AI Analysis
Final verdict: SAFE
The package shows no signs of obfuscation or credential harvesting, suggesting it is likely safe to use.
- No obfuscation detected
- No credentials harvesting detected
Per-check LLM notes
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
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-gfx906
Your task is to develop a Python-based mini-application that leverages the 'amd-torch-device-gfx906' package to perform efficient tensor operations on AMD GPUs. This application will serve as a basic demonstration of how to integrate and utilize the package for deep learning tasks. The application should include the following functionalities: 1. **Initialization and Setup**: Start by importing necessary libraries including 'amd-torch-device-gfx906', PyTorch, and any other required packages. Ensure your environment is set up correctly to recognize and use AMD GPUs. 2. **Data Preparation**: Create or load a dataset suitable for tensor operations, such as MNIST or CIFAR-10. Preprocess the data if necessary (e.g., normalization). 3. **Model Creation**: Design a simple neural network model using PyTorch. The model should be optimized to run on AMD GPUs via 'amd-torch-device-gfx906'. 4. **Training Loop**: Implement a training loop where the model learns from the dataset. Monitor the performance and adjust parameters as needed. 5. **Evaluation**: After training, evaluate the model's performance on a separate validation or test dataset. 6. **Visualization**: Include functionality to visualize the training process and results. This could be through graphs showing accuracy and loss over time. 7. **Documentation**: Provide clear documentation explaining each part of the code, the purpose of 'amd-torch-device-gfx906', and how it enhances the application's performance. The goal is to showcase the capabilities of 'amd-torch-device-gfx906' in handling complex tensor operations efficiently on AMD GPUs, making this application not only functional but also educational.
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