| Name | kaizen-deploy JSON |
| Version |
1.2.3
JSON |
| download |
| home_page | None |
| Summary | kaizen-deploy is a Configuration Management tool used for installing KIMS(Kubernetes Incident Management System anywhere.). |
| upload_time | 2025-01-05 07:47:32 |
| maintainer | Arjun Babu |
| docs_url | None |
| author | Arjun Babu |
| requires_python | >=3.6 |
| license | MIT |
| keywords |
kaizen-deploy
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
Kaizen-Deploy is an open-source tool that can be used for deploying KIMS(Kubernetes Incident Management System). This is an open-source project however, authorization rights and confidential modules belongs to the owner itself. Self written modules based on Python3 works with the tool.
The KIMS (Kubernetes Incident Management System) is a comprehensive tool that is used to handle monitoring and incident management for a Kubernetes cluster. KIMS monitors for any form of abnormalities and failures of K8s deployment and pods. Once a failure is detected, KIMS gathers all the event and logs of the failure, review that with an AI LLM (GPT-4/Anthropic, etc) and gains the issue summary, possible causes and resolution steps for mitigating the issue. KIMS also has got a UI dashboard where the user can login and view the cluster health. Also, all the incident logs, container statuses, AI recommendations, etc can be viewed from this UI.
To deploy KIMS into a Kubernetes cluster, thats where Kaizen-Deploy is used. You just need to provide the below requirements:
1. AI Details that involve model/LLM endpoints, token, etc (Detailed useage is described in the below parameters section)
2. Public DNS endpoint for the UI (Load balancer DNS).
3. Your deployment pipeline job should have access to the Kubernetes cluster, Python3 installed, kubectl CLI installed.
In terms of AI models, as of now, KIMS only support Azure OpenAi Services.
[](https://pypi.python.org/pypi/yt2mp3/)
[](https://pypi.python.org/pypi/yt2mp3/)
[](https://pypi.org/project/kaizen-deploy/)
[](https://pypi.org/project/kaizen-deploy/1.2.3/)
## Installation Prerequisite:
##### Linux
In the case of Linux based systems, set environment variable path and provide execution permission:
```Shell
ln -s /usr/local/Lib/kaizen-deploy/main.py /usr/local/bin/kaizen
chmod +x /usr/local/Lib/kaizen-deploy/main.py
```
## Command usage:
`kaizen-deploy --scroll [yaml file]`
## Requirements
* Python >= 3.9.6
## Parameters
[](https://pypi.org/project/kaizen-deploy/)
[](https://pypi.org/project/kaizen-deploy/)
|**Parameter**|**Choices/Defaults**|**Comments**|
|-------------|--------------------|------------|
|**clusterName** |Any| Name of the Kubernetes cluster.
|**jutsu/database/username** |Any| Name of the database user. This username can be used to login to the KIMS PostgreSQL server to check data feeds.
|**jutsu/database/password** |Any| Password for the database user. This password can be used to login to the KIMS PostgreSQL server to check data feeds.
|**jutsu/AICloudProvisioner** |**Choices:** **azure** or **aws** or **anthropic**| Name of the AI Cloud provisioner. OpenAi in Azure or Bedrock in AWS or Anthropic can be used.
|**jutsu/azureOpenAI/deploymentEndpoint** |If Azure is used as the AI Cloud provisioner| OpenAi deployment endpoint URL.
|**jutsu/azureOpenAI/apiKey** |If Azure is used as the AI Cloud provisioner| OpenAi deployment API key.
|**jutsu/azureOpenAI/apiVersion** |If Azure is used as the AI Cloud provisioner| OpenAi deployment API version..
|**jutsu/azureOpenAI/deploymentName** |If Azure is used as the AI Cloud provisioner| OpenAi deployment name (Eg: gpt-4).
|**jutsu/alertManager/teamsWebhookURL** |Any| The Microsoft Webhook URL/Workflow URL for sending Kubernetes incident notification.
|**jutsu/alertManager/kimsUIURL** |Any| The Microsoft Webhook URL/Workflow URL for sending Kubernetes incident notification.
### Example
```YAML
---
clusterName: docker-desktop
jutsu:
database:
username: postgres
password: postgres
AICloudProvisioner: azure
azureOpenAI:
deploymentEndpoint: "https://test.openai.azure.com/openai/deployments/gpt-4/chat/completions?api-version=2024-08-01-preview"
apiKey: "DWFDF7832bDQDds8wQwc23SQsfdsqd23sdQW"
apiVersion: "2024-02-01"
deploymentName: "gpt-4"
alertManager:
teamsWebhookURL: "https://prod-13.centralindia.logic.azure.com:443/workflows/34nkjb34b3b141/triggers/manual/paths/invoke?api-version=2016-06-01&sp=%2Ftriggers%2Fmanual%2Frun&sv=1.0&si3wqqwlfMZpiQsfds_sdbqs1h-ZqcY"
kimsUIURL: "http://kims-incident-dashboard-v1.eastus.cloudapp.azure.com:31000/incidents"
```
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"description": "Kaizen-Deploy is an open-source tool that can be used for deploying KIMS(Kubernetes Incident Management System). This is an open-source project however, authorization rights and confidential modules belongs to the owner itself. Self written modules based on Python3 works with the tool.\n\nThe KIMS (Kubernetes Incident Management System) is a comprehensive tool that is used to handle monitoring and incident management for a Kubernetes cluster. KIMS monitors for any form of abnormalities and failures of K8s deployment and pods. Once a failure is detected, KIMS gathers all the event and logs of the failure, review that with an AI LLM (GPT-4/Anthropic, etc) and gains the issue summary, possible causes and resolution steps for mitigating the issue. KIMS also has got a UI dashboard where the user can login and view the cluster health. Also, all the incident logs, container statuses, AI recommendations, etc can be viewed from this UI.\n\nTo deploy KIMS into a Kubernetes cluster, thats where Kaizen-Deploy is used. You just need to provide the below requirements:\n1. AI Details that involve model/LLM endpoints, token, etc (Detailed useage is described in the below parameters section)\n2. Public DNS endpoint for the UI (Load balancer DNS).\n3. Your deployment pipeline job should have access to the Kubernetes cluster, Python3 installed, kubectl CLI installed.\n\nIn terms of AI models, as of now, KIMS only support Azure OpenAi Services. \n\n[](https://pypi.python.org/pypi/yt2mp3/) \n[](https://pypi.python.org/pypi/yt2mp3/)\n\n[](https://pypi.org/project/kaizen-deploy/)\n[](https://pypi.org/project/kaizen-deploy/1.2.3/)\n\n\n\n\n## Installation Prerequisite: \n\n##### Linux\nIn the case of Linux based systems, set environment variable path and provide execution permission: \n```Shell\nln -s /usr/local/Lib/kaizen-deploy/main.py /usr/local/bin/kaizen\nchmod +x /usr/local/Lib/kaizen-deploy/main.py\n```\n\n## Command usage:\n`kaizen-deploy --scroll [yaml file]`\n\n## Requirements\n* Python >= 3.9.6\n\n## Parameters\n \n\n[](https://pypi.org/project/kaizen-deploy/)\n[](https://pypi.org/project/kaizen-deploy/)\n\n|**Parameter**|**Choices/Defaults**|**Comments**|\n|-------------|--------------------|------------|\n|**clusterName** |Any| Name of the Kubernetes cluster.\n|**jutsu/database/username** |Any| Name of the database user. This username can be used to login to the KIMS PostgreSQL server to check data feeds.\n|**jutsu/database/password** |Any| Password for the database user. This password can be used to login to the KIMS PostgreSQL server to check data feeds.\n|**jutsu/AICloudProvisioner** |**Choices:** **azure** or **aws** or **anthropic**| Name of the AI Cloud provisioner. OpenAi in Azure or Bedrock in AWS or Anthropic can be used.\n|**jutsu/azureOpenAI/deploymentEndpoint** |If Azure is used as the AI Cloud provisioner| OpenAi deployment endpoint URL.\n|**jutsu/azureOpenAI/apiKey** |If Azure is used as the AI Cloud provisioner| OpenAi deployment API key.\n|**jutsu/azureOpenAI/apiVersion** |If Azure is used as the AI Cloud provisioner| OpenAi deployment API version..\n|**jutsu/azureOpenAI/deploymentName** |If Azure is used as the AI Cloud provisioner| OpenAi deployment name (Eg: gpt-4).\n|**jutsu/alertManager/teamsWebhookURL** |Any| The Microsoft Webhook URL/Workflow URL for sending Kubernetes incident notification.\n|**jutsu/alertManager/kimsUIURL** |Any| The Microsoft Webhook URL/Workflow URL for sending Kubernetes incident notification.\n\n \n \n### Example\n \n```YAML\n---\n\nclusterName: docker-desktop\n\njutsu:\n database:\n username: postgres\n password: postgres\n AICloudProvisioner: azure\n azureOpenAI:\n deploymentEndpoint: \"https://test.openai.azure.com/openai/deployments/gpt-4/chat/completions?api-version=2024-08-01-preview\"\n apiKey: \"DWFDF7832bDQDds8wQwc23SQsfdsqd23sdQW\"\n apiVersion: \"2024-02-01\"\n deploymentName: \"gpt-4\"\n alertManager:\n teamsWebhookURL: \"https://prod-13.centralindia.logic.azure.com:443/workflows/34nkjb34b3b141/triggers/manual/paths/invoke?api-version=2016-06-01&sp=%2Ftriggers%2Fmanual%2Frun&sv=1.0&si3wqqwlfMZpiQsfds_sdbqs1h-ZqcY\"\n kimsUIURL: \"http://kims-incident-dashboard-v1.eastus.cloudapp.azure.com:31000/incidents\"\n```\n \n\n\n\n",
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