Enable rstcheck (#4711)

docs/source/compression/compression_utils.rst

@@ -180,10 +180,10 @@ Usage
    # Given input tensor with size (1, 1, 28, 28) and switch to full mode
    x = torch.randn(1, 1, 28, 28)
 
-   flops, params, results = count_flops_params(model, (x,) mode='full') # tuple of tensor as input
+   flops, params, results = count_flops_params(model, (x,), mode='full') # tuple of tensor as input
 
    # Format output size to M (i.e., 10^6)
-   print(f'FLOPs: {flops/1e6:.3f}M,  Params: {params/1e6:.3f}M)
+   print(f'FLOPs: {flops/1e6:.3f}M,  Params: {params/1e6:.3f}M')
    print(results)
    {
    'conv': {'flops': [60], 'params': [20], 'weight_size': [(5, 3, 1, 1)], 'input_size': [(1, 3, 2, 2)], 'output_size': [(1, 5, 2, 2)], 'module_type': ['Conv2d']}, 

docs/source/experiment/adaptdl.rst

@@ -140,11 +140,11 @@ Follow the log streaming of a certain trial:
 
 .. code-block:: bash
 
-   nnictl log trial --trial_id=<trial_id>
+   nnictl log trial --trial_id=TRIAL_ID
 
 .. code-block:: bash
 
-   nnictl log trial <experiment_id> --trial_id=<trial_id>
+   nnictl log trial EXPERIMENT_ID --trial_id=TRIAL_ID
 
 Note that *after* a trial has done and its pod has been deleted,
 no logs can be retrieved then via this command.
@@ -195,7 +195,7 @@ If having multiple experiment running at the same time, you may use
 
 .. code-block:: bash
 
-   nnictl tensorboard start <experiment_id>
+   nnictl tensorboard start EXPERIMENT_ID
 
 It will provide you the web url to access the tensorboard.
 

docs/source/experiment/customize.rst

@@ -24,7 +24,7 @@ Folder structure of code
 
 NNI's folder structure is shown below:
 
-.. code-block:: bash
+.. code-block:: text
 
    nni
      |- deployment
@@ -59,7 +59,7 @@ NNI's folder structure is shown below:
 Function annotation of TrainingService
 --------------------------------------
 
-.. code-block:: bash
+.. code-block:: typescript
 
    abstract class TrainingService {
        public abstract listTrialJobs(): Promise<TrialJobDetail[]>;
@@ -82,7 +82,7 @@ The parent class of TrainingService has a few abstract functions, users need to
 
 ClusterMetadata is the data related to platform details, for examples, the ClusterMetadata defined in remote machine server is:
 
-.. code-block:: bash
+.. code-block:: typescript
 
    export class RemoteMachineMeta {
        public readonly ip : string;
@@ -117,7 +117,7 @@ This function will return the metadata value according to the values, it could b
 
 SubmitTrialJob is a function to submit new trial jobs, users should generate a job instance in TrialJobDetail type. TrialJobDetail is defined as follow:
 
-.. code-block:: bash
+.. code-block:: typescript
 
    interface TrialJobDetail {
        readonly id: string;

docs/source/feature_engineering/overview.rst

@@ -61,8 +61,8 @@ Here is an example:
    from nni.feature_engineering.feature_selector import FeatureSelector
 
    class CustomizedSelector(FeatureSelector):
-       def __init__(self, ...):
-       ...
+       def __init__(self, *args, **kwargs):
+           ...
 
 **2. Implement fit and _get_selected features Function**
 
@@ -73,8 +73,8 @@ Here is an example:
    from nni.feature_engineering.feature_selector import FeatureSelector
 
    class CustomizedSelector(FeatureSelector):
-       def __init__(self, ...):
-       ...
+       def __init__(self, *args, **kwargs):
+           ...
 
        def fit(self, X, y, **kwargs):
            """
@@ -126,16 +126,15 @@ Here is an example:
    from nni.feature_engineering.feature_selector import FeatureSelector
 
    class CustomizedSelector(FeatureSelector, BaseEstimator):
-       def __init__(self, ...):
-       ...
+       def __init__(self, *args, **kwargs):
+           ...
 
-       def get_params(self, ...):
+       def get_params(self, *args, **kwargs):
            """
            Get parameters for this estimator.
            """
            params = self.__dict__
-           params = {key: val for (key, val) in params.items()
-           if not key.endswith('_')}
+           params = {key: val for (key, val) in params.items() if not key.endswith('_')}
            return params
 
        def set_params(self, **params):
@@ -143,8 +142,8 @@ Here is an example:
            Set the parameters of this estimator.
            """
            for param in params:
-           if hasattr(self, param):
-           setattr(self, param, params[param])
+               if hasattr(self, param):
+                   setattr(self, param, params[param])
            return self
 
 **2. Inherit the SelectorMixin Class and its Function**
@@ -157,10 +156,10 @@ Here is an example:
    from nni.feature_engineering.feature_selector import FeatureSelector
 
    class CustomizedSelector(FeatureSelector, BaseEstimator, SelectorMixin):
-       def __init__(self, ...):
+       def __init__(self, *args, **kwargs):
            ...
 
-       def get_params(self, ...):
+       def get_params(self, *args, **kwargs):
            """
            Get parameters for this estimator.
            """
@@ -174,8 +173,8 @@ Here is an example:
            Set the parameters of this estimator.
            """
            for param in params:
-           if hasattr(self, param):
-           setattr(self, param, params[param])
+               if hasattr(self, param):
+                   setattr(self, param, params[param])
            return self
 
        def get_support(self, indices=False):

docs/source/hpo/custom_algorithm.rst

@@ -19,7 +19,7 @@ Here is an example:
    from nni.tuner import Tuner
 
    class CustomizedTuner(Tuner):
-       def __init__(self, ...):
+       def __init__(self, *args, **kwargs):
            ...
 
 **2. Implement receive_trial_result, generate_parameter and update_search_space function**
@@ -29,7 +29,7 @@ Here is an example:
    from nni.tuner import Tuner
 
    class CustomizedTuner(Tuner):
-       def __init__(self, ...):
+       def __init__(self, *args, **kwargs):
            ...
 
        def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
@@ -143,7 +143,7 @@ If you want to implement a customized Assessor, there are three things to do:
    from nni.assessor import Assessor
 
    class CustomizedAssessor(Assessor):
-       def __init__(self, ...):
+       def __init__(self, *args, **kwargs):
            ...
 
 **2. Implement assess trial function**
@@ -153,7 +153,7 @@ If you want to implement a customized Assessor, there are three things to do:
    from nni.assessor import Assessor, AssessResult
 
    class CustomizedAssessor(Assessor):
-       def __init__(self, ...):
+       def __init__(self, *args, **kwargs):
            ...
 
        def assess_trial(self, trial_history):

docs/source/hpo/custom_algorithm_installation.rst

@@ -28,7 +28,6 @@ You can follow below steps to build a customized tuner/assessor/advisor, and reg
 
 Reference following instructions to create:
 
-
 * `customized tuner <../Tuner/CustomizeTuner.rst>`_
 * `customized assessor <../Assessor/CustomizeAssessor.rst>`_
 * `customized advisor <../Tuner/CustomizeAdvisor.rst>`_
@@ -101,9 +100,9 @@ Run following command to register the customized algorithms as builtin algorithm
 
 .. code-block:: bash
 
-   nnictl algo register --meta <path_to_meta_file>
+   nnictl algo register --meta PATH_TO_META_FILE
 
-The ``<path_to_meta_file>`` is the path to the yaml file your created in above section.
+The ``PATH_TO_META_FILE`` is the path to the yaml file your created in above section.
 
 Reference `customized tuner example <#example-register-a-customized-tuner-as-a-builtin-tuner>`_ for a full example.
 
@@ -128,7 +127,7 @@ List builtin algorithms
 
 Run following command to list the registered builtin algorithms:
 
-.. code-block:: bash
+.. code-block:: text
 
    nnictl algo list
    +-----------------+------------+-----------+--------=-------------+------------------------------------------+
@@ -213,7 +212,7 @@ Check the registered builtin algorithms
 
 Then run command ``nnictl algo list``\ , you should be able to see that demotuner is installed:
 
-.. code-block:: bash
+.. code-block:: text
 
    +-----------------+------------+-----------+--------=-------------+------------------------------------------+
    |      Name       |    Type    |   source  |      Class Name      |               Module Name                |

docs/source/hpo/overview.rst

@@ -29,7 +29,7 @@ Following code snippet demonstrates a naive HPO process:
                     best_accuracy = accuracy
                     best_hyperparameters = (learning_rate, momentum, activation_type)
 
-     print('Best hyperparameters:', best_hyperparameters)
+    print('Best hyperparameters:', best_hyperparameters)
 
 You may have noticed, the example will train 4×10×3=120 models in total.
 Since it consumes so much computing resources, you may want to:

docs/source/nas/exploration_strategy.rst

@@ -239,7 +239,7 @@ To run the tutorial, follow the steps below:
 
 2. **Search**: Based on the architecture of simplified PFLD, the setting of multi-stage search space and hyper-parameters for searching should be firstly configured to construct the supernet. For example,
 
-   .. code-block:: bash
+   .. code-block::
 
       from lib.builder import search_space
       from lib.ops import PRIMITIVES
@@ -249,13 +249,13 @@ To run the tutorial, follow the steps below:
       # configuration of hyper-parameters
       # search_space defines the multi-stage search space
       nas_config = NASConfig(
-            model_dir="./ckpt_save",
-            nas_lr=0.01,
-            mode="mul",
-            alpha=0.25,
-            beta=0.6,
-            search_space=search_space,
-         )
+         model_dir="./ckpt_save",
+         nas_lr=0.01,
+         mode="mul",
+         alpha=0.25,
+         beta=0.6,
+         search_space=search_space,
+      )
       # lookup table to manage the information
       lookup_table = LookUpTable(config=nas_config, primitives=PRIMITIVES)
       # created supernet

docs/source/nas/hardware_aware_nas.rst

@@ -63,7 +63,7 @@ Then run one-shot ProxylessNAS demo:
 
 .. code-block:: bash
 
-   python ${NNI_ROOT}/examples/nas/oneshot/proxylessnas/main.py --applied_hardware <hardware> --reference_latency <reference latency (ms)>
+   python ${NNI_ROOT}/examples/nas/oneshot/proxylessnas/main.py --applied_hardware HARDWARE --reference_latency REFERENCE_LATENCY_MS
 
 How the demo works
 ^^^^^^^^^^^^^^^^^^

docs/source/release.rst

@@ -1506,7 +1506,7 @@ NNICTL new features and updates
 
   Before v0.3, NNI only supports running single experiment once a time. After this release, users are able to run multiple experiments simultaneously. Each experiment will require a unique port, the 1st experiment will be set to the default port as previous versions. You can specify a unique port for the rest experiments as below:
 
-  .. code-block:: bash
+  .. code-block:: text
 
      nnictl create --port 8081 --config <config file path>
 

docs/source/sharings/hpo_comparison.rst

@@ -20,9 +20,9 @@ Hyperparameter Optimization algorithms are list below:
 
 All algorithms run in NNI local environment.
 
-Machine Environment：
+Machine Environment:
 
-.. code-block:: bash
+.. code-block:: text
 
    OS: Linux Ubuntu 16.04 LTS
    CPU: Intel(R) Xeon(R) CPU E5-2690 v3 @ 2.60GHz 2600 MHz
@@ -215,7 +215,7 @@ The performance of ``DB_Bench`` is associated with the machine configuration and
 Machine configuration
 ^^^^^^^^^^^^^^^^^^^^^
 
-.. code-block:: bash
+.. code-block:: text
 
    RocksDB:    version 6.1
    CPU:        6 * Intel(R) Xeon(R) CPU E5-2690 v4 @ 2.60GHz

docs/source/sharings/kd_example.rst

@@ -37,9 +37,9 @@ PyTorch code
 
 The complete code for fine-tuning the pruned model can be found :githublink:`here <examples/model_compress/pruning/finetune_kd_torch.py>`
 
-.. code-block:: python
+.. code-block:: bash
 
-      python finetune_kd_torch.py --model [model name] --teacher-model-dir [pretrained checkpoint path]  --student-model-dir [pruned checkpoint path] --mask-path [mask file path]
+   python finetune_kd_torch.py --model [model name] --teacher-model-dir [pretrained checkpoint path]  --student-model-dir [pruned checkpoint path] --mask-path [mask file path]
 
 Note that: for fine-tuning a pruned model, run :githublink:`basic_pruners_torch.py <examples/model_compress/pruning/basic_pruners_torch.py>` first to get the mask file, then pass the mask path as argument to the script.
 

docs/source/sharings/nni_colab_support.rst

@@ -6,40 +6,42 @@ NNI can easily run on Google Colab platform. However, Colab doesn't expose its p
 How to Open NNI's Web UI on Google Colab
 ----------------------------------------
 
-
 #. Install required packages and softwares.
 
-.. code-block:: bash
+   .. code-block:: bash
 
-   ! pip install nni # install nni
-   ! wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip # download ngrok and unzip it
-   ! unzip ngrok-stable-linux-amd64.zip
-   ! mkdir -p nni_repo
-   ! git clone https://github.com/microsoft/nni.git nni_repo/nni # clone NNI's offical repo to get examples
+      ! pip install nni # install nni
+      ! wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip # download ngrok and unzip it
+      ! unzip ngrok-stable-linux-amd64.zip
+      ! mkdir -p nni_repo
+      ! git clone https://github.com/microsoft/nni.git nni_repo/nni # clone NNI's offical repo to get examples
 
 
-#. Register a ngrok account `here <https://ngrok.com/>`__\ , then connect to your account using your authtoken.
+#. Register a ngrok account `here <https://ngrok.com/>`__, then connect to your account using your authtoken.
 
-.. code-block:: bash
+   .. code-block:: bash
 
-   ! ./ngrok authtoken <your-authtoken>
+      ! ./ngrok authtoken YOUR_AUTH_TOKEN
 
 
 #. Start an NNI example on a port bigger than 1024, then start ngrok with the same port. If you want to use gpu, make sure gpuNum >= 1 in config.yml. Use ``get_ipython()`` to start ngrok since it will be stuck if you use ``! ngrok http 5000 &``.
 
-.. code-block:: bash
+   .. code-block:: bash
+
+      ! nnictl create --config nni_repo/nni/examples/trials/mnist-pytorch/config.yml --port 5000 &
+
+   .. code-block:: python
 
-   ! nnictl create --config nni_repo/nni/examples/trials/mnist-pytorch/config.yml --port 5000 &
-   get_ipython().system_raw('./ngrok http 5000 &')
+      get_ipython().system_raw('./ngrok http 5000 &')
 
 
 #. Check the public url.
 
-.. code-block:: bash
+   .. code-block:: bash
 
-   ! curl -s http://localhost:4040/api/tunnels # don't change the port number 4040
+      ! curl -s http://localhost:4040/api/tunnels # don't change the port number 4040
 
-You will see an url like http://xxxx.ngrok.io after step 4, open this url and you will find NNI's Web UI. Have fun :)
+   You will see an url like http://xxxx.ngrok.io after step 4, open this url and you will find NNI's Web UI. Have fun :)
 
 Access Web UI with frp
 ----------------------

docs/source/sharings/op_evo_examples.rst

@@ -118,7 +118,7 @@ Citing OpEvo
 
 If you feel OpEvo is helpful, please consider citing the paper as follows:
 
-.. code-block:: bash
+.. code-block:: bib
 
    @misc{gao2020opevo,
        title={OpEvo: An Evolutionary Method for Tensor Operator Optimization},

docs/source/sharings/squad_evolution_examples.rst

@@ -146,9 +146,9 @@ Among those files, ``trial.py`` and ``graph_to_tf.py`` are special.
            if topo_i == '|':
                continue
            if graph.layers[topo_i].graph_type == LayerType.input.value:
-               # ......
+               ...
            elif graph.layers[topo_i].graph_type == LayerType.attention.value:
-               # ......
+               ...
            # More layers to handle
 
 As we can see, this function is actually a compiler, that converts the internal model DAG configuration (which will be introduced in the ``Model configuration format`` section) ``graph``\ , to a Tensorflow computation graph.
@@ -162,6 +162,7 @@ performs topological sorting on the internal graph representation, and the code
 .. code-block:: python
 
    for _, topo_i in enumerate(topology):
+       ...
 
 performs actually conversion that maps each layer to a part in Tensorflow computation graph.
 

pipelines/fast-test.yml

@@ -20,8 +20,11 @@ stages:
 
     - script: |
         cd docs
-        # rstcheck -r source
-      displayName: rstcheck (disabled for now)  # TODO: rstcheck
+        rstcheck -r source \
+          --ignore-directives automodule,autoclass,autofunction,cardlinkitem,codesnippetcard,argparse \
+          --ignore-roles githublink --ignore-substitutions release \
+          --report warning
+      displayName: rstcheck
 
     - script: |
         cd docs