modify main README
@@ -25,6 +25,46 @@ After following the setup documents, you can go through the sample notebooks lin
* Inference with the registered model on AKS-HCI cluster
* Test model
+* [Distributed PyTorch Training with DistributedDataParallel](notebooks/distributed-cifar10/distributed-pytorch-cifar10.ipynb) (Image Classification)
+
+ This notebook demonstrates an example of Image classification with PyTorch, including,
+ * Distributed training using PyTorch with 2 worker nodes on AKS-HCI cluster and the training data is stored in on-premise NFS Server
+ * Register model
+ * Inference with the registered model on AKS-HCI cluster
+ * Test model
+
+* [Distributed Image Classification with Tensorflow](notebooks/distributed-cifar10/distributed-tf2-cifar10.ipynb) (Image Classification, not finished)
+
+ This notebook demonstrates an example of Image classification with TensorFlow, including,
+ * Distributed training using Tensorflow with 2 worker nodes on AKS-HCI cluster and the training data is stored in on-premise NFS Server
+ * TBD
+
+* [Object Segmentation with Transfer Learning](notebooks/object-segmentation-on-azure-stack/object_segmentation-akshci.ipynb) (Object Segmentation)
+
+ Object segmentation using pre-trained Mask R-CNN model on PyTorch. AML pipeline steps are used for data preprocessing. Training data are stored in on-premise NFS server, and the intermediate data are stored in default datastore associated with the ML workspace. The whole flow includes,
+ * Use AML pipelines to read training data from on-premise NFS server, do data preprocessing and generate intermediate data to default datastore
+ * Use AML pipelines to trigger train step on AKS-HCI cluster
+ * Register model
+ * Inference with the registered model on AKS-HCI cluster
+ * Test model
+
+* [AML Pipelines with NYC-TAXI-DATA](notebooks/pipeline/nyc-taxi-data-regression-model-building.ipynb) (Structured Text Data Prediction)
+
+ This notebook demonstrates an example of Structured Text Data Prediction, preparing / preprocessing / training data in default datastore associated with the ML workspace. The whole flow includes,
+ * Download and upload training data to default datastore
+ * Use AML pipelines to preprocess and train
+ * Cleanse data in parallel
+ * Merge cleansed data
+ * Normalize data
+ * Transform data
+ * Split data
+ * Train model
+ * Register model
+ * Inference with the registered model on AKS-HCI cluster
+ * Test model
+
+* [Model Download and Upload](notebooks/download-upload-model/AML-model-download-upload.ipynb)
+
## CLI v2 Examples
### Prerequisites
