tinyms 0.3.2

TinyMS is an Easy-to-Use deep learning development toolkit.

TinyMS

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TinyMS is an Easy-to-Use deep learning framework development toolkit based on MindSpore, designed to provide quick-start guidelines for machine learning beginners.

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 to quickly install or upgrade TinyMS project.

Quick start

Have no idea what to do with TinyMS❓ See the Quick Start 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

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)

Network construction

from tinyms.model import lenet5 net = lenet5(class_num=10)

Model train/evaluation

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)

Model prediction

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))

API documentation

If you are interested in learning TinyMS API, please find TinyMS Python API in API Documentation.

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!	Teacher's profile+DeepLearning Course Introduction	-	Published	2021.3.30
EP02	How we teach computers to understand pictures? Three Ways to Install TinyMS	It uncovers the magic of computer vision + three ways to install TinyMS (Ubuntu, Win10, Docker)	TinyMS Installation For Beginners	Published	2020.3.31
EP03	Learn Shell Script in 30 Minutes	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)	Published	2020.4.1
EP04	Learn Python in 30 Minutes(Part I.)	Python installation, basic syntax, primitive data types and operators	Learn Python in 30 Minutes	Published	2021.4.23
EP05	Learn Python in 30 Minutes(Part II.)	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	Published	2022.1.10

Community

For any developers who are not familiar with how TinyMS community works, please find the Contributing Guidelines to get started.

Release Notes

The release notes, see our RELEASE.

License

Apache License 2.0