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

Project description

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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.

TinyMS Architecture

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

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

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