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@@ -0,0 +1 @@ +/jai-sdk-0.23.0.tar.gz diff --git a/python-jai-sdk.spec b/python-jai-sdk.spec new file mode 100644 index 0000000..5327ef9 --- /dev/null +++ b/python-jai-sdk.spec @@ -0,0 +1,355 @@ +%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 + +[](https://pypi.org/project/jai-sdk/) +[](https://img.shields.io/badge/python-3.7%20%7C%203.8-blue) +[](https://jai-sdk.readthedocs.io/en/latest/?badge=latest) +[](https://codecov.io/gh/jquant/jai-sdk) +[](https://github.com/jquant/jai-sdk/blob/main/LICENSE) +[](https://github.com/google/yapf) +[](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 + +[](https://pypi.org/project/jai-sdk/) +[](https://img.shields.io/badge/python-3.7%20%7C%203.8-blue) +[](https://jai-sdk.readthedocs.io/en/latest/?badge=latest) +[](https://codecov.io/gh/jquant/jai-sdk) +[](https://github.com/jquant/jai-sdk/blob/main/LICENSE) +[](https://github.com/google/yapf) +[](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 + +[](https://pypi.org/project/jai-sdk/) +[](https://img.shields.io/badge/python-3.7%20%7C%203.8-blue) +[](https://jai-sdk.readthedocs.io/en/latest/?badge=latest) +[](https://codecov.io/gh/jquant/jai-sdk) +[](https://github.com/jquant/jai-sdk/blob/main/LICENSE) +[](https://github.com/google/yapf) +[](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 <Python_Bot@openeuler.org> - 0.23.0-1 +- Package Spec generated @@ -0,0 +1 @@ +6c1e74419b59482092f6f68b32cee9fa jai-sdk-0.23.0.tar.gz |