td-model-store 0.1.5

Store and load ML models (XGBoost, LightGBM, CatBoost) in Treasure Data Plazma via pytd

td-model-store

Store and load ML models (XGBoost, LightGBM, CatBoost) in Treasure Data DB via pytd.

Features

• API: Save and load models with just a few lines of code
• Multiple Model Types: Supports XGBoost, LightGBM, and CatBoost
• Chunked Storage: Automatically handles large models by chunking data
• Session Management: Track model versions with session IDs
• Model Versioning: Load specific versions or the latest model
• Seamless Integration: Works directly with Treasure Data via pytd

Installation

Basic Installation

pip install td-model-store

Quick Start

Saving a Model

from td_model_store import save_model
import xgboost as xgb

# Train your model
model = xgb.XGBClassifier()
model.fit(X_train, y_train)

# Save to Treasure Data
metadata = save_model(
    model=model,
    database="my_database",
    table="ml_model_store",
    model_name="my_xgboost_model",
    apikey="your_td_api_key",
    endpoint="https://api.treasuredata.com"
)

print(f"Model saved with session_id: {metadata['session_id']}")

Loading a Model

from td_model_store import load_model

# Load the latest version of a model
model = load_model(
    database="my_database",
    table="ml_model_store",
    model_name="my_xgboost_model",
    apikey="your_td_api_key",
    endpoint="https://api.treasuredata.com"
)

# Use the model for predictions
predictions = model.predict(X_test)

API Reference

save_model()

Save an ML model to a Treasure Data table.

Parameters

• model (required): Trained model object (XGBClassifier, XGBRegressor, LGBMClassifier, LGBMRegressor, CatBoostClassifier, or CatBoostRegressor)
• database (required): Target TD database name
• table (str, default="ml_model_store"): Target table name
• model_name (str, optional): Name tag for the model. Defaults to "xgboost_model", "lightgbm_model", or "catboost_model" based on model type
• session_id (int, optional): Unique session identifier. Defaults to current unix timestamp
• apikey (str, required): TD API key. Can also be set via TD_API_KEY or TDX_API_KEY* env vars
• endpoint (str, default="https://api.treasuredata.com"): TD API endpoint. Use region-specific endpoints:
  • US: https://api.treasuredata.com
  • EU: https://api.eu01.treasuredata.com
  • Japan: https://api.treasuredata.co.jp
  • Asia Pacific: https://api.ap02.treasuredata.com
• chunk_size (int, default=130000): Max characters per chunk (must be < 131072)

Returns

Dictionary containing:

• session_id: Session identifier
• model_name: Model name
• model_type: Model type (xgboost, lightgbm, or catboost)
• model_format: Serialization format
• num_chunks: Number of chunks
• size_bytes: Model size in bytes

Example

metadata = save_model(
    model=my_model,
    database="production_db",
    table="models",
    model_name="fraud_detector_v1",
    session_id=20240420001,
    apikey="your_td_api_key",
    endpoint="https://api.treasuredata.com"
)

load_model()

Load an ML model from a Treasure Data table.

Parameters

• database (required): Source TD database name
• table (str, default="ml_model_store"): Source table name
• model_name (str, optional): Filter by model name. If not provided, loads the latest session
• session_id (int, optional): Load a specific session. If not provided, loads the max session_id (optionally filtered by model_name)
• apikey (str, required): TD API key. Can also be set via TD_API_KEY or TDX_API_KEY* env vars
• endpoint (str, default="https://api.treasuredata.com"): TD API endpoint. Use region-specific endpoints:
  • US: https://api.treasuredata.com
  • EU: https://api.eu01.treasuredata.com
  • Japan: https://api.treasuredata.co.jp
  • Asia Pacific: https://api.ap02.treasuredata.com

Returns

The reconstructed ML model (XGBoost, LightGBM, or CatBoost)

Example

# Load latest version of a specific model
model = load_model(
    database="production_db",
    table="models",
    model_name="fraud_detector_v1",
    apikey="your_td_api_key",
    endpoint="https://api.treasuredata.com"
)

# Load a specific session
model = load_model(
    database="production_db",
    table="models",
    session_id=20240420001,
    apikey="your_td_api_key",
    endpoint="https://api.treasuredata.com"
)

# Load the absolute latest model (any name)
model = load_model(
    database="production_db",
    table="models",
    apikey="your_td_api_key",
    endpoint="https://api.treasuredata.com"
)

Complete Examples

XGBoost Example

from td_model_store import save_model, load_model
import xgboost as xgb
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# Prepare data
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Train model
model = xgb.XGBClassifier(n_estimators=100, max_depth=3)
model.fit(X_train, y_train)

# Save to TD
metadata = save_model(
    model=model,
    database="ml_models",
    model_name="iris_classifier",
    apikey="your_td_api_key",
    endpoint="https://api.treasuredata.com"
)

# Load from TD
loaded_model = load_model(
    database="ml_models",
    model_name="iris_classifier",
    apikey="your_td_api_key",
    endpoint="https://api.treasuredata.com"
)

# Verify
score = loaded_model.score(X_test, y_test)
print(f"Model accuracy: {score:.3f}")

LightGBM Example

from td_model_store import save_model, load_model
import lightgbm as lgb
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split

# Prepare data
X, y = load_boston(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Train model
model = lgb.LGBMRegressor(n_estimators=100)
model.fit(X_train, y_train)

# Save to TD
metadata = save_model(
    model=model,
    database="ml_models",
    model_name="boston_price_predictor",
    session_id=20240420001,
    apikey="your_td_api_key",
    endpoint="https://api.treasuredata.com"
)

# Load from TD
loaded_model = load_model(
    database="ml_models",
    session_id=20240420001,
    apikey="your_td_api_key",
    endpoint="https://api.treasuredata.com"
)

# Make predictions
predictions = loaded_model.predict(X_test)

CatBoost Example

from td_model_store import save_model, load_model
from catboost import CatBoostClassifier
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split

# Prepare data
X, y = load_digits(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Train model
model = CatBoostClassifier(iterations=100, verbose=False)
model.fit(X_train, y_train)

# Save to TD
metadata = save_model(
    model=model,
    database="ml_models",
    model_name="digit_classifier",
    apikey="your_td_api_key",
    endpoint="https://api.treasuredata.com"
)

print(f"Saved {metadata['num_chunks']} chunks, {metadata['size_bytes']} bytes")

# Load from TD
loaded_model = load_model(
    database="ml_models",
    model_name="digit_classifier",
    apikey="your_td_api_key",
    endpoint="https://api.treasuredata.com"
)

# Make predictions
predictions = loaded_model.predict(X_test)

Configuration

API Key and Endpoint

The apikey parameter is required for all operations. Specify the correct endpoint for your Treasure Data region:

# US region (default)
save_model(
    model=model,
    database="my_database",
    apikey="your_td_api_key",
    endpoint="https://api.treasuredata.com"
)

# EU region
save_model(
    model=model,
    database="my_database",
    apikey="your_td_api_key",
    endpoint="https://api.eu01.treasuredata.com"
)

# Japan region
save_model(
    model=model,
    database="my_database",
    apikey="your_td_api_key",
    endpoint="https://api.treasuredata.co.jp"
)

# Asia Pacific region
save_model(
    model=model,
    database="my_database",
    apikey="your_td_api_key",
    endpoint="https://api.ap02.treasuredata.com"
)

Model Storage Schema

Models are stored in the following table schema:

Column	Type	Description
time	int	Unix timestamp
session_id	int	Unique session identifier
model_name	string	Model name tag
model_type	string	Model type (xgboost, lightgbm, catboost)
model_format	string	Serialization format (ubj, txt, cbm)
chunk_index	int	Chunk index (0-based)
total_chunks	int	Total number of chunks
chunk_data	string	Base64-encoded model data chunk
created_at	string	ISO timestamp

How It Works

1. Serialization: Models are serialized to their native format:

   • XGBoost: .ubj (Universal Binary JSON)
   • LightGBM: .txt (text format)
   • CatBoost: .cbm (CatBoost binary)

2. Chunking: Large models are split into chunks (default 130KB per chunk) to comply with Treasure Data's bulk import limits

3. Storage: Chunks are stored as base64-encoded strings in a Treasure Data table via pytd's bulk_import

4. Retrieval: When loading, chunks are reassembled, decoded, and deserialized back into the original model object

Requirements

• Python >= 3.8
• pytd >= 1.0.0
• pandas >= 1.0.0
• At least one of: xgboost, lightgbm, or catboost

Logging

The library uses Python's standard logging module. Enable logging to see save/load operations:

import logging
logging.basicConfig(level=logging.INFO)

Example output:

INFO:td_model_store.model_store:Saved model 'my_model' (type=xgboost) to ml_models.ml_model_store | session_id=1713628800 | chunks=5 | size=12345 bytes
INFO:td_model_store.model_loader:Loaded model 'my_model' (type=xgboost) from ml_models.ml_model_store | session_id=1713628800 | chunks=5 | size=12345 bytes

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Support

For issues and questions, please open an issue on GitHub or contact the maintainers.