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Getting Started with MLCLI

This guide will help you get started with MLCLI in just a few minutes.

Prerequisites

  • Python 3.10 or higher
  • pip package manager

Installation

From PyPI (Recommended)

pip install mlcli-toolkit

From Source

git clone https://github.com/codeMaestro78/mlcli.git
cd mlcli
pip install -e .

Verify Installation

mlcli --version
mlcli --help

Your First Model

1. Prepare Your Data

Create a CSV file with your data. MLCLI expects:

  • Features in columns
  • Target variable in one column
  • No missing values (or handle them in preprocessing)

Example data/sample.csv:

feature1,feature2,feature3,target
1.2,3.4,5.6,0
2.3,4.5,6.7,1
...

2. Create a Configuration File

Create configs/my_config.json:

{
  "dataset": {
    "path": "data/sample.csv",
    "type": "csv",
    "target_column": "target"
  },
  "model": {
    "type": "random_forest",
    "params": {
      "n_estimators": 100,
      "max_depth": 10,
      "random_state": 42
    }
  },
  "training": {
    "test_size": 0.2,
    "random_state": 42
  },
  "output": {
    "model_dir": "models",
    "save_format": ["pickle", "onnx"]
  }
}

3. Train Your Model

mlcli train --config configs/my_config.json

4. Evaluate Your Model

mlcli evaluate --model models/rf_model.pkl --data data/test.csv --target target

5. Make Predictions

mlcli predict --model models/rf_model.pkl --data data/new_data.csv --output predictions.csv

Using the Interactive UI

For a guided experience, use the Terminal UI:

mlcli ui

Navigate using arrow keys and Enter to select options.

Next Steps

Getting Help

# General help
mlcli --help

# Command-specific help
mlcli train --help
mlcli tune --help