Metadata-Version: 2.1
Name: tinyms
Version: 0.1.0
Summary: TinyMS is an Easy-to-Use deep learning development toolkit.
Home-page: https://tinyms.readthedocs.io/en/latest/
Author: The TinyMS Authors
Author-email: wanghui71leon@gmail.com
License: Apache 2.0
Download-URL: https://github.com/tinyms-ai/tinyms/tags
Project-URL: Sources, https://github.com/tinyms-ai/tinyms
Project-URL: Issue Tracker, https://github.com/tinyms-ai/tinyms/issues
Description: # TinyMS
        
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        English | [查看中文](./README_CN.md)
        
        TinyMS is an Easy-to-Use deep learning development toolkit based on [MindSpore](https://www.mindspore.cn/en/), designed to providing quick-start guidelines for machine learning beginners.
        
        ## Installation
        
        Please checkout the [install document](https://tinyms.readthedocs.io/en/latest/quickstart/install.html) to quickly install or upgrade TinyMS project.
        
        ## Quick start
        
        Have no idea what to do with TinyMS❓ See the [Quick Start](https://tinyms.readthedocs.io/en/latest/quickstart/quickstart_in_one_minute.html) to implement the image classification application in one minutes❗
        
        Besides, here are some use cases listed to demonstrate how TinyMS simplifies the code flow for users.
        
        ### Data loading and preprocess
        
        <table>
        <tr>
        <td>
        
        ```python
        from tinyms.data import MnistDataset, download_dataset
        from tinyms.vision import mnist_transform
        
        data_path = download_dataset('mnist')
        mnist_ds = MnistDataset(data_path, shuffle=True)
        mnist_ds = mnist_transform.apply_ds(mnist_ds)
        ```
        
        </td>
        </tr>
        </table>
        
        ### Network construction
        
        <table>
        <tr>
        <td>
        
        ```python
        from tinyms.model import lenet5
        
        net = lenet5(class_num=10)
        ```
        
        </td>
        </tr>
        </table>
        
        ### Model train/evaluation
        
        <table>
        <tr>
        <td>
        
        ```python
        from tinyms.model import Model
        
        model = Model(net)
        model.compile(loss_fn=net_loss, optimizer=net_opt, metrics=net_metrics)
        model.train(epoch_size, train_dataset)
        model.save_checkpoint('./checkpoint_lenet.ckpt')
        ···
        model.load_checkpoint('./checkpoint_lenet.ckpt')
        model.eval(eval_dataset)
        ```
        
        </td>
        </tr>
        </table>
        
        ### Model prediction
        
        <table>
        <tr>
        <td>
        
        ```python
        from PIL import Image
        import tinyms as ts
        from tinyms.model import Model, lenet5
        from tinyms.vision import mnist_transform
        
        img = Image.open(img_path)
        img = mnist_transform(img)
        
        net = lenet5(class_num=10)
        model = Model(net)
        model.load_checkpoint('./checkpoint_lenet.ckpt')
        
        input = ts.expand_dims(ts.array(img), 0)
        res = model.predict(input).asnumpy()
        print("The label is:", mnist_transform.postprocess(res))
        ```
        
        </td>
        </tr>
        </table>
        
        ## API documentation
        
        If you are interested in learning TinyMS API, please find TinyMS Python API in [API Documentation](https://tinyms.readthedocs.io/en/latest/tinyms/tinyms.html).
        
        ## Community
        
        For any developers who are not familiar with how TinyMS community works, please find the [Contributing Guidelines](https://tinyms.readthedocs.io/en/latest/community/contributing.html) to get started.
        
        ## Release Notes
        
        The release notes, see our [RELEASE](https://github.com/tinyms-ai/tinyms/blob/main/RELEASE.md).
        
        ## License
        
        This work is licensed under [Apache License 2.0](https://github.com/tinyms-ai/tinyms/blob/main/LICENSE).
        
Keywords: machine learning toolkit
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.7
Description-Content-Type: text/markdown
