jai-sdk 0.25.0

JAI - Trust your data

Jai SDK - Trust your data

Installation

The source code is currently hosted on GitHub at: https://github.com/jquant/jai-sdk

The latest version of JAI-SDK can be installed from pip:

pip install jai-sdk --user

Nowadays, JAI supports python 3.7+. For more information, here is our documentation.

Getting your auth key

JAI requires an auth key to organize and secure collections. You can quickly generate your free-forever auth-key by running the command below:

from jai import get_auth_key
get_auth_key(email='[e-mail]', firstName='Jai', lastName='Z')

ATTENTION: Your auth key will be sent to your e-mail, so please make sure to use a valid address and check your spam folder.

How does it work?

With JAI, you can train models in the cloud and run inference on your trained models. Besides, you can achieve all your models through a REST API endpoint.

First, you can set your auth key into an environment variable or use a :file:.env file or :file:.ini file. Please check the section How to configure your auth key for more information.

Bellow an example of the content of the :file:.env file:

JAI_AUTH="xXxxxXXxXXxXXxXXxXXxXXxXXxxx"

In the below example, we'll show how to train a simple supervised model (regression) using the California housing dataset, run a prediction from this model, and call this prediction directly from the REST API.

import pandas as pd
from jai import Jai
from sklearn.datasets import fetch_california_housing

# Load dataset
data, labels = fetch_california_housing(as_frame=True, return_X_y=True)
model_data = pd.concat([data, labels], axis=1)

# Instanciating JAI class
j = Jai()

# Send data to JAI for feature extraction
j.fit(
    name='california_supervised',   # JAI collection name
    data=model_data,    # Data to be processed
    db_type='Supervised',   # Your training type ('Supervised', 'SelfSupervised' etc)
    verbose=2,
    hyperparams={
        'learning_rate': 3e-4,
        'pretraining_ratio': 0.8
    },
    label={
        'task': 'regression',
        'label_name': 'MedHouseVal'
    },
    overwrite=True)
# Run prediction
j.predict(name='california_supervised', data=data)

In this example, you could train a supervised model with the California housing dataset and run a prediction with some data.

JAI supports many other training models, like self-supervised model training. Besides, it also can train on different data types, like text and images. You can find a complete list of the model types supported by JAI on The Fit Method.

Read our documentation

For more information, here is our documentation.