amd-torch-device-all

v0.0.1.dev0 suspicious
5.0
Medium Risk

Placeholder for amd-torch-device-all

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low risks in terms of network usage, shell execution, and code obfuscation. However, the metadata risk score is high due to missing maintainer history and author information, suggesting potential low effort or malicious intent.

  • Metadata risk is high due to missing maintainer history and author information.
  • No other significant security risks identified.
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 signs of malicious activity.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows several red flags such as lack of maintainer history and a missing author name, indicating 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-all 
Create a Python-based mini-application named 'AMD-GPU-Optimizer' that leverages the 'amd-torch-device-all' package to optimize and enhance the performance of PyTorch models on AMD GPUs. This application should allow users to select different PyTorch models (such as ResNet, VGG, etc.), upload their own datasets, and then run these models on an AMD GPU with optimized settings provided by 'amd-torch-device-all'.

The application should include the following features:
1. User-friendly interface to select a pre-trained model from a dropdown menu.
2. Ability to upload custom datasets via a file input field.
3. Configuration options for setting up the AMD GPU device using 'amd-torch-device-all', such as memory allocation, precision modes, and parallel processing configurations.
4. Performance metrics display showing inference time and accuracy before and after optimization.
5. A summary report detailing the optimization process and results.

Utilize 'amd-torch-device-all' to handle the setup and configuration of the AMD GPU, ensuring that the application takes full advantage of the hardware capabilities for enhanced performance. Additionally, include a feature that allows users to compare the performance of the same model running on CPU versus AMD GPU with and without optimization.

💬 Discussion Feed

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