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# 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)
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"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>\n\n# TinyMS\n\n[](https://pypi.org/project/tinyms)\n[](https://badge.fury.io/py/tinyms)\n[](https://pepy.tech/project/tinyms)\n[](https://hub.docker.com/r/tinyms/tinyms)\n[](https://github.com/tinyms-ai/tinyms/actions/workflows/install_and_test.yml)\n[](https://readthedocs.org/projects/tinyms)\n[](https://github.com/tinyms-ai/tinyms/releases)\n[](https://github.com/tinyms-ai/tinyms/blob/main/LICENSE)\n[](https://join.slack.com/t/mindspore/shared_invite/zt-dgk65rli-3ex4xvS4wHX7UDmsQmfu8w)\n[](https://github.com/tinyms-ai/tinyms/pulls)\n\nEnglish | [\u67e5\u770b\u4e2d\u6587](./README_CN.md)\n\nTinyMS 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.\n\n<p align=\"center\"><img src=\"docs/pic/tinyms-architecture.png\" alt=\"TinyMS Architecture\" width=\"800\" /></p>\n\n## Installation\n\n| Distribution | Version | Command |\n| :----------- | :------ | :------ |\n| PyPI | x.y.z | `pip install tinyms==x.y.z` |\n| | latest | `pip install git+https://github.com/tinyms-ai/tinyms.git` |\n| Docker | x.y.z | `docker pull tinyms==x.y.z` |\n| | latest | - |\n\n> **NOTICE:** The `x.y.z` version shown above should be replaced with the real version number.\n\nPlease checkout the [install document](https://tinyms.readthedocs.io/en/latest/quickstart/install.html) to quickly install or upgrade TinyMS project.\n\n## Quick start\n\nHave no idea what to do with TinyMS\u2753 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\u2757\n\nBesides, here are some use cases listed to demonstrate how TinyMS simplifies the code flow for users.\n\n### Data loading and preprocess\n\n<table>\n<tr>\n<td>\n\n```python\nfrom tinyms.data import MnistDataset, download_dataset\nfrom tinyms.vision import mnist_transform\n\ndata_path = download_dataset('mnist')\nmnist_ds = MnistDataset(data_path, shuffle=True)\nmnist_ds = mnist_transform.apply_ds(mnist_ds)\n```\n\n</td>\n</tr>\n</table>\n\n### Network construction\n\n<table>\n<tr>\n<td>\n\n```python\nfrom tinyms.model import lenet5\n\nnet = lenet5(class_num=10)\n```\n\n</td>\n</tr>\n</table>\n\n### Model train/evaluation\n\n<table>\n<tr>\n<td>\n\n```python\nfrom tinyms.model import Model\n\nmodel = Model(net)\nmodel.compile(loss_fn=net_loss, optimizer=net_opt, metrics=net_metrics)\nmodel.train(epoch_size, train_dataset)\nmodel.save_checkpoint('./checkpoint_lenet.ckpt')\n\u00b7\u00b7\u00b7\nmodel.load_checkpoint('./checkpoint_lenet.ckpt')\nmodel.eval(eval_dataset)\n```\n\n</td>\n</tr>\n</table>\n\n### Model prediction\n\n<table>\n<tr>\n<td>\n\n```python\nfrom PIL import Image\nimport tinyms as ts\nfrom tinyms.model import Model, lenet5\nfrom tinyms.vision import mnist_transform\n\nimg = Image.open(img_path)\nimg = mnist_transform(img)\n\nnet = lenet5(class_num=10)\nmodel = Model(net)\nmodel.load_checkpoint('./checkpoint_lenet.ckpt')\n\ninput = ts.expand_dims(ts.array(img), 0)\nres = model.predict(input).asnumpy()\nprint(\"The label is:\", mnist_transform.postprocess(res))\n```\n\n</td>\n</tr>\n</table>\n\n## API documentation\n\nIf you are interested in learning TinyMS API, please find TinyMS Python API in [API Documentation](https://tinyms.readthedocs.io/en/latest/tinyms/tinyms.html).\n\n## Tutorial\n\nFor a more detailed step-by-step video tutorial, please refer to the following website.\n\n| Episode | Title | Content | Docs | Status | Update Time |\n| ------- | ----- | ------- | ---- | ------ | ----------- |\n| 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 |\n| 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 |\n| 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\u901f\u6210Shell\u811a\u672c.md) | Published | 2020.4.1 |\n| 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 |\n| 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 |\n\n## Community\n\nFor 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.\n\n## Release Notes\n\nThe release notes, see our [RELEASE](https://github.com/tinyms-ai/tinyms/blob/main/RELEASE.md).\n\n## License\n\n[Apache License 2.0](https://github.com/tinyms-ai/tinyms/blob/main/LICENSE)\n\n\n",
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