Action History
The Action History feature provides a detailed record of all actions performed on resources within the platform. This includes actions such as creating, starting, stopping, and deleting notebook instances, training jobs, tuning jobs, model endpoints, and network volumes.
Accessing Action History
Access the Action history through the url: https://aiplatform.console.greennode.ai/action
Understanding the Action History Table
The Action History table displays the following information for each action:
Column | Description |
| Action Name | The type of action performed (e.g., Create Notebook Instance, Start Training Job). |
Resource Info | The name and ID of the resource affected by the action. |
Resource Type | The type of resource (e.g., Notebook Instance, Training Job, Model Endpoint, Network Volume). |
Resource Configuration | The configuration details of the resource, such as core count, memory size, or storage capacity. |
Action Time | The timestamp when the action was performed. |
Filtering and Searching
You can filter and search the Action History table to find specific actions:
- Time Range: Filter actions within a specific time range.
- Resource Type: Filter actions based on the type of resource affected (e.g., Notebook Instances, Training Jobs).
- Resource Information: Search for actions involving a specific resource name or id.
Utilizing Action History
The Action History feature can be used for various purposes:
- Monitoring Resource Usage: Track resource consumption patterns to optimize resource allocation and cost management.
- Troubleshooting Issues: Review the history of actions to identify potential causes of errors or performance issues.
- Auditing and Compliance: Maintain a detailed audit trail for compliance and security purposes.
- Capacity Planning: Analyze historical usage data to forecast future resource needs.
- User Behavior Analysis: Understand how users interact with the platform to identify potential areas for improvement.
By effectively utilizing the Action History feature, you can gain valuable insights into resource usage, user behavior, and system performance.
Related Articles
GreenNode AI Platform Release Note 2024
This central hub provides comprehensive information about the latest updates, new features, enhancements, and bug fixes introduced in each release of the GreenNode AI Platform in 2024. Our goal is to keep you informed and empowered to make the most ...GreenNode AI Platform Release Notes 2025
This central hub provides comprehensive information about the latest updates, new features, enhancements, and bug fixes introduced in each release of the GreenNode AI Platform in 2025. Our goal is to keep you informed and empowered to make the most ...Payment Method
Depositing Credits to Your GreenNode Account via Stripe This guide provides a step-by-step walkthrough of how to deposit credits into your GreenNode account using Stripe. Having a credit balance allows you to access and utilize GreenNode's AI ...Manage a model tuning job
Model tuning, also known as hyperparameter optimization, is the process of adjusting the hyperparameters of a machine learning model to improve its performance. Hyperparameters are settings that determine the learning process of a model and are not ...Billing Method
In this section, you will learn about the billing methods available for our Greennode AI Platform. Understanding the billing structure is essential for managing costs effectively and optimizing resource utilization. Billing Method Billing method: Pay ...
