Question

我在Azure ML中创建了一个简单的实验，并使用http客户端触发它。在Azure ML工作区中，执行时一切正常。但是，当我使用http客户端触发实验时，实验会超时并失败。设置http客户端的超时值似乎不起作用。

我们是否可以设置此超时值以便实验不会失败？

Answer 1

确保正确设置客户端超时值。如果为Web服务供电的服务器超时，则它将发回一个HTTP状态代码504 BackendScoreTimeout（或可能409 GatewayTimeout）的响应。但是，如果您根本没有收到回复，那么您的客户就不会等待足够长的时间。

您可以在ML Studio中运行实验，从而获得大量时间。转到实验属性以查找其运行时间，然后将该时间量的两倍作为超时值。

Answer 2

我发布的Azure ML实验与Web服务有类似的问题。大多数时候它运行正常，而有时它返回时出现超时错误。问题是实验本身有 90秒的运行时间限制。因此，很可能您的实验的运行时间超过此限制并返回超时错误。 HTH

Answer 3

看起来无法根据a feature request that is still marked as "planned" as of 4/1/2018设置此超时。

来自MSDN forums from 2017的建议是使用批处理执行服务，该服务启动机器学习实验，然后异步询问是否已完成。

以下是Azure ML Web服务管理示例代码中的代码段（所有注释均来自其示例代码）：

        using (HttpClient client = new HttpClient())
        {
            var request = new BatchExecutionRequest()
            {

                Outputs = new Dictionary<string, AzureBlobDataReference> () {
                    {
                        "output",
                        new AzureBlobDataReference()
                        {
                            ConnectionString = storageConnectionString,
                            RelativeLocation = string.Format("{0}/outputresults.file_extension", StorageContainerName) /*Replace this with the location you would like to use for your output file, and valid file extension (usually .csv for scoring results, or .ilearner for trained models)*/
                        }
                    },
                },    

                GlobalParameters = new Dictionary<string, string>() {
                }
            };

            client.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue("Bearer", apiKey);

            // WARNING: The 'await' statement below can result in a deadlock
            // if you are calling this code from the UI thread of an ASP.Net application.
            // One way to address this would be to call ConfigureAwait(false)
            // so that the execution does not attempt to resume on the original context.
            // For instance, replace code such as:
            //      result = await DoSomeTask()
            // with the following:
            //      result = await DoSomeTask().ConfigureAwait(false)

            Console.WriteLine("Submitting the job...");

            // submit the job
            var response = await client.PostAsJsonAsync(BaseUrl + "?api-version=2.0", request);

            if (!response.IsSuccessStatusCode)
            {
                await WriteFailedResponse(response);
                return;
            }

            string jobId = await response.Content.ReadAsAsync<string>();
            Console.WriteLine(string.Format("Job ID: {0}", jobId));

            // start the job
            Console.WriteLine("Starting the job...");
            response = await client.PostAsync(BaseUrl + "/" + jobId + "/start?api-version=2.0", null);
            if (!response.IsSuccessStatusCode)
            {
                await WriteFailedResponse(response);
                return;
            }

            string jobLocation = BaseUrl + "/" + jobId + "?api-version=2.0";
            Stopwatch watch = Stopwatch.StartNew();
            bool done = false;
            while (!done)
            {
                Console.WriteLine("Checking the job status...");
                response = await client.GetAsync(jobLocation);
                if (!response.IsSuccessStatusCode)
                {
                    await WriteFailedResponse(response);
                    return;
                }

                BatchScoreStatus status = await response.Content.ReadAsAsync<BatchScoreStatus>();
                if (watch.ElapsedMilliseconds > TimeOutInMilliseconds)
                {
                    done = true;
                    Console.WriteLine(string.Format("Timed out. Deleting job {0} ...", jobId));
                    await client.DeleteAsync(jobLocation);
                }
                switch (status.StatusCode) {
                    case BatchScoreStatusCode.NotStarted:
                        Console.WriteLine(string.Format("Job {0} not yet started...", jobId));
                        break;
                    case BatchScoreStatusCode.Running:
                        Console.WriteLine(string.Format("Job {0} running...", jobId));
                        break;
                    case BatchScoreStatusCode.Failed:
                        Console.WriteLine(string.Format("Job {0} failed!", jobId));
                        Console.WriteLine(string.Format("Error details: {0}", status.Details));
                        done = true;
                        break;
                    case BatchScoreStatusCode.Cancelled:
                        Console.WriteLine(string.Format("Job {0} cancelled!", jobId));
                        done = true;
                        break;
                    case BatchScoreStatusCode.Finished:
                        done = true;
                        Console.WriteLine(string.Format("Job {0} finished!", jobId));
                        ProcessResults(status);
                        break;
                }

                if (!done) {
                    Thread.Sleep(1000); // Wait one second
                }
            }
        }

Azure ML Web服务超时

3 个答案: