jai-sdk 0.25.0
JAI - Trust your data
License: MIT
Requires Python: >=3.9

# Jai SDK - Trust your data

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# Installation

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

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

```sh
pip install jai-sdk --user
```

Nowadays, JAI supports python 3.7+. For more information, here is our [documentation](https://jai-sdk.readthedocs.io/en/latest/).

# 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:

```python
from jai import get_auth_key
get_auth_key(email='email@mail.com', 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](https://jai-sdk.readthedocs.io/en/latest/source/overview/set_authentication.html>) for more information.

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

```text
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.

```python
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](https://jai-sdk.readthedocs.io/en/latest/source/using_jai/fit.html).

# Read our documentation

For more information, here is our [documentation](https://jai-sdk.readthedocs.io/en/latest/).