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|
%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.aliyun.com/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
* Fri Jun 09 2023 Python_Bot <Python_Bot@openeuler.org> - 0.23.0-1
- Package Spec generated
|
