AI Analysis
Final verdict: SUSPICIOUS
The package has minimal risks associated with network calls, shell execution, and obfuscation. However, the metadata suggests low maintenance efforts and unclear intentions, raising suspicion.
- Low maintenance and unclear purpose
- Minimal technical risks but suspicious metadata
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 execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent related to code obfuscation.
- Credentials: No credential harvesting patterns detected, suggesting no immediate risk of secret or credential theft.
- Metadata: The package shows signs of low maintenance and potential low effort, raising concerns about its legitimacy and purpose.
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-torchvision-device-gfx1100
Create a small machine learning project that leverages the 'amd-torchvision-device-gfx1100' package to optimize image processing tasks on AMD GPUs. This project will serve as a proof of concept for enhancing the performance of deep learning models using specific AMD GPU architectures. Here’s a step-by-step guide on how to develop this project: 1. **Setup Environment**: Begin by setting up your development environment. Ensure you have Python installed along with necessary libraries such as PyTorch and torchvision. Install the 'amd-torchvision-device-gfx1100' package specifically for optimizing operations on AMD GPUs with the gfx1100 architecture. 2. **Project Structure**: Organize your project into several directories including 'data', 'models', 'utils', and 'scripts'. Use 'data' to store datasets, 'models' for model definitions, 'utils' for helper functions, and 'scripts' for training and evaluation scripts. 3. **Data Preparation**: In the 'data' directory, prepare a dataset suitable for image classification tasks. Use torchvision.datasets to load and preprocess common datasets like CIFAR-10 or ImageNet. 4. **Model Definition**: Define a simple convolutional neural network (CNN) in the 'models' directory. Utilize the 'amd-torchvision-device-gfx1100' package to ensure that the model is optimized for the gfx1100 architecture, which might include specific kernel configurations or memory optimizations. 5. **Training Script**: Write a script in the 'scripts' directory to train your CNN. This script should initialize the model, define the loss function, optimizer, and training loop. Use the 'amd-torchvision-device-gfx1100' package to enable hardware-specific optimizations during training. 6. **Evaluation and Testing**: After training, evaluate your model on a validation set to measure its accuracy. Implement a testing phase where you can test different optimization parameters provided by 'amd-torchvision-device-gfx1100' to see their impact on performance. 7. **Documentation and Reporting**: Document your findings in a README file, detailing the setup process, performance metrics, and any challenges faced. Include visualizations of training progress and performance comparisons with and without the 'amd-torchvision-device-gfx1100' optimizations. The goal is to showcase how leveraging specialized packages like 'amd-torchvision-device-gfx1100' can enhance the efficiency and effectiveness of deep learning models on specific hardware setups.
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