amd-torch-device

v0.0.1.dev0 suspicious
4.0
Medium Risk

Placeholder for amd-torch-device

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low technical risks but raises concerns due to incomplete metadata, suggesting potential low effort or malicious intent.

  • Lack of maintainer history
  • Missing author information
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution detected, indicating no immediate risk of command injection or system exploitation.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows several red flags including lack of maintainer history and missing author information, suggesting potential low effort or malicious intent.

📦 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-torch-device 
Create a real-time image processing application using the Python package 'amd-torch-device'. This application will leverage AMD GPUs for accelerated processing of images, demonstrating the capabilities of the 'amd-torch-device' package in handling computationally intensive tasks efficiently.

The application should have the following functionalities:
1. Allow users to upload an image file from their local device.
2. Use the 'amd-torch-device' package to load and process the image using a pre-trained PyTorch model optimized for AMD GPUs.
3. Implement a feature that allows users to apply various image transformations such as resizing, rotating, and flipping.
4. Display the processed image back to the user in real-time, showcasing the speed and efficiency of the AMD GPU acceleration.
5. Provide a performance metric display showing the time taken to process the image without and with the use of the AMD GPU.

Detailed Steps:
- Set up a basic Flask web server to handle file uploads and serve HTML pages.
- Integrate the 'amd-torch-device' package into your project to ensure optimal usage of AMD GPUs.
- Pre-load a PyTorch model that is compatible with 'amd-torch-device' and ready for inference.
- Create a form on the front-end where users can select an image file to upload.
- Upon uploading, the application should utilize the 'amd-torch-device' package to process the image using the loaded model.
- Implement JavaScript to update the front-end in real-time as the image is being processed.
- After processing, display the transformed image alongside a performance comparison chart.

The goal is to demonstrate not only the ease of integration and use of the 'amd-torch-device' package but also its effectiveness in speeding up image processing tasks.

💬 Discussion Feed

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