Add test for documentation build (#1924)

azure-pipelines.yml

@@ -31,6 +31,7 @@ jobs:
       python3 -m pip install tensorflow==1.13.1 --user
       python3 -m pip install keras==2.1.6 --user
       python3 -m pip install gym onnx --user
+      python3 -m pip install sphinx==1.8.3 sphinx-argparse==0.2.5 sphinx-markdown-tables==0.0.9 sphinx-rtd-theme==0.4.2 sphinxcontrib-websupport==1.1.0 recommonmark==0.5.0 --user
       sudo apt-get install swig -y
       nnictl package install --name=SMAC
       nnictl package install --name=BOHB
@@ -69,6 +70,10 @@ jobs:
       cd test
       python3 cli_test.py
     displayName: 'nnicli test'
+  - script: |
+      cd docs/en_US/
+      sphinx-build -M html . _build -W
+    displayName: 'Sphinx Documentation Build check'
 
 - job: 'basic_test_pr_macOS'
   pool:

docs/en_US/Compressor/Pruner.md

@@ -342,5 +342,3 @@ You can view example for more information
 
 - **sparsity:** How much percentage of convolutional filters are to be pruned.
 - **op_types:** Only Conv2d is supported in ActivationMeanRankFilterPruner
-
-***
\ No newline at end of file

docs/en_US/Compressor/Quantizer.md

@@ -5,11 +5,9 @@ Quantizer on NNI Compressor
 We provide Naive Quantizer to quantizer weight to default 8 bits, you can use it to test quantize algorithm without any configure.
 
 ### Usage
-tensorflow
-```python nni.compression.tensorflow.NaiveQuantizer(model_graph).compress()
-```
 pytorch
-```python nni.compression.torch.NaiveQuantizer(model).compress()
+```python 
+model = nni.compression.torch.NaiveQuantizer(model).compress()
 ```
 
 ***

docs/en_US/Release.md

@@ -186,7 +186,7 @@
   * Run trial jobs on the GPU running non-NNI jobs
 * Kubeflow v1beta2 operator
   * Support Kubeflow TFJob/PyTorchJob v1beta2
-* [General NAS programming interface](AdvancedFeature/GeneralNasInterfaces.md)
+* [General NAS programming interface](https://github.com/microsoft/nni/blob/v0.8/docs/en_US/GeneralNasInterfaces.md)
   * Provide NAS programming interface for users to easily express their neural architecture search space through NNI annotation
   * Provide a new command `nnictl trial codegen` for debugging the NAS code
   * Tutorial of NAS programming interface, example of NAS on MNIST, customized random tuner for NAS
@@ -299,7 +299,7 @@
 * Support [Metis tuner](Tuner/MetisTuner.md) as a new NNI tuner. Metis algorithm has been proofed to be well performed for **online** hyper-parameter tuning.
 * Support [ENAS customized tuner](https://github.com/countif/enas_nni), a tuner contributed by github community user, is an algorithm for neural network search, it could learn neural network architecture via reinforcement learning and serve a better performance than NAS.
 * Support [Curve fitting assessor](Assessor/CurvefittingAssessor.md) for early stop policy using learning curve extrapolation.
-* Advanced Support of [Weight Sharing](AdvancedFeature/AdvancedNas.md): Enable weight sharing for NAS tuners, currently through NFS.
+* Advanced Support of [Weight Sharing](https://github.com/microsoft/nni/blob/v0.5/docs/AdvancedNAS.md): Enable weight sharing for NAS tuners, currently through NFS.
 
 #### Training Service Enhancement
 

docs/en_US/TrainingService/PaiYarnMode.md

@@ -106,7 +106,7 @@ nnictl create --config exp_paiYarn.yml
 ```
 to start the experiment in paiYarn mode. NNI will create OpenpaiYarn job for each trial, and the job name format is something like `nni_exp_{experiment_id}_trial_{trial_id}`.
 You can see jobs created by NNI in the OpenpaiYarn cluster's web portal, like:
-![](../../img/nni_paiYarn_joblist.jpg)
+![](../../img/nni_pai_joblist.jpg)
 
 Notice: In paiYarn mode, NNIManager will start a rest server and listen on a port which is your NNI WebUI's port plus 1. For example, if your WebUI port is `8080`, the rest server will listen on `8081`, to receive metrics from trial job running in Kubernetes. So you should `enable 8081` TCP port in your firewall rule to allow incoming traffic.
 

docs/en_US/conf.py

@@ -72,7 +72,7 @@ language = None
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
 # This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', 'Release_v1.0.md']
 
 # The name of the Pygments (syntax highlighting) style to use.
 pygments_style = None

docs/en_US/examples.rst

@@ -11,3 +11,4 @@ Examples
     EvolutionSQuAD<./TrialExample/SquadEvolutionExamples>
     GBDT<./TrialExample/GbdtExample>
     RocksDB <./TrialExample/RocksdbExamples>
+    KDExample <./TrialExample/KDExample>

docs/en_US/model_compression.rst

@@ -18,7 +18,7 @@ For details, please refer to the following tutorials:
     Overview <Compressor/Overview>
     Level Pruner <Compressor/Pruner>
     AGP Pruner <Compressor/Pruner>
-    L1Filter Pruner <Compressor/L1FilterPruner>
+    L1Filter Pruner <Compressor/l1filterpruner>
     Slim Pruner <Compressor/SlimPruner>
     Lottery Ticket Pruner <Compressor/LotteryTicketHypothesis>
     FPGM Pruner <Compressor/Pruner>

docs/en_US/training_services.rst

@@ -6,5 +6,6 @@ Introduction to NNI Training Services
     Local<./TrainingService/LocalMode>
     Remote<./TrainingService/RemoteMachineMode>
     OpenPAI<./TrainingService/PaiMode>
+    OpenPAI Yarn Mode<./TrainingService/PaiYarnMode>
     Kubeflow<./TrainingService/KubeflowMode>
     FrameworkController<./TrainingService/FrameworkControllerMode>

src/sdk/pynni/nni/ppo_tuner/ppo_tuner.py

@@ -503,8 +503,11 @@ class PPOTuner(Tuner):
         """
         Generate parameters, if no trial configration for now, self.credit plus 1 to send the config later
 
+        Parameters
+        ----------
         parameter_id : int
-            Unique identifier for requested hyper-parameters. This will later be used in :meth:`receive_trial_result`.
+            Unique identifier for requested hyper-parameters.
+            This will later be used in :meth:`receive_trial_result`.
         **kwargs
             Not used
 
@@ -512,6 +515,7 @@ class PPOTuner(Tuner):
         -------
         dict
             One newly generated configuration
+
         """
         if self.first_inf:
             self.trials_result = [None for _ in range(self.inf_batch_size)]