%global _empty_manifest_terminate_build 0 Name: python-jai-sdk Version: 0.23.0 Release: 1 Summary: JAI - Trust your data License: MIT URL: https://github.com/jquant/jai-sdk Source0: https://mirrors.nju.edu.cn/pypi/web/packages/a3/0a/829a3cff28cd700fa27b783e049debaacc90333180341ce3c49455a9d4bf/jai-sdk-0.23.0.tar.gz BuildArch: noarch Requires: python3-numpy Requires: python3-pandas Requires: python3-tqdm Requires: python3-pillow Requires: python3-psutil Requires: python3-pydantic Requires: python3-decouple Requires: python3-matplotlib Requires: python3-requests Requires: python3-scikit-learn %description # Jai SDK - Trust your data [![PyPI Latest Release](https://img.shields.io/pypi/v/jai-sdk.svg)](https://pypi.org/project/jai-sdk/) [![Python Version](https://img.shields.io/badge/python-3.7%20%7C%203.8-blue)](https://img.shields.io/badge/python-3.7%20%7C%203.8-blue) [![Documentation Status](https://readthedocs.org/projects/jai-sdk/badge/?version=latest)](https://jai-sdk.readthedocs.io/en/latest/?badge=latest) [![codecov](https://codecov.io/gh/jquant/jai-sdk/branch/main/graph/badge.svg)](https://codecov.io/gh/jquant/jai-sdk) [![License](https://img.shields.io/pypi/l/jai-sdk.svg)](https://github.com/jquant/jai-sdk/blob/main/LICENSE) [![Code style: yapf](https://img.shields.io/badge/code%20style-yapf-blue)](https://github.com/google/yapf) [![Downloads](https://pepy.tech/badge/jai-sdk)](https://pepy.tech/project/jai-sdk) # 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/). %package -n python3-jai-sdk Summary: JAI - Trust your data Provides: python-jai-sdk BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-jai-sdk # Jai SDK - Trust your data [![PyPI Latest Release](https://img.shields.io/pypi/v/jai-sdk.svg)](https://pypi.org/project/jai-sdk/) [![Python Version](https://img.shields.io/badge/python-3.7%20%7C%203.8-blue)](https://img.shields.io/badge/python-3.7%20%7C%203.8-blue) [![Documentation Status](https://readthedocs.org/projects/jai-sdk/badge/?version=latest)](https://jai-sdk.readthedocs.io/en/latest/?badge=latest) [![codecov](https://codecov.io/gh/jquant/jai-sdk/branch/main/graph/badge.svg)](https://codecov.io/gh/jquant/jai-sdk) [![License](https://img.shields.io/pypi/l/jai-sdk.svg)](https://github.com/jquant/jai-sdk/blob/main/LICENSE) [![Code style: yapf](https://img.shields.io/badge/code%20style-yapf-blue)](https://github.com/google/yapf) [![Downloads](https://pepy.tech/badge/jai-sdk)](https://pepy.tech/project/jai-sdk) # 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/). %package help Summary: Development documents and examples for jai-sdk Provides: python3-jai-sdk-doc %description help # Jai SDK - Trust your data [![PyPI Latest Release](https://img.shields.io/pypi/v/jai-sdk.svg)](https://pypi.org/project/jai-sdk/) [![Python Version](https://img.shields.io/badge/python-3.7%20%7C%203.8-blue)](https://img.shields.io/badge/python-3.7%20%7C%203.8-blue) [![Documentation Status](https://readthedocs.org/projects/jai-sdk/badge/?version=latest)](https://jai-sdk.readthedocs.io/en/latest/?badge=latest) [![codecov](https://codecov.io/gh/jquant/jai-sdk/branch/main/graph/badge.svg)](https://codecov.io/gh/jquant/jai-sdk) [![License](https://img.shields.io/pypi/l/jai-sdk.svg)](https://github.com/jquant/jai-sdk/blob/main/LICENSE) [![Code style: yapf](https://img.shields.io/badge/code%20style-yapf-blue)](https://github.com/google/yapf) [![Downloads](https://pepy.tech/badge/jai-sdk)](https://pepy.tech/project/jai-sdk) # 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/). %prep %autosetup -n jai-sdk-0.23.0 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-jai-sdk -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Mon May 29 2023 Python_Bot - 0.23.0-1 - Package Spec generated