vespatune 0.0.5

vespatune: no-code training for tabular models

VespaTune

Gradient Boosting + Optuna: no brainer

• Web UI for training, monitoring, and managing models
• Tune models directly from CSV files
• Real-time training progress with WebSocket updates
• Export models to ONNX format for deployment

Installation

Install using pip:

pip install vespatune

Quick Start

Web UI (Recommended)

Start the web interface:

vespatune

This launches the VespaTune UI at http://127.0.0.1:9999 where you can:

• Upload train/validation CSV files
• Configure model type, target columns, and hyperparameters
• Start training with real-time progress monitoring
• View trial results and metrics
• Download trained models and artifacts
• Manage multiple training runs

You can also specify host and port:

vespatune --host 0.0.0.0 --port 8080

CLI

Train a model with explicit train/valid split:

vespatune train \
  --train_filename train.csv \
  --valid_filename valid.csv \
  --output outputs/my_model \
  --model xgboost

Or let VespaTune auto-split your data:

vespatune train \
  --train_filename data.csv \
  --output outputs/my_model \
  --model xgboost

Make predictions:

vespatune predict \
  --model_path outputs/my_model \
  --test_filename test.csv \
  --output_filename predictions.csv

Serve a trained model for predictions:

vespatune serve --model_path outputs/my_model --host 0.0.0.0 --port 8000

Python API

from vespatune import VespaTune

# With explicit validation file
vtune = VespaTune(
    train_filename="train.csv",
    valid_filename="valid.csv",
    output="outputs/my_model",
    model_type="xgboost",  # or "lightgbm" or "catboost"
    targets=["target"],
    num_trials=100,
    time_limit=3600,
)
vtune.train()

# Or with auto-split (no validation file needed)
vtune = VespaTune(
    train_filename="data.csv",
    output="outputs/my_model",
    model_type="xgboost",
    targets=["target"],
    num_trials=100,
)
vtune.train()

Web UI Features

The web interface provides:

• File Upload: Drag and drop CSV files for training (validation file is optional)
• Auto-Split: If no validation file is provided, automatically splits training data
• Auto Column Detection: Automatically detects columns for target and ID selection
• Model Selection: Choose between XGBoost, LightGBM, or CatBoost
• Real-time Monitoring: Watch training progress with live trial updates via WebSocket
• Metrics Visualization: View loss curves and hyperparameter importance
• Run Management: Start, stop, and delete training runs
• Artifact Downloads: Download trained models, configs, and ONNX exports

Parameters

Required

Parameter	Description
train_filename	Path to training CSV file
output	Path to output directory for model artifacts

Optional

Parameter	Default	Description
valid_filename	None	Path to validation CSV file (auto-splits training data if not provided)
model_type	"xgboost"	Model to use: "xgboost", "lightgbm", "catboost", or "logreg"
test_filename	None	Path to test CSV file (predictions saved if provided)
task	None	"classification" or "regression" (auto-detected if not specified)
idx	"id"	Name of the ID column
targets	["target"]	List of target column names
features	None	List of feature columns (all non-id/target columns if not specified)
categorical_features	None	List of categorical columns (auto-detected if not specified)
use_gpu	False	Whether to use GPU for training
seed	42	Random seed for reproducibility
num_trials	1000	Number of Optuna trials for hyperparameter tuning
time_limit	None	Time limit for optimization in seconds

Supported Models

XGBoost

• Default model with extensive hyperparameter search
• Supports GPU acceleration
• Best for general-purpose tasks

LightGBM

• Native categorical feature support
• Fast training on large datasets
• Supports GPU acceleration

CatBoost

• Best native categorical feature handling
• Robust to overfitting
• Supports GPU acceleration

Logistic Regression

• Linear model for classification tasks only
• Searches over preprocessing (imputation, scaling) and regularization
• Fast training, interpretable coefficients

Data Splitting

VespaTune supports two modes:

1. Explicit split: Provide both train_filename and valid_filename
2. Auto-split: Provide only train_filename - VespaTune automatically creates a 5-fold split and uses fold 0 (80% train, 20% valid)

For manual control over splits, use the splitter utility:

vespatune splitter \
  --data_filename data.csv \
  --output splits/ \
  --target target \
  --task classification \
  --num_folds 5

Or via Python:

from vespatune import VespaTuneSplitter

splitter = VespaTuneSplitter(
    data_filename="data.csv",
    output="splits/",
    target="target",
    task="classification",
    num_folds=5,
)
splitter.split()

This creates fold_0_train.csv, fold_0_valid.csv, etc. for k-fold cross-validation.

Prediction

Using the trained model

from vespatune import VespaTunePredict

predictor = VespaTunePredict(model_path="outputs/my_model")

# Predict on file
predictor.predict_file("test.csv", "predictions.csv")

# Predict single sample
prediction = predictor.predict_single({"feature1": 1.0, "feature2": "A"})

Using ONNX model

from vespatune import VespaTuneONNXPredict

predictor = VespaTuneONNXPredict(model_path="onnx_model/")

# Predict on file
predictor.predict_file("test.csv", "predictions.csv")

# Predict single sample
prediction = predictor.predict_single({"feature1": 1.0, "feature2": "A"})

Standalone Preprocessing

Use VespaTuneProcessor when you want to preprocess data independently and pass it to an external ONNX runtime or inference system:

from vespatune import VespaTuneProcessor
import onnxruntime as ort

# Load preprocessor from model or ONNX export directory
processor = VespaTuneProcessor(model_path="outputs/my_model")

# Transform DataFrame
processed = processor.transform(df)  # Returns float32 numpy array

# Transform single sample
processed = processor.transform_single({"feature1": 1.0, "feature2": "A"})

# Get feature metadata
processor.get_feature_names()        # Input feature names
processor.get_categorical_features() # Categorical feature names
processor.get_feature_names_out()    # Output feature names after transform
processor.get_input_schema()         # Pydantic schema for API validation

# Pass to ONNX runtime
session = ort.InferenceSession("model.onnx")
predictions = session.run(None, {"input": processed})

CLI Reference

Default (UI)

vespatune [--host HOST] [--port PORT]

options:
  --host                Host to serve on (default: 127.0.0.1)
  --port                Port to serve on (default: 9999)
  --version, -v         Display VespaTune version

train

vespatune train --help

options:
  --train_filename      Path to training file (required)
  --valid_filename      Path to validation file (optional, auto-splits if not provided)
  --output              Path to output directory (required)
  --model               Model type: xgboost, lightgbm, catboost, logreg (default: xgboost)
  --test_filename       Path to test file
  --task                Task type: classification, regression
  --idx                 ID column name
  --targets             Target column(s), separate multiple by ';'
  --features            Feature columns, separate by ';'
  --use_gpu             Use GPU for training
  --seed                Random seed (default: 42)
  --num_trials          Number of Optuna trials (default: 100)
  --time_limit          Time limit in seconds

predict

vespatune predict --help

options:
  --model_path          Path to trained model directory (required)
  --test_filename       Path to test file (required)
  --output_filename     Path to output predictions file (required)

export

vespatune export --help

options:
  --model_path          Path to trained model directory (required)
  --output_dir          Path to ONNX output directory

serve

vespatune serve --help

options:
  --model_path          Path to ONNX export directory
  --host                Host to bind (default: 127.0.0.1)
  --port                Port to bind (default: 9999)
  --workers             Number of workers (default: 1)
  --reload              Enable auto-reload for development

splitter

vespatune splitter --help

options:
  --data_filename       Path to data file (required)
  --output              Path to output directory (required)
  --target              Target column name (required)
  --task                Task type: classification, regression (required)
  --num_folds           Number of folds (default: 5)

Output Files

After training, the following files are created in the output directory:

File	Description
vtune_model.final	Trained model
vtune.config	Model configuration
vtune.best_params	Best hyperparameters from Optuna
vtune.preprocessor.joblib	Fitted preprocessor (encoding, scaling, imputation)
vtune.target_encoder	Target encoder (for classification)
params.db	Optuna study database
train.feather	Processed training data
valid.feather	Processed validation data
onnx/	ONNX export directory (after export)
_splits/	Auto-generated train/valid splits (only if no validation file provided)

Example

from vespatune import VespaTune

# Train with LightGBM
vtune = VespaTune(
    train_filename="data/train.csv",
    valid_filename="data/valid.csv",
    output="outputs/lgb_model",
    model_type="lightgbm",
    targets=["price"],
    task="regression",
    num_trials=200,
    time_limit=1800,
    use_gpu=False,
    seed=42,
)
vtune.train()