Metadata-Version: 2.1
Name: jai-sdk
Version: 0.22.4
Summary: JAI - Trust your data
Home-page: https://github.com/jquant/jai-sdk
Author: JQuant
Author-email: jedis@jquant.com.br
License: MIT
Description: # 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/).
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Requires-Python: >=3.7
Description-Content-Type: text/markdown
