Dgx Toolbox

C+ 70 completed
Other
monorepo / markdown · small
326
Files
42,767
LOC
2
Frameworks
7
Languages

Pipeline State

completed
Run ID
#1545733
Phase
done
Progress
0%
Started
2026-04-16 23:32:01
Finished
2026-04-16 23:32:01
LLM tokens
0

Pipeline Metadata

Stage
Cataloged
Decision
proceed
Novelty
54.60
Framework unique
Isolation
Last stage change
2026-05-10 03:34:51
Deduplication group #51624
Member of a group with 14 similar repo(s) — this repo is canonical view group →
Provenance: Repobility (https://repobility.com) — every score reproducible from /scan/

AI Prompt

Create a comprehensive ML/AI development environment setup tool, similar to the DGX Spark Toolbox. I need it to manage and provide scripts for setting up various components like inference, data labeling, evaluation, and fine-tuning. The system should support building Docker images for different toolboxes, handle prerequisites like setting up aliases and environment variables, and include sections for configuration using YAML/TOML files. It should also provide a status check and potentially integrate with services like Ollama or LiteLLM.
mlops ai docker fastapi python bash devops tooling monorepo
Generated by gemma4:latest

Catalog Information

Create a comprehensive ML/AI development environment setup tool, similar to the DGX Spark Toolbox. I need it to manage and provide scripts for setting up various components like inference, data labeling, evaluation, and fine-tuning. The system should support building Docker images for different toolboxes, handle prerequisites like setting up aliases and environment variables, and include sections for configuration using YAML/TOML files. It should also provide a status check and potentially integ

Tags

mlops ai docker fastapi python bash devops tooling monorepo

Quality Score

C+
70.4/100
Structure
76
Code Quality
88
Documentation
74
Testing
75
Practices
44
Security
41
Dependencies
90

Strengths

  • Well-documented README with substantial content
  • CI/CD pipeline configured (github_actions)
  • Good test coverage (42% test-to-source ratio)
  • Code linting configured (ruff (possible))
  • Containerized deployment (Docker)

Weaknesses

  • No LICENSE file — legal ambiguity for contributors
  • Potential hardcoded secrets in 5 files
  • 850 duplicate lines detected — consider DRY refactoring
  • 1 'god files' with >500 LOC need decomposition

Recommendations

  • Add a LICENSE file (MIT recommended for open source)
  • Move hardcoded secrets to environment variables or a secrets manager

Languages

markdown
59.6%
python
23.4%
shell
15.7%
yaml
1.0%
toml
0.1%
sql
0.1%
json
0.0%

Frameworks

FastAPI pytest

Symbols

method82
function76
variable73
constant34
class24
property9

API Endpoints (8)

Source: Repobility analyzer (https://repobility.com)
MethodPathHandlerFramework
Repobility · severity-and-effort ranking · https://repobility.com
POST/correctsubmit_correctionPython
POST/jobssubmit_jobPython
GET/jobslist_jobsPython
GET/jobs/{job_id}get_job_statusPython
POST/probeprobeFastAPI/Flask
GET/queueget_hitl_queuePython
POST/suggest-tuningsuggest_tuningPython
POST/v1/chat/completionschat_completionsFastAPI

Quality Timeline

1 quality score recorded.

View File Metrics
Repobility · MCP-ready · https://repobility.com

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