Azure ML pipeline run failed with status message ServiceError: InternalServerError.
404 error when viewing executionlogs.txt, stderrlogs.txt, and stdoutlogs.txt.
A pipeline run completed the day before. No changes were made between these runs.
Compute cluster properties:
VM size: Standard_D3_v2 (4 cores, 14 GB RAM, 200 GB disk)
Processing unit: CPU - General purpose
OS type: Linux
Location: East US
You need to delete and create the endpoint again and this should work.
Also, for Error 404, make sure that URL is correct.
The root cause of the issue:
Time-based retention policy was set on the storage account level. This immutability policy prevented Azure Machine Learning from writing log files to the workspaceblobstore, hence the 404 error when viewing any of the log file followed by InternalServerError.
Solution:
Allow protected writes to append blobs
Related
I want to use a Spark pool in Synapse, but for every notebook execution fails with the following error:
InvalidHttpRequestToLivy: Your Spark job requested 24 vcores.
However, the workspace has a 0 core limit.
Try reducing the numbers of vcores requested or increasing your vcore quota.
HTTP status code: 400.
A simple print statement gives me the same error. How can I fix this?
i have gcloud app deploy but there is error in amount limit
this is the error
Updating service [default] (this may take several minutes)...failed.
ERROR: (gcloud.app.deploy) Error Response: [8] Flex operation projects/soy-alchemy-285213/regions/asia-southeast2/operations/80ed8da6-58dd-4ecb-a929-5e0462a8b224 error [RESOURCE_EXHAUSTED]: An internal error occurred while processing task /app-engine-flex/insert_flex_deployment/flex_create_resources>2021-01-12T16:32:36.907Z2608.fj.0: The requested amount of instances has exceeded GCE's default quota. Please see https://cloud.google.com/compute/quotas for more information on GCE resources
when i go to console, there is many service name in google cloud with limit, i dont know witch one i have to increase the quota
i finally using virtual machine rather app angine, deploy node js in virtual machine is simple enough
only configuration nginx is little hard
Like this closed issue https://github.com/Azure/azure-functions-host/issues/5540 I have issues figuring out what setting I should be changing to allow 100MB files to be uploaded
The weird thing is that the system is deployed in Azure where big files are allowed, but no one have made any changes to settings that should affect this.
So is there some local.settings.json setting that I am missing that is default different when hosting in Azure when compared to localhost
Error:
Microsoft.Azure.WebJobs.Host.FunctionInvocationException: Exception
while executing function: MessageReceiver --->
System.InvalidOperationException: Exception binding parameter
'request' --->
Microsoft.AspNetCore.Server.Kestrel.Core.BadHttpRequestException:
Request body too large.
There is https://learn.microsoft.com/en-us/dotnet/api/microsoft.aspnetcore.server.kestrel.core.kestrelserverlimits.maxrequestbodysize?view=aspnetcore-3.1
But I cant figure out how to set that when running Azure functions, in the startup I cant set it and setting [DisableRequestSizeLimit] or [RequestSizeLimit(100000000)] on top of my Azure function have no effect
A bug has been reported with problems on Windows https://github.com/Azure/azure-functions-core-tools/issues/2262
The HTTP request length is limited to 100 MB (104,857,600 bytes), and the URL length is limited to 4 KB (4,096 bytes). These limits are specified by the httpRuntime element of the runtime's Web.config file.
If a function that uses the HTTP trigger doesn't complete within 230 seconds, the Azure Load Balancer will time out and return an HTTP 502 error. The function will continue running but will be unable to return an HTTP response. For long-running functions, we recommend that you follow async patterns and return a location where you can ping the status of the request. For information about how long a function can run, see Scale and hosting - Consumption plan.
For more details, you could refer to this article.
I am encountering the following behavior with Azure Batch. I am using Shipyard to start a pool of 500 low-priority nodes to perform a list of 400.000 tasks. The pool size is managed using auto-scaling.
At first, the pool seems to be running just fine. The number of nodes increases to maximum capacity and the tasks complete as expected. However, after some time (having completed a sizable amount of tasks), I start to encounter 'start task failed' errors. The pool then quickly starts degrading until all nodes crash due to this same error.
This is the error I get in the stdout.txt file of one of the crashed nodes:
Login Succeeded
2020-03-04T09:09:07UTC - INFO - Docker registry logins completed.
2020-03-04T09:09:07UTC - WARNING - No Singularity registry servers found.
2020-03-04T09:13:37,840996225+00:00 - ERROR - Cascade Docker exited with non-zero exit code: 1
This seems to be an issue related to pulling the Docker image? Although it worked without issue on other nodes before.
I am aware that this is not a lot of information to go on, but I am having trouble figuring out what information is relevant and what's not.
UPDATE
After updating to shipyard 3.9.1, this is the output in stdout.txt for one of the crashed nodes (start task failed):
2020-03-05T08:23:43,784166638+00:00 - DEBUG - Pulling Docker Image: mcr.microsoft.com/azure-batch/shipyard:3.9.1-cargo (fallback: 0)
2020-03-05T08:23:58,876629647+00:00 - ERROR - Error response from daemon: Get https://mcr.microsoft.com/v2/: net/http: request canceled while waiting for connection (Client.Timeout exceeded while awaiting headers)
2020-03-05T08:23:58,878254953+00:00 - ERROR - No fallback registry specified, terminating
Please see the GitHub issue https://github.com/Azure/batch-shipyard/issues/340. You will likely need to upgrade your Batch Shipyard version and recreate your pool.
I am running a simple dataflow job and get the below error. I am able to run the job only in the East Asia region. All other 5 regions give me the below error.
Startup of the worker pool in zone us-central1-a failed to bring up any of
the desired 1 workers.
ZONE_RESOURCE_POOL_EXHAUSTED:
The zone 'projects/lmcdemo1111/zones/us-central1-a' does not have enough resources available to fulfill the request.
Try a different zone, or try again later
Any thoughts on how to fix this?