kladml 0.5.2

KladML SDK - Enterprise-grade MLOps toolkit

KladML

Build ML pipelines with pluggable backends. Simple. Modular. Yours.

⭐ Star us on GitHub to support the project!

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Why KladML?

Feature	KladML	MLflow	ClearML
Interface-based	✅ Pluggable	❌ Hardcoded	❌ Hardcoded
Server required	❌ No	⚠️ Optional	✅ Yes
Local-first	✅ SQLite default	✅ Yes	❌ No
Learning curve	🟢 Minutes	🟡 Days	🔴 Weeks
Hierarchy	✅ Workspace/Proj/Fam	❌ Exp/Run	❌ Project/task
User Interface	✅ TUI (Terminal)	⚠️ Web UI	✅ Web UI
Custom backends	✅ Easy	⚠️ Complex	❌ No

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Installation

# Core (lightweight, no UI)
pip install kladml

# Full CLI (for terminal usage with TUI)
pip install "kladml[cli]"

Quick Start

Zero to Training in 60 Seconds

# The universal quickstart - auto-detects data type and suggests pipeline
kladml quickstart --data my_data.csv

# Output:
# 📊 Analyzing data...
#    Data type: TABULAR (5 columns, 1000 rows)
#
# ? What task do you want to perform?
#   > Classification (detected 'label' column)
#
# 🔧 Selected: XGBoostClassifier + ClassificationEvaluator
# 🚀 Training...
# ✅ Complete! Results saved to data/projects/quickstart/run_001/

Traditional Workflow

# Initialize workspace
kladml init

# Launch Interactive TUI
kladml ui

# Manual training with config
kladml train --config data/configs/my_config.yaml

# Evaluate a trained model
kladml eval --run run_001 --evaluator AnomalyEvaluator

# Hyperparameter tuning with Optuna
kladml tune --config config.yaml --n-trials 50

Create Your Model

from kladml import TimeSeriesModel, ExperimentRunner

class MyForecaster(TimeSeriesModel):
    def train(self, X_train, y_train=None, **kwargs):
        # Your training logic
        return {"loss": 0.1}
    
    def predict(self, X, **kwargs):
        return predictions
    
    def evaluate(self, X_test, y_test=None, **kwargs):
        return {"mae": 0.5, "mse": 0.25}
    
    def save(self, path: str):
        pass
    
    def load(self, path: str):
        pass

# Run with experiment tracking
runner = ExperimentRunner()
result = runner.run(
    model_class=MyForecaster,
    train_data=train_data,
    experiment_name="my-experiment",
)

Supported Data Types

Data Type	Auto-Detection	Default Pipeline
TABULAR	CSV/Parquet with numeric columns	XGBoost
TIMESERIES	Has datetime column/index	Transformer/Gluformer
IMAGE	Folder with JPG/PNG	ResNet50
TEXT	CSV with text columns	BERT

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Architecture

KladML uses dependency injection with abstract interfaces. Swap implementations without changing your code:

┌─────────────────────────────────────────────────────────────┐
│                      Your Code                              │
├─────────────────────────────────────────────────────────────┤
│                   ExperimentRunner                          │
├─────────────────────────────────────────────────────────────┤
│  StorageInterface  │  ConfigInterface  │  TrackerInterface  │
├─────────────────────────────────────────────────────────────┤
│  LocalStorage      │  YamlConfig       │  LocalTracker      │
│  S3Storage         │  EnvConfig        │  MLflowTracker     │
│  (your impl)       │  (your impl)      │  (your impl)       │
└─────────────────────────────────────────────────────────────┘

Implement Custom Backends

from kladml.interfaces import StorageInterface

class S3Storage(StorageInterface):
    """Custom S3 implementation."""
    
    def upload_file(self, local_path, bucket, key):
        # Your S3 logic
        ...

# Plug it in
runner = ExperimentRunner(storage=S3Storage())

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Interfaces

Interface	Description	Default
StorageInterface	Object storage (files, artifacts)	LocalStorage
ConfigInterface	Configuration management	YamlConfig
PublisherInterface	Real-time metric publishing	ConsolePublisher
TrackerInterface	Experiment tracking	LocalTracker (MLflow + SQLite)

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Configuration

Create kladml.yaml:

project:
  name: my-project
  version: 0.1.0

training:
  device: auto  # auto | cpu | cuda | mps

storage:
  artifacts_dir: ./artifacts

Or use environment variables:

export KLADML_TRAINING_DEVICE=cuda
export KLADML_STORAGE_ARTIFACTS_DIR=/data/artifacts

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CLI Commands

kladml --help                 # Show all commands
kladml init                   # Initialize workspace
kladml version                # Show version

# Training
kladml train quick ...        # Quick training (no DB setup)
kladml train single ...       # Full training with project/experiment

# Evaluation
kladml eval run ...           # Evaluate a model
kladml eval info              # Show available evaluators

# Data
kladml data inspect <path>    # Analyze a dataset
kladml data summary <dir>     # Summary of datasets in directory
kladml data convert ...       # Convert PKL -> HDF5

# Models
kladml models export ...      # Export to TorchScript

# Organization
kladml project list           # List all projects
kladml family list ...        # List families
kladml experiment list ...    # List experiments

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Contributing

PRs welcome! See CONTRIBUTING.md for guidelines.

git clone https://github.com/kladml/kladml.git
cd kladml
pip install -e ".[dev]"
pytest

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License

MIT License - see LICENSE for details.

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Documentation · PyPI · GitHub

Made in 🇮🇹 by the KladML Team