GNN documentation

docs/minified-benchmarks/bert.md

@@ -13,7 +13,7 @@ virtualenv -p python3 ./env && source ./env/bin/activate && pip install pip==24.
 git clone https://github.com/mlcommons/training && cd ./training/language_model/tensorflow/bert
 ```
 
-Go to mlcube directory and study what tasks MLCube implements.
+Go to the mlcube directory and study what tasks MLCube implements.
 
 ```shell
 cd ./mlcube
@@ -80,7 +80,7 @@ In the Google Cloud console, search for the Cloud TPU API page, then click Enabl
 
 Then go to the virtual machine sections and select [TPUs](https://console.cloud.google.com/compute/tpus)
 
-Select create TPU node, fill in all the needed parameters, the recommended TPU type in the [readme](../README.md#on-tpu-v3-128) is v3-128 and the recommended TPU software version is 2.4.0.
+Select create TPU node, fill in all the needed parameters, the recommended TPU type in the [readme](https://github.com/mlcommons/training/blob/3283fc35e68deb88f7197155964f7c3858705649/language_model/tensorflow/bert/README.md#on-tpu-v3-128) is v3-128 and the recommended TPU software version is 2.4.0.
 
 The 3 most important parameters you need to remember are: `project name`, `TPU name`, and `TPU Zone`.
 

docs/minified-benchmarks/gnn.md

@@ -0,0 +1,50 @@
+# Graph Neural Network
+
+The benchmark reference for Graph Neural Network can be found in this [link](https://github.com/mlcommons/training/tree/master/graph_neural_network), and here is the PR for the minified benchmark implementation: [link](https://github.com/mlcommons/training/pull/762).
+
+## Project setup
+
+An important requirement is that you must have Docker installed.
+
+```bash
+# Create Python environment and install MLCube Docker runner 
+virtualenv -p python3 ./env && source ./env/bin/activate && pip install pip==24.0 && pip install mlcube-docker
+# Fetch the implementation from GitHub
+git clone https://github.com/mlcommons/training && cd ./training
+git fetch origin pull/762/head:feature/mlcube_graph_nn && git checkout feature/mlcube_graph_nn
+cd ./graph_neural_network/mlcube
+```
+
+Inside the mlcube directory run the following command to check implemented tasks.
+
+```shell
+mlcube describe
+```
+
+### MLCube tasks
+
+Download dataset.
+
+```shell
+mlcube run --task=download_data -Pdocker.build_strategy=always
+```
+
+Process dataset.
+
+```shell
+mlcube run --task=process_data -Pdocker.build_strategy=always
+```
+
+Train GNN.
+
+```shell
+mlcube run --task=train -Pdocker.build_strategy=always
+```
+
+### Execute the complete pipeline
+
+You can execute the complete pipeline with one single command.
+
+```shell
+mlcube run --task=download_data,process_data,train -Pdocker.build_strategy=always
+```

docs/minified-benchmarks/introduction.md

@@ -6,7 +6,7 @@ A minified benchmark is a reduced version of a MLCommons training benchmark desi
 
 The main advantages of these minified benchmarks are:
 
-- **Faster Execution**: Minified benchmarks are quicker to run (between 10 to 15 mintues), allowing for faster iteration and validation.
+- **Faster Execution**: Minified benchmarks are quicker to run (between 10 to 15 minutes), allowing for faster iteration and validation.
 - **Easier implementation**: By using MLCube users don't need to worry about installing everything from scratch.
 - **Reference preparation**: Minified benchmarks could be used as an introductory step for users interested in executing the MLCommons reference benchmarks.
 
@@ -18,3 +18,4 @@ The main advantages of these minified benchmarks are:
 - [ResNet](resnet.md)
 - [Bert](bert.md)
 - [Object Detection](object-detection.md)
+- [Graph Neural Network](gnn.md)

docs/minified-benchmarks/object-detection.md

@@ -58,7 +58,7 @@ It's also possible to execute the two tasks in one single instruction:
 mlcube run --task=download_demo,demo -Pdocker.build_strategy=always
 ```
 
-### Aditonal options
+### Additional options
 
 Parameters defined at **mculbe/mlcube.yaml** could be overridden using: `--param=input`
 

mkdocs.yml

@@ -34,6 +34,7 @@ nav:
     - ResNet: minified-benchmarks/resnet.md
     - Bert: minified-benchmarks/bert.md
     - Object Detection: minified-benchmarks/object-detection.md
+    - Graph Neural Network: minified-benchmarks/gnn.md
 
 theme:
   features: