backend.ai-manager

v26.4.3 safe
3.0
Low Risk

Backend.AI Manager

🤖 AI Analysis

Final verdict: SAFE

The package exhibits low risks across all critical areas with no signs of malicious activities. The metadata risk is slightly elevated due to the maintainer's single package history, but this alone does not indicate malicious intent.

  • No network calls
  • No shell execution
  • No obfuscation
  • No credential harvesting
  • Maintainer has only one package
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution patterns detected, indicating no immediate signs of malicious activity.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has only one package, indicating a new or less active account which could be suspicious but not conclusive.

📦 Package Quality Overall: Medium (5.4/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://docs.backend.ai/
  • Detailed PyPI description (31565 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 410 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 9 unique contributor(s) across 100 commits in lablup/backend.ai
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository lablup/backend.ai appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Lablup Inc. and contributors" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with backend.ai-manager 
Create a fully functional mini-application named 'AI-Compute-Dashboard' using the Python package 'backend.ai-manager'. This application will serve as a user-friendly interface for managing and monitoring AI computations on a cluster of machines. Here are the steps and features you need to implement:

1. **Setup Environment**: Begin by setting up your development environment. Ensure you have Python installed along with the necessary dependencies including 'backend.ai-manager'. Install the package via pip.
2. **Application Structure**: Design a clean and modular application structure. Use Flask for the web framework due to its simplicity and ease of use for web applications.
3. **User Authentication**: Implement a basic authentication system where users can sign up and log in to access their computation sessions. Use JWT tokens for secure session management.
4. **Dashboard Overview**: Develop a dashboard that provides an overview of all active computation sessions. This includes details such as session ID, start time, status (running, paused, completed), and estimated completion time.
5. **Session Management**: Allow users to create, manage, and terminate computation sessions directly from the dashboard. Users should be able to specify the type of machine they want to run their computations on (e.g., CPU, GPU).
6. **Resource Monitoring**: Integrate real-time resource usage monitoring for each active session. Display metrics like CPU usage, memory usage, and disk I/O.
7. **Job Scheduling**: Implement a job scheduling feature that allows users to queue jobs and set priorities based on computational requirements.
8. **Logging and Alerts**: Provide logging capabilities for each session, allowing users to view detailed logs. Additionally, set up alert notifications for critical events such as job failures or high resource usage.
9. **Custom Scripts Execution**: Enable users to upload and execute custom scripts within their computation sessions. Ensure that these scripts can interact with the environment provided by 'backend.ai-manager'.
10. **Documentation and Testing**: Write comprehensive documentation for the application and ensure thorough testing across different scenarios.

In this project, the 'backend.ai-manager' package will be utilized extensively for managing the lifecycle of computation sessions, handling resource allocation, and interfacing with the underlying cluster infrastructure. Make sure to leverage the package's capabilities to simplify the implementation of complex functionalities.

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