gke gateway :- error ensuring load balancer: generic::invalid_argument: Insert: Invalid value for field 'resource.target'

This error  comes about when you do not have a proxy subnet created for your region when you're trying to deploy load balancer or when creating k8s API gateway's gateway resources. 

These load balancers use a Google-managed proxy layer, and for internal communication between the proxy and your backend VMs, GCP needs a reserved IP range within your VPC. That’s where the proxy-only subnet comes in.

You can create it by going here:

https://cloud.google.com/load-balancing/docs/proxy-only-subnets#proxy_only_subnet_create

You can see after i apply the fixes, my gateway resources starts to sync successfully. 


And then allocated a ip for my gateway here:- 







Comments

Post a Comment

Popular posts from this blog

gemini cli getting file not defined error

Image
 While trying to run gemini cli on my windows linux subsystem, i bump into this error here - "/Roaming/npm/node_modules/@google/gemini-cli/node_modules/undici/lib/web/webidl/index.js:512 webidl.is.File = webidl.util.MakeTypeAssertion(File)"  "ReferenceError: File is not defined" In Node.js, there’s no built-in File (until very recent Node 20+ with experimental WHATWG APIs). So, unless polyfilled, File is undefined, leading to the ReferenceError. And to resolve this, I had to upgrade to node 22 (might work for node 20) and then I was able to run it.
Read more

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

vllm : Failed to infer device type

Trying to run vllm without gpu will typicaly lands you into this issue. To resolve this issue: we can use set these both. Setting one does not work for me. So i had to configure both settings below:- 1.  environment variable CUDA_VISIBLE_DEVICES = "" 2. in the command line, change to use device cpu and remove  tensor-parallel-size. For example, python3 -m vllm.entrypoints.openai.api_server --port 8080 --model deepseek-ai/DeepSeek-R1 --device cpu --trust-remote-code --max-model-len 4096" References https://docs.vllm.ai/en/latest/getting_started/troubleshooting.html#failed-to-infer-device-type
Read more