Small edits (#1042)

docs/hyperdatasets/dataviews.md

@@ -146,6 +146,8 @@ You can retrieve the Dataview frames using [`DataView.to_list()`](../references/
 This example uses an ROI query to filter for frames containing at least one ROI with the label `cat`:
 
 ```python
+from allegroai import DataView, IterationOrder
+
 # Create a Dataview object for an iterator that randomly returns frames according to queries
 myDataView = DataView(iteration_order=IterationOrder.random, iteration_infinite=False)
 
@@ -166,6 +168,8 @@ list_of_frames = myDataView.to_list()
 This example uses an ROI query to filter for frames containing at least one ROI with either the label `cat` OR the label `dog`:
 
 ```python
+from allegroai import DataView
+
 # Add a query for a Dataset version 
 myDataView.add_query(
     dataset_name='myDataset', 
@@ -189,6 +193,8 @@ list_of_frames = myDataView.to_list()
 This example uses an ROI query to filter for frames containing at least one ROI with both the label `Car` AND the label `partly_occluded`:
 
 ```python
+from allegroai import DataView
+
 # Add a query for a Dataset version
 myDataView.add_query(
     dataset_name='myDataset', 
@@ -207,6 +213,8 @@ This example uses an ROI query to filter for frames containing at least one ROI
 have the label `partly_occluded`:
 
 ```python
+from allegroai import DataView
+
 # Add a query for a Dataset version
 # Use a Lucene Query
 #   "label" is a key in the rois dictionary of a frame
@@ -229,6 +237,8 @@ ROI with the label `Person`. The example demonstrates using the `roi_queries` pa
 with a list of [`DataView.RoiQuery`](../references/hyperdataset/dataview.md#roiquery) objects:
 
 ```python
+from allegroai import DataView
+
 myDataview = DataView()
 myDataview.add_multi_query(
     dataset_id=self._dataset_id,
@@ -247,6 +257,8 @@ contain ROIs with the label `Person`. To exclude an ROI, pass `must_not=True` in
 object. 
 
 ```python
+from allegroai import DataView
+
 myDataview = DataView()
 myDataview.add_multi_query(
     dataset_id=self._dataset_id,
@@ -266,6 +278,8 @@ This example demonstrates an ROI query filtering for frames containing the ROI l
 from two versions of one Dataset, and one version of another Dataset:
 
 ```python
+from allegroai import DataView
+
 # Add queries:
 
 # The 1st Dataset version 
@@ -310,6 +324,8 @@ They use the same logical OR, AND, NOT AND matching as ROI queries.
 This example demonstrates a frame query filtering for frames containing the meta key `city` value of `bremen`:
         
 ```python
+from allegroai import DataView
+
 # Add a frame query for frames with the meta key "city" value of "bremen"
 myDataView.add_query(
     dataset_name='myDataset',
@@ -388,6 +404,8 @@ This example demonstrates creating a Dataview and setting its parameters to iter
 manually terminated: 
 
 ```python
+from allegroai import DataView, IterationOrder
+
 # Create a Dataview object for an iterator that returns frames
 myDataView = DataView()
 
@@ -399,6 +417,8 @@ myDataView.set_iteration_parameters(order=IterationOrder.random, infinite=True)
 This example demonstrates creating a DataView and setting its parameters to iterate and return all frames matching a query:
 
 ```python
+from allegroai import DataView, IterationOrder
+
 # Create a Dataview object for an iterator for frames
 myDataView = DataView(iteration_order=IterationOrder.random, iteration_infinite=False)
 
@@ -425,6 +445,8 @@ This example demonstrates creating a Dataview and setting its parameters to iter
 Dataset version contains fewer than that number of frames matching the query, then fewer are returned by the iterator.
 
 ```python
+from allegroai import DataView, IterationOrder
+
 # Create a Dataview object for an iterator for frames
 myDataView = DataView(iteration_order=IterationOrder.random, iteration_infinite=True)
 
@@ -447,6 +469,8 @@ This example adjusts an imbalance in the input data to improve training for `Car
 one ROI labeled with both `Car` and `largely_occluded` will be input.
 
 ```python
+from allegroai import DataView, IterationOrder
+
 myDataView = DataView(iteration_order=IterationOrder.random, iteration_infinite=True)
 
 myDataView.add_query(
@@ -478,6 +502,8 @@ then use the labels you map **to** when setting enumeration values.
 For example, if the labels `truck`, `van`, and `car` are mapped **to** `vehicle`, then set enumeration for `vehicle`. 
 
 ```python
+from allegroai import DataView, IterationOrder
+
 # Create a Dataview object for an iterator that randomly returns frames according to queries
 myDataView = DataView(iteration_order=IterationOrder.random, iteration_infinite=True)
 
@@ -528,6 +554,8 @@ third uses `Car` (upper case "C").
 The example maps `Car` (upper case "C") to `car` (lower case "c"):
 
 ```python
+from allegroai import DataView, IterationOrder
+
 # Create a Dataview object for an iterator that randomly returns frames according to queries 
 myDataView = DataView(iteration_order=IterationOrder.random, iteration_infinite=True)
 
@@ -567,6 +595,8 @@ Dataview objects can be retrieved by the Dataview ID or name using the [`DataVie
 class method.
 
 ```python
+from allegroai import DataView
+
 my_dataview = DataView.get(dataview_id='<dataview_id>')
 ```
 

docs/hyperdatasets/webapp/webapp_dataviews.md

@@ -1,5 +1,5 @@
 ---
-title: The Dataview table
+title: The Dataview Table
 ---
 
 The **Dataview table** is a [customizable](#customizing-the-dataview-table) list of Dataviews associated with a project.

docs/release_notes/clearml_agent/ver_0_12.md

@@ -22,7 +22,7 @@ title: Version 0.12
 * Windows support! YES, you can now have Windows machines as part of your cluster (notice --docker is not supported on Windows).
 * Add initial Conda package manager support (still in beta).
 * Add --gpus and --cpu-only for easier GPU control when running multiple `trains-agent` instances on the same machine.
-* [python_binary](https://github.com/clearml/clearml-agent/blob/master/docs/trains.conf#L35) can now be specified (the default is the same Python binary executing the `trains-agent`).
+* [python_binary](https://github.com/clearml/clearml-agent/blob/831b36c4246bb4dfe150407461e0d01166bc6e92/docs/trains.conf#L35) can now be specified (the default is the same Python binary executing the `trains-agent`).
 * Fix Issue ([GitHub Issue #2](https://github.com/clearml/clearml-agent/issues/2)).
 
 ### Trains Agent 0.12.0

docs/webapp/applications/apps_llama_deployment.md

@@ -80,6 +80,7 @@ values from the file, which can be modified before launching the app instance
 
 <div class="max-w-65">
 
-![llama deployment app form](../../img/apps_llama_form.png)
+![llama deployment app form](../../img/apps_llama_form.png#light-mode-only)
+![llama deployment app form](../../img/apps_llama_form.png#dark-mode-only)
 
 </div>