ascend-tui

v1.0.0 suspicious
4.0
Medium Risk

Ascend TUI - GPU→NPU 端到端迁移与推理加速 Multi-Agent

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has minimal risks associated with network calls, shell execution, and obfuscation. However, the metadata quality is questionable, suggesting potential issues with transparency or maintenance efforts.

  • Metadata risk is elevated due to low effort and potential lack of transparency.
  • No direct evidence of malicious activity, but the overall quality of the package metadata raises concerns.
Per-check LLM notes
  • Network: No network calls detected, which is normal for many tools not requiring internet access.
  • Shell: No shell execution patterns detected, indicating the package does not execute external commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
  • Metadata: The package shows signs of low effort and potential lack of transparency, raising suspicion.

📦 Package Quality Overall: Low (2.8/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Brief PyPI description (570 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 18 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 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

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 8.0

4 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with ascend-tui 
Create a command-line interface (CLI) tool using the 'ascend-tui' Python package to facilitate the migration of machine learning models from GPUs to NPUs, while also providing real-time performance metrics and optimization suggestions. This tool will serve as a bridge between developers working with traditional GPU-based models and those looking to leverage the power of NPUs for enhanced performance and energy efficiency.

### Key Features:
1. **Model Migration**: Allow users to input a model file trained on a GPU, and the tool will automatically convert it to a format compatible with NPUs, ensuring minimal changes to the original model architecture.
2. **Performance Metrics**: Display real-time performance metrics such as inference time, accuracy, and energy consumption when running the model on an NPU. Compare these metrics against the same model running on a GPU to provide a clear picture of the benefits of NPU usage.
3. **Optimization Suggestions**: Based on the performance metrics, suggest ways to optimize the model further for NPU execution. This could include adjusting model parameters, fine-tuning the model, or suggesting different NPU configurations.
4. **User Interface**: Utilize the 'ascend-tui' package to create an intuitive and user-friendly text-based interface for interacting with the tool. Ensure that all commands are clearly documented and easy to understand.
5. **Logging and Reporting**: Implement logging functionality to record the entire process of model migration and optimization, including any errors encountered and their resolutions. Provide a reporting feature that summarizes the results of the migration and optimization process.

### How 'ascend-tui' Package is Utilized:
- Use 'ascend-tui' to handle the user interaction aspects of the CLI tool, making sure that the interface is responsive and provides feedback at each step of the process.
- Leverage 'ascend-tui' functionalities for managing the migration process, ensuring seamless integration between GPU-trained models and NPUs.
- Employ 'ascend-tui' to monitor the performance of the models during inference, collecting data that can be used to generate real-time performance metrics.
- Utilize 'ascend-tui' for displaying optimization suggestions based on the collected performance data, helping users make informed decisions about how to improve their models for NPU execution.

This project aims to streamline the process of transitioning machine learning models to NPUs, offering developers a powerful yet accessible tool for enhancing the performance and efficiency of their applications.

💬 Discussion Feed

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