static void ProcessResults(BatchScoreStatus status)
{
foreach (var output in status.Results)
{
var blobLocation = output.Value;
Console.WriteLine(string.Format("The result '{0}' is available at the following Azure Storage location:", output.Key));
Console.WriteLine(string.Format("BaseLocation: {0}", blobLocation.BaseLocation));
Console.WriteLine(string.Format("RelativeLocation: {0}", blobLocation.RelativeLocation));
Console.WriteLine(string.Format("SasBlobToken: {0}", blobLocation.SasBlobToken));
Console.WriteLine();
}
}
static void ProcessResults(BatchScoreStatus status)
{
bool first = true;
foreach (var output in status.Results)
{
var blobLocation = output.Value;
Console.WriteLine(string.Format("The result '{0}' is available at the following Azure Storage location:", output.Key));
Console.WriteLine(string.Format("BaseLocation: {0}", blobLocation.BaseLocation));
Console.WriteLine(string.Format("RelativeLocation: {0}", blobLocation.RelativeLocation));
Console.WriteLine(string.Format("SasBlobToken: {0}", blobLocation.SasBlobToken));
Console.WriteLine();
// Save the first output to disk
if (first)
{
first = false;
SaveBlobToFile(blobLocation, string.Format("The results for {0}", output.Key));
}
}
}
static async Task InvokeBatchExecutionService()
{
// How this works:
//
// 1. Assume the input is present in a local file (if the web service accepts input)
// 2. Upload the file to an Azure blob - you'd need an Azure storage account
// 3. Call the Batch Execution Service to process the data in the blob. Any output is written to Azure blobs.
// 4. Download the output blob, if any, to local file
const string BaseUrl = "https://ussouthcentral.services.azureml.net/workspaces/a84f9ba84d5c48a582bfac621d24ff3e/services/86fe4131d2d645fbadd233fa6f8e45db/jobs";
const string StorageAccountName = "mystorageacct"; // Replace this with your Azure Storage Account name
const string StorageAccountKey = "Dx9WbMIThAvXRQWap/aLnxT9LV5txxw=="; // Replace this with your Azure Storage Key
const string StorageContainerName = "mycontainer"; // Replace this with your Azure Storage Container name
string storageConnectionString = string.Format("DefaultEndpointsProtocol=https;AccountName={0};AccountKey={1}", StorageAccountName, StorageAccountKey);
const string apiKey = "abc123"; // Replace this with the API key for the web service
// set a time out for polling status
const int TimeOutInMilliseconds = 120 * 1000; // Set a timeout of 2 minutes
UploadFileToBlob("..\\azure-machine-learning-getting-started\\3-azure-machine-learning-getting-started-m3-exercise-files\\german.data.csv" /*Replace this with the location of your input file*/,
"input1datablob.csv" /*Replace this with the name you would like to use for your Azure blob; this needs to have the same extension as the input file */,
StorageContainerName, storageConnectionString);
using (HttpClient client = new HttpClient())
{
var request = new BatchExecutionRequest()
{
Inputs = new Dictionary <string, AzureBlobDataReference>()
{
{
"input1",
new AzureBlobDataReference()
{
ConnectionString = storageConnectionString,
RelativeLocation = string.Format("{0}/input1datablob.csv", StorageContainerName)
}
},
},
Outputs = new Dictionary <string, AzureBlobDataReference>()
{
{
"output1",
new AzureBlobDataReference()
{
ConnectionString = storageConnectionString,
RelativeLocation = string.Format("/{0}/output1results.csv", StorageContainerName)
}
},
},
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
}
}
}
}
static async Task InvokeBatchExecutionService()
{
// How this works:
//
// 1. Assume the input is present in a local file (if the web service accepts input)
// 2. Upload the file to an Azure blob - you'd need an Azure storage account
// 3. Call the Batch Execution Service to process the data in the blob. Any output is written to Azure blobs.
// 4. Download the output blob, if any, to local file
const string BaseUrl = "https://ussouthcentral.services.azureml.net/subscriptions/3ad4a5841cf644859e2f3dc64fc51528/services/ad5cd4eaf3f041589164680d5679ff01/jobs";
const string StorageAccountName = "mlstorage33"; // Replace this with your Azure Storage Account name
const string StorageAccountKey = "CRCbe2eBK0b1/XWDvoPipZigM/CEO+AAel8i3gNsixDxwEFW2QcNJH7n9/nhqSw1hZ6QORZcywbvfOotmgqtYQ=="; // Replace this with your Azure Storage Key
const string StorageContainerName = "mldata"; // Replace this with your Azure Storage Container name
string storageConnectionString = string.Format("DefaultEndpointsProtocol=https;AccountName={0};AccountKey={1}", StorageAccountName, StorageAccountKey);
const string apiKey = "Swo+LRPS+vgc7HsUNeDg5Ql5Xyptnb/Zll8+on3qeGdQPizu8rNAlZr0BMJtLomdnV4bywh+pl1SXK/5JAcOcg=="; // Replace this with the API key for the web service
// set a time out for polling status
const int TimeOutInMilliseconds = 120 * 1000; // Set a timeout of 2 minutes
UploadFileToBlob("E:\\Download\\Train Data\\traindatanocopy.csv" /*Replace this with the location of your input file, and valid file extension (usually .csv)*/,
"input1datablob.csv" /*Replace this with the name you would like to use for your Azure blob; this needs to have the same extension as the input file */,
StorageContainerName, storageConnectionString);
UploadFileToBlob("E:\\Download\\Train Data\\channels.csv" /*Replace this with the location of your input file, and valid file extension (usually .csv)*/,
"input2datablob.csv" /*Replace this with the name you would like to use for your Azure blob; this needs to have the same extension as the input file */,
StorageContainerName, storageConnectionString);
using (HttpClient client = new HttpClient())
{
var request = new BatchExecutionRequest()
{
Inputs = new Dictionary <string, AzureBlobDataReference>()
{
{
"input1",
new AzureBlobDataReference()
{
ConnectionString = storageConnectionString,
RelativeLocation = string.Format("{0}/input1datablob.csv", StorageContainerName)
}
},
{
"input2",
new AzureBlobDataReference()
{
ConnectionString = storageConnectionString,
RelativeLocation = string.Format("{0}/input2datablob.csv", StorageContainerName)
}
},
},
Outputs = new Dictionary <string, AzureBlobDataReference>()
{
{
"output1",
new AzureBlobDataReference()
{
ConnectionString = storageConnectionString,
RelativeLocation = string.Format("{0}/output1results.csv", 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)*/
}
},
{
"output2",
new AzureBlobDataReference()
{
ConnectionString = storageConnectionString,
RelativeLocation = string.Format("{0}/output2results.ilearner", 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
}
}
}
}
static void ProcessResults(BatchScoreStatus status)
{
var blobLocation = status.Result;
SaveBlobToFile(blobLocation, "The results");
}
static async Task InvokeBatchExecutionService()
{
// How this works:
//
// 1. Assume the input is present in a local file (if the web service accepts input)
// 2. Upload the file to an Azure blob - you'd need an Azure storage account
// 3. Call the Batch Execution Service to process the data in the blob. Any output is written to Azure blobs.
// 4. Download the output blob, if any, to local file
const string BaseUrl = "https://ussouthcentral.services.azureml.net/subscriptions/1a917ae725664354b19e4acf4039a381/services/c01a944d35a9426880392b47f3f21466/jobs";
const string StorageAccountName = "storage account"; // Replace this with your Azure Storage Account name
const string StorageAccountKey = "storage key"; // Replace this with your Azure Storage Key
const string StorageContainerName = "bescontainer"; // Replace this with your Azure Storage Container name
string storageConnectionString = string.Format("DefaultEndpointsProtocol=https;AccountName={0};AccountKey={1}", StorageAccountName, StorageAccountKey);
const string apiKey = "API key"; // Replace this with the API key for the web service
// set a time out for polling status
const int TimeOutInMilliseconds = 120 * 1000; // Set a timeout of 2 minutes
UploadFileToBlob("E:\\Labfiles\\Lab10\\Starter\\Binary Classification dataset.csv" /*Replace this with the location of your input file, and valid file extension (usually .csv)*/,
"BESdata.csv" /*Replace this with the name you would like to use for your Azure blob; this needs to have the same extension as the input file */,
StorageContainerName, storageConnectionString);
using (HttpClient client = new HttpClient())
{
var request = new BatchExecutionRequest()
{
Inputs = new Dictionary <string, AzureBlobDataReference>()
{
{
"input1",
new AzureBlobDataReference()
{
ConnectionString = storageConnectionString,
RelativeLocation = string.Format("{0}/BESdata.csv", StorageContainerName)
}
},
},
Outputs = new Dictionary <string, AzureBlobDataReference>()
{
{
"output1",
new AzureBlobDataReference()
{
ConnectionString = storageConnectionString,
RelativeLocation = string.Format("{0}/BESoutput.csv", 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>()
{
{
"blobpath", "bescontainer/BESpredictions.csv"
},
}
};
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
}
}
Console.ReadLine();
}
}