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
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.
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.