Update docs (#373)

docs/hyperdatasets/dataset.md

@@ -43,6 +43,18 @@ from allegroai import DatasetVersion
 myDataset = DatasetVersion.create_new_dataset(dataset_name='myDataset Two')
 ```
 
+When creating a dataset, you can put it into a project. In this case, the dataset will adhere to the [access rules](../webapp/webapp_profile.md#access-rules) 
+specified for its containing project. Use `dataset_project` parameter in `Dataset.create` or `DatasetVersion.create_new_dataset` 
+to specify a project name. 
+
+```python
+myDataset_1 = Dataset.create(dataset_name="myDataset", dataset_project="myDataset Project")
+
+myDataset_2 = DatasetVersion.create_new_dataset(
+  dataset_name="myDataset_2", dataset_project="myDatasetProject_2"
+)
+```
+
 To raise a `ValueError` exception if the Dataset exists, specify the `raise_if_exists` parameters as `True`.
 
 * With `Dataset.create`
@@ -102,7 +114,33 @@ Delete a Dataset even if it contains versions whose status is *Published*.
 ```python
 Dataset.delete(dataset_name='MyDataset', delete_all_versions=True, force=True)
 ```
-    
+  
+Delete a Dataset and the sources associated with its deleted frames:
+
+```python
+Dataset.delete(
+  dataset_name='MyDataset', delete_all_versions=True, force=True, delete_sources=True
+)
+```
+
+This supports deleting sources located in AWS S3, GCP, and Azure Storage (not local storage). The `delete_sources` 
+parameter is ignored if `delete_all_versions` is `False`.  You can view the deletion process’ progress by passing 
+`show_progress=True` (`tqdm` required).  
+
+### Tagging Datasets
+
+Tags can be added to datasets, allowing to easily identify and group experiments.
+
+Add tags to a dataset:
+```python
+MyDataset.add_tags(["coco", "dogs"])
+```
+
+Remove tags from a dataset:
+
+```python
+MyDataset.remove_tags(["dogs"])
+```
 
 ## Dataset Versioning
 

docs/hyperdatasets/dataviews.md

@@ -363,49 +363,6 @@ myDataView.add_query(
 )
 ```
 
-### Mapping ROI Labels
-
-ROI label translation (label mapping) enables combining labels for training, combining disparate datasets, and hiding 
-certain labels for training.
-
-This example demonstrates consolidating two disparate Datasets. Two Dataset versions use `car` (lower case "c"), but the
-third uses `Car` (upper case "C"). 
-The example maps `Car` (upper case "C") to `car` (lower case "c").
-
-```python
-# Create a Dataview object for an iterator that randomly returns frames according to queries 
-myDataView = DataView(iteration_order=IterationOrder.random, iteration_infinite=True)
-
-# The 1st Dataset (version) - "car" with lowercase "c"
-myDataView.add_query(
-    dataset_name='myDataset', 
-    version_name='myVersion', 
-    roi_query='car'
-)
-
-# The 2nd Dataset (version) - "car" with lowercase "c"
-myDataView.add_query(
-    dataset_name='dataset_2', 
-    version_name='aVersion',  
-    roi_query='car'
-)
-
-# A 3rd Dataset (version) - "Car" with uppercase "C"
-myDataView.add_query(
-    dataset_name='dataset_3', 
-    version_name='training',
-    roi_query='Car'
-)
-
-# Use a mapping rule to translate "Car" (uppercase) to "car" (lowercase)
-myDataView.add_mapping_rule(
-    dataset_name='dataset_3',
-    version_name='training', 
-    from_labels=['Car'], 
-    to_label='car'
-)
-```
-
 ### Setting Label Enumeration Values
 
 Set label enumeration values to maintain data conformity across multiple codebases and datasets. 
@@ -459,3 +416,98 @@ myDataView.set_labels(
     {"cat": 1, "dog": 2, "bird": 3, "sheep": 4, "cow": 5, "ignore": -1,}
 )
 ```
+
+### Mapping ROI Labels
+
+ROI label translation (label mapping) enables combining labels for training, combining disparate datasets, and hiding 
+certain labels for training.
+
+This example demonstrates consolidating two disparate Datasets. Two Dataset versions use `car` (lower case "c"), but the
+third uses `Car` (upper case "C"). 
+The example maps `Car` (upper case "C") to `car` (lower case "c").
+
+```python
+# Create a Dataview object for an iterator that randomly returns frames according to queries 
+myDataView = DataView(iteration_order=IterationOrder.random, iteration_infinite=True)
+
+# The 1st Dataset (version) - "car" with lowercase "c"
+myDataView.add_query(
+    dataset_name='myDataset', 
+    version_name='myVersion', 
+    roi_query='car'
+)
+
+# The 2nd Dataset (version) - "car" with lowercase "c"
+myDataView.add_query(
+    dataset_name='dataset_2', 
+    version_name='aVersion',  
+    roi_query='car'
+)
+
+# A 3rd Dataset (version) - "Car" with uppercase "C"
+myDataView.add_query(
+    dataset_name='dataset_3', 
+    version_name='training',
+    roi_query='Car'
+)
+
+# Use a mapping rule to translate "Car" (uppercase) to "car" (lowercase)
+myDataView.add_mapping_rule(
+    dataset_name='dataset_3',
+    version_name='training', 
+    from_labels=['Car'], 
+    to_label='car'
+)
+```
+
+### Accessing Frames
+
+Dataview objects can be retrieved by the Dataview ID or name using the [DataView.get](../references/hyperdataset/dataview.md#dataviewget) 
+class method.
+
+```python
+my_dataview = DataView.get(dataview_id='12344kg2p3hf8')
+```
+
+Access the Dataview's frames as a python list, dictionary, or through a pythonic iterator.
+
+The `DataView.to_dict` method returns a list of dictionaries, where each dictionary represents a frame. Use the 
+`projection` parameter to specify a subset of the frame fields to be included in the result. Input a list of strings, 
+where each string represents a frame field or subfield (using dot-separated notation). 
+
+For example, the code below specifies that the frame dictionaries should include only the `id` and `sources` fields and 
+the `dataset.id` subfield:  
+
+```python
+my_dataview = DataView.get(dataview_id='<dataview_id>')
+my_dataview.to_dict(projection=['id', 'dataset.id', 'sources'])
+```
+
+The method returns a list of dictionaries that looks something like this:
+
+```json
+[   
+  {
+    "id": "<dataview_id>",
+    "dataset": {
+      "id": "<dataset_id>"
+    },
+    "sources": [
+      {
+        "id": "<id>",
+        "uri": "<uri>",
+        "timestamp": <timestamp>,
+        "preview": {
+          "uri": "<uri>",
+          "timestamp": <timestamp>
+        }
+      }
+    ]
+  },
+  #   additional dictionaries with the same format here
+]
+```
+
+Since the `to_list`/`to_dict` methods return all the frames in the dataview, it is recommended to use the [`DataView.get_iterator`](../references/hyperdataset/dataview.md#get_iterator) 
+method, which returns an iterator of the dataview. You can also specify the desired frame fields in this method using 
+the `projection` parameter, just like in the `DataView.to_dict` method, as described above.

docs/hyperdatasets/frame_groups.md

@@ -50,7 +50,11 @@ To add FrameGroups to a Dataset Version:
   
 1. Append the FrameGroup object to a list of frames
   
-1. Add that list to a DatasetVersion.
+1. Add that list to a DatasetVersion using the [`DatasetVersion.add_frames`](../references/hyperdataset/hyperdatasetversion.md#add_frames) 
+method. Use the `upload_retries` parameter to set the number of times the upload of a frame should be retried in case of 
+failure, before marking the frame as failed and continuing to upload the next frames. In the case that a single frame in 
+the FrameGroup fails to upload, the entire group will not be registered. The method returns a list of frames that were 
+not successfully registered or uploaded. 
 
 ```python
 # Create a FrameGroup object

docs/hyperdatasets/single_frames.md

@@ -222,8 +222,14 @@ For more information, see the `SingleFrame` class description.
 
 ### Adding SingleFrames to a Dataset Version
 
-Use the `DatasetVersion.add_frames` method to add SingleFrames to a [Dataset version](dataset.md#dataset-versioning)
-(see [Creating snapshots](dataset.md#creating-snapshots) or [Creating child versions](dataset.md#creating-child-versions)).
+Use the [`DatasetVersion.add_frames`](../references/hyperdataset/hyperdatasetversion.md#add_frames) method to add 
+SingleFrames to a [Dataset version](dataset.md#dataset-versioning) (see [Creating snapshots](dataset.md#creating-snapshots) 
+or [Creating child versions](dataset.md#creating-child-versions)). Frames that are already a part of the dataset version 
+will only be updated. 
+
+Use the `upload_retries` parameter to set the number of times the upload of a frame should be retried in case of failure, 
+before marking the frame as failed and continuing to upload the next frames. The method returns a list of frames that 
+were not successfully registered or uploaded. 
 
 ```python
 from allegroai import DatasetVersion, SingleFrame