Update readme.md

Author: mx-iao

training/readme.md

@@ -2,19 +2,19 @@
 These notebook tutorials cover the various scenarios for training machine learning and deep learning models with Azure Machine Learning.
 
 ## Sample notebooks
-- [01.train-hyperparameter-tune-deploy-with-pytorch](https://github.com/Azure/MachineLearningNotebooks/tree/master/training/01.train-hyperparameter-tune-deploy-with-pytorch)  
+- [01.train-hyperparameter-tune-deploy-with-pytorch](./01.train-hyperparameter-tune-deploy-with-pytorch/01.train-hyperparameter-tune-deploy-with-pytorch.ipynb)  
 Train, hyperparameter tune, and deploy a PyTorch image classification model that distinguishes bees vs. ants using transfer learning. Azure ML concepts covered:
   - Create a remote compute target (Batch AI cluster)
   - Upload training data using `Datastore`
   - Run a single-node `PyTorch` training job
   - Hyperparameter tune model with HyperDrive
   - Find and register the best model
   - Deploy model to ACI
-- [02.distributed-pytorch-with-horovod](https://github.com/Azure/MachineLearningNotebooks/tree/master/training/02.distributed-pytorch-with-horovod)  
+- [02.distributed-pytorch-with-horovod](./02.distributed-pytorch-with-horovod/02.distributed-pytorch-with-horovod.ipynb)  
 Train a PyTorch model on the MNIST dataset using distributed training with Horovod. Azure ML concepts covered:
   - Create a remote compute target (Batch AI cluster)
   - Run a two-node distributed `PyTorch` training job using Horovod
-- [03.train-hyperparameter-tun-deploy-with-tensorflow](https://github.com/Azure/MachineLearningNotebooks/tree/master/training/03.train-hyperparameter-tune-deploy-with-tensorflow)  
+- [03.train-hyperparameter-tun-deploy-with-tensorflow](./03.train-hyperparameter-tune-deploy-with-tensorflow/03.train-hyperparameter-tune-deploy-with-tensorflow.ipynb)  
 Train, hyperparameter tune, and deploy a TensorFlow model on the MNIST dataset. Azure ML concepts covered:
   - Create a remote compute target (Batch AI cluster)
   - Upload training data using `Datastore`
@@ -24,29 +24,29 @@ Train, hyperparameter tune, and deploy a TensorFlow model on the MNIST dataset.
   - Hyperparameter tune model with HyperDrive
   - Find and register the best model
   - Deploy model to ACI
-- [04.distributed-tensorflow-with-horovod](https://github.com/Azure/MachineLearningNotebooks/tree/master/training/04.distributed-tensorflow-with-horovod)  
+- [04.distributed-tensorflow-with-horovod](./04.distributed-tensorflow-with-horovod/04.distributed-tensorflow-with-horovod.ipynb)  
 Train a TensorFlow word2vec model using distributed training with Horovod. Azure ML concepts covered:
   - Create a remote compute target (Batch AI cluster)
   - Upload training data using `Datastore`
   - Run a two-node distributed `TensorFlow` training job using Horovod
-- [05.distributed-tensorflow-with-parameter-server](https://github.com/Azure/MachineLearningNotebooks/tree/master/training/05.distributed-tensorflow-with-parameter-server)  
+- [05.distributed-tensorflow-with-parameter-server](./05.distributed-tensorflow-with-parameter-server/05.distributed-tensorflow-with-parameter-server.ipynb)  
 Train a TensorFlow model on the MNIST dataset using native distributed TensorFlow (parameter server). Azure ML concepts covered:
   - Create a remote compute target (Batch AI cluster)
   - Run a two workers, one parameter server distributed `TensorFlow` training job
-- [06.distributed-cntk-with-custom-docker](https://github.com/Azure/MachineLearningNotebooks/tree/master/training/06.distributed-cntk-with-custom-docker)  
+- [06.distributed-cntk-with-custom-docker](./06.distributed-cntk-with-custom-docker/06.distributed-cntk-with-custom-docker.ipynb)  
 Train a CNTK model on the MNIST dataset using the Azure ML base `Estimator` with custom Docker image and distributed training. Azure ML concepts covered:
   - Create a remote compute target (Batch AI cluster)
   - Upload training data using `Datastore`
   - Run a base `Estimator` training job using a custom Docker image from Docker Hub
   - Distributed CNTK two-node training job via MPI using base `Estimator`
 
-- [07.tensorboard](https://github.com/Azure/MachineLearningNotebooks/tree/master/training/07.tensorboard)  
+- [07.tensorboard](./07.tensorboard/07.tensorboard.ipynb)  
 Train a TensorFlow MNIST model locally, on a DSVM, and on Batch AI and view the logs live on TensorBoard. Azure ML concepts covered:
   - Run the training job locally with Azure ML and run TensorBoard locally. Start (and stop) an Azure ML `TensorBoard` object to stream and view the logs
   - Run the training job on a remote DSVM and stream the logs to TensorBoard
   - Run the training job on a remote Batch AI cluster and stream the logs to TensorBoard
   - Start a `Tensorboard` instance that displays the logs from all three above runs in one
-- [08.export-run-history-to-tensorboard](https://github.com/Azure/MachineLearningNotebooks/tree/master/training/08.export-run-history-to-tensorboard)
+- [08.export-run-history-to-tensorboard](./08.export-run-history-to-tensorboard/08.export-run-history-to-tensorboard.ipynb)
   - Start an Azure ML `Experiment` and log metrics to `Run` history
   - Export the `Run` history logs to TensorBoard logs
   - View the logs in TensorBoard