ailine-core

v0.5.5 safe
2.0
Low Risk

ML experiment lineage tracker with snapshot-based reproducibility.

🤖 AI Analysis

Final verdict: SAFE

The package 'ailine-core' has been assessed as safe due to its low risk scores across all categories, with no indications of malicious activities or supply-chain attacks.

  • No network calls detected
  • Git and DVC commands are likely benign for version control and data versioning
Per-check LLM notes
  • Network: No network calls detected.
  • Shell: Git and DVC commands are likely used for version control and data versioning purposes.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.

📦 Package Quality Overall: Low (4.6/10)

○ 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://www.igorzaton.com/ailine/index.html
  • Detailed PyPI description (12737 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 157 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 4 unique contributor(s) across 46 commits in IgorZaton/ailine
  • 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 score 10.0

Found 6 shell execution pattern(s)

  • n PATH", ) proc = subprocess.run( ["dvc", "--version"], check=False, capture_output=T
  • t-demo' first." ) subprocess.run(["git", "clone", repo_url, constants.REPO_DIR], check=True)
  • f.write(repo_url) subprocess.run(["git", "fetch"], check=True, cwd=constants.REPO_DIR) _w
  • constants.REPO_DIR}") subprocess.run(["dvc", "add", dataset], check=True, cwd=constants.REPO_DIR)
  • `.""" try: proc = subprocess.run( ["git", "-C", repo_root, "config", "--get", "re
  • ry: _MLFLOW_PROCESS = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
✓ 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 score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "Igor Zaton" 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 ailine-core 
Develop a mini-application called 'ML Experiment Tracker' that leverages the 'ailine-core' Python package to track machine learning experiments. This application will allow users to record and manage their ML experiments efficiently, ensuring reproducibility through snapshot-based tracking. Here are the steps and features to implement:

1. **Setup**: Initialize the application by setting up a basic Flask web framework for the frontend and backend integration. Ensure 'ailine-core' is installed and configured within your environment.

2. **Experiment Tracking**: Implement functionality where users can log new experiments. Each experiment entry should include details such as experiment name, description, start time, end time, and tags for categorization.

3. **Snapshot Management**: Use 'ailine-core' to capture snapshots of the model state at various points during training. These snapshots should include metadata like hyperparameters, metrics, and any other relevant information.

4. **Reproducibility**: Enable users to load previous experiment snapshots and reproduce the exact experiment conditions, including model weights and training parameters. Provide a user-friendly interface for selecting and loading these snapshots.

5. **Visualization**: Integrate visualizations for experiment metrics over time. Users should be able to compare different experiments visually, highlighting improvements or issues in performance.

6. **Search & Filter**: Allow users to search and filter experiments based on tags, date ranges, or specific metrics. This feature should help in quickly locating relevant experiments from a large dataset.

7. **User Interface**: Design an intuitive and responsive UI using HTML/CSS/JavaScript frameworks like Bootstrap or React. Ensure that the UI is clean, easy to navigate, and visually appealing.

8. **Security**: Implement basic security measures such as user authentication and authorization to protect sensitive data and ensure only authorized users can access and modify experiment records.

9. **Documentation**: Write comprehensive documentation detailing how to install, configure, and use the application. Include examples and best practices for utilizing 'ailine-core' effectively.

By following these guidelines, you'll create a robust tool for managing and reproducing machine learning experiments, making it easier for researchers and developers to iterate and improve their models.