Octomil Python
C 69 completed
Ai Ml
library / python · small
189
Files
48,294
LOC
1
Frameworks
7
Languages
Pipeline State
completedRun ID
#366824Phase
doneProgress
1%Started
Finished
2026-04-13 01:31:02LLM tokens
0Pipeline Metadata
Stage
CatalogedDecision
proceedNovelty
55.40Framework unique
—Isolation
—Last stage change
2026-05-10 03:35:28Deduplication group #50476
Member of a group with 10 similar repo(s) — canonical #17208 view group →
Top concepts (2)
Project DescriptionTesting
Repobility (the analyzer behind this table) · https://repobility.com
AI Prompt
Create a Python library tool called Octomil for managing and running ML models locally and facilitating collaborative training. The tool should allow users to serve models locally with an OpenAI-compatible API endpoint, support registering models, managing rollouts, and setting up A/B tests using the SDK. Additionally, I need functionality for federated learning, enabling training across devices without centralizing data, and a CLI with commands for serving, pulling, pushing, deploying, converting, checking, benchmarking, and logging in.
python mlops machine-learning library cli federated-learning api
Generated by gemma4:latest
Catalog Information
Octomil is a tool for running ML models locally and training them together.
Description
Octomil allows users to serve machine learning models locally, making it possible to train multiple models simultaneously. It provides an OpenAI-compatible API and supports various commands for model management, deployment, and security scanning. Octomil also enables federated learning, allowing data to be trained across devices without centralization.
الوصف
يسمح أوكتوميل بتشغيل النماذج العصبية المحلية وتدريبها معًا. يتيح أيضًا API متوافق مع OpenAI ويدعم مجموعة من الأوامر لمراقبة النماذج والتنفيذ والفحص الأمني. كما يمكنه تدريب البيانات عبر الأجهزة دون المركزية.
Novelty
9/10Tags
machine-learning federated-learning model-management deployment security-scanning
Claude Models
claude-opus-4.6
Quality Score
C
68.6/100
Structure
82
Code Quality
63
Documentation
74
Testing
85
Practices
54
Security
44
Dependencies
60
Strengths
- CI/CD pipeline configured (github_actions)
- Good test coverage (62% test-to-source ratio)
- Code linting configured (ruff (possible))
- Consistent naming conventions (snake_case)
- Containerized deployment (Docker)
- Properly licensed project
Weaknesses
- Potential hardcoded secrets in 2 files
- 3778 duplicate lines detected \u2014 consider DRY refactoring
- 9 'god files' with >500 LOC need decomposition
Recommendations
- Move hardcoded secrets to environment variables or a secrets manager
Security & Health
9.3h
Tech Debt (A)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (0)
Citation: Repobility (2026). State of AI-Generated Code. https://repobility.com/research/
MIT
License
4.2%
Duplication
Languages
Frameworks
pytest
Concepts (2)
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| auto_description | Project Description | Run ML models locally. Train them together. | 80% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| auto_category | Testing | testing | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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