Archai accelerates your Neural Architecture Search (NAS) through fast, reproducible and modular research, enabling the generation of efficient deep networks for various applications.

Installation • Quickstart • Tasks • Documentation • Support

Installation

Archai can be installed through various methods, however, it is recommended to utilize a virtual environment such as conda or pyenv for optimal results.

To install Archai via PyPI, the following command can be executed:

pip install archai

Archai requires Python 3.8+ and PyTorch 1.7.0+ to function properly.

For further information, please consult the installation guide.

Quickstart

In this quickstart example, we will apply Archai in Natural Language Processing to find the optimal Pareto-frontier Transformers' configurations according to a set of objectives.

Creating the Search Space

We start by importing the TransformerFlexSearchSpace class which represents the search space for the Transformer architecture:

from archai.discrete_search.search_spaces.nlp.transformer_flex.search_space import TransformerFlexSearchSpace

space = TransformerFlexSearchSpace("gpt2")

Defining Search Objectives

Next, we define the objectives we want to optimize. In this example, we use NonEmbeddingParamsProxy, TransformerFlexOnnxLatency, and TransformerFlexOnnxMemory to define the objectives:

from archai.discrete_search.api.search_objectives import SearchObjectives
from archai.discrete_search.evaluators.nlp.parameters import NonEmbeddingParamsProxy
from archai.discrete_search.evaluators.nlp.transformer_flex_latency import TransformerFlexOnnxLatency
from archai.discrete_search.evaluators.nlp.transformer_flex_memory import TransformerFlexOnnxMemory

search_objectives = SearchObjectives()
search_objectives.add_objective(
   "non_embedding_params",
   NonEmbeddingParamsProxy(),
   higher_is_better=True,
   compute_intensive=False,
   constraint=(1e6, 1e9),
)
search_objectives.add_objective(
   "onnx_latency",
   TransformerFlexOnnxLatency(space),
   higher_is_better=False,
   compute_intensive=False,
)
search_objectives.add_objective(
   "onnx_memory",
   TransformerFlexOnnxMemory(space),
   higher_is_better=False,
   compute_intensive=False,
)

Initializing the Algorithm

We use the EvolutionParetoSearch algorithm to conduct the search:

from archai.discrete_search.algos.evolution_pareto import EvolutionParetoSearch

algo = EvolutionParetoSearch(
   space,
   search_objectives,
   None,
   "tmp",
   num_iters=5,
   init_num_models=10,
   seed=1234,
)

Performing the Search

Finally, we call the search() method to start the NAS process:

algo.search()

The algorithm will iterate through different network architectures, evaluate their performance based on the defined objectives, and ultimately produce a frontier of Pareto-optimal results.

Tasks

To demonstrate and showcase the capabilities/functionalities of Archai, a set of end-to-end tasks are provided:

• Text Generation.
• Face Segmentation.

Documentation

The official documentation also provides a series of notebooks.

Support

If you have any questions or feedback about the Archai project or the open problems in Neural Architecture Search, please feel free to contact us using the following information:

• Email: archai@microsoft.com
• Website: https://github.com/microsoft/archai/issues

We welcome any questions, feedback, or suggestions you may have and look forward to hearing from you.

Team

Archai has been created and maintained by Shital Shah, Debadeepta Dey, Gustavo de Rosa, Caio Mendes, Piero Kauffmann, Chris Lovett, Allie Del Giorno, Mojan Javaheripi, and Ofer Dekel at Microsoft Research.

Contributions

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repositories using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademark

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

License

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