ai-dynamo

v1.2.0.post1 safe
3.0
Low Risk

Distributed Inference Framework

🤖 AI Analysis

Final verdict: SAFE

The package ai-dynamo v1.2.0.post1 shows very low risks across all categories checked. It does not engage in network calls, shell executions, or obfuscation techniques that could indicate malicious behavior. The metadata has minor issues but nothing that suggests a supply-chain attack.

  • No network calls
  • No shell executions
  • Minor metadata issues
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell executions detected, indicating no direct system command risks.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package has some minor issues but no clear signs of being malicious or part of a supply-chain attack.

📦 Package Quality Overall: Medium (5.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (16948 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

  • Type checker (mypy / pyright / pytype) referenced in project
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 49 unique contributor(s) across 100 commits in ai-dynamo/dynamo
  • Active 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: nvidia.com>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://www.apache.org/licenses/LICENSE-2.0
✓ Git Repository History

Repository ai-dynamo/dynamo appears legitimate

⚠ 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 ai-dynamo 
Your task is to create a distributed machine learning inference system using the 'ai-dynamo' package. This system will allow users to submit image classification tasks across multiple nodes in a cluster. Each node will use pre-trained models to classify images and return results back to the user. Here’s a detailed breakdown of your project:

1. **Setup**: Begin by installing 'ai-dynamo' and setting up a local development environment. Ensure you have access to multiple nodes (simulated or real).
2. **Node Configuration**: Configure each node to load a pre-trained model suitable for image classification (e.g., ResNet, VGG). Use 'ai-dynamo' to manage the distribution of these models across nodes.
3. **User Interface**: Develop a simple web interface where users can upload images. Upon submission, the image should be split into chunks if necessary and sent to different nodes for parallel processing.
4. **Inference Process**: Implement the logic to route image chunks to available nodes, receive predictions from each node, and aggregate the results. Use 'ai-dynamo' for efficient communication and data handling between nodes.
5. **Result Aggregation & Presentation**: Once all nodes have completed their tasks, aggregate the results and present a unified output to the user via the web interface. Highlight any discrepancies or anomalies in the predictions.
6. **Monitoring & Logging**: Include functionality to monitor the status of each node and log activities such as start times, completion times, and any errors encountered during inference.
7. **Scalability Testing**: Test your system with varying numbers of nodes and image sizes to ensure it scales efficiently. Document your findings and any adjustments made to improve performance.
8. **Documentation & Deployment**: Write comprehensive documentation detailing how to set up and run the system. Prepare a deployment plan for a cloud-based environment using 'ai-dynamo'.

Throughout the project, focus on leveraging 'ai-dynamo' to streamline the setup, management, and execution of distributed tasks. Ensure your solution is robust, scalable, and user-friendly.