AI Analysis
Final verdict: SUSPICIOUS
The package has a low risk of obfuscation and credential harvesting but shows signs of low maintenance and lacks author information, raising suspicion about its legitimacy.
- Low maintenance indicators
- Lack of author information
Per-check LLM notes
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows signs of low maintenance and could potentially be suspicious due to the lack of author information and GitHub repository.
Package Quality Overall: Low (1.2/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
○ Low
Documentation
1.0
No documentation detected
No documentation URL, doc files, or meaningful description found
○ Low
Contributing Guide
2.0
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low
Type Annotations
1.0
No type annotations detected
No type annotations, py.typed marker, or stub files detected
○ 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
Email domain looks legitimate: example.com>
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 packageAuthor name is missing or very shortAuthor "" 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 amd-torch-device-gfx908
Create a machine learning inference application using the 'amd-torch-device-gfx908' Python package. This application will be designed to run on AMD GPUs that support the gfx908 architecture, such as the Radeon Instinct MI60/MI50 series. Your goal is to build a tool that can load pre-trained models from PyTorch and perform real-time inference on user-provided input data. Here’s a step-by-step guide on how to develop this application: 1. **Project Setup**: Start by setting up your Python environment. Ensure you have installed the 'amd-torch-device-gfx908' package, PyTorch, and any other necessary libraries. Use conda or pip for package management. 2. **Model Loading**: Implement a feature to load pre-trained models from PyTorch. These could include popular models like ResNet, VGG, or any other deep learning model trained for image classification, object detection, or segmentation tasks. 3. **Input Handling**: Design a user-friendly interface where users can upload images or video frames. For simplicity, you may choose to work with images initially. 4. **Inference Engine**: Utilize the 'amd-torch-device-gfx908' package to optimize and run the loaded model on the AMD GPU. Ensure that the application efficiently leverages the hardware capabilities of the gfx908 architecture. 5. **Result Display**: After performing inference, display the results to the user. For image classification tasks, show the predicted labels along with confidence scores. For more complex tasks like object detection or segmentation, visualize the detected objects or segmented regions over the input images. 6. **Performance Metrics**: Include basic performance metrics like inference time per frame/image to give users an idea of how fast the application runs on their system. 7. **Error Handling and Logging**: Add robust error handling and logging mechanisms to capture any issues during model loading, input processing, or inference phases. Suggested Features: - Support for multiple model types (classification, regression, segmentation). - Real-time feedback through visualizations. - Option to save the output predictions. - Command-line interface for batch processing of images/videos. This project aims to showcase the power of AMD GPUs and the 'amd-torch-device-gfx908' package in accelerating deep learning inference tasks.
💬 Discussion Feed
No discussion yet. Be the first to share your thoughts!
Report Abuse / Security Issue