AI Analysis
Final verdict: SUSPICIOUS
The package has a low risk of obfuscation or credential theft but shows signs of poor metadata quality, which raises some suspicion about its legitimacy.
- Low obfuscation risk
- Low credential risk
- Poor metadata quality
Per-check LLM notes
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
- Credentials: No credential harvesting patterns detected, suggesting no immediate risk to stored secrets.
- Metadata: The package shows signs of low effort and potential lack of transparency, raising suspicion.
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-gfx1150
Create a machine learning model trainer application that leverages the 'amd-torch-device-gfx1150' package for optimizing performance on AMD GPUs with GFX1150 architecture. This application should allow users to train custom models using PyTorch, with specific optimizations for AMD GPUs. Here’s a step-by-step guide on how to build this application: 1. **Setup Environment**: Ensure your environment is set up with Python, PyTorch, and the 'amd-torch-device-gfx1150' package installed. 2. **Model Selection**: Provide a user-friendly interface where users can select from pre-configured models like CNNs, RNNs, or LSTM networks. 3. **Dataset Integration**: Allow users to upload their datasets or choose from common datasets available in torchvision. 4. **Training Parameters Configuration**: Users should be able to adjust training parameters such as batch size, number of epochs, learning rate, etc. 5. **Optimization with 'amd-torch-device-gfx1150'**: Use the 'amd-torch-device-gfx1150' package to optimize the training process specifically for AMD GPUs with GFX1150 architecture. This includes setting up the device configuration and applying any necessary performance tweaks. 6. **Real-time Monitoring**: Implement real-time monitoring of the training progress, including loss function values and accuracy metrics. 7. **Save & Load Models**: Enable users to save trained models and load them for further training or inference. 8. **Documentation & Help**: Provide comprehensive documentation and help sections to assist users in understanding how to use the application effectively. Suggested Features: - Support for multiple loss functions and optimizers. - Automatic scaling of batch sizes based on GPU memory availability. - Option to export models in ONNX format for deployment. - Detailed logs and error messages for troubleshooting. The 'amd-torch-device-gfx1150' package will be crucial in configuring the AMD GPU for optimal performance during the training phase. It will handle specifics such as setting up the correct device type, managing memory efficiently, and applying any AMD-specific optimizations to speed up the training process.
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