auto-trainer-api

v0.9.22 safe
4.0
Medium Risk

API for interfacing with the core acquisition process via platform and language agnostic message queues.

🤖 AI Analysis

Final verdict: SAFE

The package exhibits low risks across all categories with only minor concerns regarding metadata完整性。但是，根据给出的信息，“metadata风险”评分为5/10，这意味着有一些需要注意的地方，比如缺少作者信息和仓库链接。尽管这些因素可能表明开发者的透明度不足，但不足以证明存在恶意行为或供应链攻击。

  • 低网络风险
  • 无shell执行风险
  • 无代码混淆迹象
  • 无凭证风险
  • 部分元数据缺失
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution detected, indicating no immediate risk of command injection or similar attacks.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows some red flags such as missing author information and lack of repository links, but no clear signs of typosquatting or other malicious intent.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 4 test file(s) found

  • 4 test file(s) detected (e.g. test_api_basics.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (7974 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

  • 41 type-annotated function signatures detected in source
○ 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 auto-trainer-api 
Create a fully-functional mini-app that leverages the 'auto-trainer-api' package to manage and optimize the training of machine learning models across various platforms and languages. Your application should facilitate the setup, execution, monitoring, and optimization of training processes through a user-friendly interface.

Steps to develop the application:
1. **Setup**: Initialize your environment with the necessary packages, including 'auto-trainer-api'. Ensure that your application supports multiple platforms and languages for seamless integration.
2. **Configuration**: Design a configuration module that allows users to specify parameters such as model type, dataset source, hyperparameters, and training duration. This should also include options for selecting different messaging queue technologies supported by 'auto-trainer-api'.
3. **Training Process Management**: Implement functionalities within your app to start, pause, resume, and stop the training process based on user commands or predefined conditions. Use 'auto-trainer-api' to communicate with the training system and manage these actions efficiently.
4. **Monitoring and Logging**: Develop real-time monitoring and logging capabilities to track the progress of each training session. Users should be able to view metrics like accuracy, loss, and training time. Leverage 'auto-trainer-api' to retrieve this information from the training process.
5. **Optimization Suggestions**: Based on the performance data collected during training, provide suggestions for optimizing the model's performance. This could include adjusting hyperparameters, switching datasets, or changing the model architecture.
6. **User Interface**: Create a simple yet effective user interface that integrates all the above features. It should allow users to interact with the application easily and see results clearly.

Suggested Features:
- Support for multiple messaging queue technologies (e.g., RabbitMQ, AWS SQS).
- Integration with popular machine learning frameworks (TensorFlow, PyTorch).
- Detailed reports and visualizations for training performance.
- Automatic retries for failed training sessions.
- Multi-user support with role-based access control.

How to Utilize 'auto-trainer-api':
- Use 'auto-trainer-api' to send commands to start, stop, or modify the training process.
- Employ it for receiving status updates and performance metrics from ongoing training sessions.
- Leverage its capabilities to manage communication between different components of the application, ensuring smooth operation regardless of the underlying technology stack.

💬 Discussion Feed

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