide sled](https://oxide.computer/), you will need to deploy a Kubernetes cluster to it.
+The good folks at Ainekko have created
+[a single-binary Kubernetes installer and controller for 0xide sleds](https://github.com/aifoundry-org/oxide-controller).
+
+## Deployment
+
+Once you have your Kubernetes cluster up and running, and credentials to access it,
+deploy Clowder to the Kubernetes cluster.
+
+You can deploy Clowder using either `helm` or `kubectl`. The recommended way is to use `helm`, but you can also use `kubectl` directly.
+
+### With helm
+
+The easiest way to install Clowder is using [helm](https://helm.sh/) package manager:
+
+```sh
+helm install clowder oci://docker.io/aifoundryorg/clowder
+```
+
+### With kubectl
+
+Otherwise you can use kubernetes cli directly. For this to work you should clone
+this repository first:
+
+```sh
+git clone https://github.com/clowder-dev/clowder.git
+cd clowder
+```
+
+Then deploy all the parts with a single command:
+
+```sh
+kubectl apply -f k8s/
+```
+
+## Local Access
+
+The Clowder API is exposed using a single Kubernetes `Service`. If you want it exposed to your local
+machine, use `kubectl port-forward`:
+
+```sh
+# Start this and leave running in a separate terminal window.
+kubectl port-forward svc/nekko-lb-svc 3090:3090
+```
+
+## Use it!
+
+For our examples, we will assumg you have forwarded the Clowder API to your local machine on port `3090`, per the above instructions.
+If you are running it elsewhere, you will need to adjust the API URL accordingly.
+
+Let's download a model and run inference on it. For access, we need the authentication token for the Clowder API.
+Unless configured otherwise, the default token is `nekko-admin-token`. Since we did a default quickstart installation, we can use this token to
+access the API.
+
+First, download the model. We will use the [SmolLM2-135M-Instruct](https://huggingface.co/unsloth/SmolLM2-135M-Instruct-GGUF) model from Hugging Face.
+
+Let's get a list of physical nodes to decide on which node to deploy our model with runtime:
+
+```sh
+kubectl get nodes
+NAME                                 STATUS   ROLES                       AGE   VERSION
+ainekko-control-plane-0-10ahb6ro     Ready    control-plane,etcd,master   85m   v1.32.4+k3s1
+ainekko-worker-1747986577-r7qql3ie   Ready    <none>                      85m   v1.32.4+k3s1
+```
+
+Mark that the worker node is called `ainekko-worker-1747986577-r7qql3ie`. We will use this node to deploy our model.
+In the future, you will be able to:
+
+* use the Clowder API to get a list of nodes
+* select a node based on its labels
+* select a node based on its capabilities, such as GPU or CPU
+* tell Clowder to automatically select a node for you
+
+For now, we will use the node name directly.
+
+```sh
+curl -H "Authorization: Bearer nekko-admin-token" \
+  -X POST \
+  --data '{"modelUrl": "hf:///unsloth//SmolLM2-135M-Instruct-GGUF/SmolLM2-135M-Instruct-Q4_K_M.gguf", "modelAlias": "smol", "nodeName": "ainekko-worker-1747986577-r7qql3ie", "credentials": "YOUR_HUGGING_FACE_TOKEN"}' \
+  -i \
+  http://localhost:3090/api/v1/workers
+```
+
+This downloads the model and starts a worker runtime pod, let's check:
+
+```sh
+curl -H "Authorization: Bearer nekko-admin-token" http://localhost:3090/api/v1/workers/list
+```
+
+This gives the result:
+
+```json
+{"count":1,"status":"success","workers":[{"name":"","model_name":"smol","model_alias":"smol"}]}
+```
+
+We can now use `http://localhost:3090/v1/chat_completions` as we would any
+OpenAI API compatible chat completions endpoint.
+
+### Web UI
+
+By default, Open WebUI client app is deployed on the cluster.
+
+Expose it locally with:
+
+```sh
+# Start this and leave it running in a separate terminal window.
+kubectl port-forward svc/open-webui 4080:4080
+```
+
+Now we can open the UI at [http://localhost:4080](http://localhost:4080), select the model
+and have a chat.
+
+
+
+