Documentation updates (#368)

Co-authored-by: Javier <jaalvare@microsoft.com>
Co-authored-by: Anton Schwaighofer <antonsc@microsoft.com>

docs/building_models.md

@@ -48,7 +48,7 @@ class Prostate(ProstateBase):
     def __init__(self) -> None:
         super().__init__(
             ground_truth_ids=["femur_r", "femur_l", "rectum", "prostate"],
-            azure_dataset_id="id-of-your-blob-containing-prostate-data")
+            azure_dataset_id="name-of-your-AML-dataset-with-prostate-data")
 ```
 The allowed parameters and their meanings are defined in [`SegmentationModelBase`](/InnerEye/ML/config.py).
 The class name must be the same as the basename of the file containing it, so `Prostate.py` must contain `Prostate`. 
@@ -64,12 +64,14 @@ class HeadAndNeck(HeadAndNeckBase):
     def __init__(self) -> None:
         super().__init__(
             ground_truth_ids=["parotid_l", "parotid_r", "smg_l", "smg_r", "spinal_cord"]
-            azure_dataset_id="id-of-your-blob-containing-prostate-data")
+            azure_dataset_id="name-of-your-AML-dataset-with-prostate-data")
 ```
 
 ### Training a new model
 
-* Set up your model configuration as above.
+* Set up your model configuration as above and update `azure_dataset_id` to the name of your Dataset in the AML workspace.
+It is enough to put your dataset into blob storage. The dataset should be a contained in a folder at the root of the datasets container. 
+The InnerEye runner will check if there is a dataset in the AzureML workspace already, and if not, generate it directly from blob storage.
 
 * Train a new model, for example `Prostate`:
 ```shell script

docs/setting_up_aml.md

@@ -64,8 +64,13 @@ You can skip this if you have chosen automatic deployment above.
 
 We recommend using [low priority](https://docs.microsoft.com/en-us/azure/batch/batch-low-pri-vms) clusters, since 
 they only cost a fraction of the dedicated VMs.
-As a reference, the Prostate model and the Head and Neck model require VMs with 4 GPUs with at least 16GB of memory
+As a reference:
-per GPU, for example `Standard_ND24s`, `Standard_NC24s_v3` or `Standard_NC24s_v2`.
+* The Prostate, HeadAndNeck, and the Lung model require VMs with 4 GPUs with at least 24GB of memory
+per GPU, for example `Standard_ND24s` (4 GPUs, 24GB per GPU).
+* It is possible to train all of these models on machines with fewer GPUs, or GPUs with less memory. If using GPUs with
+less memory, some model parameters will need to be adjusted. As a starting point, we would suggest reducing the `train_batch_size`,
+and if that is not sufficient, reducing the `crop_size`, bearing in mind though that the size of the crops has a large
+impact on the model's accuracy.
 
 You need to ensure that your Azure subscription actually has a quota for accessing GPU machines. To see your quota,
 find your newly created AzureML workspace in the [Azure portal](http://portal.azure.com), using the search bar at the