kladml 0.2.0

KladML SDK - Enterprise-grade MLOps toolkit

KladML

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

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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
Custom backends	✅ Easy	⚠️ Complex	❌ No

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Installation

pip install kladml

Quick Start

# Initialize a project
kladml init my-project
cd my-project

# Train the Gluformer model
kladml train single \
    --model gluformer \
    --data data/datasets/dataset.pkl \
    --project my-project \
    --experiment v1

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",
)

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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 <name>            # Initialize new project
kladml train single ...       # Train a model
kladml data convert ...       # Convert datasets (PKL -> HDF5)
kladml version                # Show version

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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