diff --git a/website/.gitignore b/website/.gitignore index 3502ef7..a0c58e0 100644 --- a/website/.gitignore +++ b/website/.gitignore @@ -142,3 +142,5 @@ dist .svelte-kit # End of https://www.toptal.com/developers/gitignore/api/node + +build/ diff --git a/website/AGENTS.md b/website/AGENTS.md index 2dc1f8d..6c7fcc9 100644 --- a/website/AGENTS.md +++ b/website/AGENTS.md @@ -4,7 +4,8 @@ This file defines rules for contributors and automation agents working in `websi ## 1. Structure & Metadata -- **Frontmatter**: All `.mdx` files must start with the following (wrap titles in single quotes): +- **File Extension**: All documentation files must use the `.mdx` extension. +- **Frontmatter**: All documentation files must start with the following (wrap titles in single quotes): ```mdx --- title: @@ -12,7 +13,7 @@ This file defines rules for contributors and automation agents working in `websi sidebar_position: <position> --- ``` -- **Categories**: Every subfolder under `docs/` containing docs must have a `_category_.yaml` with `label` and `position`. +- **Categories**: Every subfolder under `docs/` containing docs must have a `_category_.yaml` with `label`, `position`, and `collapsed: false`. - **Placement**: Place new docs in appropriate subfolders (e.g., `getting-started`, `reference`). Create a new folder with `_category_.yaml` only if necessary. - **Artifacts**: Never edit `versioned_docs/` or `versioned_sidebars/`. Only edit source files in `docs/`. diff --git a/website/css/custom.css b/website/css/custom.css index b8a0ebd..6ed1ea6 100644 --- a/website/css/custom.css +++ b/website/css/custom.css @@ -55,3 +55,34 @@ html.docs-doc-id-home .theme-doc-markdown article:has(a.card h2[title='Home']) { align-items: center; justify-content: center; } + +.prism-code { + counter-reset: line-number; + tab-size: 4; +} + +.prism-code .token-line::marker { + color: var(--ifm-color-gray-700); + content: counter(line-number); +} + +.prism-code .token-line { + counter-increment: line-number; + display: list-item; + padding-left: var(--ifm-pre-padding); + margin-left: var(--ifm-global-spacing); +} + +:root { + --docusaurus-highlighted-code-line-bg: rgb(240, 240, 180); +} + +[data-theme='dark'] { + --docusaurus-highlighted-code-line-bg: rgb(60, 60, 0); +} + +div.docusaurus-mermaid-container { + margin: 0 auto 20px; + display: flex; + justify-content: center; +} diff --git a/website/docs/getting-started/_category_.yaml b/website/docs/getting-started/_category_.yaml new file mode 100644 index 0000000..438717d --- /dev/null +++ b/website/docs/getting-started/_category_.yaml @@ -0,0 +1,3 @@ +position: 1 +label: Getting Started +collapsed: false diff --git a/website/docs/getting-started/prerequisites.mdx b/website/docs/getting-started/prerequisites.mdx new file mode 100644 index 0000000..609542f --- /dev/null +++ b/website/docs/getting-started/prerequisites.mdx @@ -0,0 +1,451 @@ +--- +title: 'Prerequisites' +sidebar_position: 1 +--- + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +This document introduces the prerequisites for the MoAI Inference Framework and provides instructions on how to install them. + +:::info +To follow this document, you need to understand the configuration of the Kubernetes cluster where the MoAI Inference Framework will be installed. Since Moreh provides support for installing the MoAI Inference Framework at customer sites, if you encounter any difficulties, you can request assistance from the Moreh team. +::: + +## Target system + +To install the MoAI Inference Framework, you must have + +* Kubernetes 1.29 or later +* At least one worker node equipped with accelerators supported by the MoAI Inference Framework (e.g., AMD GPUs) +* `cluster-admin` privilege for the Kubernetes cluster +* A StorageClass defined in the Kubernetes cluster (required for storing the monitoring metrics, model weights, etc.) +* A Docker private registry accessible from the Kubernetes cluster + +--- + +## cert-manager + +[cert-manager](https://cert-manager.io/) is a powerful and extensible X.509 certificate controller for Kubernetes workloads. It is essential for managing TLS certificates within the MoAI Inference Framework. + +Deploy `cert-manager` using the following command: + +```shell +helm upgrade -i cert-manager oci://quay.io/jetstack/charts/cert-manager \ + --version v1.18.4 \ + -n cert-manager \ + --create-namespace \ + --set crds.enabled=true +``` + +--- + +## moai-inference-framework + +The `moai-inference-framework` Helm chart deploys the dependencies required by the MoAI Inference Framework, excluding GPU and network-related components. To deploy it, you first need to add Moreh's Helm chart repository. + +```shell +helm repo add moreh https://moreh-dev.github.io/helm-charts +``` + +If you have already added the repository, make sure to update it. + +```shell +helm repo update moreh +``` + +The container images for the MoAI Inference Framework are distributed through a private repository on [Amazon ECR](https://aws.amazon.com/ecr/) `255250787067.dkr.ecr.ap-northeast-2.amazonaws.com`, and you need to obtain an authorization token to download them. To facilitate this, the `moai-inference-framework` chart defines the installation of an ECR token refresher. You need to specify your AWS credentials in the values file to configure the ECR token refresher. + +:::warning +**The AWS credentials (`AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY`) should have been provided to you along with your purchase or trial issuance of the MoAI Inference Framework**. If you did not receive this information, please contact your point of purchase separately. +::: + +Create a `moai-inference-framework-values.yaml` file as follows. **You need to replace `<AWS_ACCESS_KEY_ID>` and `<AWS_SECRET_ACCESS_KEY>` with your own values**. + +```yaml title="moai-inference-framework-values.yaml" {3-4} +ecrTokenRefresher: + aws: + accessKeyId: <AWS_ACCESS_KEY_ID> + secretAccessKey: <AWS_SECRET_ACCESS_KEY> +``` + +In addition, if dependencies such as keda, kube-prometheus-stack, lws are already installed in your cluster, you should skip their installation by setting the corresponding values to `false` in the `moai-inference-framework-values.yaml` file. Refer to [moai-inference-framework README](https://github.com/moreh-dev/mif/tree/main/deploy/helm/moai-inference-framework) to see the full list of dependencies. + +Then, deploy the `moai-inference-framework` chart using the following command: + +```shell +helm upgrade -i mif moreh/moai-inference-framework \ + --version v0.3.0 \ + -n mif \ + --create-namespace \ + -f moai-inference-framework-values.yaml +``` + +--- + +## moai-inference-preset + +The `moai-inference-preset` Helm chart deploys the presets for the MoAI Inference Framework. The presets define preconfigured ways to run inference containers (e.g., Moreh vLLM containers) in the MoAI Inference Framework. + +```shell +helm upgrade -i moai-inference-preset moreh/moai-inference-preset \ + --version v0.3.0 \ + -n mif +``` + +--- + +## AMD GPU operator + +This section describes how to set up the AMD GPU Operator on a Kubernetes cluster. See [AMD GPU Operator / Kubernetes (Helm)](https://instinct.docs.amd.com/projects/gpu-operator/en/release-v1.4.0/installation/kubernetes-helm.html) for more details. + +Add the ROCm's GPU Operator Helm chart repository. + +```shell +helm repo add rocm https://rocm.github.io/gpu-operator +helm repo update rocm +``` + +Create a namespace for the AMD GPU Operator. + +```shell +kubectl create namespace amd-gpu +``` + +During the installation of the AMD GPU Operator, the GPU driver image needs to be built and pushed to a Docker registry. For more details, see [AMD GPU Operator / Preparing Pre-compiled Driver Images](https://instinct.docs.amd.com/projects/gpu-operator/en/release-v1.4.0/drivers/precompiled-driver.html). The private registry mentioned earlier in the "target system" section can be used for this purpose. + +Create a Docker registry secret in the `amd-gpu` namespace to enable access to the private registry. **Set the `<registry>`, `<username>`, and `<password>` values to the information for your private registry**. + +```shell {3-5} +kubectl create secret -n amd-gpu \ + docker-registry private-registry \ + --docker-server=<registry> \ + --docker-username=<username> \ + --docker-password=<password> +``` + +Then, create a `gpu-operator-values.yaml` file with the following content. **Please replace `<registry>` on line 7 with the URL of your private registry**. You may also change the image name `amdgpu-driver`, if necessary, according to your private registry's policies. + +```yaml title="gpu-operator-values.yaml" {7} +deviceConfig: + spec: + driver: + enable: true + version: "6.4.3" + blacklist: true + image: "<registry>/amdgpu-driver" + imageRegistrySecret: + name: private-registry + imageRegistryTLS: + insecure: false + insecureSkipTLSVerify: false + tolerations: &tolerations + - key: amd.com/gpu + operator: Exists + effect: NoSchedule + devicePlugin: + devicePluginTolerations: *tolerations + metricsExporter: + prometheus: + serviceMonitor: + enable: true + interval: 10s + labels: + release: mif + tolerations: *tolerations + +node-feature-discovery: + worker: + tolerations: *tolerations +``` + +:::info +If the AMD GPU Operator is already installed on your system, verify that the toleration key is set to `amd.com/gpu`. MoAI Inference Framework assumes this name. +::: + +You can install the AMD GPU Operator as follows. + +```shell +helm upgrade -i gpu-operator rocm/gpu-operator-charts \ + --version v1.4.0 \ + -n amd-gpu \ + -f gpu-operator-values.yaml +``` + +Note that installing the operator and GPU driver may take some time. After the installation is complete, you can verify that the gpu-operator pods are running using the following command. + +```shell +kubectl get pods -n amd-gpu +``` + +Expected output: + +``` +NAME READY STATUS RESTARTS AGE +default-device-plugin-fxj66 1/1 Running 0 108s +default-metrics-exporter-r2l6h 1/1 Running 0 108s +default-node-labeller-qhqdl 1/1 Running 0 2m35s +gpu-operator-gpu-operator-charts-controller-manager-69856dhd67k 1/1 Running 0 4m20s +gpu-operator-kmm-controller-7b5dd7b48b-fpcv6 1/1 Running 0 4m20s +gpu-operator-kmm-webhook-server-c7bfc864-tfqdb 1/1 Running 0 4m20s +gpu-operator-node-feature-discovery-gc-7649c47d5d-55rcn 1/1 Running 0 4m20s +gpu-operator-node-feature-discovery-master-fc889959c-sx7wv 1/1 Running 0 4m20s +gpu-operator-node-feature-discovery-worker-4tnns 1/1 Running 0 4m20s +``` + +:::info +You can monitor the installation progress using the `kubectl get pods -n amd-gpu -w` command instead. +::: + +--- + +## RDMA device plugin + +### Host driver and OFED installation + +You need to install the device drivers and OFED software for InfiniBand or RoCE NICs on the **host OS**. Follow the instructions provided by your hardware vendor. + +**This must be completed before joining the node to the Kubernetes cluster**. By running the following command on the host OS, you can verify that the OFED software has been installed correctly and that it recognizes the NICs. If no devices are shown, there is an issue with the installation. + +```shell +ibv_devices +``` + +Expected output: + +``` +device node GUID +<device_name> <16-hex GUID> +<device_name> <16-hex GUID> +... +``` + +### RDMA device plugin installation + +This section describes how to install the **rdma-shared-device-plugin**. See [k8s-rdma-shared-dev-plugin / README](https://github.com/Mellanox/k8s-rdma-shared-dev-plugin/blob/master/README.md) for more details. + +First, create a `rdma-shared-device-plugin.yaml` file as follows. **You need to replace `<device>` on line 21 with your RDMA NIC's network interface name**. If multiple NICs are installed on the server, you must list all interface names (e.g., `"devices": ["ib0", "ib1"]`). + +:::info +You can check the network interface names using the `ip addr` command. +::: + +```yaml title="rdma-shared-device-plugin.yaml" {21} +--- +apiVersion: v1 +kind: ConfigMap +metadata: + name: rdma-devices + namespace: kube-system + labels: + app.kubernetes.io/name: rdma-shared-device-plugin + app.kubernetes.io/version: v1.5.2 + app.kubernetes.io/instance: rdma-shared-device-plugin +data: + config.json: | + { + "periodicUpdateInterval": 300, + "configList": [ + { + "resourcePrefix": "mellanox", + "resourceName": "hca", + "rdmaHcaMax": 1000, + "devices": [ + "<device>" + ] + } + ] + } + +--- +apiVersion: apps/v1 +kind: DaemonSet +metadata: + name: rdma-shared-device-plugin + namespace: kube-system + labels: + app.kubernetes.io/name: rdma-shared-device-plugin + app.kubernetes.io/version: v1.5.2 + app.kubernetes.io/instance: rdma-shared-device-plugin +spec: + selector: + matchLabels: + app.kubernetes.io/name: rdma-shared-device-plugin + app.kubernetes.io/instance: rdma-shared-device-plugin + updateStrategy: + rollingUpdate: + maxUnavailable: "30%" + template: + metadata: + labels: + app.kubernetes.io/name: rdma-shared-device-plugin + app.kubernetes.io/version: v1.5.2 + app.kubernetes.io/instance: rdma-shared-device-plugin + spec: + hostNetwork: true + priorityClassName: system-node-critical + tolerations: + - key: amd.com/gpu + operator: Exists + effect: NoSchedule + containers: + - name: device-plugin + image: ghcr.io/mellanox/k8s-rdma-shared-dev-plugin:v1.5.2 + imagePullPolicy: IfNotPresent + securityContext: + privileged: true + volumeMounts: + - name: device-plugin + mountPath: /var/lib/kubelet/device-plugins + - name: plugins-registry + mountPath: /var/lib/kubelet/plugins_registry + - name: config + mountPath: /k8s-rdma-shared-dev-plugin + - name: devs + mountPath: /dev/ + volumes: + - name: device-plugin + hostPath: + path: /var/lib/kubelet/device-plugins + - name: plugins-registry + hostPath: + path: /var/lib/kubelet/plugins_registry + - name: config + configMap: + name: rdma-devices + items: + - key: config.json + path: config.json + - name: devs + hostPath: + path: /dev/ +``` + +:::info +If the RDMA device plugin is already installed on your system, verify that the resource name is set to `mellanox/hca`. MoAI Inference Framework assumes this name. This does not imply that the actual hardware vendor must be Mellanox. +::: + +Then, create an rdma-shared-device-plugin DaemonSet using the following command. + +```shell +kubectl apply -f rdma-shared-device-plugin.yaml +``` + +You can verify that the rdma-shared-device-plugin pods are running using the following command. + +```shell +kubectl get pods -n kube-system -l app.kubernetes.io/instance=rdma-shared-device-plugin +``` + +Expected output: + +``` +NAME READY STATUS RESTARTS AGE +rdma-shared-device-plugin-wh9fz 1/1 Running 0 7s +``` + +--- + +## Node labeling for heterogeneous accelerators + +The `moai-accelerator` NodeFeatureRule enables the identification of heterogeneous accelerators by assigning vendor and model labels to nodes. This metadata facilitates targeted scheduling or automated selection of optimal accelerator. For a full list of supported hardware, please refer to [supported devices](../reference/supported-devices.mdx). + +Apply the `moai-accelerator` NodeFeatureRule using the following command. + +```shell +kubectl apply -f https://raw.githubusercontent.com/moreh-dev/mif/refs/heads/main/config/nfd/moai-accelerator.yaml +``` + +--- + +## Gateway + +Add the Gateway API and Gateway API Inference Extension CRDs. + +```shell +kubectl apply --server-side -f https://github.com/kubernetes-sigs/gateway-api/releases/download/v1.3.0/standard-install.yaml +kubectl apply -f https://github.com/kubernetes-sigs/gateway-api-inference-extension/releases/download/v1.1.0/manifests.yaml +``` + +You can verify the CRDs are installed using the following command. + +```shell +kubectl get crd | grep 'networking.k8s*.io' +``` + +Expected output: + +``` +gatewayclasses.gateway.networking.k8s.io 2025-12-12T02:03:07Z +gateways.gateway.networking.k8s.io 2025-12-12T02:03:07Z +grpcroutes.gateway.networking.k8s.io 2025-12-12T02:03:07Z +httproutes.gateway.networking.k8s.io 2025-12-12T02:03:07Z +inferencepools.inference.networking.k8s.io 2025-12-12T02:03:08Z +referencegrants.gateway.networking.k8s.io 2025-12-12T02:03:07Z +``` + +You can use any gateway controller compatible with the Gateway API Inference Extension. using either **Istio** or **Kgateway** is recommended, and installation instructions for both are provided below. + +<Tabs groupId="gateway-class"> +<TabItem value="istio" label="Istio (Default)" default> + +Add the Istio Helm chart repository. + +```shell +helm repo add istio https://istio-release.storage.googleapis.com/charts +helm repo update istio +``` + +Install the Istio base chart. + +```shell +helm upgrade -i istio-base istio/base \ + --version 1.28.3 \ + -n istio-system \ + --create-namespace +``` + +Create a `istiod-values.yaml` file and install the Istio control plane. + +```yaml title="istiod-values.yaml" +pilot: + env: + PILOT_ENABLE_ALPHA_GATEWAY_API: "true" + ENABLE_GATEWAY_API_INFERENCE_EXTENSION: "true" +``` + +```shell +helm upgrade -i istiod istio/istiod \ + --version 1.28.3 \ + -n istio-system \ + -f istiod-values.yaml +``` + +</TabItem> +<TabItem value="kgateway" label="Kgateway"> + +Install the Kgateway CRDs. + +```shell +helm upgrade -i kgateway-crds oci://cr.kgateway.dev/kgateway-dev/charts/kgateway-crds \ + --version v2.1.1 \ + -n kgateway-system \ + --create-namespace +``` + +Create a `kgateway-values.yaml` file and install the Kgateway controller. + +```yaml title="kgateway-values.yaml" +inferenceExtension: + enabled: true +``` + +```shell +helm upgrade -i kgateway oci://cr.kgateway.dev/kgateway-dev/charts/kgateway \ + --version v2.1.1 \ + -n kgateway-system \ + -f kgateway-values.yaml +``` + +</TabItem> +</Tabs> diff --git a/website/docs/getting-started/quickstart.mdx b/website/docs/getting-started/quickstart.mdx new file mode 100644 index 0000000..9be9e02 --- /dev/null +++ b/website/docs/getting-started/quickstart.mdx @@ -0,0 +1,463 @@ +--- +title: 'Quickstart' +sidebar_position: 2 +--- + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +This quickstart launches two vLLM instances (pods) of the Llama 3.2 1B Instruct model and serves them through a single endpoint as an example. Please make sure to install all [prerequisites](./prerequisites.mdx), including the following versions of the components, before starting this quickstart guide. + +- **moai-inference-framework** v0.2.0 +- **moai-inference-preset** v0.2.0 + +## Required tools + +To follow this quickstart, you need to install a few tools on the client machine. + +### kubectl + +This section describes how to install **kubectl**. See [Kubernetes / Install and Set Up kubectl on Linux](https://kubernetes.io/docs/tasks/tools/install-kubectl-linux/) for more details. + +You can install kubectl binary with curl on Linux as follows. **Please replace `<kubernetesVersion>` and `<kubeconfigPath>` with your desired Kubernetes version and the path to your kubeconfig file, respectively**. If you did not set up the target cluster yourself, please request the relevant information from your administrator. + +```shell {1,4} +KUBECTL_VERSION=<kubernetesVersion> +curl -LO https://dl.k8s.io/release/${KUBECTL_VERSION}/bin/linux/amd64/kubectl +sudo install -o root -g root -m 0755 kubectl /usr/local/bin/kubectl +export KUBECONFIG=<kubeconfigPath> +``` + +You can verify the installation by running the following command. Note that the printed version may vary depending on your cluster version. + +```shell +kubectl version +``` + +Expected output: + +``` +Client Version: v1.32.9 +Kustomize Version: v5.5.0 +Server Version: v1.32.8 +``` + +### Helm + +You can install **Helm** by running the following command. See [Helm / Installing Helm](https://helm.sh/docs/intro/install/) for more details. + +```shell +curl https://raw.githubusercontent.com/helm/helm/main/scripts/get-helm-3 | bash +``` + +You can verify the installation by running the following command. Note that the printed version may vary depending on the Helm version installed. + +```shell +helm version +``` + +Expected output: + +``` +version.BuildInfo{Version:"v3.19.0", GitCommit:"3d8990f0836691f0229297773f3524598f46bda6", GitTreeState:"clean", GoVersion:"go1.24.7"} +``` + +### jq + +You need to install **jq** to format JSON responses from the inference endpoint. See [Download jq](https://jqlang.github.io/jq/download/) for more details. + +On Ubuntu or Debian, you can install jq as follows. + +```shell +sudo apt-get update && sudo apt-get install -y jq +``` + +You can verify the installation by running the following command. Note that the printed version may vary depending on the version installed. + +```shell +jq --version +``` + +Expected output: + +``` +jq-1.7.1 +``` + +--- + +## Deployment + +Deploy at least three components to completely run the MoAI Inference Framework on the cluster. + +- **Gateway**: A component that receives all requests through a single endpoint and distributes them to one of the vLLM pods. +- **"Heimdall" scheduler**: A component that determines the rules the Gateway uses to select the proper destination for each request. +- **"Odin" inference service**: A collection of vLLM pods running across different GPUs/servers. + +```mermaid +graph TB + clients@{ shape: procs, label: "Clients" } --> gateway[Gateway] + gateway <--> heimdall[Heimdall] + subgraph Odin + vllm1[vLLM 1] + vllm2[vLLM 2] + end + gateway --> vllm1[vLLM 1] + gateway --> vllm2[vLLM 2] +``` + +### Kubernetes namespace + +You need to have a namespace for deploying and running the components of the MoAI Inference Framework. This guide assumes the namespace is named `quickstart`. + +```shell +kubectl create namespace quickstart +kubectl label namespace quickstart mif=enabled +``` + +### Gateway + +Gateway is not a component provided by the MoAI Inference Framework. You may use any gateway controller that is compatible with the [Gateway API Inference Extension](https://gateway-api-inference-extension.sigs.k8s.io/) in Kubernetes, though using **Istio** or **Kgateway** is recommended. + +Create a `gateway.yaml` file to add the Gateway resource to the `quickstart` namespace. The contents of `gateway.yaml` vary depending on which gateway controller you use, and this guide provides instructions for both Istio and Kgateway. If you did not set up the target Kubernetes cluster yourself, ask your administrator to check which gateway controller is currently installed and available. The inference endpoint will be exposed on port 80 as specified in the `gateway.yaml` file. + +<Tabs groupId="gateway-class"> +<TabItem value="istio" label="Istio (Default)" default> + +```yaml title="gateway.yaml" +apiVersion: v1 +kind: ConfigMap +metadata: + name: mif-gateway-infrastructure +data: + service: | + spec: + type: ClusterIP + ports: + - name: http + port: 80 + targetPort: http + + deployment: | + spec: + template: + metadata: + annotations: + proxy.istio.io/config: | + accessLogFile: /dev/stdout + accessLogEncoding: JSON + spec: + containers: + - name: istio-proxy + resources: + limits: null + ports: + - name: http + containerPort: 80 + +--- +apiVersion: gateway.networking.k8s.io/v1 +kind: Gateway +metadata: + name: mif +spec: + gatewayClassName: istio + infrastructure: + parametersRef: + group: '' + kind: ConfigMap + name: mif-gateway-infrastructure + listeners: + - name: http + protocol: HTTP + port: 80 + allowedRoutes: + namespaces: + from: All +``` + +</TabItem> +<TabItem value="kgateway" label="Kgateway"> + +```yaml title="gateway.yaml" +apiVersion: gateway.kgateway.dev/v1alpha1 +kind: GatewayParameters +metadata: + name: mif-gateway-infrastructure +spec: + kube: + service: + type: ClusterIP + +--- +apiVersion: gateway.networking.k8s.io/v1 +kind: Gateway +metadata: + name: mif +spec: + gatewayClassName: kgateway + infrastructure: + parametersRef: + group: gateway.kgateway.dev + kind: GatewayParameters + name: mif-gateway-infrastructure + listeners: + - name: http + protocol: HTTP + port: 80 + allowedRoutes: + namespaces: + from: All +``` + +</TabItem> +</Tabs> + +Run the following command. + +```shell +kubectl apply -n quickstart -f gateway.yaml +``` + +You can verify that the Gateway resource is created using the following command. + +```shell +kubectl get pod -n quickstart -l gateway.networking.k8s.io/gateway-name=mif +``` + +Expected output: + +``` +NAME READY STATUS RESTARTS AGE +mif-istio-78789865b7-747nz 1/1 Running 0 27s +``` + +### Heimdall + +To deploy Heimdall and Odin, you must first add the Moreh Helm chart repository. + +```shell +helm repo add moreh https://moreh-dev.github.io/helm-charts +helm repo update moreh +``` + +This quickstart uses a simple scheduling rule that selects the vLLM pod with fewer queued requests between the two pods. Create a `heimdall-values.yaml` file as shown below and deploy the Heimdall scheduler using this file. **Note that you need to set `gatewayClassName` on line 20 to `kgateway` if you are using Kgateway as the gateway controller**. + +```yaml title="heimdall-values.yaml" +global: + imagePullSecrets: + - name: moreh-registry + +config: + apiVersion: inference.networking.x-k8s.io/v1alpha1 + kind: EndpointPickerConfig + plugins: + - type: single-profile-handler + - type: queue-scorer + - type: max-score-picker + schedulingProfiles: + - name: default + plugins: + - pluginRef: queue-scorer + - pluginRef: max-score-picker + +gateway: + name: mif + gatewayClassName: istio + +inferencePool: + targetPorts: + - number: 8000 +``` + +```shell +helm upgrade -i heimdall moreh/heimdall \ + --version v0.6.1 \ + -n quickstart \ + -f heimdall-values.yaml +``` + +You can verify that the Heimdall pod is running and the related objects (ReplicaSet, Deployment and Service) are created as follows. + +```shell +kubectl get all -n quickstart -l app.kubernetes.io/instance=heimdall +``` + +Expected output: + +``` +NAME READY STATUS RESTARTS AGE +pod/heimdall-7d54fcbfff-chw94 1/1 Running 0 70s + +NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE +service/heimdall ClusterIP 10.110.35.57 <none> 9002/TCP,9090/TCP,5557/TCP 70s + +NAME READY UP-TO-DATE AVAILABLE AGE +deployment.apps/heimdall 1/1 1 1 70s + +NAME DESIRED CURRENT READY AGE +replicaset.apps/heimdall-7d54fcbfff 1 1 1 70s +``` + +### Odin + +This quickstart launches two vLLM instances using the `vllm-meta-llama-llama-3.2-1b-instruct-amd-mi250-tp2` preset, which includes the model name and model-specific arguments. Each pod utilizes two GPU devices to run the Llama 3.2 1B Instruct model with Tensor Parallelism (TP) set to 2. + +:::info +For more information on presets, please refer to the [Odin Preset](/features/preset) +::: + +To enable the vLLM pods to download model parameters from Hugging Face, you must generate a Hugging Face token at [Hugging Face / Access Tokens](https://huggingface.co/settings/tokens). In addition, you need to accept the model license at [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct). + +:::info +In production environments, it is common to download the model parameters to a storage volume in advance and load them at runtime. Refer to the [Hugging Face model management with persistent volume](/best_practices/hf_model_management_with_pv) for more details. +::: + +Create a `vllm-llama3-1b-instruct-tp2.yaml` file with the following contents. **Please replace `<huggingFaceToken>` on line 20 with your Hugging Face token that has accepted the model license**. + +```yaml title="vllm-llama3-1b-instruct-tp2.yaml" {20} +apiVersion: odin.moreh.io/v1alpha1 +kind: InferenceService +metadata: + name: vllm-llama3-1b-instruct-tp2 +spec: + replicas: 2 + inferencePoolRefs: + - name: heimdall + templateRefs: + - name: vllm + - name: vllm-meta-llama-llama-3.2-1b-instruct-amd-mi250-tp2 + parallelism: + tensor: 2 + template: + spec: + containers: + - name: main + env: + - name: HF_TOKEN + value: <huggingFaceToken> +``` + +- `replicas` on line 6 specifies the number of vLLM pods. +- `inferencePoolRefs` on line 7-8 specifies the Heimdall's InferencePool where this vLLM pod will register to. +- `templateRefs` on line 9-11 specifies the Odin Template resources; `vllm` is a runtime base, and `vllm-meta-llama-llama-3.2-1b-instruct-amd-mi250-tp2` is a model-specific template. + +After that, you can deploy the Odin InferenceService by running the following command: + +```shell +kubectl apply -n quickstart -f vllm-llama3-1b-instruct-tp2.yaml +``` + +You can wait for the Odin InferenceService to be ready as follows: + +```shell +kubectl wait inferenceservice -n quickstart vllm-llama3-1b-instruct-tp2 \ + --for=condition=Ready \ + --timeout=15m +``` + +You can verify that the vLLM pods are running and the related objects (ReplicaSet and Deployment) are created as follows: + +```shell +kubectl get all -n quickstart -l app.kubernetes.io/name=vllm-llama3-1b-instruct-tp2 +``` + +Expected output: + +``` +NAME READY STATUS RESTARTS AGE +pod/vllm-llama3-1b-instruct-tp2-64d68f6466-klzll 1/1 Running 0 2m54s +pod/vllm-llama3-1b-instruct-tp2-64d68f6466-nzr89 1/1 Running 0 2m54s + +NAME READY UP-TO-DATE AVAILABLE AGE +deployment.apps/vllm-llama3-1b-instruct-tp2 2/2 2 2 2m54s + +NAME DESIRED CURRENT READY AGE +replicaset.apps/vllm-llama3-1b-instruct-tp2-64d68f6466 2 2 2 2m54s +``` + +--- + +## Usage + +You can set up port forwarding as follows to send API requests to the inference endpoint from your local machine. This forwards port 80 of the ingress gateway to port 8000 of the local machine (`localhost`). + +```shell +SERVICE=$(kubectl -n quickstart get service -l gateway.networking.k8s.io/gateway-name=mif -o name) +kubectl -n quickstart port-forward $SERVICE 8000:80 +``` + +Then, you can send a request to the inference endpoint as follows. Note that `jq` is used only to format the JSON response for better readability and is not required for the request to function. + +```shell +curl http://localhost:8000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "meta-llama/Llama-3.2-1B-Instruct", + "messages": [ + { + "role": "developer", + "content": "You are a helpful assistant." + }, + { + "role": "user", + "content": "Hello!" + } + ] + }' | jq '.' +``` + +Response: + +```json +{ + "id": "chatcmpl-5613ccb4-d168-40df-a5b7-842ab4a00d6a", + "object": "chat.completion", + "created": 1761484035, + "model": "meta-llama/Llama-3.2-1B-Instruct", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": "Hello! How can I assist you today? Do you have a specific question or problem you'd like to talk about, or are you just looking for some information on a particular topic?", + "refusal": null, + "annotations": null, + "audio": null, + "function_call": null, + "tool_calls": [], + "reasoning_content": null + }, + "logprobs": null, + "finish_reason": "stop", + "stop_reason": null, + "token_ids": null + } + ], + "service_tier": null, + "system_fingerprint": null, + "usage": { + "prompt_tokens": 48, + "total_tokens": 86, + "completion_tokens": 38, + "prompt_tokens_details": null + }, + "prompt_logprobs": null, + "prompt_token_ids": null, + "kv_transfer_params": null +} +``` + +You can also run various benchmarking tools on this inference endpoint to evaluate the performance. However, for reliable performance evaluation, your client and the Kubernetes cluster must be connected through a sufficiently fast and stable network (e.g., the same local network). + +--- + +## Cleanup + +To delete all the resources created in this quickstart, run the following commands. + +```shell +kubectl delete -n quickstart -f vllm-llama3-1b-instruct-tp2.yaml +helm uninstall -n quickstart heimdall +kubectl delete -n quickstart -f gateway.yaml +``` diff --git a/website/docs/reference/_category_.yaml b/website/docs/reference/_category_.yaml index 99d89dd..192b930 100644 --- a/website/docs/reference/_category_.yaml +++ b/website/docs/reference/_category_.yaml @@ -1,2 +1,3 @@ position: 999 label: Reference +collapsed: false diff --git a/website/docs/reference/heimdall/_category_.yaml b/website/docs/reference/heimdall/_category_.yaml index 23fa43c..a1e64de 100644 --- a/website/docs/reference/heimdall/_category_.yaml +++ b/website/docs/reference/heimdall/_category_.yaml @@ -1,2 +1,3 @@ position: 1 label: Heimdall +collapsed: false diff --git a/website/docs/reference/odin/_category_.yaml b/website/docs/reference/odin/_category_.yaml index 9cedca8..3ab173f 100644 --- a/website/docs/reference/odin/_category_.yaml +++ b/website/docs/reference/odin/_category_.yaml @@ -1,2 +1,3 @@ -position: 999 +position: 2 label: Odin +collapsed: false diff --git a/website/docs/reference/supported-devices.mdx b/website/docs/reference/supported-devices.mdx new file mode 100644 index 0000000..f5ee36e --- /dev/null +++ b/website/docs/reference/supported-devices.mdx @@ -0,0 +1,65 @@ +--- +title: 'Supported devices' +sidebar_position: 3 +--- + +## Accelerator labels + +The MoAI Inference Framework provides features to efficiently utilize clusters with heterogeneous accelerators. + +Each accelerator is identified by labels prefixed with `moai.moreh.io`. Users can specificy the accelerator on which a model should run, or allow the framework to automatically select the optimal accelerator. Below is the list of accelerator labels supported by the MoAI Inference Framework. + +| Label | Accelerator | +| :----------------------------------------------------------------------------------------------- | :------------------------------ | +| `moai.moreh.io/accelerator.vendor: amd`<br/>`moai.moreh.io/accelerator.model: mi355x` | AMD Instinct MI355X GPU | +| `moai.moreh.io/accelerator.vendor: amd`<br/>`moai.moreh.io/accelerator.model: mi350x` | AMD Instinct MI350X GPU | +| `moai.moreh.io/accelerator.vendor: amd`<br/>`moai.moreh.io/accelerator.model: mi325x` | AMD Instinct MI325X GPU | +| `moai.moreh.io/accelerator.vendor: amd`<br/>`moai.moreh.io/accelerator.model: mi308x` | AMD Instinct MI308X GPU | +| `moai.moreh.io/accelerator.vendor: amd`<br/>`moai.moreh.io/accelerator.model: mi300x` | AMD Instinct MI300X GPU | +| `moai.moreh.io/accelerator.vendor: amd`<br/>`moai.moreh.io/accelerator.model: mi300x-hf` | AMD Instinct MI300X HF GPU | +| `moai.moreh.io/accelerator.vendor: amd`<br/>`moai.moreh.io/accelerator.model: mi300a` | AMD Instinct MI300A APU | +| `moai.moreh.io/accelerator.vendor: amd`<br/>`moai.moreh.io/accelerator.model: mi250x` | Half of AMD Instinct MI250X GPU | +| `moai.moreh.io/accelerator.vendor: amd`<br/>`moai.moreh.io/accelerator.model: mi250` | Half of AMD Instinct MI250 GPU | +| `moai.moreh.io/accelerator.vendor: amd`<br/>`moai.moreh.io/accelerator.model: mi210` | AMD Instinct MI210 GPU | +| `moai.moreh.io/accelerator.vendor: amd`<br/>`moai.moreh.io/accelerator.model: mi100` | AMD Instinct MI100 GPU | +| `moai.moreh.io/accelerator.vendor: nvidia`<br/>`moai.moreh.io/accelerator.model: h100` | NVIDIA H100 SXM GPU | +| `moai.moreh.io/accelerator.vendor: nvidia`<br/>`moai.moreh.io/accelerator.model: h100-pcie` | NVIDIA H100 PCIe GPU | +| `moai.moreh.io/accelerator.vendor: nvidia`<br/>`moai.moreh.io/accelerator.model: h100-80gb-hbm3` | NVIDIA H100 80GB HBM3 GPU | +| `moai.moreh.io/accelerator.vendor: nvidia`<br/>`moai.moreh.io/accelerator.model: h800` | NVIDIA H800 GPU | +| `moai.moreh.io/accelerator.vendor: nvidia`<br/>`moai.moreh.io/accelerator.model: h800-pcie` | NVIDIA H800 PCIe GPU | +| `moai.moreh.io/accelerator.vendor: nvidia`<br/>`moai.moreh.io/accelerator.model: a100-80gb-pcie` | NVIDIA A100 80GB PCIe GPU | +| `moai.moreh.io/accelerator.vendor: nvidia`<br/>`moai.moreh.io/accelerator.model: a100-40gb-pcie` | NVIDIA A100 40GB PCIe GPU | +| `moai.moreh.io/accelerator.vendor: tenstorrent`<br/>`moai.moreh.io/accelerator.model: blackhole` | Tenstorrent Blackhole NPU | +| `moai.moreh.io/accelerator.vendor: tenstorrent`<br/>`moai.moreh.io/accelerator.model: wormhole` | Tenstorrent Wormhole NPU | + +--- + +## Checking available accelerators + +You can use the following command to see which accelerators are currently available in the cluster and how they are labeled by the MoAI Inference Framework. + +```shell +kubectl get nodes -L moai.moreh.io/accelerator.vendor,moai.moreh.io/accelerator.model +``` + +Expected output: + +```shell +NAME STATUS ROLES AGE VERSION ACCELERATOR.VENDOR ACCELERATOR.MODEL +cpu-001 Ready control-plane 1d v1.32.5 +mi250-001 Ready <none> 1d v1.32.5 amd mi250 +mi300x-001 Ready <none> 1d v1.32.5 amd mi300x +``` + +To list nodes that match a specific label, use the `-l` option: + +```shell +kubectl get nodes -l moai.moreh.io/accelerator.model=mi300x +``` + +Expected output: + +```shell +NAME STATUS ROLES AGE VERSION +mi300x-001 Ready <none> 1d v1.32.5 +``` diff --git a/website/docusaurus.config.ts b/website/docusaurus.config.ts index f396fa1..477c0cb 100644 --- a/website/docusaurus.config.ts +++ b/website/docusaurus.config.ts @@ -1,5 +1,6 @@ import type {Config} from '@docusaurus/types'; import type * as Preset from '@docusaurus/preset-classic'; +import {themes} from 'prism-react-renderer'; const config: Config = { title: 'Moreh', @@ -9,7 +10,7 @@ const config: Config = { trailingSlash: true, favicon: '/moreh-icon.png', staticDirectories: ['static'], - onBrokenLinks: 'warn', + onBrokenLinks: 'throw', plugins: [ [ '@cmfcmf/docusaurus-search-local', @@ -19,6 +20,15 @@ const config: Config = { }, ], ], + + markdown: { + mermaid: true, + hooks: { + onBrokenMarkdownLinks: 'warn', + }, + }, + themes: ['@docusaurus/theme-mermaid'], + presets: [ [ 'classic', @@ -71,6 +81,11 @@ const config: Config = { }, ], }, + prism: { + additionalLanguages: ['bash', 'toml', 'yaml'], + theme: themes.nightOwlLight, + darkTheme: themes.vsDark, + }, footer: { style: 'dark', copyright: diff --git a/website/package-lock.json b/website/package-lock.json index f350a6c..fa256ca 100644 --- a/website/package-lock.json +++ b/website/package-lock.json @@ -11,6 +11,8 @@ "@cmfcmf/docusaurus-search-local": "^2.0.1", "@docusaurus/core": "3.9.2", "@docusaurus/preset-classic": "3.9.2", + "@docusaurus/theme-mermaid": "^3.9.2", + "prism-react-renderer": "^2.4.1", "react": "^18.0.0", "react-dom": "^18.0.0" }, @@ -401,6 +403,19 @@ "@algolia/requester-common": "4.25.3" } }, + "node_modules/@antfu/install-pkg": { + "version": "1.1.0", + "resolved": "https://registry.npmjs.org/@antfu/install-pkg/-/install-pkg-1.1.0.tgz", + "integrity": "sha512-MGQsmw10ZyI+EJo45CdSER4zEb+p31LpDAFp2Z3gkSd1yqVZGi0Ebx++YTEMonJy4oChEMLsxZ64j8FH6sSqtQ==", + "license": "MIT", + "dependencies": { + "package-manager-detector": "^1.3.0", + "tinyexec": "^1.0.1" + }, + "funding": { + "url": "https://github.com/sponsors/antfu" + } + }, "node_modules/@babel/code-frame": { "version": "7.29.0", "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.29.0.tgz", @@ -2114,6 +2129,63 @@ "node": ">=6.9.0" } }, + "node_modules/@braintree/sanitize-url": { + "version": "7.1.2", + "resolved": "https://registry.npmjs.org/@braintree/sanitize-url/-/sanitize-url-7.1.2.tgz", + "integrity": "sha512-jigsZK+sMF/cuiB7sERuo9V7N9jx+dhmHHnQyDSVdpZwVutaBu7WvNYqMDLSgFgfB30n452TP3vjDAvFC973mA==", + "license": "MIT" + }, + 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"react-dom": "^18.0.0" }, diff --git a/website/versioned_docs/version-v0.0.0/getting_started/prerequisites.md b/website/versioned_docs/version-v0.0.0/getting_started/prerequisites.md index 3adfc2b..ffea9d4 100644 --- a/website/versioned_docs/version-v0.0.0/getting_started/prerequisites.md +++ b/website/versioned_docs/version-v0.0.0/getting_started/prerequisites.md @@ -344,7 +344,7 @@ rdma-shared-device-plugin-wh9fz 1/1 Running 0 7s ## Node labeling for heterogeneous accelerators -The `moai-accelerator` NodeFeatureRule enables the identification of heterogeneous accelerators by assigning vendor and model labels to nodes. This metadata facilitates targeted scheduling or automated selection of optimal accelerator. For a full list of supported hardware, please refer to [supported devices](supported_devices). +The `moai-accelerator` NodeFeatureRule enables the identification of heterogeneous accelerators by assigning vendor and model labels to nodes. This metadata facilitates targeted scheduling or automated selection of optimal accelerator. For a full list of supported hardware, please refer to [supported devices](./supported_devices.md). Apply the `moai-accelerator` NodeFeatureRule using the following command.