This is a tuner geared for NNI's Neural Architecture Search (NAS) interface. It uses the ppo algorithm. The implementation inherits the main logic of the ppo2 OpenAI implementation here and is adapted for the NAS scenario.
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.
We also tune :githublink:`the macro search space for image classification in the enas paper <examples/trials/nas_cifar10>` (with a limited epoch number for each trial, i.e., 8 epochs), which is implemented using the NAS interface and tuned with PPOTuner. Here is Figure 7 from the enas paper to show what the search space looks like
The figure above was the chosen architecture. Each square is a layer whose operation was chosen from 6 options. Each dashed line is a skip connection, each square layer can choose 0 or 1 skip connections, getting the output from a previous layer. Note that, in original macro search space, each square layer could choose any number of skip connections, while in our implementation, it is only allowed to choose 0 or 1.
The results are shown in figure below (see the experimenal config :githublink:`here <examples/trials/nas_cifar10/config_ppo.yml>`:
