aluminatiai

v0.3.1 suspicious
8.0
High Risk

GPU energy monitoring agent — per-job cost attribution and energy-efficient fine-tuning for AI teams

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package is assessed as suspicious due to high metadata risk associated with unusual git repository activity and maintainer history.

  • High metadata risk score of 7/10
  • Suspicious git repository activity and maintainer history
Per-check LLM notes
  • Metadata: High risk due to suspicious git repository activity and maintainer history.

📦 Package Quality Overall: Low (4.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. smoke_test.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://aluminatiai.com/docs/agent
  • Detailed PyPI description (13668 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

  • 255 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 3 commits in AgentMulder404/aluminatiai
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

  • s(payload).encode() req = urllib.request.Request( url, data=data, headers={"Content-T
  • , ) try: with urllib.request.urlopen(req, timeout=10) as resp: body = json.lo
  • , ) try: with urllib.request.urlopen(req, timeout=5): pass except Excepti
  • fault=str).encode() req = urllib.request.Request( url, data=data, headers={"Content-T
  • , ) try: with urllib.request.urlopen(req, timeout=10): return True except
⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • .merge_and_unload() model.eval() print(f"\nRunning {len(PROMPTS)} eval prompts...\n")
  • int(f"{'='*60}\n") model.eval() model.config.use_cache = True if hasattr(model, "g
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • en kill it.""" proc = subprocess.Popen( cmd, shell=True, stdout=subprocess.PIPE, stderr
  • onotonic() proc = subprocess.Popen( cmd, shell=True, stdout=subprocess.PIPE, st
  • on == "status": ret = subprocess.run(["systemctl", "status", "aluminatai-agent"]).returncode
  • return 1 subprocess.run(["systemctl", "stop", "aluminatai-agent"], stderr=subprocess
  • r=subprocess.DEVNULL) subprocess.run(["systemctl", "disable", "aluminatai-agent"], stderr=subproc
  • nt(f"Removed {path}") subprocess.run(["systemctl", "daemon-reload"]) print("Service unins
✓ 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 7.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
  • Single contributor with only 3 commit(s) — possibly throwaway account
  • All 3 commits happened within 24 hours
⚠ 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 aluminatiai 
Create a mini-application named 'EnergyWiseAI' that helps AI developers monitor and optimize the energy consumption of their machine learning models during training. This application will utilize the 'aluminatiai' package to provide real-time insights into GPU energy usage and cost attribution per job. The application should have the following functionalities:

1. **Job Setup**: Users can input details about their machine learning jobs including the model name, dataset size, and expected training duration.
2. **Real-Time Monitoring**: Display real-time GPU energy usage metrics such as power draw, temperature, and efficiency while the job is running.
3. **Cost Attribution**: Estimate and display the cost associated with running each job based on current GPU pricing and energy consumption data.
4. **Optimization Tips**: Provide suggestions on how to reduce energy consumption and costs, such as adjusting batch sizes or using more efficient hardware.
5. **Historical Data Analysis**: Allow users to review past job performance and energy consumption trends to identify areas for improvement.
6. **Custom Reporting**: Generate customizable reports summarizing job performance, energy use, and cost savings.

To implement these features, you'll need to integrate 'aluminatiai' to capture detailed GPU metrics and calculate costs based on energy usage. Additionally, consider using visualization libraries like Matplotlib or Plotly to create insightful dashboards for the real-time and historical data.

💬 Discussion Feed

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