AI Analysis
Final verdict: SAFE
The package appears to be safe based on the analysis notes. It uses common libraries for its functionality without any signs of malicious activity.
- Low network, shell, obfuscation, and credential risks.
- Moderate metadata risk due to the maintainer having only one package.
Per-check LLM notes
- Network: The use of aiohttp for network requests is common and may be necessary for legitimate functionality.
- Shell: No shell execution patterns detected, indicating low risk.
- Obfuscation: No obfuscation patterns detected, indicating low risk of code obfuscation.
- Credentials: No credential harvesting patterns detected, indicating low risk of secret or credential theft.
- Metadata: The maintainer has only one package, which may indicate a new or less active account.
Package Quality Overall: Medium (5.4/10)
○ 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://docs.backend.ai/Brief PyPI description (574 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
13 type-annotated function signatures detected in source
✦ High
Multiple Contributors
10.0
Active multi-contributor project
9 unique contributor(s) across 100 commits in lablup/backend.aiActive community — 5 or more distinct contributors
Heuristic Checks
Outbound Network Calls
score 1.5
Found 1 network call pattern(s)
ith ( aiohttp.ClientSession() as sess, sess.get(
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 lablup/backend.ai appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Lablup Inc. and 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 backend.ai-accelerator-tpu
Create a machine learning model training utility called 'TPU Trainer' that leverages the power of Google TPUs through the 'backend.ai-accelerator-tpu' package. This utility will allow users to upload their datasets, select from a variety of pre-defined ML models, and train these models on TPU resources provided by Backend.AI. The application should have a user-friendly interface where users can input their dataset URLs, specify model parameters, and monitor the progress of their training sessions. Additionally, it should provide options for hyperparameter tuning and offer real-time performance metrics during training. Upon completion, users should receive a trained model file that they can download and use in their applications. Utilize the 'backend.ai-accelerator-tpu' package to manage the TPU resources efficiently, ensuring optimal performance and resource utilization.
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