amd-torch-device-gfx1153

v0.0.1.dev0 suspicious
4.0
Medium Risk

Placeholder for amd-torch-device-gfx1153

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low direct risk indicators but raises concerns due to its metadata quality and lack of maintenance history, potentially indicating unreliability or malicious intent.

  • Metadata risk score of 6/10
  • Lack of maintainer history and author information
Per-check LLM notes
  • Network: No network calls detected, which is normal for a package focused on device-specific optimizations.
  • Shell: No shell executions detected, aligning with the expected behavior of a package aimed at enhancing torch operations on specific hardware.
  • 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 several red flags indicating low effort and potential unreliability, such as lack of maintainer history and missing author 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-gfx1153 
Create a Python-based mini-application that leverages the 'amd-torch-device-gfx1153' package to optimize the performance of machine learning models on AMD GPUs with GFX1153 architecture. Your application should include the following functionalities:

1. **Model Loading**: Allow users to load their own PyTorch models or use pre-defined models such as ResNet, VGG, or any other common deep learning model.
2. **Performance Optimization**: Utilize the 'amd-torch-device-gfx1153' package to fine-tune the model for better performance specifically on AMD GPUs with GFX1153 architecture. This could involve adjusting memory allocation, optimizing kernel execution, or utilizing specific hardware features.
3. **Benchmarking Tool**: Implement a benchmarking tool within the application to measure the performance improvement achieved by using 'amd-torch-device-gfx1153'. This tool should compare the performance before and after optimization, providing metrics like inference time, throughput, and memory usage.
4. **Visualization**: Include a simple visualization feature to display the benchmark results in a user-friendly manner, such as graphs or charts showing the performance improvements over time or under different conditions.
5. **User Interface**: Develop a basic command-line interface (CLI) for interacting with the application, allowing users to easily load models, run optimizations, and view benchmark results.
6. **Documentation**: Provide comprehensive documentation explaining how to install the application, use its features, and understand the output.

Your task is to design and implement this application from scratch, ensuring it effectively showcases the capabilities of the 'amd-torch-device-gfx1153' package. Focus on making the application easy to use while also being informative about the benefits of GPU-specific optimizations.

💬 Discussion Feed

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