amd-torch-device-gfx11

v0.0.1.dev0 suspicious
4.0
Medium Risk

Placeholder for amd-torch-device-gfx11

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low risks in terms of network, shell, and obfuscation activities, but it lacks maintainer history and essential metadata, raising concerns about its authenticity and purpose.

  • Lack of maintainer history
  • Missing critical metadata
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires network interaction for its functionality.
  • Shell: No shell execution patterns detected, indicating the package does not execute external commands.
  • 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 critical information.

📦 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-gfx11 
Your task is to develop a mini-application using the 'amd-torch-device-gfx11' Python package, which is designed to optimize PyTorch operations on AMD GPUs with GFX11 architecture. This application will serve as a benchmarking tool to compare the performance of various PyTorch models when run on different AMD GPUs with GFX11 support versus CPUs.

The application should have the following features:
1. Allow users to select from a predefined list of PyTorch models (e.g., ResNet, VGG, etc.).
2. Provide options for the user to choose between running the selected model on an AMD GPU with GFX11 support or a CPU.
3. Implement a mechanism to measure and display the time taken for each model to complete an inference operation under both conditions (GPU and CPU).
4. Offer a comparison feature that displays the speedup ratio when running the model on the AMD GPU versus the CPU.
5. Include a simple graphical interface built using a library like Tkinter or PyQt.
6. Ensure that the application logs all benchmarking results into a file for future reference.

In utilizing the 'amd-torch-device-gfx11' package, your application should:
- Automatically detect available AMD GPUs with GFX11 support and present them as options to the user.
- Use the package's capabilities to ensure optimal performance of PyTorch models on the selected GPU.
- Handle cases where no compatible GPU is detected gracefully, informing the user and allowing them to proceed with CPU-only benchmarks.

This project aims to demonstrate the efficiency gains achievable by leveraging specialized hardware like AMD GPUs with GFX11 architecture for deep learning tasks.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!