diff --git a/docs/en_US/hpo_benchmark.rst b/docs/en_US/hpo_benchmark.rst
index 490ecfa6c2..8e58674620 100644
--- a/docs/en_US/hpo_benchmark.rst
+++ b/docs/en_US/hpo_benchmark.rst
@@ -34,11 +34,11 @@ Run predefined benchmarks on existing tuners
 
 This script runs the benchmark 'nnivalid', which consists of a regression task, a binary classification task, and a multi-class classification task. After the script finishes, you can find a summary of the results in the folder results_[time]/reports/. To run on other predefined benchmarks, change the ``benchmark`` variable in ``runbenchmark_nni.sh``. Some benchmarks are defined in ``/examples/trials/benchmarking/automlbenchmark/nni/benchmarks``\ , and others are defined in ``/examples/trials/benchmarking/automlbenchmark/automlbenchmark/resources/benchmarks/``. One example of larger benchmarks is "nnismall", which consists of 8 regression tasks, 8 binary classification tasks, and 8 multi-class classification tasks.
 
-By default, the script runs the benchmark on all embedded tuners in NNI. If provided a list of tuners in [tuner-names], it only runs the tuners in the list. Currently, the following tuner names are supported: "TPE", "Random", "Anneal", "Evolution", "SMAC", "GPTuner", "MetisTuner", "Hyperband", "BOHB". It is also possible to evaluate custom tuners. See the next sections for details. 
+By default, the script runs the benchmark on all embedded tuners in NNI. If provided a list of tuners in [tuner-names], it only runs the tuners in the list. Currently, the following tuner names are supported: "TPE", "Random", "Anneal", "Evolution", "SMAC", "GPTuner", "MetisTuner", "DNGOTuner", "Hyperband", "BOHB". It is also possible to evaluate custom tuners. See the next sections for details. 
 
 By default, the script runs the specified tuners against the specified benchmark one by one. To run all the experiments simultaneously in the background, set the "serialize" flag to false in ``runbenchmark_nni.sh``. 
 
-Note: the SMAC tuner and the BOHB advisor has to be manually installed before any experiments can be run on it. Please refer to `this page <https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html?highlight=nni>`_ for more details on installing SMAC and BOHB.
+Note: the SMAC tuner, DNGO tuner, and the BOHB advisor has to be manually installed before any experiments can be run on it. Please refer to `this page <https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html?highlight=nni>`_ for more details on installing SMAC and BOHB.
 
 Run customized benchmarks on existing tuners
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -62,7 +62,7 @@ To use custom tuners, first make sure that the tuner inherits from ``nni.tuner.T
 A Benchmark Example 
 ^^^^^^^^^^^^^^^^^^^
 
-As an example, we ran the "nnismall" benchmark on the following 8 tuners: "TPE", "Random", "Anneal", "Evolution", "SMAC", "GPTuner", "MetisTuner", "DngoTuner". (The DngoTuner is not available as a built-in tuner at the time this article is written.) As some of the tasks contains a considerable amount of training data, it took about 2 days to run the whole benchmark on one tuner using a single CPU core. For a more detailed description of the tasks, please check ``/examples/trials/benchmarking/automlbenchmark/nni/benchmarks/nnismall_description.txt``. For binary and multi-class classification tasks, the metric "auc" and "logloss" were used for evaluation, while for regression, "r2" and "rmse" were used. 
+As an example, we ran the "nnismall" benchmark on the following 8 tuners: "TPE", "Random", "Anneal", "Evolution", "SMAC", "GPTuner", "MetisTuner", "DNGOTuner". As some of the tasks contains a considerable amount of training data, it took about 2 days to run the whole benchmark on one tuner using a single CPU core. For a more detailed description of the tasks, please check ``/examples/trials/benchmarking/automlbenchmark/nni/benchmarks/nnismall_description.txt``. For binary and multi-class classification tasks, the metric "auc" and "logloss" were used for evaluation, while for regression, "r2" and "rmse" were used. 
 
 After the script finishes, the final scores of each tuner are summarized in the file ``results[time]/reports/performances.txt``. Since the file is large, we only show the following screenshot and summarize other important statistics instead. 
 
diff --git a/examples/trials/benchmarking/automlbenchmark/nni/frameworks.yaml b/examples/trials/benchmarking/automlbenchmark/nni/frameworks.yaml
index 5fa057ac0b..402449e557 100644
--- a/examples/trials/benchmarking/automlbenchmark/nni/frameworks.yaml
+++ b/examples/trials/benchmarking/automlbenchmark/nni/frameworks.yaml
@@ -5,7 +5,7 @@ NNI:
   version: 'stable'
   project: https://github.com/microsoft/nni
 
-# type in ['TPE', 'Random', 'Anneal', 'Evolution', 'SMAC', 'GPTuner', 'MetisTuner', 'Hyperband', 'BOHB']
+# type in ['TPE', 'Random', 'Anneal', 'Evolution', 'SMAC', 'GPTuner', 'MetisTuner', 'DNGOTuner', 'Hyperband', 'BOHB']
 # arch_type in ['random_forest']
 # limit_type in ['time', 'ntrials']
 # limit must be an integer
@@ -66,6 +66,14 @@ MetisTuner:
     limit_type: 'ntrials'
     trial_limit: 10
 
+DNGOTuner:
+  extends: NNI 
+  params:
+    tuner_type: 'DNGOTuner'
+    arch_type: 'random_forest'
+    limit_type: 'ntrials'
+    trial_limit: 10
+
 Hyperband:
   extends: NNI 
   params: