atomr-accel

v0.10.0 suspicious
4.0
Medium Risk

Actor-shaped face for compute-acceleration backends (NVIDIA CUDA shipping; ROCm / Metal / oneAPI / Vulkan future), on the atomr actor runtime.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal risks in terms of network usage, shell execution, and obfuscation, but the metadata suggests the author may be new or less active, warranting further investigation.

  • Author has only one package listed, indicating potential new or less active developer status.
  • No significant security risks detected in code analysis.
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires network interaction for its functionality.
  • Shell: No shell execution patterns detected, indicating the package does not execute external commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, suggesting no immediate risk of secret theft.
  • Metadata: The author has only one package, which may indicate a new or less active account, raising some suspicion but not conclusive evidence of malintent.

📦 Package Quality Overall: Medium (5.8/10)

✦ High Test Suite 9.0

Test suite present — 22 test file(s) found

  • Test runner config found: conftest.py
  • 22 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (6341 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 15 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in rustakka/atomr-accel
  • Two distinct contributors found

🔬 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository rustakka/atomr-accel appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "atomr-accel contributors" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with atomr-accel 
Create a real-time image processing application that leverages the 'atomr-accel' package for accelerating computations on various hardware backends such as NVIDIA CUDA, AMD ROCm, Apple Metal, Intel oneAPI, and Vulkan. This application will process live camera feed to apply filters and effects, demonstrating the power of compute acceleration on different hardware platforms.

### Project Overview:
- **Name**: Real-Time Image Filter App
- **Core Functionality**: Capture live video feed from a webcam, apply customizable filters and effects using 'atomr-accel', and display the processed video in real-time.
- **Features**:
  - Live video capture from a webcam.
  - Ability to switch between different compute backends (CUDA, ROCm, Metal, etc.) for processing.
  - Customizable filters and effects (e.g., grayscale, sepia, blur, edge detection).
  - Performance metrics display showing FPS (frames per second) and backend being used.
  - Option to save processed video frames to disk.

### Steps to Build the Application:
1. **Setup Environment**:
   - Install necessary libraries including 'atomr-accel' and OpenCV for handling video capture and processing.
2. **Initialize Camera**:
   - Use OpenCV to initialize the webcam and start capturing video frames.
3. **Configure 'atomr-accel'**:
   - Set up 'atomr-accel' to use the appropriate backend based on available hardware.
4. **Define Filters & Effects**:
   - Implement functions for each filter/effect using 'atomr-accel' for accelerated computation.
5. **Real-Time Processing Loop**:
   - Continuously read frames from the webcam, apply selected filters/effects using 'atomr-accel', and display the output frame-by-frame.
6. **Performance Metrics**:
   - Track and display FPS and backend information during processing.
7. **Save Output**:
   - Provide functionality to save processed frames to a file.
8. **User Interface**:
   - Develop a simple UI allowing users to select filters, choose backend, and toggle saving output.

### Utilizing 'atomr-accel':
- Leverage 'atomr-accel' to offload computationally intensive tasks (filtering, effects) to GPU/CPU based on selected backend, ensuring high performance even at high resolutions and frame rates.
- Experiment with different backends to observe performance differences and showcase the versatility of 'atomr-accel'.

💬 Discussion Feed

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