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MLos DomainOS

Status: Production
Engine: aiDevops
Domain: Deep learning for genomics (Karpathy 6-phase methodology)

GPU-aware deep-learning DomainOS for genomics. Structures every training run with Andrej Karpathy's six-phase recipe and auto-detects framework and accelerator (NVIDIA CUDA, Apple MPS, Google TPU) with Weights & Biases integration.

Field Value
Product MLos
Engine id aiDevops
Intent key model_training
Repository Hordago-Labs/aiDevops
Plugin ml-os
Maturity (registry) Production

Overview

GPU-aware deep-learning DomainOS for genomics. Structures every training run with Andrej Karpathy's six-phase recipe and auto-detects framework and accelerator (NVIDIA CUDA, Apple MPS, Google TPU) with Weights & Biases integration.

Scientific approach

MLos structures every run as 6 phases. Gates marked HITL require an explicit human-in-the-loop approval before the run advances.

# Phase Gate HITL
1 Data Inspection Inspect and understand the dataset. No
2 End-to-End Skeleton Wire a minimal training/eval loop. No
3 Overfit Verification Prove the model can overfit a small batch. No
4 Regularization Introduce regularization and augmentation. No
5 Hyperparameter Tuning Sweep hyperparameters. No
6 Final Optimization Squeeze final performance and freeze. Yes

Capabilities & evidence objects

Domain tools / skills

  • jax-training
  • pytorch-training
  • tensorflow-training
  • karpathy-recipe
  • variant-effect-prediction
  • protein-embedding
  • gpu-setup
  • wandb-integration

Evidence objects

Object Role Consumer
result.json produces co-writer
provenance.json produces evidence-audit (framework + GPU + seed lineage)

Canonical artifacts (5-artifact contract)

Emitted

MLos emits the full 5-artifact contract on every completed run.

Artifact Description
result.json Structured primary result payload for the domain run.
report.md Human-readable narrative summary of the analysis.
provenance.json Tool versions, reference data, and algorithm lineage for reproducibility.
gate_status.json Per-phase gate pass/fail decisions.
session_summary.json Session metadata for replay and audit.

Standalone quickstart

Zero platform dependency

This quickstart runs the DomainOS standalone. The Hordago platform is not required; platform composition is opt-in (see Composition below).

  1. Install the standalone ml-os plugin (Hordago-Labs/aiDevops) -- no Hordago platform required.
  2. Invoke the domain skill with a MLos intent (see the intent keywords below).
  3. Review the emitted artifacts under the run's output directory.

Intent keywords (route to this engine):

train fine-tune model deep learning neural network embeddings variant effect predict

Worked example

Train a variant-effect-prediction model on dual RTX 5090s

A user asks to train a variant-effect predictor. MLos runs detect_gpu.py, selects a framework, then walks the six Karpathy phases from data inspection through final optimization, logging to W&B and emitting training artifacts.

Validation & benchmarks

Benchmarks

  • Overfit-a-batch sanity gate
  • Held-out AUROC regression

Reproducibility. provenance.json pins framework versions, random seeds, and GPU topology; runs are replayable from the artifact bundle.

Reference

MCP fallback servers (used when the plugin is unavailable): pubmed

Source documents

  • MLos integration: references/mlos-integration.md
  • Engine catalog (Tier 2b): references/engine-catalog.md

Composition

Platform opt-in

MLos runs standalone. When composed under the Hordago platform it gains cross-domain routing, the Shared Compiler gate, and evidence-audit provenance enforcement. Platform composition is opt-in; the quickstart above has zero platform dependency.

Cross-domain dependencies

Engine Relationship
struct-os Protein-embedding models feed structure-prediction workflows.
onco-os Variant-effect-prediction models score somatic variants.