diff --git a/docs/hyperdatasets/dataviews.md b/docs/hyperdatasets/dataviews.md index 663cf455..3e32a9f0 100644 --- a/docs/hyperdatasets/dataviews.md +++ b/docs/hyperdatasets/dataviews.md @@ -364,7 +364,7 @@ order, number, timing, and reproducibility of frames for training. #### Iterate Frames Infinitely This example demonstrates creating a Dataview and setting its parameters to iterate infinitely until the script is -manually terminated. +manually terminated: ```python # Create a Dataview object for an iterator that returns frames @@ -375,7 +375,7 @@ myDataView.set_iteration_parameters(order=IterationOrder.random, infinite=True) ``` #### Iterate All Frames Matching the Query -This example demonstrates creating a DataView and setting its parameters to iterate and return all frames matching a query. +This example demonstrates creating a DataView and setting its parameters to iterate and return all frames matching a query: ```python # Create a Dataview object for an iterator for frames @@ -504,7 +504,7 @@ 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"). +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 @@ -551,9 +551,9 @@ my_dataview = DataView.get(dataview_id='') Access the Dataview's frames as a python list, dictionary, or through a pythonic iterator. -The [`DataView.to_list`](../references/hyperdataset/dataview.md#to_list) method returns the Dataview queries result as a python list. +[`DataView.to_list()`](../references/hyperdataset/dataview.md#to_list) returns the Dataview queries result as a Python list. -The [`DataView.to_dict`](../references/hyperdataset/dataview.md#to_dict) method returns a list of dictionaries, where each dictionary represents a frame. Use the +[`DataView.to_dict()`](../references/hyperdataset/dataview.md#to_dict) 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). diff --git a/docs/webapp/applications/apps_llama_deployment.md b/docs/webapp/applications/apps_llama_deployment.md index 3e5ad55c..a59f9b31 100644 --- a/docs/webapp/applications/apps_llama_deployment.md +++ b/docs/webapp/applications/apps_llama_deployment.md @@ -20,10 +20,10 @@ If the ClearML AI application Gateway is not available, the model endpoint might After starting a llama.cpp Model Deployment instance, you can view the following information in its dashboard: * Status indicator - * Active server - App instance is running and is actively in use - * Loading server - App instance is setting up - * Idle server - App instance is idle - * Stopped server - App instance is stopped + * Active server - App instance is running and is actively in use + * Loading server - App instance is setting up + * Idle server - App instance is idle + * Stopped server - App instance is stopped * Idle time - Time elapsed since last activity * App - The publicly accessible URL of the model endpoint. Active model endpoints are also available in the [Model Endpoints](../webapp_model_endpoints.md) table, which allows you to view and compare endpoint details and