Welcome to Archai

Neural Architecture Search (NAS) aims to automate the process of searching for neural architectures. Given a new dataset, it is often a tedious task of trying out many different architectures and hyperparameters manually. Even the most skilled machine learning researchers and engineers have to resort to the dark arts of finding good architectures and corresponding hyperparameters guided by some intuition and a lot of careful experimentation. The NAS community's dream is that this tedium be taken over by algorithms, freeing up precious human time for more noble pursuits.

Recently, NAS has made tremendous progress but is merely getting started. Many open problems remain. But one of the more immediate problems is fair comparison and reproducibility. To ameliorate these issues we are releasing Archai which is a performant platform for NAS algorithms. Archai has the following features:

Arhai has the following features:

• NAS for non-experts

  • Turnkey experimentation platform

• High performance PyTorch code base

• Ease of algorithm development

  • Object-oriented model definition
  • Unified abstractions for training and evaluation
  • New algorithms can be written in a few lines of code
  • Easily mix and match existing algorithm aspects
  • Easily implement both forward and backward search
  • Algorithm-agnostic pareto-front generation
    • Easily add hardware-specific constraints like memory, inference time, flops etc.

• Efficient experiment management for reproducibility and fair comparison

  • Flexible configuration system
  • Structured logging
  • Metrics management and logging
  • Declarative experimentation
  • Declarative support for wide variety of datasets
  • Custom dataset support
  • Unified final training procedure for searched models

Installation

Currently we have tested Archai on Ubuntu 16.04 LTS 64-bit and Ubuntu 18.04 LTS 64-bit on Python 3.6+ and PyTorch 1.3+.

• System prep:

  • CUDA compatible GPU
  • Anaconda package manager
  • Nvidia driver compatible with cuda 9.2 or greater

• We provide two conda environments for cuda 9.2 and cuda 10.1.

  • archaicuda101.yaml
  • archaicuda92.yaml

• conda env create -f archaicuda101.yml

• conda activate archaicuda101

• pip install -r requirements.txt

• pip install -e .

Test installation

• cd archai
• The below command will run every algorithm through a few batches of cifar10 and for both search and final training
• python scripts/main.py
• If all went well, now you have a working installation!
• Note one can also build and use the cuda 10.1 or 9.2 compatible dockers provided in the dockers folder. These dockers are useful for large scale experimentation on compute clusters.

How to use it?

├── archai
│   ├── cifar10_models
│   ├── common
│   ├── darts
│   ├── data_aug
│   ├── nas
│   ├── networks
│   ├── petridish
│   ├── random
│   └── xnas
├── archived
├── confs
├── dockers
├── docs
├── scripts
├── setup.py
├── tests
└── tools
    ├── azure

Most of the functionality resides in the archai folder. nas contains algorithm-agnostic infrastructure that is commonly used in NAS algorithms. common contains common infrastructure code that has no nas-specific code but is infrastructre code that gets widely used. Algorithm-specific code resides in appropriately named folder like darts, petridish, random, xnas

scripts contains entry-point scripts to running all algorithms.

Quick start

scripts/main.py is the main point of entry.

Run all algorithms in toy mode

python scripts/main.py runs all implemented search algorithms and final training with a few minibatches of data from cifar10. This is designed to exercise all code paths and make sure that everything is properly.

To run specific algorithms

python scripts/main.py --darts will run darts search and evaluation (final model training) using only a few minibatches of data from cifar10. python scripts/main.py --darts --full will run the full search.

Other algorithms can be run by specifying different algorithm names like petridish, xnas, random etc.

List of algorithms

Current the following algorithms are implemented:

• Petridish
• DARTS
• [Random search baseline]
• XNAS (this is currently experimental and has not been fully reproduced yet as XNAS authors have not released source code at the time of writing.)

See Roadmap for details on new algorithms coming soon.

Tutorials

Running experiments on Azure AML

See detailed instructions.

Roadmap

We are striving to rapidly update the list of algorithms and encourage pull-requests from the community of new algorithms.

Here is our current deck:

• ProxyLess NAS
• SNAS
• DATA
• RandNAS

Please file in the issues algorithms you would like to see implemented in Archai. We will try our best to accomodate.

Paper

If you use Archai in your work please cite...

Contribute

We would love your contributions, feedback, questions, and feature requests! Please file a github issue or send us a pull request. Please review the Microsoft Code of Conduct and learn more.

Contacts

Shital Shah shitals@microsoft.com

Debadeepta Dey dedey@microsoft.com

Eric Horvitz horvitz@microsoft.com

Credits

Archai utilizes several open source libraries for many of its features. These includes:fastautoaugment, tensorwatch, and many others.

License

This project is released under the MIT License. Please review the License file for further details.