I am setting up ML experiment in Azure ML Studio Designer for K-Means Clustering and have ~750 attributes that I am attempting to cluster with ~4000 rows.
When i go to execute this i get the following error:
AzureMLCompute job failed.
InvalidPropertyValue: The size of the specified property Job.Properties.CustomToolkitSettings.CommandLine
exceeds the limit of 20480 characters
There isn't much on this error but would someone be able to assist me on how to increase the limit? I don't believe this is an K-Means error but a Azure ML Studio error.
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Presently, I am using kusto query language to get the value of CPU consumption and memory utilized above a certain threshold. I have kept the scope as that adf in log analytics workspace. Whenever I run some query I am getting no result, even when the pipeline gives considerable results for these parameters in the metrics section. I have gone through these sources for finding Kusto queries:
https://www.cloudsma.com/2018/07/cpu-processes-azure-log-analytics/
https://www.catapultsystems.com/blogs/creating-next-generation-queries-for-cpu-and-memory-kpis-in-log-analytics/
https://pixelrobots.co.uk/2019/06/monitor-your-servers-cpu-usage-using-azure-log-analytics/
And also tried the ones given as the sample queries in log analytics workspace.
Besides, on using "Perf" in the queries, an error is thrown stating that
"'where' operator: Failed to resolve table or column expression named 'Perf'". How do I get the results using kusto queries?
I am new to Azure Databricks. I have two input files and python AI model, I am cleaning the input files and applying AI model on input Files to get final probabilities. Reading files, loading model, cleaning data, preprocessing the data and displaying output with probabilities taking me only few minutes.
But while I am trying to write the result to Table or parquet file it is taking me more than 4-5 hours. I have tried various approaches of repartition/partitionBy/saveAsTable but none of it is fast enough.
My output spark dataframe consists of three columns with 120000000 rows. My shared cluster size is 9-Node cluster with each Node of 56GB memory.
My doubts are :-
1.) Is it expected behavior in azure databricks with slow writing capabilities.
2.) Is it true that we can't tune spark configurations in azure databricks, azure databricks tunes itself with available memory.
The performance depends on multiple factors: To investigate further, could you please share the below details:
What is the size of the data?
What is the size of the worker type?
Share the code which you are running?
I would suggest you go through the below articles, which helps to improve the performance:
Optimize performance with caching
7 Tips to Debug Apache Spark Code Faster with Databricks
Azure Databricks Performance Notes
I have used azure databricks and have written data to azure storage and it has been fast.
Also the databricks is hosted on Azure like in Aws.So all configurations of spark can be set.
As pradeep asked, what is the datasize and number of partitions? you can get it using df.rdd.getNumPartitions().
Have you tried a repartition before write? Thanks.
I am building pipelines on Azure Data Factory, using the Mapping Data Flow activity (Azure SQL DB to Synapse). The pipelines complete in debug mode, when I enable sampling data for the sources. When I disable sampling data and run the debug, I make no progress in the pipeline. i.e. none of the transformations complete (yellow dot)
To improve this, should I increase the batch size from the source/sink (how do I determine a batch size), increase the number of partitions (how do I determine a good number of partitions)
What is the size of the Spark compute cluster you have set in the Azure Integration Runtime under data flow properties. Start there by creating an Azure IR with enough cores to provide RAM for your process. Then you can adjust the partitions and batch sizes. Much of the learnings in this area are shared here at this ADF data flow performance guide.
I am using ADF to execute Databricks notebook. At this time, I have 6 pipelines, and they are executed consequently.
Specifically, after the former is done, the latter is executed with multiple parameters by the loop box, and this keeps going. For example, after the first pipeline is done, it will trigger 3 instances of the second pipeline with different parameters, and each of these instances will trigger multiple instances of the third pipeline. As a result, the deeper I go, the more pipelines I have to run.
The issue with me is: when each pipeline is executed, it will ask Databricks to allocate a cluster to run. However, Databricks limits the number of cores to be used for each workspace, which causes the pipeline instance to fail to run.
My question is: is there any solution to control the number of pipeline instance running at the same time, or any solution to handle my issue?
Thanks in advance :-)
Why this issue occurs?
Note: Creating a Databricks clusters always have a dependency with the number of cores available in the subscription.
Before creating any databricks cluster, make sure number of cores are
available in the region selected and the VM Family vCPUs.
You can checkout the core limit of your subscription by going to Azure Portal => Subscriptions => Select your subscription => Settings “Usage + quotes” => Checkout the usage quota available for each regions.
Example: If your subscription has > 72 cores which results in success of ADF runs else results in failure.
Activity Validate failed: Databricks execution failed with error message: Unexpected failure while waiting for the cluster to be ready. Cause Unexpected state for cluster (job-200-run-1): Could not launch cluster due to cloud provider failures. azure_error_code: OperationNotAllowed, azure_error_message: Operation results in exceeding quota limits of Core. Maximum allowed: 350, Current in use: 344
I’m trying to create 6 pipelines with databricks clusters with 2 worker nodes each. Which means it requires
(6 pipelines) * (1 Driver Node + 2 Worker Node) * (4 cores) = 72 cores.
Above calculation used with VM Size Standard_DS3_v2 which has 4 cores.
Note: To create a databricks spark cluster which requires more than 4 cores i.e. (Minimum 4 cores for Driver type and 4 cores for
Worker type).
Resolutions for this issue:
Increase the core limits by raising the ticket to billing and subscription team to higher limit. With this option once you use it you will be charged for the used cores only.
Limit your job frequency so that limited number of clusters/ consider using a single job for copying multiple file so that you can limit the cluster creations which will exhaust your cores under your subscription.
To request an increase of one or more resources that support such an increase, submit an
Azure support request (select "Quota" for Issue type).
Issue type: Service and subscription limits (quotas)
Reference: Total regional vCPU limit increases
Hope this helps. Do let us know if you any further queries.
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You can limit number of activities being run in parallel at each foreach level by setting - Batch Count Parameter. ( Found under settings tab on foreach loop)
batchCount- Batch count to be used for controlling the number of parallel execution (when isSequential is set to false).
https://learn.microsoft.com/en-us/azure/data-factory/control-flow-for-each-activity
If not able to set limit at overall Pipeline level, try arriving at a minimum value of batch count in your each nested foreach loops.
I just started with using the Azure Machine Learning Services and ran into this problem. Creating a local environment and deploying my model to localhost works perfectly fine.
Can anyone identify what could have caused this error, because i do not know where to start..
I tried to create a cluster for Location "eastus2" aswell, which caused the same error.
Thank you very much in advance!
Btw, the ressource group and ressources are being created into my azure account.
Image of error
Ashvin [MSFT]
Sorry to hear that you were facing issues. We checked logs on our side using the info you provided in the screenshot. The cluster setup failed because there weren't enough cores to fit AzureML and system components in the cluster. You specified agent-vm-size of D1v2 which has 1 CPU core. By default we create 2 agents so total cores were 2. To resolve, can you please try creating a new cluster without specifying agent size? Then AzureML will create 2 agents of D3v2 which is 8 cores total. This should fit the AzureML and system components and leave some room for you to deploy your services.
If you wish a bigger cluster you could specify agent-count along with agent-vm-size to appropriately size your cluster but please have minimum total of 8 cores with each individual VM >= 2 cores to ensure cluster works smoothly. Hope this helps.
We are working on our side to add error handling to ensure request fails with clear error message.