amd-torch-device-gfx1011

v0.0.1.dev0 suspicious
4.0
Medium Risk

Placeholder for amd-torch-device-gfx1011

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has minimal direct security risks but exhibits low maintainer activity and poor metadata quality, raising concerns about its legitimacy and potential for future issues.

  • Low maintainer activity
  • Poor metadata quality
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution patterns detected, indicating no immediate risk of command injection or similar vulnerabilities.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows signs of low maintainer activity and poor metadata quality, raising suspicion but not definitive proof of malintent.

📦 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-gfx1011 
Your task is to create a mini-application that leverages the 'amd-torch-device-gfx1011' package to perform optimized tensor operations on AMD GPUs specifically designed for machine learning tasks. This application will serve as a basic framework for users to experiment with different tensor operations and observe the performance gains achieved by utilizing AMD's specialized GPU architecture.

The application should include the following core functionalities:
- Initialization of the AMD GPU device using 'amd-torch-device-gfx1011'.
- Ability to load and manipulate various types of tensors on the GPU.
- Support for common tensor operations such as addition, multiplication, and convolution.
- Performance measurement tools to compare the speed and efficiency of these operations when run on the AMD GPU versus a CPU.
- A user-friendly interface allowing users to input tensor dimensions and operation types.

Additionally, consider adding these optional features to enhance the application:
- Visualization of tensor data before and after operations.
- Integration with popular ML frameworks like PyTorch or TensorFlow for more complex model training.
- Real-time logging and analysis of GPU usage during operations.

Ensure your application provides clear instructions for installation and setup, including any necessary dependencies for 'amd-torch-device-gfx1011'. Your goal is to showcase the capabilities of AMD GPUs in handling machine learning tasks efficiently and to provide a practical tool for developers interested in exploring this technology.

💬 Discussion Feed

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