aimodelground 0.1.0

Privacy-first local AI model builder — async DAG workflow, pluggable connectors, guided training pipeline

aimodelground

Privacy-first, locally-installed ML model builder.

Upload data from any source, let the app guide you step-by-step through training, and get a deployable model — entirely on your machine. No cloud, no telemetry, no data leaving your system.

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Installation

pip install aimodelground

Install ML plugins (choose what you need):

pip install aimodelground-classical   # RandomForest, XGBoost, LightGBM
pip install aimodelground-dl          # CNN (images), LSTM (sequences)
pip install aimodelground-llm         # LoRA fine-tuning for text

Requires Python 3.11+

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Quick start

aimodelground init my-model      # create project
cp data.csv my-model/data/raw/  # add your data
cd my-model
aimodelground run               # start pipeline
aimodelground approve review_data  # approve a gate
aimodelground run               # continue
aimodelground ui                # open web interface
aimodelground deploy            # view deployment guide

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How it works

aimodelground runs your data through a configurable DAG pipeline with human-in-the-loop gates:

ingest → merge → validate → profile → rank_algos
            [GATE: review data]
                        ↓
         train_rf ──┐
         train_xgb ─┤→ eval_join → [GATE: review results] → export → DEPLOY.md
         train_lgb ─┘

Each [GATE] pauses and waits for your review. Every run is versioned — replay from any node, compare runs, update models with new data.

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CLI reference

Command	Description
aimodelground --version	Show version
aimodelground init <name>	Create project
aimodelground run	Start/resume pipeline
aimodelground run --from <node>	Replay from node, reuse upstream
aimodelground status	Show DAG node states
aimodelground approve <node>	Approve a gate
aimodelground skip <node>	Skip a node
aimodelground retry <node>	Reset failed node
aimodelground logs <node>	Show node logs
aimodelground runs	List all runs
aimodelground compare <a> <b>	Diff eval metrics
aimodelground tune	Optuna hyperparameter search
aimodelground export [--format]	Re-export model (pickle/onnx)
aimodelground deploy	Print deployment guide
aimodelground ui [--port N]	Open web interface
aimodelground features list	List saved feature sets
aimodelground features info <n>	Feature set details
aimodelground features delete <n>	Delete feature set
aimodelground models list	View all trained models
aimodelground models update [id]	Update model with new data

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Pipeline configuration (pipeline.yaml)

nodes:
  - id: ingest_csv
    type: task
    plugin: connectors.file
    config:
      paths: ["data/raw/*.csv"]

  - id: merge
    type: task
    plugin: core.merge
    depends_on: [ingest_csv]

  - id: validate
    type: task
    plugin: validators.schema
    depends_on: [merge]
    config:
      required_columns: [age, income, label]
      max_null_pct: 0.1

  - id: profile
    type: task
    plugin: core.profile
    depends_on: [merge]

  - id: rank_algos
    type: task
    plugin: core.automl_ranker
    depends_on: [profile]

  - id: review_data
    type: gate
    depends_on: [rank_algos, validate]
    message: "Review data before training"

  - id: train_rf
    type: task
    plugin: ml.classical.random_forest
    depends_on: [review_data]
    config:
      target_col: label

  - id: train_xgb
    type: task
    plugin: ml.classical.xgboost
    depends_on: [review_data]
    config:
      target_col: label

  - id: eval_join
    type: parallel_join
    depends_on: [train_rf, train_xgb]

  - id: review_results
    type: gate
    depends_on: [eval_join]
    message: "Review results and pick model"

  - id: export
    type: task
    plugin: core.export
    depends_on: [review_results]
    config:
      format: onnx

  - id: deploy_advisor
    type: task
    plugin: core.deploy_advisor
    depends_on: [export]

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Data connectors

Plugin	Source
connectors.file	CSV, JSON, Parquet, Excel, Arrow (DuckDB, glob patterns)
connectors.sql	PostgreSQL, MySQL, SQLite (SQLAlchemy DSN)
connectors.rest_poll	HTTP API polling
connectors.websocket	WebSocket stream
connectors.kafka	Kafka topic
connectors.image	PNG/JPG/TIFF directory → image_path + label
connectors.audio	WAV/MP3/FLAC directory → MFCC features
connectors.s3	Amazon S3 (DuckDB httpfs, IAM/keys/MinIO)
connectors.gcs	Google Cloud Storage (DuckDB httpfs)
connectors.feature_store	Saved feature sets

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ML plugins

aimodelground-classical

pip install aimodelground-classical

Plugin	Algorithm	Update support
ml.classical.random_forest	RandomForest	warm_start
ml.classical.xgboost	XGBoost	incremental
ml.classical.lightgbm	LightGBM	incremental

All produce: accuracy/F1/RMSE, SHAP feature importance, pickle + ONNX export.

aimodelground-dl

pip install aimodelground-dl

Plugin	Architecture
ml.dl.cnn_image	3-layer CNN for image classification
ml.dl.lstm_tabular	2-layer LSTM for sequential/tabular data

aimodelground-llm

pip install aimodelground-llm

Plugin	Method
ml.llm.lora_text	LoRA fine-tuning on GPT-2, Llama, Mistral, Phi

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Core pipeline plugins

Plugin	Purpose
core.merge	Concat all connector outputs
core.profile	Compute DataProfile (row count, column types, nulls)
validators.schema	Validate required columns + null thresholds
core.automl_ranker	Rank installed ML plugins by suitability
core.automl_tuner	Optuna hyperparameter search (CV-based)
core.export	Export best model (pickle/ONNX/safetensors)
core.deploy_advisor	Generate DEPLOY.md
core.feature_store_save	Save processed data as named feature set
core.model_update	Update existing model with new data

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Feature store

aimodelground features list
aimodelground features info <name>
aimodelground features versions <name>
aimodelground features delete <name>

# Save features in pipeline
- id: save_features
  type: task
  plugin: core.feature_store_save
  depends_on: [merge]
  config:
    feature_name: customer_features_v1

# Load in future run
- id: load_features
  type: task
  plugin: connectors.feature_store
  config:
    name: customer_features_v1

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Model update

aimodelground models list
aimodelground models update --data data/raw/new.csv --target label
aimodelground models update run_001/random_forest --n-estimators 100

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Versioned runs

aimodelground runs
aimodelground compare run_001 run_002
aimodelground run --from validate    # replay, reuse upstream outputs

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Web UI

aimodelground ui --port 8765

• Pipeline — live DAG, approve/skip buttons, SSE real-time updates
• Data — file upload, schema, null stats
• Results — leaderboard, Plotly charts, run comparison
• Deploy — rendered deployment guide

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Project structure

my-project/
  pipeline.yaml         # DAG definition
  project.db            # SQLite state
  data/raw/             # Input data
  runs/
    run_001/
      artifacts/        # Models, parquets, ranking.json
      logs/             # Node logs
      eval_report.json
      DEPLOY.md         # Deployment guide
      export/           # Exported model
  .modelbuilder/
    features/           # Feature store data
    feature_store.db

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Contributing

See CONTRIBUTING.md.

Releasing

See RELEASING.md.

Changelog

See CHANGELOG.md.

License

Apache 2.0 — see LICENSE