Getting Started#


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

pip install jai-sdk --user

Nowadays, JAI supports python 3.7+.

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='', firstName='Jai', lastName='Z')


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 .env file or .ini file. Please check the section How to configure your auth key for more information.

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


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
...     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.

What to do next?#

Visit Jai in 5 Minutes to get a more complex and detailed example of how to use JAI correctly.

Read about The Fit Method if you want a complete overview of what models JAI can train and what you can do to get your better model.