AI Analysis
Final verdict: SAFE
The package shows very low risk indicators with no network calls, shell executions, or credential harvesting attempts. The metadata risk is slightly elevated due to the author's limited presence on PyPI, but there are no signs of malicious intent.
- No network calls detected
- No shell execution patterns
- No credential harvesting patterns
Per-check LLM notes
- Network: No network calls detected, which is normal for most machine learning libraries.
- 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 author has only one package on PyPI and lacks PyPI classifiers, indicating low effort or a new/inactive account, but no clear signs of malicious intent.
Package Quality Overall: Low (4.2/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
7.0
Some documentation present
Documentation URL: "Documentation" -> https://qualcomm.github.io/aimet-pages/releases/latest/indexDetailed PyPI description (11585 chars)
○ 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
✦ High
Multiple Contributors
10.0
Active multi-contributor project
10 unique contributor(s) across 100 commits in quic/aimetActive community — 5 or more distinct contributors
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: quicinc.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository quic/aimet appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author "Qualcomm Innovation Center, Inc." 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 aimet-torch
Create a machine learning application using PyTorch that leverages the 'aimet-torch' package to optimize a pre-trained neural network model for inference on mobile devices. Your application should include the following steps: 1. **Model Selection**: Choose a pre-trained neural network model from torchvision.models, such as ResNet-18, suitable for image classification tasks. 2. **Data Preparation**: Prepare a dataset of images (e.g., CIFAR-10) for evaluation purposes. Ensure you have both training and validation datasets. 3. **Model Evaluation**: Evaluate the performance of the pre-trained model on your prepared dataset before any optimization. 4. **Quantization Using AIMET**: Utilize AIMET’s Quantization Aware Training (QAT) feature to quantize the model to 8-bit precision, which is essential for deployment on resource-constrained devices like smartphones. 5. **Compression Using AIMET**: Apply AIMET’s Low-Rank Factorization and Sparsity techniques to reduce the model size while maintaining acceptable accuracy. 6. **Post-Optimization Evaluation**: Re-evaluate the optimized model's performance on the same dataset to observe any changes in accuracy. 7. **Inference Benchmarking**: Compare the inference speed of the original and optimized models on a simulated mobile device environment (consider using tools like ONNX Runtime). 8. **Reporting**: Document the process, including initial and final model sizes, accuracy, and inference times. Provide insights into how these optimizations impact the model's performance. This project aims to demonstrate the practical application of AIMET in preparing deep learning models for efficient deployment on edge devices.