atomr-infer

v0.8.0 safe
3.0
Low Risk

Multi-runtime GPU + remote inference as a supervised actor system on the atomr actor runtime.

🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activity and poses minimal risks across all assessed categories. The metadata risk slightly increases due to the author's limited presence, but overall it is considered safe.

  • No network calls or shell executions detected
  • No obfuscation or credential risk observed
  • Author has only one package listed
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require internet access.
  • Shell: No shell executions detected, indicating no immediate risk of command injection or unauthorized system access.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
  • Metadata: The author has only one package, which could indicate a new or less active account, but no other red flags were identified.

📦 Package Quality Overall: Low (4.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://docs.rs/atomr-infer
  • Detailed PyPI description (11597 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 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 40 commits in rustakka/atomr-infer
  • Small but multi-author team (3–4 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository rustakka/atomr-infer appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "atomr-infer 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-infer 
Your task is to develop a real-time image classification web application using the 'atomr-infer' package. This application will leverage multi-runtime GPU capabilities and remote inference to classify images uploaded by users. The goal is to create a user-friendly interface where users can upload an image, and the application will provide a classification result almost instantly.

Step 1: Set up your development environment.
- Install necessary packages including 'atomr-infer', Flask for the web framework, and any other dependencies required for handling image uploads and displaying results.

Step 2: Build the backend.
- Use 'atomr-infer' to set up a supervised actor system that can handle image classification tasks efficiently. Configure it to utilize available GPUs for faster processing.
- Develop a REST API endpoint that accepts POST requests containing image files. This endpoint should trigger the classification process through the 'atomr-infer' system.
- Implement error handling and logging mechanisms to ensure robustness.

Step 3: Develop the frontend.
- Create a simple yet elegant HTML/CSS/JavaScript front end using Flask's template engine. The UI should allow users to select and upload an image.
- On successful upload, the application should display a loading indicator while the backend processes the request.
- Once the classification result is received from the backend, update the UI to show the predicted class along with a confidence score.

Suggested Features:
- User authentication to track performance and usage.
- Support for multiple classification models to switch between different types of classifications (e.g., objects, animals).
- A gallery feature showing recent classifications made by all users.
- An admin dashboard to monitor system health and manage users.

How 'atomr-infer' is utilized:
- For setting up the supervised actor system that runs inference tasks.
- To configure the multi-runtime GPU support for parallel processing.
- For remote inference setup if the application needs to scale beyond local resources.
- To manage the workflow of receiving requests, processing them, and returning results.

💬 Discussion Feed

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