Using Powershell for Azure Devops to refer to predefined variables.



Using Powershell for Azure Devops Linux agent to refer to variables can be hard to get right essentially just use $(build.SourceBranchName) - the build.SourceBranchName is what listed in the documentation.

You can see one example given below :-





- task: PowerShell@2
      displayName: 'PowerShell contruct VersionSuffix if not Master'
      inputs:
        targetType: 'inline'
        script: |
         Write-Host "Setting up version info"
         $VersionSuffix =  'prerelease' + '.$(build.SourceBranchName).' + $(build.buildid)
         write-host "##vso[task.setvariable variable=VersionSuffix]$VersionSuffix"
         Write-Output "##vso[build.updatebuildnumber]$($env:VersionPrefix)-$VersionSuffix"

        failOnStderr: true
      condition: and(succeeded(), ne(variables['Build.SourceBranch'], 'refs/heads/master'))

Comments

Post a Comment

Popular posts from this blog

NodeJS: Error: spawn EINVAL in window for node version 20.20 and 18.20

Encounter this issue when running autorest (that uses nodejs).  I am using node version:  v18.20.2 After i revert to use node v18.12.0, then i was able to get my app to run without this error. Since i am working with Azure devops yaml, i was able to use the following to resolve my issue.  - task : NodeTool@0   inputs :     versionSource : 'spec'     versionSpec : '18.18.0'
Read more

llama cpp running it in google colab

Image
There are 2 options to do this in Google  colab.  Option 1 (Easiest)  With this option, we can just install the relevant package and then run the model.  First we need to install the required packages using the following command:-  ! pip install -U llama-cpp-python Next,  we will try to get the model using the following command # !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained(   repo_id= "unsloth/Qwen3-0.6B-GGUF" ,   filename= "Qwen3-0.6B-IQ4_NL.gguf" , ) We can see the model being downloaded:- And then we will run the following command to test this model llm.create_chat_completion(   messages = [     {       "role" : "user" ,       "content" : "What is the capital of France?"     }   ] ) And the output will look something like this:-  Option 2 llama.cpp is a powerful inference engine and if you wanted to get it running in google colab, you co...
Read more