SparkSnail · SparkSnail · May 12, 2020 · May 8, 2020 · May 8, 2020 · May 8, 2020
diff --git a/docs/en_US/AdvancedFeature/MultiPhase.md b/docs/en_US/AdvancedFeature/MultiPhase.md
diff --git a/docs/en_US/NAS/NasGuide.md b/docs/en_US/NAS/NasGuide.md
@@ -156,12 +156,23 @@ model = Net()
 apply_fixed_architecture(model, "model_dir/final_architecture.json")
 ```
 
-The JSON is simply a mapping from mutable keys to one-hot or multi-hot representation of choices. For example
+The JSON is simply a mapping from mutable keys to choices. Choices can be expressed in:
+
+* A string: select the candidate with corresponding name.
+* A number: select the candidate with corresponding index.
+* A list of string: select the candidates with corresponding names.
+* A list of number: select the candidates with corresponding indices.
+* A list of boolean values: a multi-hot array.
+
+For example,
 
 ```json
 {
-    "LayerChoice1": [false, true, false, false],
-    "InputChoice2": [true, true, false]
+    "LayerChoice1": "conv5x5",
+    "LayerChoice2": 6,
+    "InputChoice3": ["layer1", "layer3"],
+    "InputChoice4": [1, 2],
+    "InputChoice5": [false, true, false, false, true]
 }
 ```
 

diff --git a/docs/en_US/Release.md b/docs/en_US/Release.md
@@ -206,7 +206,7 @@
 
 * Documentation
     - Update the docs structure  -Issue #1231
-    - [Multi phase document improvement](AdvancedFeature/MultiPhase.md)   -Issue #1233  -PR #1242
+    - (deprecated) Multi phase document improvement   -Issue #1233  -PR #1242
          + Add configuration example
     - [WebUI description improvement](Tutorial/WebUI.md)  -PR #1419
 
@@ -234,12 +234,10 @@
     * Add `enas-mode`  and `oneshot-mode` for NAS interface: [PR #1201](https://github.com/microsoft/nni/pull/1201#issue-291094510)
 * [Gaussian Process Tuner with Matern kernel](Tuner/GPTuner.md) 
 
-* Multiphase experiment supports
+* (deprecated) Multiphase experiment supports
     * Added new training service support for multiphase experiment: PAI mode supports multiphase experiment since v0.9.
     * Added multiphase capability for the following builtin tuners: 
         * TPE, Random Search, Anneal, Naïve Evolution, SMAC, Network Morphism, Metis Tuner.
-
-    For details, please refer to [Write a tuner that leverages multi-phase](AdvancedFeature/MultiPhase.md)
 
 * Web Portal
     * Enable trial comparation in Web Portal. For details, refer to [View trials status](Tutorial/WebUI.md)
@@ -549,4 +547,3 @@ Initial release of Neural Network Intelligence (NNI).
   * Support CI by providing out-of-box integration with [travis-ci](https://github.com/travis-ci) on ubuntu
 * Others
   * Support simple GPU job scheduling
-
diff --git a/docs/en_US/TrainingService/FrameworkControllerMode.md b/docs/en_US/TrainingService/FrameworkControllerMode.md
@@ -26,7 +26,7 @@ NNI supports running experiment using [FrameworkController](https://github.com/M
 
 ## Setup FrameworkController
 
-Follow the [guideline](https://github.com/Microsoft/frameworkcontroller/tree/master/example/run) to set up FrameworkController in the Kubernetes cluster, NNI supports FrameworkController by the stateful set mode.
+Follow the [guideline](https://github.com/Microsoft/frameworkcontroller/tree/master/example/run) to set up FrameworkController in the Kubernetes cluster, NNI supports FrameworkController by the stateful set mode. If your cluster enforces authorization, you need to create a service account with granted permission for FrameworkController, and then pass the name of the FrameworkController service account to the NNI Experiment Config. [refer](https://github.com/Microsoft/frameworkcontroller/tree/master/example/run#run-by-kubernetes-statefulset)
 
 ## Design
 
@@ -83,6 +83,7 @@ If you use Azure Kubernetes Service, you should  set `frameworkcontrollerConfig`
 ```yaml
 frameworkcontrollerConfig:
   storage: azureStorage
+  serviceAccountName: {your_frameworkcontroller_service_account_name}
   keyVault:
     vaultName: {your_vault_name}
     name: {your_secert_name}

diff --git a/docs/en_US/Tuner/BuiltinTuner.md b/docs/en_US/Tuner/BuiltinTuner.md
@@ -68,10 +68,6 @@ tuner:
 
 Random search is suggested when each trial does not take very long (e.g., each trial can be completed very quickly, or early stopped by the assessor), and you have enough computational resources. It's also useful if you want to uniformly explore the search space. Random Search can be considered a baseline search algorithm. [Detailed Description](./HyperoptTuner.md)
 
-**classArgs Requirements:**
-
-* **optimize_mode** (*maximize or minimize, optional, default = maximize*) - If 'maximize', the tuner will try to maximize metrics. If 'minimize', the tuner will try to minimize metrics.
-
 **Example Configuration:**
 
 ```yaml

diff --git a/docs/en_US/Tuner/CustomizeTuner.md b/docs/en_US/Tuner/CustomizeTuner.md
@@ -51,7 +51,7 @@ class CustomizedTuner(Tuner):
     ...
 ```
 
-`receive_trial_result` will receive the `parameter_id, parameters, value` as parameters input. Also, Tuner will receive the `value` object are exactly same value that Trial send. If `multiPhase` is set to `true` in the experiment configuration file, an additional `trial_job_id` parameter is passed to `receive_trial_result` and `generate_parameters` through the `**kwargs` parameter.
+`receive_trial_result` will receive the `parameter_id, parameters, value` as parameters input. Also, Tuner will receive the `value` object are exactly same value that Trial send.
 
 The `your_parameters` return from `generate_parameters` function, will be package as json object by NNI SDK. NNI SDK will unpack json object so the Trial will receive the exact same `your_parameters` from Tuner.
 
@@ -109,4 +109,4 @@ More detail example you could see:
 
 ### Write a more advanced automl algorithm
 
-The methods above are usually enough to write a general tuner. However, users may also want more methods, for example, intermediate results, trials' state (e.g., the methods in assessor), in order to have a more powerful automl algorithm. Therefore, we have another concept called `advisor` which directly inherits from `MsgDispatcherBase` in [`src/sdk/pynni/nni/msg_dispatcher_base.py`](https://github.com/Microsoft/nni/tree/master/src/sdk/pynni/nni/msg_dispatcher_base.py). Please refer to [here](CustomizeAdvisor.md) for how to write a customized advisor.
+The methods above are usually enough to write a general tuner. However, users may also want more methods, for example, intermediate results, trials' state (e.g., the methods in assessor), in order to have a more powerful automl algorithm. Therefore, we have another concept called `advisor` which directly inherits from `MsgDispatcherBase` in [`src/sdk/pynni/nni/msg_dispatcher_base.py`](https://github.com/Microsoft/nni/tree/master/src/sdk/pynni/nni/msg_dispatcher_base.py). Please refer to [here](CustomizeAdvisor.md) for how to write a customized advisor.
diff --git a/docs/en_US/Tutorial/ExperimentConfig.md b/docs/en_US/Tutorial/ExperimentConfig.md
@@ -17,7 +17,6 @@ This document describes the rules to write the config file, and provides some ex
     + [trainingServicePlatform](#trainingserviceplatform)
     + [searchSpacePath](#searchspacepath)
     + [useAnnotation](#useannotation)
-    + [multiPhase](#multiphase)
     + [multiThread](#multithread)
     + [nniManagerIp](#nnimanagerip)
     + [logDir](#logdir)
@@ -94,8 +93,6 @@ searchSpacePath:
 #choice: true, false, default: false
 useAnnotation:
 #choice: true, false, default: false
-multiPhase:
-#choice: true, false, default: false
 multiThread:
 tuner:
   #choice: TPE, Random, Anneal, Evolution
@@ -130,8 +127,6 @@ searchSpacePath:
 #choice: true, false, default: false
 useAnnotation:
 #choice: true, false, default: false
-multiPhase:
-#choice: true, false, default: false
 multiThread:
 tuner:
   #choice: TPE, Random, Anneal, Evolution
@@ -171,8 +166,6 @@ trainingServicePlatform:
 #choice: true, false, default: false
 useAnnotation:
 #choice: true, false, default: false
-multiPhase:
-#choice: true, false, default: false
 multiThread:
 tuner:
   #choice: TPE, Random, Anneal, Evolution
@@ -283,12 +276,6 @@ Use annotation to analysis trial code and generate search space.
 
 Note: if __useAnnotation__ is true, the searchSpacePath field should be removed.
 
-### multiPhase
-
-Optional. Bool. Default: false.
-
-Enable [multi-phase experiment](../AdvancedFeature/MultiPhase.md).
-
 ### multiThread
 
 Optional. Bool. Default: false.

diff --git a/docs/en_US/Tutorial/SetupNniDeveloperEnvironment.md b/docs/en_US/Tutorial/SetupNniDeveloperEnvironment.md
@@ -67,10 +67,8 @@ It doesn't need to redeploy, but the nnictl may need to be restarted.
 
 #### TypeScript
 
-* If `src/nni_manager` will be changed, run `yarn watch` continually under this folder. It will rebuild code instantly.
-* If `src/webui` or `src/nasui` is changed, use **step 3** to rebuild code.
-
-The nnictl may need to be restarted.
+* If `src/nni_manager` is changed, run `yarn watch` continually under this folder. It will rebuild code instantly. The nnictl may need to be restarted to reload NNI manager.
+* If `src/webui` or `src/nasui` are changed, run `yarn start` under the corresponding folder. The web UI will refresh automatically if code is changed.
 
 
 ---

diff --git a/docs/en_US/hpo_advanced.rst b/docs/en_US/hpo_advanced.rst
@@ -4,7 +4,6 @@ Advanced Features
 ..  toctree::
     :maxdepth: 2
 
-    Enable Multi-phase <AdvancedFeature/MultiPhase>
     Write a New Tuner <Tuner/CustomizeTuner>
     Write a New Assessor <Assessor/CustomizeAssessor>
     Write a New Advisor <Tuner/CustomizeAdvisor>

diff --git a/docs/zh_CN/AdvancedFeature/MultiPhase.md b/docs/zh_CN/AdvancedFeature/MultiPhase.md
diff --git a/examples/nas/spos/network.py b/examples/nas/spos/network.py
@@ -147,8 +147,10 @@ def _initialize_weights(self):
 
 def load_and_parse_state_dict(filepath="./data/checkpoint-150000.pth.tar"):
     checkpoint = torch.load(filepath, map_location=torch.device("cpu"))
+    if "state_dict" in checkpoint:
+        checkpoint = checkpoint["state_dict"]
     result = dict()
-    for k, v in checkpoint["state_dict"].items():
+    for k, v in checkpoint.items():
         if k.startswith("module."):
             k = k[len("module."):]
         result[k] = v