intellx-api 0.0.0.0

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IntelLX

intellx is a Python package that provides a simple interface for machine learning model management, training, and inference.

Installation

pip install intellx-api

Configuration

Before using IntelLX, you need to set up your API key. You can do this in two ways:

1. Create a .env file in your project directory:

INTELLX_API_KEY=your_api_key_here

2. Set an environment variable:

export INTELLX_API_KEY=your_api_key_here

CLI Usage

IntelLX provides a command-line interface for building and running experiments.

Building Configuration

Create a configuration file for your experiment:

intellx build --stages feature_engineering,model_tune,model_train,model_evaluate

Available stages:

• feature_engineering
• model_tune
• model_train
• model_evaluate

Add --verbose flag for detailed output:

intellx build --stages model_train,model_evaluate --verbose

Running Experiments

After creating and configuring intellx_config.yaml, run your experiment:

intellx run

API Usage

Model Prediction

from intellx import predict

# Make predictions
data = {
    "feature1": [1, 2, 3],
    "feature2": ["A", "B", "C"]
}
predictions = predict(model_name="my_model", data=data)

# Use specific model state/version
predictions = predict(model_name="my_model", data=data, model_state="production")

Get Model Information

from intellx import get_model_info

# Get model metadata
model_info = get_model_info(model_name="my_model")

# Get information for specific model state
model_info = get_model_info(model_name="my_model", model_state="production")

Get Model Features

from intellx import get_model_features

# Get list of features used by the model
features = get_model_features(model_name="my_model")

# Get features for specific model state
features = get_model_features(model_name="my_model", model_state="production")

Configuration File Structure

The intellx_config.yaml file structure contains:

user_name: "your_username"
project_name: "your_project"
experiment_name: "your_experiment"
experiment_description: "description"
problem_type: "classification"
data_injestion:
  data_source: ""
  data_config: ""
  target: ""
verbose: false

# Optional sections based on selected stages
feature_engineering:
  step1:
    task: ""
    expected_output: ""
  step2:
    task: ""
    expected_output: ""

model_tune:
  model1:
    model_name: ""
    parameters:
      param1:
        name: ""
        type: ""
        range: ""
    objective:
      metric: ""
      sampler: ""
      trials: ""

model_train:
  model1:
    name: ""
    source: ""
    input: ""
    output: ""

model_evaluate:
  metric1:
    name: ""
    source: ""
    input: ""
    output: ""

Error Handling

The package includes built-in error handling for:

• Missing API keys
• Invalid API keys
• Missing configuration files
• Invalid configuration formats
• API request errors

For any errors, check the error message for specific instructions on how to resolve the issue.

Support

Visit intellx.bydata.com to manage your runs and access additional documentation.