Bulk delete all the old jobs from the batch account
Deleting a Job also deletes all Tasks that are part of that Job, and all Job statistics. This also overrides the retention period for Task data; that is, if the Job contains Tasks which are still retained on Compute Nodes, the Batch service deletes those Tasks' working directories and all their contents. When a Delete Job request is received, the Batch service sets the Job to the deleting state. All update operations on a Job that is in deleting state will fail with status code 409 (Conflict), with additional information indicating that the Job is being deleted. We can use below PowerShell command to delete one single job using job id: Remove-AzBatchJob -Id "Job-000001" -BatchContext $Context But if you have a large number of jobs and wants to delete them simultaneously, then you can refer below PowerShell command for the same: # Replace with your Azure Batch account name, resource group, and subscription ID $accountName = "yourBatchAccountName" $resourceGroupName = "yourResourceGroupName" # Authenticate to Azure Connect-AzAccount # Get the Batch account context $batchContext = Get-AzBatchAccount -Name $accountName -ResourceGroupName $resourceGroupName # Get all batch jobs with creation time before May 2024 # Replace the creation time date accordingly $jobsForDelete = Get-AzBatchJob -BatchContext $batchContext | Where-Object {$_.CreationTime -lt "2024-05-01"} # List the jobs Write-Host "Jobs to be deleted:" foreach ($job in $jobsForDelete) { Write-Host $job.Id # Write-Host "Deleting jobs..." Remove-AzBatchJob -Id $job.Id -BatchContext $batchContext -Force } The above script will delete all the jobs created before the creation date. You can accordingly modify the parameters as per your requirement.
Finding Azure Batch Python client in Conda packaging
I've started working with Azure Batch and use Python, with my Python environment managed by Anaconda. I'd like to install the Azure Batch Client Library and Azure Batch Management Client Library from the Azure SDK for Python in my Anaconda environments, preferably using conda instead of pip. These are the azure.batch and azure.mgmt.batch modules (or "module packages"; whatever they're called), found in the azure-batch and azure-mgmt-batchPyPI packages. But I don't know where to find them in the Conda packaging of Azure SDK for Python. The Azure SDK for Python introduced a Conda packaging of it back in 2021, and its use is described in the "How to install Azure library packages for Python" page. The Conda packaging differs from the PyPI packaging. The Python Azure SDK modules are packaged in to a smaller number of packages in the Conda form, with sometimes different naming. Is the Azure Batch client library available in the Microsoft-supplied Conda packages somewhere (the ones in the microsoft conda channel, instead of the conda-forge channel)? If so, which Conda package? And more generally, if I know what Azure SDK for Python module I want, or what PyPI package it's in, how can I find out which microsoft-channel Conda package it's in? I haven't been able to find a list of which module is in which Conda package anywhere. There's an azure-batch conda package in the conda-forge channel (instead of the microsoft channel). But if I understand correctly, those conda-forge Azure packages are the old ones from before the 2021 introduction of the microsoft conda channel's packaging, and have different dependencies and stuff. I'd prefer to install the Azure Batch client from the microsoft-channel Conda packages, instead of the conda-forge channel package or from PyPI/pip, for consistency with my other Azure Python packages, which are all installed from the microsoft-channel Conda packages. I've read that mixing interdependent packages from different channels can sometimes cause problems, and if you're mixing conda-managed and pip-managed packages in an Anaconda environment, you're supposed to install all the conda packages first, then the pip packages, and then don't go back and install or update any conda packages afterwards, or something like that.Optimizing ETL Workflows: A Guide to Azure Integration and Authentication with Batch and Storage
Unlock the Power of Azure: Transform Your ETL Pipelines Dive into the world of data transformation and discover how to build a solid foundation for your ETL pipelines with Azure’s powerful trio: Data Factory, Batch, and Storage. Learn to navigate the complexities of data authentication and harness the full potential of Synapse Pipeline for seamless integration and advanced data processing. Ready to revolutionize your data strategy? This guide is your key to mastering Azure’s services and optimizing your workflows.Install Python on a windows node using a start task with Azure Batch
It is common that customers contact the Azure Batch Team to provide instructions on how to install Python using the start task feature. I would like to provide the steps to perform this task in case that someone needs to work in a similar case.SSL/TLS connection issue troubleshooting guide
You may experience exceptions or errors when establishing TLS connections with Azure services. Exceptions are vary dramatically depending on the client and server types. A typical ones such as "Could not create SSL/TLS secure channel." "SSL Handshake Failed", etc. In this article we will discuss common causes of TLS related issue and troubleshooting steps.
How to build customized container image for Azure Batch
Sometimes, users will need to install some necessary software/packages in the environment before the task is executed. This can be easily done by using Start task feature of Azure Batch. But when there are many dependencies to be installed, for example 20 packages in Linux and the installation of some of them will take long time such as Tensorflow, it will cause additional problems such as long start task running time when Azure Batch starts Batch node every time or even possible timeout issue or start task failure issue. In order to avoid this issue, user has two options: Custom image and Container. Both of these two features are supported in Azure Batch. The way of using custom image is already explained here. This blog will mainly explain how to use the container feature. One additional advantage is that for some special scenarios when the Batch task (application) has strong dependencies on a specific Operation System (OS) version, such as the Linux Ubuntu 18.04 which will reach EOL on Apr 30 th 2023, what user needs to further consider is only the compatibility between the container OS and the host OS(selected when you create Batch pool) if he selects the container feature. And it will be easier for user to recreate/modify the environment itself as he only needs to modify the Dockerfile, but not to recreate a new Virtual Machine to capture a new custom image. In this blog, the container image will be based on Linux Ubuntu 20.04. Assume that the Batch application needs a specific Python package called numpy as dependency. This blog will install this package based on a requirement.txt file, capture it into a container image, create a Batch pool based on this container image and verify the environment is good by running Batch task. Note: This is just one simplest example. For real situation, user will need to modify the Dockerfile and include much more his own files when he creates the container image. Pre-requisites: An Azure Batch account An Azure Container Registry The commands and/or the packages of installing dependencies Docker Desktop on Windows (The example used in this blog will all be with Windows OS, including the computer where we create image, the container image OS and the host OS. For Linux system, please kindly change the configuration.) Step 1: Create the own container image In order to create a new container image, user needs to prepare the following files: The Docker File Other dependency components which will be needed (For example the requirement.txt in this scenario) Note: Please pay attention that the Dockerfile should be without any extension name. The Dockerfile.txt will not work. Dockerfile: # Use an official Ubuntu 20.04 runtime as a base image FROM ubuntu:20.04 # Set the working directory to /app WORKDIR /app # Copy the current directory contents into the container at /app ADD . /app # Install pip3 RUN apt-get update RUN apt-get -y install python3-pip # Install numpy package RUN pip3 install -r requirement.txt # Make port 80 available to the world outside this container # EXPOSE 80 requirement.txt: Note: In rare situation, Batch task may need to wait until a request is sent from other client sides to Batch node. In this scenario, please kindly add the EXPOSE {port} line to export the port of the container to host machine. For more details about how to write Dockerfile, please check this official document from Docker. Once the files are prepared, user can start creating the container image. Open Docker Desktop and make sure to login Right click the tray icon and select Switch to Linux containers. Open Powershell in the folder containing Dockerfile and following command: docker build -t {container_name} . The expected result is like the following one. (This step might take lots of time to finish depending on the network speed as Docker is downloading the container image as base.) Find the username and password from Azure Container Registry and use the following command to login: docker login {registry_name}.azurecr.io -u {username} -p {password} Use the following commands to upload the container from local Docker to Azure Container Registry. docker tag {container_name} {registry_name}.azurecr.io/{repository} docker push {registry_name}.azurecr.io/{repository} Until here, the container is successfully created and uploaded into Azure Container Registry. Step 2: Use the container image to run Batch task User can then create a new pool and configure the container related setting in this node. The other configuration can be set as usual. Once the node is idle, please add a new job with a new task and link it to this new Batch pool. While creating the new task, please remember to select the correct Image. The following example uses a command line to let container python environment import numpy package, set variable a with value 1, then print out the value of variable a. The expected output should be just “1”. This proved that our container is successfully created and the installation of python package is also good, otherwise there will be an error message reporting the module not found like following one. Conclusion The real user scenario will be more complicated, but the principal way of creating the customized container will be the same. The example Dockerfile in this blog shows how to: Run command to install dependencies Copy the necessary dependency files into Docker image These two steps are also the main steps which user needs to customize.
Create Azure Batch Pool with PowerShell
The aim of this blog is to show you how to create a Batch pool using PowerShell commandwith several features enabled: Scenario 1: Add Batch pool to the Azure Virtual Network with or without public IP address Scenario 2: Add application package to the Batch pool Scenario 3: Add certificate to the Batch pool Scenario 4: Add user account to the Batch pool Scenario 5: Add start task to the Batch pool Scenario 6: Mounting the file system to the Batch pool Scenario 7: Given the image reference (built-in image and custom image) to the Batch pool
How to detect and delete unusable nodes in Batch Service automatically by PowerShell Script
In some circumstances, Azure Batch might set thenode statetounusable due to different reasons. When a node isunusable, tasks can't be scheduled to the node, but it still charges. If it’s not needed to find the root cause of nodes becoming unusable, we can locate these unusable batch nodes and delete them timely to avoid the unexpected cost. This article provides a PowerShell script that can detect all unusable nodes in a batch account/pool and delete them. Please download the script sample from this link. Some Tips: 1.If the auto-scale feature is enabled in a batch pool, it’s needed to disable the auto-scale before removing the nodes. 2.It’s needed to make sure the batch pool is in steady status before enabling auto-scale. Otherwise, there could be some issues. For example, the auto-scale can be enabled without any errors or warnings but it won’t work as expected, because the actual samples of metric data can’t be obtained. 3.When an unusable node is deleted, it would be very hard to troubleshoot the root cause of nodes becoming unusable. If the root cause is needed, you need to disable the auto-scale so that the problematic node can be reserved. 4.This PowerShell script can be configured in an Azure Function App with Timer triggers to detect and delete the unusable nodes periodically. Reference links: 1.Details of unusable batch node: Node in unusable state. 2.Install the latest Batch PowerShell module: Batch PowerShell module.Azure Batch Pool resizing failed - Allocation failed due to marketplace purchase eligibilty
For Azure Batch users, when you tried to resize your Batch pool, but it failed with error message ofAllocation failed due to marketplace purchase eligibility. You can follow the below steps to check if it’s the same scenario with this blog.
