alaas 0.1.7

A Distributed MLOps System for Efficient Active Learning

ALaaS: Active Learning as a Service.

Active Learning as a Service (ALaaS) is a fast and scalable framework for automatically selecting a subset to be labeled from a full dataset so to reduce labeling cost. It provides a out-of-the-box and standalone experience for users to quickly utilize active learning.

ALaaS is featured for

• :hatching_chick: Easy-to-use With <10 lines of code to start the system to employ active learning.
• :rocket: Fast Use the stage-level parallellism to achieve over 10x speedup than under-optimized active learning process.
• :collision: Elastic Scale up and down multiple active workers, depending on the number of GPU devices.

The project is still under the active development. Welcome to join us!

Try It Out :coffee:

Free ALaaS demo on AWS

Use least confidence sampling with ResNet-18 to select images to be labeled for your tasks!

We have deployed ALaaS on AWS for demonstration. Try it by yourself!

call ALaaS with HTTP 🌐
curl \ -X POST http://13.213.29.8:8081/post \ -H 'Content-Type: application/json' \ -d '{"data":[{"uri": "https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane1.png"}, {"uri": "https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane2.png"}, {"uri": "https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane3.png"}, {"uri": "https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane4.png"}, {"uri": "https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane5.png"}], "parameters": {"budget": 3}, "execEndpoint":"/query"}'
call ALaaS with gRPC 🔐
from alaas.client import Client url_list = [ 'https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane1.png', 'https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane2.png', 'https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane3.png', 'https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane4.png', 'https://www.cs.toronto.edu/~kriz/cifar-10-sample/airplane5.png' ] client = Client('grpc://13.213.29.8:60035') print(client.query_by_uri(url_list, budget=3))

Then you will see 3 data samples (the most informative) has been selected from all the 5 data points by ALaaS.

Installation :construction:

You can easily install the ALaaS by PyPI,

pip install alaas

The package of ALaaS contains both client and server parts. You can build an active data selection service on your own servers or just apply the client to perform data selection.

:warning: For deep learning frameworks like TensorFlow and Pytorch, you may need to install manually since the version to meet your deployment can be different.

Step by Step

0. Start the active learning server

You need to start an active learning server before conducting the data selection.

from alaas import Server

Server(config_path='./you_config.yml').start()

How to customize a configuration for your deployment scenarios can be found here.

1. Querying data from client

You can easily start the data selection by the following code,

from alaas.client import Client

client = Client('http://0.0.0.0:60035')
queries = client.query_by_uri(<url_list>, budget=<budget number>)

The output data is a subset uris/data in your request, which means the selection results for further data labeling.

Support Strategy :art:

Currently we supported several active learning strategies shown in the following table,

Type	Setting	Abbr	Strategy	Year	Reference
Random	Pool-base	RS	Random Sampling	-	-
Uncertainty	Pool	LC	Least Confidence Sampling	1994	DD Lew et al.
Uncertainty	Pool	MC	Margin Confidence Sampling	2001	T Scheffer et al.
Uncertainty	Pool	RC	Ratio Confidence Sampling	2009	B Settles et al.
Uncertainty	Pool	VRC	Variation Ratios Sampling	1965	EH Johnson et al.
Uncertainty	Pool	ES	Entropy Sampling	2009	B Settles et al.
Uncertainty	Pool	MSTD	Mean Standard Deviation	2016	M Kampffmeyer et al.
Uncertainty	Pool	BALD	Bayesian Active Learning Disagreement	2017	Y Gal et al.
Clustering	Pool	KCG	K-Center Greedy Sampling	2017	Ozan Sener et al.
Clustering	Pool	KM	K-Means Sampling	2011	Z Bodó et al.
Clustering	Pool	CS	Core-Set Selection Approach	2018	Ozan Sener et al.
Diversity	Pool	DBAL	Diverse Mini-batch Sampling	2019	Fedor Zhdanov
Adversarial	Pool	DFAL	DeepFool Active Learning	2018	M Ducoffe et al.

Citation

Our tech report is available on arxiv. Please cite as:

@article{huang2022active,
  title={Active-Learning-as-a-Service: An Efficient MLOps System for Data-Centric AI},
  author={Huang, Yizheng and Zhang, Huaizheng and Li, Yuanming and Lau, Chiew Tong and You, Yang},
  journal={arXiv preprint arXiv:2207.09109},
  year={2022}
}

Contributors ✨

Thanks goes to these wonderful people (emoji key):

Yizheng Huang 🚇 ⚠️ 💻

Huaizheng 🖋 ⚠️ 📖

Yuanming Li ⚠️ 💻

This project follows the all-contributors specification. Contributions of any kind welcome!

Acknowledgement

• Jina - Build cross-modal and multimodal applications on the cloud

License

The theme is available as open source under the terms of the Apache 2.0 License.