diff --git a/docs/en/installation/ai-cluster.mdx b/docs/en/installation/ai-cluster.mdx index e1118a4..67d926d 100644 --- a/docs/en/installation/ai-cluster.mdx +++ b/docs/en/installation/ai-cluster.mdx @@ -8,7 +8,7 @@ weight: 30 To begin, you will need to deploy the **Alauda AI Operator**. This is the core engine for all Alauda AI products. By default, it uses the **KServe** `Raw Deployment` mode for the inference backend, which is particularly recommended for resource-intensive generative workloads. This mode provides a straightforward way to deploy models and offers robust, customizable deployment capabilities by leveraging foundational Kubernetes functionalities. -If your use case requires `Serverless` functionality, which enables advanced features like **scaling to zero on demand** for cost optimization, you can optionally install the **Alauda AI Model Serving Operator**. This operator is not part of the default installation and can be added at any time to enable `Serverless` functionality. +If your use case requires `Serverless` functionality, which enables advanced features like **scaling to zero on demand** for cost optimization, you can optionally install the **Knative CE Operator**. This operator is not part of the default installation and can be added at any time to enable `Serverless` functionality. @@ -26,19 +26,19 @@ If your use case requires `Serverless` functionality, which enables advanced fea _Download package: aml-operator.xxx.tgz_ -- **Alauda AI Model Serving Operator** +- **Knative CE Operator** - Alauda AI Model Serving Operator provides serverless model inference. + Knative CE Operator provides serverless model inference. - _Download package: kserveless-operator.xxx.tgz_ + _Download package: knative-operator.ALL.v1.x.x-yymmdd.tgz_ :::info -You can download the app named 'Alauda AI' and 'Alauda AI Model Serving' from the Marketplace on the Customer Portal website. +You can download the app named 'Alauda AI' and 'Knative CE Operator' from the Marketplace on the Customer Portal website. ::: ## Uploading -We need to upload both `Alauda AI` and `Alauda AI Model Serving` to the cluster where Alauda AI is to be used. +We need to upload both `Alauda AI` and `Knative CE Operator` to the cluster where Alauda AI is to be used. @@ -66,7 +66,7 @@ export PLATFORM_ADMIN_PASSWORD= # [!code callout] export CLUSTER= # [!code callout] export AI_CLUSTER_OPERATOR_NAME= # [!code callout] -export KSERVELESS_OPERATOR_PKG_NAME= # [!code callout] +export KNATIVE_CE_OPERATOR_PKG_NAME= # [!code callout] VIOLET_EXTRA_ARGS=() IS_EXTERNAL_REGISTRY= @@ -97,9 +97,9 @@ violet push \ ${VIOLET_EXTRA_ARGS[@]} # [!code highlight] -# Push **KServeless** operator package to destination cluster +# Push **Knative CE Operator** package to destination cluster violet push \ - ${KSERVELESS_OPERATOR_PKG_NAME} \ + ${KNATIVE_CE_OPERATOR_PKG_NAME} \ --platform-address=${PLATFORM_ADDRESS} \ --platform-username=${PLATFORM_ADMIN_USER} \ --platform-password=${PLATFORM_ADMIN_PASSWORD} \ @@ -114,14 +114,14 @@ violet push \ 3. `${PLATFORM_ADMIN_PASSWORD}` is the password of the ACP platform admin. 4. `${CLUSTER}` is the name of the cluster to install the Alauda AI components into. 5. `${AI_CLUSTER_OPERATOR_NAME}` is the path to the Alauda AI Cluster Operator package tarball. -6. `${KSERVELESS_OPERATOR_PKG_NAME}` is the path to the KServeless Operator package tarball. +6. `${KNATIVE_CE_OPERATOR_PKG_NAME}` is the path to the Knative CE Operator package tarball. 7. `${REGISTRY_ADDRESS}` is the address of the external registry. 8. `${REGISTRY_USERNAME}` is the username of the external registry. 9. `${REGISTRY_PASSWORD}` is the password of the external registry. -After configuration, execute the script file using `bash ./uploading-ai-cluster-packages.sh` to upload both `Alauda AI` and `Alauda AI Model Serving` operator. +After configuration, execute the script file using `bash ./uploading-ai-cluster-packages.sh` to upload both `Alauda AI` and `Knative CE Operator`. @@ -137,7 +137,7 @@ In **Administrator** view: 2. At the top of the console, from the **Cluster** dropdown list, select the destination cluster where you want to install Alauda AI. 3. Select **Alauda AI**, then click **Install**. - **Install Alauda AI** window will popup. + **Install Alauda AI** window will pop up. 4. Then in the **Install Alauda AI** window. 5. Leave **Channel** unchanged. @@ -195,9 +195,6 @@ In **Administrator** view: By default, the configuration uses `SelfSigned` certificate type for securing ingress traffic to your cluster, the certificate is stored in the `knative-serving-cert` secret that is specified in the **Domain Certificate Secret** field. - - To use certificate provided by your own, store the certificate secret in the `istio-system` namespace, then update the value of the - **Domain Certificate Secret** field, and change the value of the **Domain Certificate Secret** field to `Provided`. ::: 11. In the **Serverless Configuration** section, set **Knative Serving Provider** to **Operator**; leave all other parameters blank. @@ -230,35 +227,35 @@ Now, the core capabilities of Alauda AI have been successfully deployed. If you Serverless functionality is an optional capability that requires an additional operator and instance to be deployed. -### 1. Installing the Alauda AI Model Serving Operator +### 1. Installing the Knative CE Operator -### Prerequisites - -The `Serverless` capability relies on the **Istio** `Gateway` for its networking. Please install the **Service Mesh** first by following the [documentation](./pre-configuration.mdx#deploy-service-mesh). +:::info +Starting from **Knative CE Operator**, the Knative networking layer switches to **Kourier**, so installing **Istio** is no longer required. +::: -### Procedure +#### Procedure In **Administrator** view: 1. Click **Marketplace / OperatorHub**. 2. At the top of the console, from the **Cluster** dropdown list, select the destination cluster where you want to install. -3. Select **Alauda AI Model Serving**, then click **Install**. +3. Search for and select **Knative CE Operator**, then click **Install**. - **Install Alauda AI Model Serving** window will popup. + **Install Knative CE Operator** window will pop up. -4. Then in the **Install Alauda AI Model Serving** window. +4. Then in the **Install Knative CE Operator** window. 5. Leave **Channel** unchanged. -6. Check whether the **Version** matches the **Alauda AI Model Serving** version you want to install. -7. Leave **Installation Location** unchanged, it should be `kserveless-operator` by default. +6. Check whether the **Version** matches the **Knative CE Operator** version you want to install. +7. Leave **Installation Location** unchanged. 8. Select **Manual** for **Upgrade Strategy**. 9. Click **Install**. -### Verification +#### Verification -Confirm that the **Alauda AI Model Serving** tile shows one of the following states: +Confirm that the **Knative CE Operator** tile shows one of the following states: - `Installing`: installation is in progress; wait for this to change to `Installed`. - `Installed`: installation is complete. @@ -266,79 +263,70 @@ Confirm that the **Alauda AI Model Serving** tile shows one of the following sta -### 2. Creating Alauda AI Model Serving Instance - -Once **Alauda AI Model Serving Operator** is installed, you can create an instance. There are two ways to do this: +### 2. Creating Knative Serving Instance -#### **Automated Creation (Recommended)** +Once **Knative CE Operator** is installed, you need to create the `KnativeServing` instance manually. -You can have the instance automatically created and managed by the `AmlCluster` by editing its parameters. -In **Administrator** view: - -1. Click **Marketplace / OperatorHub**. -2. At the top of the console, from the **Cluster** dropdown list, select the destination cluster where you previously installed the `AmlCluster`. -3. Select **Alauda AI**, then **Click**. -4. In the **Alauda AI** page, click **All Instances** from the tab. -5. Click name **default**. -6. Locate **Actions** dropdown list and select update. - - **update default** form will show up. - -7. In the **Serverless Configuration** section: - 1. Set **Knative Serving Provider** to `Legacy`. - 2. Set **BuiltIn Knative Serving** to `Managed`. - -8. Leave all other parameters unchanged. Click **Update**. - - - -#### **Manual Creation and Integration** - -You can manually create the `KnativeServing (knativeservings.components.aml.dev)` instance. +#### Procedure + +1. Create the `knative-serving` namespace. + + ```bash + kubectl create ns knative-serving + ``` + +2. In the **Administrator** view, navigate to **Operators** -> **Installed Operators**. +3. Select the **Knative CE Operator**. +4. Under **Provided APIs**, locate **KnativeServing** and click **Create Instance**. +5. Switch to **YAML view**. +6. Replace the content with the following YAML: +7. Click **Create**. + + ```yaml + apiVersion: operator.knative.dev/v1beta1 + kind: KnativeServing + metadata: + name: knative-serving + namespace: knative-serving + spec: + config: + deployment: + registries-skipping-tag-resolving: kind.local,ko.local,dev.local,private-registry # [!code callout] + domain: + example.com: "" + features: + kubernetes.podspec-affinity: enabled + kubernetes.podspec-hostipc: enabled + kubernetes.podspec-hostnetwork: enabled + kubernetes.podspec-init-containers: enabled + kubernetes.podspec-nodeselector: enabled + kubernetes.podspec-persistent-volume-claim: enabled + kubernetes.podspec-persistent-volume-write: enabled + kubernetes.podspec-securitycontext: enabled + kubernetes.podspec-tolerations: enabled + kubernetes.podspec-volumes-emptydir: enabled + queueproxy.resource-defaults: enabled + network: + domain-template: '{{.Name}}.{{.Namespace}}.{{.Domain}}' + ingress-class: kourier.ingress.networking.knative.dev + ingress: + kourier: + enabled: true + ``` - - -In **Administrator** view: - -1. Click **Marketplace / OperatorHub**. -2. At the top of the console, from the **Cluster** dropdown list, select the destination cluster where you want to install. -3. Select **Alauda AI Model Serving**, then **Click**. -4. In the **Alauda AI Model Serving** page, click **All Instances** from the tab. -5. Click **Create**. - - **Select Instance Type** window will pop up. - -6. Locate the **KnativeServing** tile in **Select Instance Type** window, then click **Create**. - - **Create KnativeServing** form will show up. - -7. Keep `default-knative-serving` unchanged for **Name**. -8. Keep `knative-serving` unchanged for **Knative Serving Namespace**. -9. In the **Ingress Gateway** section, configure the following: - 1. Set the **Ingress Gateway Istio Revision** to a value that corresponds to your Istio version (e.g., `1-22`). - 2. Set a valid domain for the **Domain** field. - 3. Set the appropriate **Domain Certificate Type**. + - :::info - For details on configuring the domain and certificate type, refer to the [relevant section](#procedure-1). - ::: +1. `private-registry` is a placeholder for your private registry address. You can find this in the **Administrator** view, then click **Clusters**, select `your cluster`, and check the **Private Registry** value in the **Basic Info** section. -10. In the **Values** section, configure the following: - 1. Select **Deploy Flavor** from dropdown: - 1. `single-node` for non HA deployments. - 2. `ha-cluster` for HA cluster deployments (**Recommended** for production). - 2. Set **Global Registry Address** to Match Your Cluster + - You can find your cluster's private registry address by following these steps: - 1. In the Web Console, go to Administrator / Clusters. - 2. Select your target **cluster**. - 3. On the **Overview** tab, find the `Private Registry address` value in the **Basic Info** section. + - +### 3. Integrate with AmlCluster -Configure the `AmlCluster` instance to integrate with a `KnativeServing` instance. +Configure the `AmlCluster` instance to integrate with the `KnativeServing` instance. @@ -348,10 +336,10 @@ In the **AmlCluster** instance update window, you will need to fill in the requi After the initial installation, you will find that only the **Knative Serving Provider** is set to `Operator`. You will now need to provide values for the following parameters: ::: -* **APIVersion**: `components.aml.dev/v1alpha1` +* **APIVersion**: `operator.knative.dev/v1beta1` * **Kind**: `KnativeServing` -* **Name**: `default-knative-serving` -* Leave **Namespace** blank. +* **Name**: `knative-serving` +* **Namespace**: `knative-serving` ## Replace GitLab Service After Installation diff --git a/docs/en/installation/fine-tuning.mdx b/docs/en/installation/fine-tuning.mdx index 6d329ee..e29fe1b 100644 --- a/docs/en/installation/fine-tuning.mdx +++ b/docs/en/installation/fine-tuning.mdx @@ -47,11 +47,8 @@ spec: type: SelfSigned domain: '*.example.com' knativeServing: - istioConfig: - controlPlane: - autoRevisionMode: legacy - managementState: Managed - providerType: Legacy + managementState: Removed + providerType: Operator kserve: managementState: Managed values: diff --git a/docs/en/overview/architecture.mdx b/docs/en/overview/architecture.mdx index 6ca6735..417a725 100644 --- a/docs/en/overview/architecture.mdx +++ b/docs/en/overview/architecture.mdx @@ -22,7 +22,7 @@ NOTE: Alauda AI uses some general Kubernetes, ACP components including: | aml-controller | Manages Alauda AI namespaces on workload clusters. Namespaces will be automatically configured a Model Repo space and corresponding resources. | Self-developed | | | aml-api-deploy | Provides high-level APIs for "Lich" | Self-developed | | | Gitlab (with Minio or S3) | Model repository backend storage and version tracking. | Open source | MIT | -| kserve-controller | (Optionally with knative serving and istio enabled) Manages AI inference services and inference service runtimes. | Open source | Apache Version 2.0 | +| kserve-controller | (Optionally with knative serving enabled) Manages AI inference services and inference service runtimes. | Open source | Apache Version 2.0 | | workspace-controller | Manages workbench instances (jupyter notebooks, codeserver) | Open source | Apache Version 2.0 | | Volcano | Plugin to provide co-scheduling (gang-scheduling) features for AI training jobs. Also manages "volcanojob" resource to run general training workloads. | Open source | Apache Version 2.0 | | MLFlow | Track training, evaluation jobs by storing, visualizing metrics and artifacts | Open source | Apache Version 2.0 |