update readme (microsoft#3827)

Co-authored-by: Scarlett Li <[email protected]> Co-authored-by: kvartet <[email protected]> Co-authored-by: --help <[email protected]>
syswyl · Jun 15, 2021 · 8450724 · 8450724
1 parent cf95cfc
commit 8450724
Show file tree

Hide file tree

Showing 3 changed files with 65 additions and 99 deletions.
diff --git a/README.md b/README.md
@@ -123,25 +123,19 @@ Within the following table, we summarized the current NNI capabilities, we are g
   <li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#SMAC">SMAC</a></li>
   <li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#MetisTuner">Metis Tuner</a></li>
   <li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#GPTuner">GP Tuner</a></li>
+  <li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#DNGOTuner">DNGO Tuner</a></li>
   </ul>
-  <b>RL Based</b>
-  <ul>
-  <li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#PPOTuner">PPO Tuner</a> </li>
-  </ul>
   </ul>
-  <a href="https://nni.readthedocs.io/en/stable/NAS/Overview.html">Neural Architecture Search</a>
+  <a href="https://nni.readthedocs.io/en/stable/NAS/Overview.html">Neural Architecture Search (Retiarii)</a>
   <ul>
-  <ul>
-  <li><a href="https://nni.readthedocs.io/en/stable/NAS/ENAS.html">ENAS</a></li>
-  <li><a href="https://nni.readthedocs.io/en/stable/NAS/DARTS.html">DARTS</a></li>
-  <li><a href="https://nni.readthedocs.io/en/stable/NAS/PDARTS.html">P-DARTS</a></li>
-  <li><a href="https://nni.readthedocs.io/en/stable/NAS/CDARTS.html">CDARTS</a></li>
-  <li><a href="https://nni.readthedocs.io/en/stable/NAS/SPOS.html">SPOS</a></li>
-  <li><a href="https://nni.readthedocs.io/en/stable/NAS/Proxylessnas.html">ProxylessNAS</a></li>
-  <li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#NetworkMorphism">Network Morphism</a></li>
-  <li><a href="https://nni.readthedocs.io/en/stable/NAS/TextNAS.html">TextNAS</a></li>
-  <li><a href="https://nni.readthedocs.io/en/stable/NAS/Cream.html">Cream</a></li>
-  </ul>
+  <li><a href="https://nni.readthedocs.io/en/stable/NAS/ENAS.html">ENAS</a></li>
+  <li><a href="https://nni.readthedocs.io/en/stable/NAS/DARTS.html">DARTS</a></li>
+  <li><a href="https://nni.readthedocs.io/en/stable/NAS/SPOS.html">SPOS</a></li>
+  <li><a href="https://nni.readthedocs.io/en/stable/NAS/Proxylessnas.html">ProxylessNAS</a></li>
+  <li><a href="https://nni.readthedocs.io/en/stable/NAS/FBNet.html">FBNet</a></li>
+  <li><a href="https://nni.readthedocs.io/en/stable/NAS/ExplorationStrategies.html">Reinforcement Learning</a></li>
+  <li><a href="https://nni.readthedocs.io/en/stable/NAS/ExplorationStrategies.html">Regularized Evolution</a></li>
+  <li><a href="https://nni.readthedocs.io/en/stable/NAS/Overview.html">More...</a></li>
   </ul>
   <a href="https://nni.readthedocs.io/en/stable/Compression/Overview.html">Model Compression</a>
   <ul>
@@ -154,11 +148,13 @@ Within the following table, we summarized the current NNI capabilities, we are g
   <li><a href="https://nni.readthedocs.io/en/stable/Compression/Pruner.html#simulatedannealing-pruner">SimulatedAnnealing Pruner</a></li>
   <li><a href="https://nni.readthedocs.io/en/stable/Compression/Pruner.html#admm-pruner">ADMM Pruner</a></li>
   <li><a href="https://nni.readthedocs.io/en/stable/Compression/Pruner.html#autocompress-pruner">AutoCompress Pruner</a></li>
+  <li><a href="https://nni.readthedocs.io/en/stable/Compression/Overview.html">More...</a></li>
   </ul>
   <b>Quantization</b>
   <ul>
   <li><a href="https://nni.readthedocs.io/en/stable/Compression/Quantizer.html#qat-quantizer">QAT Quantizer</a></li>
   <li><a href="https://nni.readthedocs.io/en/stable/Compression/Quantizer.html#dorefa-quantizer">DoReFa Quantizer</a></li>
+  <li><a href="https://nni.readthedocs.io/en/stable/Compression/Quantizer.html#bnn-quantizer">BNN Quantizer</a></li>
   </ul>
   </ul>
   <a href="https://nni.readthedocs.io/en/stable/FeatureEngineering/Overview.html">Feature Engineering (Beta)</a>
@@ -208,6 +204,8 @@ Within the following table, we summarized the current NNI capabilities, we are g
   <li><a href="https://nni.readthedocs.io/en/stable/Tuner/CustomizeTuner.html">CustomizeTuner</a></li>
   <li><a href="https://nni.readthedocs.io/en/stable/Assessor/CustomizeAssessor.html">CustomizeAssessor</a></li>
   <li><a href="https://nni.readthedocs.io/en/stable/Tutorial/InstallCustomizedAlgos.html">Install Customized Algorithms as Builtin Tuners/Assessors/Advisors</a></li>
+  <li><a href="https://nni.readthedocs.io/en/stable/NAS/QuickStart.html#define-your-model-space">Define NAS Model Space</a></li>
+  <li><a href="https://nni.readthedocs.io/en/stable/NAS/ApiReference.html">NAS/Retiarii APIs</a></li>
   </ul>
   </td>
   <td style="border-top:#FF0000 solid 0px;">

diff --git a/docs/en_US/Tuner/BuiltinTuner.rst b/docs/en_US/Tuner/BuiltinTuner.rst
@@ -497,47 +497,6 @@ As a strategy in a Sequential Model-based Global Optimization (SMBO) algorithm,
  selection_num_warm_up: 100000
  selection_num_starting_points: 250
 
-:raw-html:`<a name="PPOTuner"></a>`
-
-PPO Tuner
-^^^^^^^^^
-
-..
-
- Built-in Tuner Name: **PPOTuner**
-
-
-Note that the only acceptable types within the search space are ``layer_choice`` and ``input_choice``. For ``input_choice``\ , ``n_chosen`` can only be 0, 1, or [0, 1]. Note, the search space file for NAS is usually automatically generated through the command `nnictl ss_gen <../Tutorial/Nnictl.rst>`__.
-
-**Suggested scenario**
-
-PPOTuner is a Reinforcement Learning tuner based on the PPO algorithm. PPOTuner can be used when using the NNI NAS interface to do neural architecture search. In general, the Reinforcement Learning algorithm needs more computing resources, though the PPO algorithm is relatively more efficient than others. It's recommended to use this tuner when you have a large amount of computional resources available. You could try it on a very simple task, such as the :githublink:`mnist-nas <examples/nas/legacy/classic_nas>` example. `See details <./PPOTuner.rst>`__
-
-**classArgs Requirements:**
-
-
-* **optimize_mode** (*'maximize' or 'minimize'*\ ) - If 'maximize', the tuner will try to maximize metrics. If 'minimize', the tuner will try to minimize metrics.
-* **trials_per_update** (*int, optional, default = 20*\ ) - The number of trials to be used for one update. It must be divisible by minibatch_size. ``trials_per_update`` is recommended to be an exact multiple of ``trialConcurrency`` for better concurrency of trials.
-* **epochs_per_update** (*int, optional, default = 4*\ ) - The number of epochs for one update.
-* **minibatch_size** (*int, optional, default = 4*\ ) - Mini-batch size (i.e., number of trials for a mini-batch) for the update. Note that trials_per_update must be divisible by minibatch_size.
-* **ent_coef** (*float, optional, default = 0.0*\ ) - Policy entropy coefficient in the optimization objective.
-* **lr** (*float, optional, default = 3e-4*\ ) - Learning rate of the model (lstm network); constant.
-* **vf_coef** (*float, optional, default = 0.5*\ ) - Value function loss coefficient in the optimization objective.
-* **max_grad_norm** (*float, optional, default = 0.5*\ ) - Gradient norm clipping coefficient.
-* **gamma** (*float, optional, default = 0.99*\ ) - Discounting factor.
-* **lam** (*float, optional, default = 0.95*\ ) - Advantage estimation discounting factor (lambda in the paper).
-* **cliprange** (*float, optional, default = 0.2*\ ) - Cliprange in the PPO algorithm, constant.
-
-**Example Configuration:**
-
-.. code-block:: yaml
-
- # config.yml
- tuner:
- builtinTunerName: PPOTuner
- classArgs:
- optimize_mode: maximize
-
 :raw-html:`<a name="PBTTuner"></a>`
 
 PBT Tuner
@@ -573,6 +532,8 @@ Population Based Training (PBT) bridges and extends parallel search methods and
 
 Note that, to use this tuner, your trial code should be modified accordingly, please refer to `the document of PBTTuner <./PBTTuner.rst>`__ for details.
 
+:raw-html:`<a name="DNGOTuner"></a>`
+
 DNGO Tuner
 ^^^^^^^^^^