@@ -19,7 +19,8 @@ NNI currently supports the NAS algorithms listed below and is adding more. Users
| [P-DARTS](PDARTS.md) | [Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation](https://arxiv.org/abs/1904.12760) is based on DARTS. It introduces an efficient algorithm which allows the depth of searched architectures to grow gradually during the training procedure. |
| [SPOS](SPOS.md) | [Single Path One-Shot Neural Architecture Search with Uniform Sampling](https://arxiv.org/abs/1904.00420) constructs a simplified supernet trained with a uniform path sampling method and applies an evolutionary algorithm to efficiently search for the best-performing architectures. |
| [CDARTS](CDARTS.md) | [Cyclic Differentiable Architecture Search](https://arxiv.org/abs/****) builds a cyclic feedback mechanism between the search and evaluation networks. It introduces a cyclic differentiable architecture search framework which integrates the two networks into a unified architecture.|
| [ProxylessNAS](Proxylessnas.md) | [ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware](https://arxiv.org/abs/1812.00332).|
| [ProxylessNAS](Proxylessnas.md) | [ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware](https://arxiv.org/abs/1812.00332). It removes proxy, directly learns the architectures for large-scale target tasks and target hardware platforms. |
| [TextNAS](TextNAS.md) | [TextNAS: A Neural Architecture Search Space tailored for Text Representation](https://arxiv.org/pdf/1912.10729.pdf). It is a neural architecture search algorithm tailored for text representation. |
One-shot algorithms run **standalone without nnictl**. Only the PyTorch version has been implemented. Tensorflow 2.x will be supported in a future release.
The current implementation only supports search space types in `float`, including `uniform`, `normal`. The support of other search space types is ongoing.
This is a tuner geared for NNI's Neural Architecture Search (NAS) interface. It uses the [ppo algorithm](https://arxiv.org/abs/1707.06347). The implementation inherits the main logic of the ppo2 OpenAI implementation [here](https://github.com/openai/baselines/tree/master/baselines/ppo2) and is adapted for the NAS scenario.
It can successfully tune the [mnist-nas example](https://github.com/microsoft/nni/tree/master/examples/trials/mnist-nas), and has the following result:
We had successfully tuned the mnist-nas example and has the following result:
**NOTE: we are refactoring this example to the latest NAS interface, will publish the example codes after the refactor.**
