amd-torchvision-device-gfx1152

v0.0.1.dev0 suspicious
4.0
Medium Risk

Placeholder for amd-torchvision-device-gfx1152

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has minimal direct risks but exhibits low development effort and potential lack of transparency, raising suspicion about its origin and purpose.

  • Metadata risk due to low effort and lack of transparency
  • Potential supply-chain attack due to suspicious metadata
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell executions detected, which is expected for a typical package.
  • Obfuscation: No obfuscation patterns detected, suggesting low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating no immediate risk of secret theft.
  • Metadata: The package shows signs of low effort and potential lack of transparency, which raises concerns.

📦 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 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 amd-torchvision-device-gfx1152 
Create a Python-based image classification tool using the 'amd-torchvision-device-gfx1152' package. This project aims to showcase the capabilities of this package in optimizing image processing tasks on AMD GPUs with GFX1152 architecture. Your task is to develop a user-friendly application that allows users to upload images and receive real-time predictions about the content of those images. Here’s a detailed breakdown of what your application should achieve:

1. **Setup Environment**: Ensure you have Python installed, along with necessary libraries such as torch, torchvision, and the 'amd-torchvision-device-gfx1152' package. Note that the 'amd-torchvision-device-gfx1152' package is specifically optimized for AMD GPUs with GFX1152 architecture, so ensure your system meets these requirements.

2. **Model Selection and Training**: Use a pre-trained model from torchvision.models, but modify it slightly if necessary to work seamlessly with the 'amd-torchvision-device-gfx1152' optimizations. Focus on fine-tuning the model on a dataset of your choice (e.g., CIFAR-10, ImageNet subset), ensuring that the training process leverages the specific optimizations provided by 'amd-torchvision-device-gfx1152'.

3. **User Interface**: Develop a simple web interface where users can upload images directly. Utilize Flask or Django for backend development and HTML/CSS/JavaScript for the frontend. The interface should allow users to select an image file from their device and upload it to the server.

4. **Real-Time Classification**: Once an image is uploaded, use the trained model to classify the image content. Display the top 3 predicted classes along with their probabilities. Ensure the application provides fast response times by leveraging the 'amd-torchvision-device-gfx1152' package's performance enhancements.

5. **Performance Metrics**: Implement a feature to display performance metrics such as inference time and accuracy. This will help users understand the efficiency gains from using 'amd-torchvision-device-gfx1152'.

6. **Documentation and Testing**: Write comprehensive documentation detailing how to set up the environment, run the application, and interpret results. Also, include testing scripts to validate the functionality and performance of your application.

This project not only serves as a practical application but also as a demonstration of how specialized packages like 'amd-torchvision-device-gfx1152' can significantly enhance the performance of machine learning models on specific hardware configurations.

💬 Discussion Feed

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