32 bit:

curl -LO https://github.com/k8sgpt-ai/k8sgpt/releases/download/v0.3.6/k8sgpt_386.rpm
sudo rpm -ivh k8sgpt_386.rpm

64 bit:

curl -LO https://github.com/k8sgpt-ai/k8sgpt/releases/download/v0.3.6/k8sgpt_amd64.rpm
sudo rpm -ivh -i k8sgpt_amd64.rpm

32 bit:

curl -LO https://github.com/k8sgpt-ai/k8sgpt/releases/download/v0.3.6/k8sgpt_386.deb
sudo dpkg -i k8sgpt_386.deb

64 bit:

curl -LO https://github.com/k8sgpt-ai/k8sgpt/releases/download/v0.3.6/k8sgpt_amd64.deb
sudo dpkg -i k8sgpt_amd64.deb

32 bit:

curl -LO https://github.com/k8sgpt-ai/k8sgpt/releases/download/v0.3.6/k8sgpt_386.apk
apk add k8sgpt_386.apk

64 bit:

curl -LO https://github.com/k8sgpt-ai/k8sgpt/releases/download/v0.3.6/k8sgpt_amd64.apk
apk add k8sgpt_amd64.apk

==> Installing k8sgpt from k8sgpt-ai/k8sgpt Error: The following formula cannot be installed from a bottle and must be
built from the source. k8sgpt Install Clang or run brew install gcc.

If you install gcc as suggested, the problem will persist. Therefore, you need to install the build-essential package.

   sudo apt-get update
   sudo apt-get install build-essential

List filters

k8sgpt filters list

Add default filters

k8sgpt filters add [filter(s)]

• Simple filter : k8sgpt filters add Service
• Multiple filters : k8sgpt filters add Ingress,Pod

Remove default filters

k8sgpt filters remove [filter(s)]

• Simple filter : k8sgpt filters remove Service
• Multiple filters : k8sgpt filters remove Ingress,Pod

k8sgpt auth list

Remove configured backends

k8sgpt auth remove $MY_BACKEND1,$MY_BACKEND2..

List integrations

k8sgpt integrations list

Activate integrations

k8sgpt integrations activate [integration(s)]

Use integration

k8sgpt analyze --filter=[integration(s)]

Deactivate integrations

k8sgpt integrations deactivate [integration(s)]

Serve mode

k8sgpt serve

Analysis with serve mode

curl -X GET "http://localhost:8080/analyze?namespace=k8sgpt&explain=false"

To run local models, it is possible to use OpenAI compatible APIs, for instance LocalAI which uses llama.cpp and ggml to run inference on consumer-grade hardware. Models supported by LocalAI for instance are Vicuna, Alpaca, LLaMA, Cerebras, GPT4ALL, GPT4ALL-J and koala.

To run local inference, you need to download the models first, for instance you can find ggml compatible models in huggingface.com (for example vicuna, alpaca and koala).

To start the API server, follow the instruction in LocalAI.

To run k8sgpt, run k8sgpt auth new with the localai backend:

k8sgpt auth new --backend localai --model <model_name> --baseurl http://localhost:8080/v1

Now you can analyze with the localai backend:

k8sgpt analyze --explain --backend localai

Prerequisites: an Azure OpenAI deployment is needed, please visit MS official documentation to create your own.

To authenticate with k8sgpt, you will need the Azure OpenAI endpoint of your tenant "https://your Azure OpenAI Endpoint", the api key to access your deployment, the deployment name of your model and the model name itself.

To run k8sgpt, run k8sgpt auth with the azureopenai backend:

k8sgpt auth add --backend azureopenai --baseurl https://<your Azure OpenAI endpoint> --engine <deployment_name> --model <model_name>

Lastly, enter your Azure API key, after the prompt.

Now you are ready to analyze with the azure openai backend:

k8sgpt analyze --explain --backend azureopenai

There may be scenarios where you wish to have K8sGPT plugged into several default AI providers. In this case you may wish to use one as a new default, other than OpenAI which is the project default.

To view available providers

k8sgpt auth list
Default:
> openai
Active:
> openai
> azureopenai
Unused:
> localai
> noopai

To set a new default provider

k8sgpt auth default -p azureopenai
Default provider set to azureopenai

With this option, the data is anonymized before being sent to the AI Backend. During the analysis execution, k8sgpt retrieves sensitive data (Kubernetes object names, labels, etc.). This data is masked when sent to the AI backend and replaced by a key that can be used to de-anonymize the data when the solution is returned to the user.

2. Payload sent to the AI backend:

Error: HorizontalPodAutoscaler uses StatefulSet/tGLcCRcHa1Ce5Rs as ScaleTargetRef which does not exist.

3. Payload returned by the AI:

The Kubernetes system is trying to scale a StatefulSet named tGLcCRcHa1Ce5Rs using the HorizontalPodAutoscaler, but it cannot find the StatefulSet. The solution is to verify that the StatefulSet name is spelled correctly and exists in the same namespace as the HorizontalPodAutoscaler.

4. Payload returned to the user:

The Kubernetes system is trying to scale a StatefulSet named fake-deployment using the HorizontalPodAutoscaler, but it cannot find the StatefulSet. The solution is to verify that the StatefulSet name is spelled correctly and exists in the same namespace as the HorizontalPodAutoscaler.

Anonymization does not currently apply to events.

Config file locations:

As a prerequisite AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY are required as environmental variables.

Adding a remote cache Note: this will create the bucket if it does not exist

k8sgpt cache add --region <aws region> --bucket <name>

Listing cache items

k8sgpt cache list

Removing the remote cache Note: this will not delete the bucket

k8sgpt cache remove --bucket <name>