updating to use new file dataset class

Author: Trevor Bye

articles/machine-learning/service/how-to-train-keras.md

@@ -52,13 +52,9 @@ First, import the necessary Python libraries.
 
 ```Python
 import os
-import urllib
-import shutil
 import azureml
-
 from azureml.core import Experiment
 from azureml.core import Workspace, Run
-
 from azureml.core.compute import ComputeTarget, AmlCompute
 from azureml.core.compute_target import ComputeTargetException
 ```
@@ -75,43 +71,36 @@ ws = Workspace.from_config()
 
 ### Create an experiment
 
-Create an experiment and a folder to hold your training scripts. In this example, create an experiment called "keras-mnist".
+Create an experiment called "keras-mnist" in your workspace.
 
 ```Python
-script_folder = './keras-mnist'
-os.makedirs(script_folder, exist_ok=True)
-
 exp = Experiment(workspace=ws, name='keras-mnist')
 ```
 
-### Upload dataset and scripts
-
-The [datastore](how-to-access-data.md) is a place where data can be stored and accessed by mounting or copying the data to the compute target. Each workspace provides a default datastore. Upload the data and training scripts to the datastore so that they can be easily accessed during training.
+### Create a file dataset
 
-1. Download the MNIST dataset locally.
+A `FileDataset` object references one or multiple files in your workspace datastore or public urls. The files can be of any format, and the class provides you with the ability to download or mount the files to your compute. By creating a `FileDataset`, you create a reference to the data source location. If you applied any transformations to the data set, they will be stored in the data set as well. The data remains in its existing location, so no extra storage cost is incurred. See the [how-to](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-create-register-datasets) guide on the `Dataset` package for more information.
 
-    ```Python
-    os.makedirs('./data/mnist', exist_ok=True)
+```python
+from azureml.core.dataset import Dataset
 
-    urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz', filename = './data/mnist/train-images.gz')
-    urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz', filename = './data/mnist/train-labels.gz')
-    urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz', filename = './data/mnist/test-images.gz')
-    urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz', filename = './data/mnist/test-labels.gz')
-    ```
-
-1. Upload the MNIST dataset to the default datastore.
-
-    ```Python
-    ds = ws.get_default_datastore()
-    ds.upload(src_dir='./data/mnist', target_path='mnist', overwrite=True, show_progress=True)
-    ```
+web_paths = [
+            'http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz',
+            'http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz',
+            'http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz',
+            'http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz'
+            ]
+dataset = Dataset.File.from_files(path=web_paths)
+```
 
-1. Upload the Keras training script, `keras_mnist.py`, and the helper file, `utils.py`.
+Use the `register()` method to register the data set to your workspace so they can be shared with others, reused across various experiments, and referred to by name in your training script.
 
-    ```Python
-    shutil.copy('./keras_mnist.py', script_folder)
-    shutil.copy('./utils.py', script_folder)
-    ```
+```python
+dataset = dataset.register(workspace=ws,
+                           name='mnist dataset',
+                           description='training and test dataset',
+                           create_new_version=True)
+```
 
 ## Create a compute target
 
@@ -139,11 +128,22 @@ For more information on compute targets, see the [what is a compute target](conc
 
 The [TensorFlow estimator](https://docs.microsoft.com/python/api/azureml-train-core/azureml.train.dnn.tensorflow?view=azure-ml-py) provides a simple way of launching TensorFlow training jobs on compute target. Since Keras runs on top of TensorFlow, you can use the TensorFlow estimator and import the Keras library using the `pip_packages` argument.
 
+First get the data from the workspace datastore using the `Dataset` class.
+
+```python
+dataset = Dataset.get_by_name(ws, 'mnist dataset')
+
+# list the files referenced by mnist dataset
+dataset.to_path()
+```
+
 The TensorFlow estimator is implemented through the generic [`estimator`](https://docs.microsoft.com//python/api/azureml-train-core/azureml.train.estimator.estimator?view=azure-ml-py) class, which can be used to support any framework. Additionally, create a dictionary `script_params` that contains the DNN hyperparameter settings. For more information about training models using the generic estimator, see [train models with Azure Machine Learning using estimator](how-to-train-ml-models.md)
 
-```Python
+```python
+from azureml.train.dnn import TensorFlow
+
 script_params = {
-    '--data-folder': ds.path('mnist').as_mount(),
+    '--data-folder': dataset.as_named_input('mnist').as_mount(),
     '--batch-size': 50,
     '--first-layer-neurons': 300,
     '--second-layer-neurons': 100,