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Quick Demo

ml-audit https://github.com/pytorch/pytorch

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Motivation

Most machine learning repositories:

• Cannot be reliably reproduced
• Lack dependency and environment clarity
• Provide no benchmark guarantees
• Hide system-level bottlenecks

This tool evaluates repositories through a systems lens, focusing on:

• Reproducibility signals
• Engineering maturity
• ML systems design patterns

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Installation

pip install -e .

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Usage

ml-audit https://github.com/user/repo

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GitHub Action Usage

name: ML Reproducibility Audit
on:
  pull_request:
  workflow_dispatch:
jobs:
  audit:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: OmprakashSahani/ml-repro-audit/.github/actions/ml-audit@v1
        with:
          repo-url: https://github.com/user/repo

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Example Script

Run a quick audit using:

./examples/run_audit.sh

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JSON Output

ml-audit https://github.com/user/repo --json

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Example Output

Repository: user/repo

Structure Analysis:
- has_readme: YES
- has_license: YES
- has_ci: NO
- has_benchmarks: YES

Reproducibility Score: 7.5/10
Risk Level: MEDIUM

Code Quality Signals:
- has_pinned_dependencies: YES
- has_seed_control: NO
- has_training_loop: YES

ML Systems Detection:
- uses_pytorch: YES
- uses_distributed: YES
- uses_all_reduce: YES

Insights:
- No CI/CD detected → changes are not automatically validated
- Missing seed control → results may not be reproducible

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JSON Output Example

{
  "repository": "user/repo",
  "score": 7.5,
  "risk": "MEDIUM",
  "analysis": {},
  "quality": {},
  "patterns": {},
  "insights": []
}

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Features

• GitHub API integration (with authentication support)
• Repository structure analysis (CI/CD, benchmarks, datasets)
• Code quality analysis (dependencies, determinism, training loops)
• Reproducibility scoring with weighted signals
• Risk classification (LOW / MEDIUM / HIGH)
• ML systems pattern detection (PyTorch, distributed training, all-reduce)
• Code-level inspection via GitHub API
• Insight generation based on system signals
• JSON output for automation and pipelines
• Rich CLI interface (tables, colors)

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GitHub Integration

This tool integrates directly with the GitHub API to:

• Fetch repository metadata and file structure
• Inspect source code for ML system patterns
• Analyze engineering signals across repositories

It is designed as a developer tool to audit and improve repository quality within the GitHub ecosystem.

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Use Cases

• Evaluate reproducibility of ML repositories before use
• Audit open-source projects for engineering quality
• Compare ML infrastructure practices across repositories
• Integrate into CI pipelines for repository validation

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Architecture

flowchart TD
    A[CLI Input] --> B[GitHub API]
    B --> C[File Fetcher]
    C --> D[Structure Analyzer]
    C --> E[Code Quality Analyzer]
    C --> F[ML Pattern Detector]
    D --> G[Scoring Engine]
    E --> G
    G --> H[Risk Classifier]
    D --> I[Insights Generator]
    E --> I
    F --> I
    H --> J[Report Output]
    I --> J

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Design Principles

• Reproducibility-first — treat environment and determinism as first-class concerns
• Signal over noise — focus on high-impact engineering indicators
• System-aware analysis — go beyond files into behavior and patterns
• Composable design — CLI + JSON for integration into workflows

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Evaluation Dimensions

The scoring system considers:

• Environment setup (dependencies, packaging)
• Determinism (seed control)
• Documentation
• Testing and validation
• CI/CD pipelines
• Benchmarking practices
• Dataset reproducibility
• Configuration-driven experimentation

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Roadmap

• AST-based static analysis (deeper code understanding)
• Dataset pipeline validation
• Training loop structure detection
• Performance bottleneck hints
• Multi-repo comparison
• Web dashboard (FastAPI)

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Limitations

• Heuristic-based detection (not full static analysis yet)
• Partial file sampling for performance
• GitHub API rate limits without authentication
• Static analysis only (does not execute code)

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Why This Matters

Reproducibility is a major gap in real-world ML systems.

This project explores how:

• System design decisions affect reproducibility
• Engineering practices impact reliability
• Scalability constraints influence outcomes

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