Metadata-Version: 2.1
Name: tinyms
Version: 0.3.2
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: simple_hlw@163.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: <p align="center"><img src="https://github.com/tinyms-ai/tinyms/raw/main/docs/pic/tinyms-logo.png" alt="TinyMS logo" width="300"/></p>
        
        # TinyMS
        
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        English | [查看中文](./README_CN.md)
        
        TinyMS is an Easy-to-Use deep learning framework development toolkit based on [MindSpore](https://www.mindspore.cn/en/), designed to provide quick-start guidelines for machine learning beginners.
        
        <p align="center"><img src="docs/pic/tinyms-architecture.png" alt="TinyMS Architecture" width="800" /></p>
        
        ## Installation
        
        | Distribution | Version | Command |
        | :----------- | :------ | :------ |
        | PyPI | x.y.z | `pip install tinyms==x.y.z` |
        |  | latest | `pip install git+https://github.com/tinyms-ai/tinyms.git` |
        | Docker | x.y.z | `docker pull tinyms==x.y.z` |
        |  | latest | - |
        
        > **NOTICE:** The `x.y.z` version shown above should be replaced with the real version number.
        
        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).
        
        ## Tutorial
        
        For a more detailed step-by-step video tutorial, please refer to the following website.
        
        | Episode | Title | Content | Docs | Status | Update Time |
        | ------- | ----- | ------- | ---- | ------ | ----------- |
        | EP01    | [How to learn Deep Learning? The Most Efficient Way For Beginners!](https://www.bilibili.com/video/BV1MB4y1P79S) | Teacher's profile+DeepLearning Course Introduction | - | Published | 2021.3.30 |
        | EP02    | [How we teach computers to understand pictures? Three Ways to Install TinyMS](https://www.bilibili.com/video/BV18v41187fX) | It uncovers the magic of computer vision + three ways to install TinyMS (Ubuntu, Win10, Docker) | [TinyMS Installation For Beginners](https://tinyms.readthedocs.io/zh_CN/latest/quickstart/install.html) | Published | 2020.3.31 |
        | EP03    | [Learn Shell Script in 30 Minutes](https://www.bilibili.com/video/BV1vy4y1b7jh) | It covers the essential concepts such as using variables, basic operators, loops & functions and so on. It also gives you an insight by scaling down some real-time scenarios and demonstrating them using the docker container. | [Learn Shell Script in 30 Minutes (doc)](https://github.com/tinyms-ai/tinyms/blob/main/tutorials/EP03/30min速成Shell脚本.md) | Published | 2020.4.1 |
        | EP04    | [Learn Python in 30 Minutes(Part I.)](https://www.bilibili.com/video/BV1Tp4y1b7UG?spm_id_from=333.999.0.0) | Python installation, basic syntax, primitive data types and operators| [Learn Python in 30 Minutes](https://github.com/tinyms-ai/tinyms/blob/main/tutorials/EP04/Quickstart-for-Python-in-30-minutes.md) | Published | 2021.4.23    |
        | EP05    | [Learn Python in 30 Minutes(Part II.)](https://www.bilibili.com/video/BV1XS4y1Z7yp?spm_id_from=333.999.0.0) | Python conditional statements, loop statements, iterators, generators, functions, class, module, advanced usages, and several most commonly used Python libraries in deep learning | [Learn Python in 30 Minutes](https://github.com/tinyms-ai/tinyms/blob/main/tutorials/EP04/Quickstart-for-Python-in-30-minutes.md) | Published | 2022.1.10    |
        
        ## 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
        
        [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
