зеркало из https://github.com/microsoft/archai.git
158 строки
6.7 KiB
Markdown
158 строки
6.7 KiB
Markdown
<h1 align="center">
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<img src="https://user-images.githubusercontent.com/9354770/171523113-70c7214b-8298-4d7e-abd9-81f5788f6e19.png" alt="Archai logo" width="384px" />
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<br />
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</h1>
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<div align="center">
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<b>Archai</b> accelerates your Neural Architecture Search (NAS) through <b>fast</b>, <b>reproducible</b> and <b>modular</b> research, enabling the generation of efficient deep networks for various applications.
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</div>
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<br />
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<div align="center">
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<img src ="https://img.shields.io/github/release/microsoft/archai?style=flat-square" alt="Release version" />
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<img src ="https://img.shields.io/github/issues-raw/microsoft/archai?style=flat-square" alt="Open issues" />
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<img src ="https://img.shields.io/github/contributors/microsoft/archai?style=flat-square" alt="Contributors" />
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<img src ="https://img.shields.io/pypi/dm/archai?style=flat-square" alt="PyPI downloads" />
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<img src ="https://img.shields.io/github/license/microsoft/archai?color=red&style=flat-square" alt="License" />
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</div>
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<br />
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<div align="center">
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<a href="#installation">Installation</a> •
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<a href="#quickstart">Quickstart</a> •
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<a href="#tasks">Tasks</a> •
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<a href="#documentation">Documentation</a> •
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<a href="#support">Support</a>
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</div>
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## Installation
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Archai can be installed through various methods, however, it is recommended to utilize a virtual environment such as `conda` or `pyenv` for optimal results.
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To install Archai via PyPI, the following command can be executed:
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```bash
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pip install archai
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```
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**Archai requires Python 3.8+ and PyTorch 1.7.0+ to function properly.**
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For further information, please consult the [installation guide](https://microsoft.github.io/archai/getting_started/installation.html).
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## Quickstart
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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.
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### Creating the Search Space
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We start by importing the `TransformerFlexSearchSpace` class which represents the search space for the Transformer architecture:
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```python
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from archai.discrete_search.search_spaces.nlp.transformer_flex.search_space import TransformerFlexSearchSpace
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space = TransformerFlexSearchSpace("gpt2")
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```
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### Defining Search Objectives
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Next, we define the objectives we want to optimize. In this example, we use `NonEmbeddingParamsProxy`, `TransformerFlexOnnxLatency`, and `TransformerFlexOnnxMemory` to define the objectives:
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```python
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from archai.discrete_search.api.search_objectives import SearchObjectives
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from archai.discrete_search.evaluators.nlp.parameters import NonEmbeddingParamsProxy
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from archai.discrete_search.evaluators.nlp.transformer_flex_latency import TransformerFlexOnnxLatency
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from archai.discrete_search.evaluators.nlp.transformer_flex_memory import TransformerFlexOnnxMemory
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search_objectives = SearchObjectives()
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search_objectives.add_objective(
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"non_embedding_params",
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NonEmbeddingParamsProxy(),
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higher_is_better=True,
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compute_intensive=False,
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constraint=(1e6, 1e9),
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)
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search_objectives.add_objective(
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"onnx_latency",
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TransformerFlexOnnxLatency(space),
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higher_is_better=False,
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compute_intensive=False,
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)
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search_objectives.add_objective(
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"onnx_memory",
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TransformerFlexOnnxMemory(space),
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higher_is_better=False,
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compute_intensive=False,
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)
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```
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### Initializing the Algorithm
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We use the `EvolutionParetoSearch` algorithm to conduct the search:
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```python
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from archai.discrete_search.algos.evolution_pareto import EvolutionParetoSearch
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algo = EvolutionParetoSearch(
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space,
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search_objectives,
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None,
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"tmp",
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num_iters=5,
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init_num_models=10,
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seed=1234,
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)
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```
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### Performing the Search
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Finally, we call the `search()` method to start the NAS process:
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```python
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algo.search()
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```
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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.
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## Tasks
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To demonstrate and showcase the capabilities/functionalities of Archai, a set of end-to-end tasks are provided:
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* [Text Generation](https://github.com/microsoft/archai/blob/main/tasks/text_generation).
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* [Face Segmentation](https://github.com/microsoft/archai/blob/main/tasks/face_segmentation).
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## Documentation
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The [official documentation](https://microsoft.github.io/archai) also provides a series of [notebooks](https://microsoft.github.io/archai/getting_started/notebooks.html).
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## Support
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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:
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* Email: archai@microsoft.com
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* Website: https://github.com/microsoft/archai/issues
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We welcome any questions, feedback, or suggestions you may have and look forward to hearing from you.
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### Team
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Archai has been created and maintained by [Shital Shah](https://shital.com), [Debadeepta Dey](https://debadeepta.com), [Gustavo de Rosa](https://www.microsoft.com/en-us/research/people/gderosa), Caio Mendes, [Piero Kauffmann](https://www.microsoft.com/en-us/research/people/pkauffmann), [Chris Lovett](https://lovettsoftware.com), Allie Del Giorno, Mojan Javaheripi, and [Ofer Dekel](https://www.microsoft.com/en-us/research/people/oferd) at Microsoft Research.
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### Contributions
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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.
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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.
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This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
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### Trademark
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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.
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### License
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This project is released under the MIT License. Please review the [file](https://github.com/microsoft/archai/blob/main/LICENSE) for more details.
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