kailash-ml

name	kailash-ml
description	Kailash ML 1.0.0 — MANDATORY for ALL ML training/inference/feature/drift/AutoML/RL work. Engine-first km.* surface (train/register/serve/track/diagnose/watch/dashboard/seed/reproduce/resume/lineage/rl_train/engine_info/list_engines/autolog) + 18 engines. Raw sklearn / pytorch / numpy training loops BLOCKED.

Kailash ML 1.0.0 — Classical / Deep Learning / RL Lifecycle

Production ML lifecycle framework built on Kailash Core SDK — engine-first km.* verb surface, 18-engine discovery registry, polars-native, ONNX-default serialisation, Agent Tool Discovery for Kaizen integration, wave-released with 6 sibling packages.

1.0.0 Engine-First Surface (Canonical)

Single entry: import kailash_ml as km. Zero-arg construction. 14 lifecycle verbs + 2 discovery verbs grouped in __all__:

import kailash_ml as km

async with km.track("demo") as run:                        # Group 1 lifecycle
    result = await km.train(df, target="y")                # Group 1 lifecycle
    registered = await km.register(result, name="demo")    # Group 1 lifecycle
server = await km.serve("demo@production")                 # Group 1 lifecycle
# $ kailash-ml-dashboard  (separate shell)                 # Group 1 lifecycle

km.diagnose(model)                                         # Group 1 — DLDiagnostics / RAGDiagnostics / RLDiagnostics
km.watch(model, reference_df)                              # Group 1 — DriftMonitor
km.seed(42); await km.reproduce(run_id)                    # Group 1 — reproducibility
await km.resume(run_id)                                    # Group 1 — checkpoint resume
graph = await km.lineage("demo@v1", tenant_id=None)        # Group 1 — LineageGraph; ambient tenant via get_current_tenant_id()
await km.rl_train(env, policy)                             # Group 1 — RL
km.autolog()                                               # Group 1 — sklearn/lgb/Lightning/torch auto-logging

info = km.engine_info("TrainingPipeline")                  # Group 6 Engine Discovery (agents MUST use this, not imports)
engines = km.list_engines()                                # Group 6 — 18-engine catalog per §E1.1

Quick Start fingerprint (pinned, regression-tested via ml-engines-v2 §16.3): c962060cf467cc732df355ec9e1212cfb0d7534a3eed4480b511adad5a9ceb00

21 Canonical Specs

Authoritative domain truth lives in specs/. Read the spec before touching the code:

Engine Core (4)

• specs/ml-engines-v2.md — MLEngine 1.0.0, 8-method MLEngine surface (per Decision 8 Lightning lock-in), TrainingResult + DeviceReport, km.* wrappers, canonical README Quick Start (§16 fingerprint contract).
• specs/ml-engines-v2-addendum.md — 18-engine catalog (§E1.1), classical-ML surface, EngineInfo / MethodSignature / ParamSpec / ClearanceRequirement frozen dataclasses (§E11.1), LineageGraph + LineageNode + LineageEdge (§E10), Pydantic-to-DataFrame adapter.
• specs/ml-backends.md — 6 first-class backends (cpu/cuda/mps/rocm/xpu/tpu), detect_backend(), precision auto, Lightning integration, hardware-gated CI, backend-compat-matrix.yaml data.
• specs/ml-diagnostics.md — DLDiagnostics cross-SDK Diagnostic Protocol, torch-hook instrumentation, plotly gated by [dl] extra.

Experiment / Registry / Serving (4)

• specs/ml-tracking.md — ExperimentTracker async-context ambient-run scope, nested runs, auto-logging, GDPR erasure, MLflow import bridge, ExperimentTracker.create() factory, get_current_run() contextvar accessor.
• specs/ml-registry.md — ModelRegistry lifecycle (staging → shadow → production → archived), RegisterResult.artifact_uris: dict[str, str] (§7.1 canonical), §7.1.1 v1.x @property back-compat shim, §7.1.2 Single-Format-Per-Row DDL Invariant, §5.6 ONNX export probe.
• specs/ml-serving.md — InferenceServer + ServeHandle, REST + MCP channels, §4.1 batch padding, §5.2 streaming backpressure, §2.5.3 pickle-fallback gate.
• specs/ml-autolog.md — sklearn / lightgbm / PyTorch Lightning / torch loop auto-logging, DDP rank-0-only (Decision 4), metric namespace discipline.

AutoML / Drift / Feature Store / Dashboard (4)

• specs/ml-automl.md — AutoMLEngine agent-infused (grid/random/bayesian/successive-halving) + cost budget + human-approval gate + PACT envelope.
• specs/ml-drift.md — DriftMonitor (KS / chi2 / PSI / Jensen-Shannon), scheduled monitoring, retraining hooks.
• specs/ml-feature-store.md — Polars-native FeatureStore on ConnectionManager, point-in-time queries, schema enforcement.
• specs/ml-dashboard.md — MLDashboard CLI + km.dashboard() launcher + Streamlit panels.

Reinforcement Learning (3)

• specs/ml-rl-core.md — RLTrainer + registries + km.rl_train() (Stable-Baselines3 + Gymnasium).
• specs/ml-rl-algorithms.md — PPO / SAC / DQN / A2C / TD3 / DDPG / MaskablePPO / Decision Transformer catalog.
• specs/ml-rl-align-unification.md — kailash-ml.rl ↔ kailash-align trajectory bridge (GRPO / RLOO / PPO-LM).

Integrations (6)

• specs/kailash-core-ml-integration.md, specs/dataflow-ml-integration.md, specs/nexus-ml-integration.md, specs/kaizen-ml-integration.md, specs/align-ml-integration.md, specs/pact-ml-integration.md.

14 Approved Decisions (pinned 2026-04-21)

1. Status vocabulary — FINISHED only; hard-migrate legacy SUCCESS/COMPLETED at install.
2. GDPR erasure — audit rows IMMUTABLE with sha256 fingerprints.
3. Cross-SDK Rust enum parity — {RUNNING, FINISHED, FAILED, KILLED} byte-identical.
4. DDP/FSDP/DeepSpeed — rank-0-only hardcoded via torch.distributed.get_rank() == 0.
5. XPU dual-path — torch.xpu native-first + intel_extension_for_pytorch fallback.
6. GPU architecture cutoff — backend-compat-matrix.yaml as data; km.doctor reads it.
7. CI runner — CPU + MPS BLOCKING now; CUDA BLOCKING when self-hosted lands.
8. Lightning hard lock-in — raw loops raise UnsupportedTrainerError; no escape hatch.
9. Rust ExperimentTracker — explicit start_run()/end_run() (AsyncDrop not stable).
10. Single canonical spec per domain; Rust overlays via loom/.claude/variants/rs/.
11. Legacy namespace sunset — at 3.0; 2.x DeprecationWarning; 1.x back-compat shim.
12. Cross-tenant admin export — MultiTenantOpError in 1.0.0; PACT-gated post-1.0.
13. Extras naming — hyphens across all specs ([rl-offline], [autolog-lightning], [feature-store]).
14. Package version at merge — kailash-ml 1.0.0 MAJOR (breaking-change list in spec body).

1.0.0 Wave Release (7 packages atomic)

• kailash 2.9.0 (ml extras alias) + kailash-pact 0.10.0 (ml_context + ClearanceRequirement) + kailash-nexus 2.2.0 (ml-endpoints mount) + kailash-kaizen 2.12.0 (§2.4 Agent Tool Discovery + SQLiteSink) + kailash-align 0.6.0 (ml-unification + rl_bridge) + kailash-dataflow 2.1.0 (TrainingContext + lineage_dataset_hash) + kailash-ml 1.0.0.

M1 release-wave patterns — see m1-release-wave for:

• Canonical 48-symbol __all__ (41 §15.9 + erase_subject + 7 Phase-1 adapters)
• km.train + km.register canonical async-await pipeline (commit fdd3040e)
• TrainingResult.trainable back-reference (commit 15033fa6)
• Release-blocking README Quick Start regression (SHA-256 fingerprint end-to-end)
• MIGRATION.md sunset contract (W33b)
• 7 integration surfaces (kailash.ml / kailash.observability.ml / dataflow.ml / nexus.ml / kaizen.ml / align.ml / pact.ml)
• 8 institutional patterns from session 2026-04-23

---

Legacy v0.x material below retained as internal-implementation reference only. The canonical user surface is the engine-first km.* verbs above; the spec files under specs/ml-*.md are the authority.

Install Matrix

pip install kailash-ml            # Core: polars, numpy, scipy, sklearn, lightgbm, xgboost, plotly, onnx
pip install kailash-ml[dl]        # + PyTorch, Lightning, transformers, timm
pip install kailash-ml[dl-gpu]    # + onnxruntime-gpu
pip install kailash-ml[rl]        # + Stable-Baselines3, Gymnasium
pip install kailash-ml[agents]    # + kailash-kaizen (agent integration)
# NOTE: [xgb] is a no-op alias — xgboost is now a base dep (xgboost>=2.0 ships
# with CUDA built in and auto-detects GPU at runtime, CPU fallback otherwise).
pip install kailash-ml[catboost]  # + CatBoost
pip install kailash-ml[explain]   # + SHAP (model explainability)
pip install kailash-ml[imbalance] # + imbalanced-learn (SMOTE, ADASYN)
pip install kailash-ml[stats]     # + statsmodels
pip install kailash-ml[full]      # Everything (CPU)
pip install kailash-ml[all-gpu]   # Everything (GPU)

13 Engines (by Priority)

#	Engine	Priority	Purpose	Key Dependency
1	FeatureStore	P0	Polars-native feature versioning, point-in-time queries	ConnectionManager
2	ModelRegistry	P0	Model versioning (staging/shadow/production/archived), ONNX export	ConnectionManager, ArtifactStore
3	TrainingPipeline	P0	sklearn/LightGBM/Lightning training with FeatureSchema	FeatureStore, ModelRegistry
4	InferenceServer	P0	REST serving via kailash-nexus, response caching, batch	ModelRegistry, kailash-nexus
5	DriftMonitor	P0	KS/chi2/PSI/Jensen-Shannon drift detection, scheduled checks	ConnectionManager
6	ExperimentTracker	P0	MLflow-compatible run tracking, metric comparison, audit	ConnectionManager
7	HyperparameterSearch	P1	Grid/random/Bayesian/successive halving optimization	TrainingPipeline
8	AutoMLEngine	P1	Multi-family model search, optional agent augmentation	HyperparameterSearch, FeatureStore
9	EnsembleEngine	P1	Blend/stack/bag/boost ensemble creation	TrainingPipeline
10	PreprocessingPipeline	P1	Auto-setup from FeatureSchema, imputation, encoding	FeatureSchema
11	DataExplorer	P2	Statistical profiling, plotly visualization, comparison	polars, plotly
12	FeatureEngineer	P2	Auto-generation, selection, importance ranking	polars
13	ModelExplainer	P2	SHAP-based global/local/dependence explanations	SHAP (requires [explain])

Additional modules: OnnxBridge, MlflowFormatReader/Writer, MLDashboard (all lazy-loaded).

Quick Start

Feature Ingestion

from kailash.db.connection import ConnectionManager
from kailash_ml import FeatureStore
from kailash_ml.types import FeatureSchema, FeatureField
import polars as pl

conn = ConnectionManager("sqlite:///ml.db")
await conn.initialize()

schema = FeatureSchema(
    name="user_churn",
    features=[
        FeatureField(name="age", dtype="float"),
        FeatureField(name="tenure_months", dtype="float"),
    ],
    target=FeatureField(name="churned", dtype="int"),
)

fs = FeatureStore(conn, table_prefix="kml_feat_")
await fs.initialize()

df = pl.read_csv("data.csv")
await fs.ingest("user_features", schema, df)

# Point-in-time retrieval
features = await fs.get_features("user_features", entity_ids=["u1", "u2"])

Training

from kailash_ml import TrainingPipeline, ModelRegistry, ModelSpec, EvalSpec
from kailash_ml.engines import LocalFileArtifactStore

registry = ModelRegistry(conn, artifact_store=LocalFileArtifactStore("./artifacts"))
await registry.initialize()

pipeline = TrainingPipeline(feature_store=fs, model_registry=registry)
result = await pipeline.train(
    schema=schema,
    model_spec=ModelSpec(model_class="sklearn.ensemble.RandomForestClassifier"),
    eval_spec=EvalSpec(metrics=["accuracy", "f1"]),
)

Drift Monitoring

from kailash_ml import DriftMonitor

# W26.e: tenant_id is REQUIRED at construction. One monitor per tenant.
monitor = DriftMonitor(conn, tenant_id="acme")
await monitor.initialize()
await monitor.set_reference("model_v1", reference_df)
report = await monitor.check_drift("model_v1", current_df)
# report.overall_drift, report.feature_results, report.recommendations

Model Explainability (requires [explain])

from kailash_ml import ModelExplainer

explainer = ModelExplainer(model=fitted_model, X=train_df, feature_names=schema.feature_names)
global_report = explainer.explain_global(max_display=10)
local_report = explainer.explain_local(X=test_df, index=0)
fig = explainer.to_plotly("summary")  # "summary", "beeswarm", "dependence"

AutoML with Agent Augmentation

from kailash_ml import AutoMLEngine
from kailash_ml.engines.automl_engine import AutoMLConfig

config = AutoMLConfig(
    task_type="classification",
    metric_to_optimize="f1",
    search_strategy="bayesian",
    search_n_trials=50,
    agent=True,            # LLM augmentation (requires kailash-ml[agents])
    auto_approve=False,    # Human approval gate
    max_llm_cost_usd=5.0,
)
engine = AutoMLEngine(feature_store=fs, model_registry=registry, config=config)
result = await engine.run(schema=schema, data=df)

Model Registry Lifecycle

# Stage transitions: staging → shadow → production → archived
await registry.promote("model_v1", version_id, target_stage="production")

# Valid transitions:
# staging    → shadow, production, archived
# shadow     → production, archived, staging
# production → archived, shadow
# archived   → staging

Preprocessing Pipeline

from kailash_ml.engines import PreprocessingPipeline

pipeline = PreprocessingPipeline()
result = pipeline.setup(
    data=df, target="churned",
    normalize=True, normalize_method="zscore",       # zscore, minmax, robust, maxabs
    imputation="knn", impute_n_neighbors=5,           # knn, iterative, default
    remove_multicollinearity=True, multicollinearity_threshold=0.9,
    fix_imbalance=True, imbalance_method="smote",     # smote, adasyn ([imbalance])
)

Nested Runs & Auto-Logging

from kailash_ml import ExperimentTracker

tracker = ExperimentTracker(conn)
await tracker.initialize()

async with tracker.run("hyperopt-sweep") as parent:
    for params in param_grid:
        async with tracker.run("trial", parent_run_id=parent.run_id) as child:
            await child.log_params(params)

Decision Tree: kailash-ml vs kailash-align vs kailash-kaizen

You Want To...	Use
Train sklearn/LightGBM/XGBoost models	kailash-ml
Manage feature pipelines	kailash-ml
Monitor model drift	kailash-ml
Export models to ONNX	kailash-ml
Fine-tune an LLM (LoRA, DPO, RLHF)	kailash-align
Serve a fine-tuned LLM via Ollama	kailash-align
Build an AI agent with tools	kailash-kaizen
Add agent intelligence to ML engines	kailash-ml[agents] (uses Kaizen under the hood)
Train RL policies (Gymnasium)	kailash-ml[rl]

Polars-Native Rule (ABSOLUTE)

Every engine accepts and returns polars.DataFrame. Conversion to numpy/pandas/LightGBM Dataset happens ONLY in interop.py at sklearn/framework boundaries.

# DO: Work in polars throughout
df = pl.read_csv("data.csv")
await fs.ingest("features", schema, df)

# DO NOT: Convert to pandas first
df_pd = pd.read_csv("data.csv")  # WRONG — polars is the native format

Interop Conversion Table

All conversions live in interop.py. Import from there only.

Function	From	To	Use When
to_sklearn_input()	polars DataFrame	(X: ndarray, y: ndarray, info: dict)	Training with sklearn
from_sklearn_output()	ndarray	polars DataFrame	Converting predictions back
to_lgb_dataset()	polars DataFrame	lightgbm.Dataset	Training with LightGBM
to_hf_dataset()	polars DataFrame	datasets.Dataset	HuggingFace integration
polars_to_arrow()	polars DataFrame	pyarrow.Table	Arrow IPC / Parquet
from_arrow()	pyarrow.Table	polars DataFrame	Ingesting Arrow data
to_pandas()	polars DataFrame	pandas.DataFrame	Legacy pandas interop
from_pandas()	pandas.DataFrame	polars DataFrame	Ingesting pandas data
polars_to_dict_records()	polars DataFrame	list[dict]	JSON serialization
dict_records_to_polars()	list[dict]	polars DataFrame	JSON deserialization

Architecture

kailash-ml/
  engines/
    _shared.py              ← Numeric dtypes, model class validation
    _feature_sql.py         ← ALL raw SQL (zero SQL in engine files)
    _guardrails.py          ← AgentGuardrailMixin (5 mandatory guardrails)
    feature_store.py        ← FeatureStore (ConnectionManager, polars-native)
    model_registry.py       ← ModelRegistry (lifecycle, SHA256 integrity)
    training_pipeline.py    ← TrainingPipeline (schema-driven)
    inference_server.py     ← InferenceServer (Nexus, ONNX, caching)
    drift_monitor.py        ← DriftMonitor (KS/chi2/PSI/JS)
    model_explainer.py      ← ModelExplainer (SHAP, [explain])
    experiment_tracker.py   ← MLflow-compatible tracking (nested runs)
    hyperparameter_search.py ← Grid/random/bayesian/successive halving
    automl_engine.py        ← Agent-infused AutoML
    ensemble.py             ← Blend/stack/bag/boost
    preprocessing.py        ← Auto-setup from FeatureSchema
  agents/                   ← 6 Kaizen agents ([agents])
    tools.py                ← Dumb data endpoints (LLM-first)
  rl/                       ← RLTrainer, EnvironmentRegistry, PolicyRegistry
  interop.py                ← SOLE conversion point
  bridge/                   ← OnnxBridge (export + verification)

Internal Module Guide

Module	Purpose	When to Touch
_shared.py	NUMERIC_DTYPES, ALLOWED_MODEL_PREFIXES, validate_model_class(), compute_metrics_by_name()	Adding new model frameworks or metrics
_feature_sql.py	ALL raw SQL for FeatureStore (zero SQL elsewhere)	Any FeatureStore schema/query change
_guardrails.py	AgentGuardrailMixin (cost budget, audit trail, approval gate)	Adding agent integration to any engine
interop.py	SOLE conversion point: polars ↔ sklearn/lgb/arrow/pandas/hf	Adding new framework interop

6 ML Agents (kailash-ml[agents])

Agents require both agent=True AND the agents extra installed. All follow LLM-first rule.

Agent	Purpose
DataScientistAgent	Data profiling recommendations
FeatureEngineerAgent	Feature generation guidance
ModelSelectorAgent	Model selection reasoning
ExperimentInterpreterAgent	Trial result analysis
DriftAnalystAgent	Drift report interpretation
RetrainingDecisionAgent	Retrain/rollback decisions

See ml-agent-guardrails for the 5 mandatory guardrails.

RL Module (kailash-ml[rl])

from kailash_ml.rl import RLTrainer, EnvironmentRegistry, PolicyRegistry

env_reg = EnvironmentRegistry()
env_reg.register("CartPole-v1")

trainer = RLTrainer(env_registry=env_reg, policy_registry=PolicyRegistry())
result = await trainer.train(env_id="CartPole-v1", algorithm="PPO", total_timesteps=100_000)

Security Checklist

When writing or reviewing kailash-ml engine code, verify:

• SQL identifiers: All interpolated identifiers pass through _validate_identifier() (from kailash.db.dialect)
• SQL types: Column types validated via _validate_sql_type() allowlist (INTEGER, REAL, TEXT, BLOB, NUMERIC)
• SQL placement: Zero raw SQL outside _feature_sql.py — all queries go through that module
• Model classes: Dynamic model imports validated via validate_model_class() against ALLOWED_MODEL_PREFIXES (sklearn., lightgbm., xgboost., catboost., kailash_ml., torch., lightning.)
• Financial fields: math.isfinite() on all cost/budget fields (NaN/Inf bypass comparisons)
• Table prefix: Regex-validated in constructor (^[a-zA-Z_][a-zA-Z0-9_]*$)
• Bounded collections: Audit trails, cost logs, trial history use deque(maxlen=N)
• Agent guardrails: Engines with agent integration inherit AgentGuardrailMixin (cost budget + approval gate)
• Interop boundary: Conversions happen ONLY in interop.py, nowhere else

Skill Files

• ml-feature-pipelines — FeatureStore, polars-only engineering, schema-driven ingestion
• ml-model-registry — ModelRegistry CRUD, lifecycle stages, MLflow compatibility
• ml-training-pipeline — TrainingPipeline, hyperparameter search, experiment tracking
• ml-inference-server — InferenceServer, Nexus exposure, ONNX serving, batch inference
• ml-agent-guardrails — 5 mandatory guardrails, AutoML, agent integration
• ml-onnx-export — PyTorch/sklearn to ONNX, verification, cross-language serving
• ml-drift-monitoring — DriftMonitor, statistical tests, alert thresholds, retraining triggers

Critical Rules

• All engines are polars-native — no pandas/numpy in pipeline code
• sklearn interop only at boundary via interop.py
• FeatureStore uses ConnectionManager, not Express (needs window functions)
• Zero raw SQL outside _feature_sql.py
• Agent-augmented engines require double opt-in (agent=True + extras installed)
• All agents follow LLM-first rule — tools are dumb data endpoints

Related Skills

• 01-core-sdk — Core workflow patterns
• 02-dataflow — Database integration (ConnectionManager)
• 03-nexus — Multi-channel deployment (InferenceServer)
• 04-kaizen — AI agent framework (ML agents)
• 35-kailash-align — LLM fine-tuning and alignment