Compute - List Nodes

服務:

Machine Learning

API 版本:

2024-04-01

取得計算中所有計算節點的詳細數據 (例如IP位址、埠等) 。

POST https://management.azure.com/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/listNodes?api-version=2024-04-01

URI 參數

名稱	位於	必要	類型	Description
computeName	path	True	string	Azure Machine Learning 計算的名稱。
resourceGroupName	path	True	string	資源群組的名稱。 名稱不區分大小寫。
subscriptionId	path	True	string	目標訂用帳戶的標識碼。
workspaceName	path	True	string	Azure Machine Learning 工作區的名稱。 Regex 模式: ^[a-zA-Z0-9][a-zA-Z0-9_-]{2,32}$
api-version	query	True	string	用於此作業的 API 版本。

回應

名稱	類型	Description
200 OK	AmlComputeNodesInformation	作業成功。 回應包含IP位址清單。
Other Status Codes	ErrorResponse	描述作業失敗原因的錯誤回應。

安全性

azure_auth

Azure Active Directory OAuth2 Flow。

類型: oauth2
Flow: implicit
授權 URL: https://login.microsoftonline.com/common/oauth2/authorize

範圍

名稱	Description
user_impersonation	模擬您的用戶帳戶

範例

Get compute nodes information for a compute

範例要求

HTTP

POST https://management.azure.com/subscriptions/34adfa4f-cedf-4dc0-ba29-b6d1a69ab345/resourceGroups/testrg123/providers/Microsoft.MachineLearningServices/workspaces/workspaces123/computes/compute123/listNodes?api-version=2024-04-01

Java

/**
 * Samples for Compute ListNodes.
 */
public final class Main {
    /*
     * x-ms-original-file:
     * specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2024-04-01/
     * examples/Compute/listNodes.json
     */
    /**
     * Sample code: Get compute nodes information for a compute.
     * 
     * @param manager Entry point to MachineLearningManager.
     */
    public static void getComputeNodesInformationForACompute(
        com.azure.resourcemanager.machinelearning.MachineLearningManager manager) {
        manager.computes().listNodes("testrg123", "workspaces123", "compute123", com.azure.core.util.Context.NONE);
    }
}

To use the Azure SDK library in your project, see this documentation. To provide feedback on this code sample, open a GitHub issue

Go

package armmachinelearning_test

import (
	"context"
	"log"

	"github.com/Azure/azure-sdk-for-go/sdk/azidentity"
	"github.com/Azure/azure-sdk-for-go/sdk/resourcemanager/machinelearning/armmachinelearning/v4"
)

// Generated from example definition: https://github.com/Azure/azure-rest-api-specs/blob/9778042723206fbc582306dcb407bddbd73df005/specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2024-04-01/examples/Compute/listNodes.json
func ExampleComputeClient_NewListNodesPager() {
	cred, err := azidentity.NewDefaultAzureCredential(nil)
	if err != nil {
		log.Fatalf("failed to obtain a credential: %v", err)
	}
	ctx := context.Background()
	clientFactory, err := armmachinelearning.NewClientFactory("<subscription-id>", cred, nil)
	if err != nil {
		log.Fatalf("failed to create client: %v", err)
	}
	pager := clientFactory.NewComputeClient().NewListNodesPager("testrg123", "workspaces123", "compute123", nil)
	for pager.More() {
		page, err := pager.NextPage(ctx)
		if err != nil {
			log.Fatalf("failed to advance page: %v", err)
		}
		for _, v := range page.Nodes {
			// You could use page here. We use blank identifier for just demo purposes.
			_ = v
		}
		// If the HTTP response code is 200 as defined in example definition, your page structure would look as follows. Please pay attention that all the values in the output are fake values for just demo purposes.
		// page.AmlComputeNodesInformation = armmachinelearning.AmlComputeNodesInformation{
		// 	Nodes: []*armmachinelearning.AmlComputeNodeInformation{
		// 		{
		// 			NodeID: to.Ptr("tvm-3601533753_1-20170719t162906z"),
		// 			NodeState: to.Ptr(armmachinelearning.NodeStateRunning),
		// 			Port: to.Ptr[int32](50000),
		// 			PrivateIPAddress: to.Ptr("13.84.190.124"),
		// 			PublicIPAddress: to.Ptr("13.84.190.134"),
		// 			RunID: to.Ptr("2f378a44-38f2-443a-9f0d-9909d0b47890"),
		// 		},
		// 		{
		// 			NodeID: to.Ptr("tvm-3601533753_2-20170719t162906z"),
		// 			NodeState: to.Ptr(armmachinelearning.NodeStateIdle),
		// 			Port: to.Ptr[int32](50001),
		// 			PrivateIPAddress: to.Ptr("13.84.190.124"),
		// 			PublicIPAddress: to.Ptr("13.84.190.134"),
		// 	}},
		// }
	}
}

To use the Azure SDK library in your project, see this documentation. To provide feedback on this code sample, open a GitHub issue

JavaScript

const { AzureMachineLearningServicesManagementClient } = require("@azure/arm-machinelearning");
const { DefaultAzureCredential } = require("@azure/identity");

/**
 * This sample demonstrates how to Get the details (e.g IP address, port etc) of all the compute nodes in the compute.
 *
 * @summary Get the details (e.g IP address, port etc) of all the compute nodes in the compute.
 * x-ms-original-file: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2024-04-01/examples/Compute/listNodes.json
 */
async function getComputeNodesInformationForACompute() {
  const subscriptionId =
    process.env["MACHINELEARNING_SUBSCRIPTION_ID"] || "34adfa4f-cedf-4dc0-ba29-b6d1a69ab345";
  const resourceGroupName = process.env["MACHINELEARNING_RESOURCE_GROUP"] || "testrg123";
  const workspaceName = "workspaces123";
  const computeName = "compute123";
  const credential = new DefaultAzureCredential();
  const client = new AzureMachineLearningServicesManagementClient(credential, subscriptionId);
  const resArray = new Array();
  for await (let item of client.computeOperations.listNodes(
    resourceGroupName,
    workspaceName,
    computeName,
  )) {
    resArray.push(item);
  }
  console.log(resArray);
}

To use the Azure SDK library in your project, see this documentation. To provide feedback on this code sample, open a GitHub issue

dotnet

using Azure;
using Azure.ResourceManager;
using System;
using System.Threading.Tasks;
using System.Xml;
using Azure.Core;
using Azure.Identity;
using Azure.ResourceManager.MachineLearning.Models;
using Azure.ResourceManager.MachineLearning;

// Generated from example definition: specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2024-04-01/examples/Compute/listNodes.json
// this example is just showing the usage of "Compute_ListNodes" operation, for the dependent resources, they will have to be created separately.

// get your azure access token, for more details of how Azure SDK get your access token, please refer to https://learn.microsoft.com/en-us/dotnet/azure/sdk/authentication?tabs=command-line
TokenCredential cred = new DefaultAzureCredential();
// authenticate your client
ArmClient client = new ArmClient(cred);

// this example assumes you already have this MachineLearningComputeResource created on azure
// for more information of creating MachineLearningComputeResource, please refer to the document of MachineLearningComputeResource
string subscriptionId = "34adfa4f-cedf-4dc0-ba29-b6d1a69ab345";
string resourceGroupName = "testrg123";
string workspaceName = "workspaces123";
string computeName = "compute123";
ResourceIdentifier machineLearningComputeResourceId = MachineLearningComputeResource.CreateResourceIdentifier(subscriptionId, resourceGroupName, workspaceName, computeName);
MachineLearningComputeResource machineLearningCompute = client.GetMachineLearningComputeResource(machineLearningComputeResourceId);

// invoke the operation and iterate over the result
await foreach (AmlComputeNodeInformation item in machineLearningCompute.GetNodesAsync())
{
    Console.WriteLine($"Succeeded: {item}");
}

Console.WriteLine($"Succeeded");

To use the Azure SDK library in your project, see this documentation. To provide feedback on this code sample, open a GitHub issue

範例回覆

狀態碼:

200

{
  "nodes": [
    {
      "nodeId": "tvm-3601533753_1-20170719t162906z",
      "privateIpAddress": "13.84.190.124",
      "publicIpAddress": "13.84.190.134",
      "port": 50000,
      "nodeState": "running",
      "runId": "2f378a44-38f2-443a-9f0d-9909d0b47890"
    },
    {
      "nodeId": "tvm-3601533753_2-20170719t162906z",
      "privateIpAddress": "13.84.190.124",
      "publicIpAddress": "13.84.190.134",
      "port": 50001,
      "nodeState": "idle"
    }
  ],
  "nextLink": "nextLink"
}

定義

名稱	Description
AmlComputeNodeInformation	與 AmlCompute 相關的計算節點資訊。
AmlComputeNodesInformation	AmlCompute 節點的結果
ErrorAdditionalInfo	資源管理錯誤其他資訊。
ErrorDetail	錯誤詳細數據。
ErrorResponse	錯誤回應
nodeState	計算節點的狀態。 值為閑置、執行中、準備、無法使用、離開和先佔。

AmlComputeNodeInformation

與 AmlCompute 相關的計算節點資訊。

名稱	類型	Description
nodeId	string	節點標識碼。 計算節點的識別碼。
nodeState	nodeState	計算節點的狀態。 值為閑置、執行中、準備、無法使用、離開和先佔。
port	number	連接埠。 節點的 SSH 埠號碼。
privateIpAddress	string	私人 IP 位址。 計算節點的私人IP位址。
publicIpAddress	string	公用 IP 位址。 計算節點的公用IP位址。
runId	string	執行標識碼。 若為任何其他 Null，則為節點上執行的實驗標識碼。

AmlComputeNodesInformation

AmlCompute 節點的結果

名稱	類型	Description
nextLink	string	接續 Token。
nodes	AmlComputeNodeInformation[]	傳回之 AmlCompute 節點詳細數據的集合。

ErrorAdditionalInfo

資源管理錯誤其他資訊。

名稱	類型	Description
info	object	其他資訊。
type	string	其他信息類型。

ErrorDetail

錯誤詳細數據。

名稱	類型	Description
additionalInfo	ErrorAdditionalInfo[]	錯誤其他資訊。
code	string	錯誤碼。
details	ErrorDetail[]	錯誤詳細資料。
message	string	錯誤訊息。
target	string	錯誤目標。

ErrorResponse

錯誤回應

名稱	類型	Description
error	ErrorDetail	錯誤物件。

nodeState

計算節點的狀態。 值為閑置、執行中、準備、無法使用、離開和先佔。

名稱	類型	Description
idle	string
leaving	string
preempted	string
preparing	string
running	string
unusable	string