AI Analysis
Final verdict: SUSPICIOUS
The package exhibits minimal direct risks but has a metadata risk due to low effort and lack of transparency, which raises concerns about its legitimacy and development process.
- Metadata risk due to low effort and potential lack of transparency.
- No direct network, shell, obfuscation, or credential risks detected.
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 expectations for a specialized library package.
- Obfuscation: No obfuscation patterns detected, suggesting legitimate code.
- Credentials: No credential harvesting patterns detected, indicating safe handling of secrets.
- Metadata: The package shows signs of low effort and potential lack of transparency, raising suspicion but not conclusive evidence of malice.
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 packageAuthor name is missing or very shortAuthor "" 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-gfx1151
Create a mini-application that leverages the 'amd-torch-device-gfx1151' package to showcase its capabilities in optimizing PyTorch operations on AMD GPUs, specifically targeting the GFX1151 architecture. Your application will serve as a benchmarking tool to compare performance between different GPU configurations while also providing a simple machine learning model training example. Here are the steps and features your application should include: 1. **Setup Environment**: Ensure your environment is set up correctly with the latest version of PyTorch and the 'amd-torch-device-gfx1151' package installed. 2. **Benchmarking Module**: Develop a module that benchmarks various PyTorch operations such as matrix multiplication, convolution, and batch normalization. This module should allow users to specify the operation, input sizes, and number of repetitions for accurate timing. 3. **GPU Configuration Detection**: Implement functionality within your application to detect the current GPU configuration and determine if it supports the GFX1151 architecture. If supported, the application should automatically configure itself to utilize the 'amd-torch-device-gfx1151' optimizations. 4. **Machine Learning Model Training Example**: Include a simple machine learning task, like training a neural network on a dataset (e.g., MNIST), demonstrating the performance improvement when using 'amd-torch-device-gfx1151'. 5. **Comparison Tool**: Provide a feature that compares the performance metrics obtained from the benchmarking module and ML training example with and without the 'amd-torch-device-gfx1151' optimizations enabled. Display these comparisons in a user-friendly manner. 6. **User Interface**: Design a simple command-line interface (CLI) that guides users through the benchmarking and training processes, displaying results and allowing them to interactively adjust settings. 7. **Documentation**: Write comprehensive documentation detailing how each part of the application works, including setup instructions, usage examples, and explanations of the 'amd-torch-device-gfx1151' package's role in enhancing performance.
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