AzureML - 如何从集群模型创建 Web 服务

Question

我创建了一个推理管道，它使用 K-means 对一组数据进行聚类。从设计器运行时，它工作正常并返回输入数据的集群分配。一旦部署为 Web 服务，该服务总是返回一组空结果。有没有人设法做类似的事情？

更新：4/22

在部署日志中我看到

消息取决于我是在生产模式还是开发测试模式下使用 AksCompute 实例。

在开发测试模式下，警告是：output_name WebServiceOutput0 存在于模式中，但不存在于数据中

在生产模式下，我得到：output_name output1 存在于模式中但不存在于数据中

兜兜转转，我返回的数据集中仍然没有得到任何东西。还记录了 MS 的支持票，所以如果他们帮助我，我会在这里发布答案。

更新：4/24

测试端点时的部署日志：

    Init: Graph has been loaded
    2023-04-24 17:32:43,725 | root | INFO | Users's init has completed successfully
2023-04-24 17:32:43,726 | root | INFO | Skipping middleware: dbg_model_info as it's not enabled.
2023-04-24 17:32:43,726 | root | INFO | Skipping middleware: dbg_resource_usage as it's not enabled.
Found swagger file: /var/azureml-app/swagger.json
Swagger file loaded.
2023-04-24 17:32:43,733 | root | INFO | Scoring timeout is found from os.environ: 60000 ms
2023-04-24 17:32:50,235 | root | INFO | 200
127.0.0.1 - - [24/Apr/2023:17:32:50 +0000] "GET /swagger.json HTTP/1.0" 200 3301 "-" "-"
2023-04-24 17:32:53,910 | root | INFO | Scoring Timer is set to 60.0 seconds
Handling http request - Start:
2023-04-24 17:32:53,911 studio.azureml.designer.serving.dagengine.request_handler INFO       |   Run: is_classic = False, with_details = False, verbose = True
2023-04-24 17:32:53,911 studio.core          INFO       |   Pre-processing - Start:
2023-04-24 17:32:53,911 studio.core          INFO       |   Pre-processing - End with 0.0001s elapsed.
2023-04-24 17:32:53,912 studio.core          INFO       |   Processing - Start:
2023-04-24 17:32:53,932 studio.core          INFO       |   Processing - End with 0.0207s elapsed.
2023-04-24 17:32:53,933 studio.core          INFO       |   Post-processing - Start:
2023-04-24 17:32:53,933 studio.azureml.designer.serving.dagengine.processor WARNING    |   |   output_name WebServiceOutput0 exists in schema but not in data
2023-04-24 17:32:53,934 studio.core          INFO       |   Post-processing - End with 0.0006s elapsed.
2023-04-24 17:32:53,934 studio.core          INFO       Handling http request - End with 0.0235s elapsed.
2023-04-24 17:32:53,940 studio.azureml.designer.serving.dagengine.request_handler DEBUG      Run: output data(raw) = {"Results": {}}
2023-04-24 17:32:53,941 | root | INFO | run() output is HTTP Response
2023-04-24 17:32:53,941 | root | INFO | 200
127.0.0.1 - - [24/Apr/2023:17:32:53 +0000] "POST /score?verbose=true HTTP/1.0" 200 15 "-" "-"
2023-04-24 17:33:04,283 | root | INFO | 200
127.0.0.1 - - [24/Apr/2023:17:33:04 +0000] "GET /swagger.json HTTP/1.0" 200 3301 "-" "-"
2023-04-24 17:33:11,437 | root | INFO | 200
127.0.0.1 - - [24/Apr/2023:17:33:11 +0000] "GET /swagger.json HTTP/1.0" 200 3301 "-" "-"
2023-04-24 17:33:12,932 | root | INFO | Scoring Timer is set to 60.0 seconds
2023-04-24 17:33:12,932 studio.core          INFO       Handling http request - Start:
2023-04-24 17:33:12,932 studio.azureml.designer.serving.dagengine.request_handler INFO       |   Run: is_classic = False, with_details = False, verbose = True
2023-04-24 17:33:12,932 studio.core          INFO       |   Pre-processing - Start:
2023-04-24 17:33:12,933 studio.core          INFO       |   Pre-processing - End with 0.0001s elapsed.
2023-04-24 17:33:12,933 studio.core          INFO       |   Processing - Start:
2023-04-24 17:33:12,949 studio.core          INFO       |   Processing - End with 0.0163s elapsed.
2023-04-24 17:33:12,949 studio.core          INFO       |   Post-processing - Start:
2023-04-24 17:33:12,949 studio.azureml.designer.serving.dagengine.processor WARNING    |   |   output_name WebServiceOutput0 exists in schema but not in data
2023-04-24 17:33:12,949 studio.core          INFO       |   Post-processing - End with 0.0001s elapsed.
2023-04-24 17:33:12,949 studio.core          INFO       Handling http request - End with 0.0170s elapsed.
2023-04-24 17:33:12,949 studio.azureml.designer.serving.dagengine.request_handler DEBUG      Run: output data(raw) = {"Results": {}}
2023-04-24 17:33:12,950 | root | INFO | run() output is HTTP Response
2023-04-24 17:33:12,950 | root | INFO | 200
127.0.0.1 - - [24/Apr/2023:17:33:12 +0000] "POST /score?verbose=true HTTP/1.0" 200 15 "-" "-"
2023-04-24 17:42:24,397 | root | INFO | 200
127.0.0.1 - - [24/Apr/2023:17:42:24 +0000] "GET /swagger.json HTTP/1.0" 200 3301 "-" "-"

Answer 1

部署到实时端点时，计算类型有两个选项。

1.AksCompute

2.Azure Container实例

喜欢下面

我尝试部署实时推理管道。 在这两个选项中。 它工作成功。这是结果。

此端点使用 Azure 容器实例并且

这是使用 AksCompute 的另一个端点。 这是我的网络服务输出结果。

和实时推理管道。

尝试使用两种计算选项创建端点。

要使用 AksCompute 创建，请按照以下步骤操作。

首先，您需要创建 kubernates 集群。 转到 Ml studio 工作区中的Compute，然后单击 新

然后选择 AksCompute 选项。 在下一页中选择位置和 VM。

单击高级设置旁边。

如果您用于开发目的，请提供计算名称并选择Dev-test。然后点击Create。 创建需要一些时间。

现在让我们部署实时推理管道。 提交推理管道后，作业开始运行，您将获得如下 Web 服务输出和选项。

在这里你可以找到部署选项，点击它。

接下来，您将获得如上的选项。 选择部署新的实时端点。 为端点命名，然后选择 AksCompute 作为计算类型，并从下拉列表中选择您之前在 Kubernetes 集群中创建的计算名称。 然后单击部署，部署将开始，这需要一些时间来部署。

部署后在端点Test选项卡中测试结果，否则您使用Postman使用端点URL和Authentication Key

此部署使用 AksCompute 并尝试使用 Azure 容器服务。 部署时，只需选择容器服务作为计算类型，然后单击部署。

如果您仍然得到空结果，请转到端点中的部署日志，在那里您可以找到管道运行的日志和如下所示的结果。 在那里你可以看到哪里出了问题。

2023-04-20 19:18:08,363 studio.module        INFO       |   |   |   |   Select column indexes from Dataset
2023-04-20 19:18:08,365 studio.core          DEBUG      |   |   |   |   return:
2023-04-20 19:18:08,365 studio.core          DEBUG      |   |   |   |   |   [0] = <DataTable (3 Rows, 1 Cols) at 0x00007FD783962430>
2023-04-20 19:18:08,365 studio.core          INFO       |   |   |   SelectColumnsModule.run - End with 0.0085s elapsed.
2023-04-20 19:18:08,365 studio.core          INFO       |   |   Executing node 4: Select Columns in Dataset - End with 0.0116s elapsed.
2023-04-20 19:18:08,373 studio.core          INFO       |   Processing - End with 0.1412s elapsed.
2023-04-20 19:18:08,374 studio.core          INFO       |   Post-processing - Start:
2023-04-20 19:18:08,374 studio.core          INFO       |   Post-processing - End with 0.0000s elapsed.
2023-04-20 19:18:08,374 studio.core          INFO      
Handling http request - End with 0.1420s elapsed.
2023-04-20 19:18:08,374 studio.azureml.designer.serving.dagengine.request_handler DEBUG      Run: output data(raw) = {"Results": {"WebServiceOutput0": [{"Assignments": 1}, {"Assignments": 0}, {"Assignments": 2}]}}
2023-04-20 19:18:08,374 | root | INFO | run() output is HTTP Response
2023-04-20 19:18:08,374 | root | INFO | 200
127.0.0.1 - - [20/Apr/2023:19:18:08 +0000] "POST /score?verbose=true HTTP/1.0" 200 96 "-" "-"