Small edits (#820)

docs/configs/clearml_conf.md

@@ -14,7 +14,7 @@ This reference page is organized by configuration file section:
 * [files](#files-section) - Define auto-generated files to apply into local file system
 
 
-See an [example configuration file](https://github.com/allegroai/clearml-agent/blob/master/docs/clearml.conf), 
+See an [example configuration file](https://github.com/allegroai/clearml-agent/blob/master/docs/clearml.conf)
 in the ClearML Agent GitHub repository. 
 
 :::info

docs/fundamentals/hyperparameters.md

@@ -95,8 +95,8 @@ making it easier to search / filter experiments. Add user properties to an exper
 
 ClearML provides methods to directly access a task's logged parameters.
 
-To get all of a task's parameters and properties (hyperparameters, configuration objects, and user properties), use the 
-[`Task.get_parameters`](../references/sdk/task.md#get_parameters) method, which will return a dictionary with the parameters, 
+To get all of a task's parameters and properties (hyperparameters, configuration objects, and user properties), use 
+[`Task.get_parameters()`](../references/sdk/task.md#get_parameters), which will return a dictionary with the parameters, 
 including their subsections (see [WebApp sections](#webapp-interface) below). 
 
 ## WebApp Interface

docs/guides/distributed/distributed_pytorch_example.md

@@ -45,8 +45,8 @@ logged together.
 
 ```python
 Task.current_task().get_logger().report_scalar(
-    'loss', 
-    'worker {:02d}'.format(dist.get_rank()), 
+    title='loss', 
+    series='worker {:02d}'.format(dist.get_rank()), 
     value=loss.item(), 
     iteration=i
 )

docs/integrations/langchain.md

@@ -15,7 +15,6 @@ Integrate ClearML with the following steps:
 1. Set up the `ClearMLCallbackHandler`. The following code creates a [ClearML Task](../fundamentals/task.md) called 
    `llm` in the `langchain_callback_demo` project, which captures your script's information, including Git details, 
    uncommitted code, and python environment: 
-
    ```python
    from langchain.callbacks import ClearMLCallbackHandler
    from langchain_openai import OpenAI
@@ -34,14 +33,13 @@ Integrate ClearML with the following steps:
 
    llm = OpenAI(temperature=0, callbacks=[clearml_callback])
    ```
-  
-  You can also pass the following parameters to the `ClearMLCallbackHandler` object:
-  * `task_type` – The type of ClearML task to create (see [task types](../fundamentals/task.md#task-types))
-  * `tags` – A list of tags to add to the task
-  * `visualize` - Set to `True` for ClearML to capture the run's Dependencies and Entities plots to the ClearML task
-  * `complexity_metrics` - Set to `True` to log complexity metrics
-  * `stream_logs` - Set to `True` to stream callback actions to ClearML Parameters.
-1. Use `ClearMLCallbackHandler.flush_tracker()` after each model request to make sure all outputs, including metrics and  
+   You can also pass the following parameters to the `ClearMLCallbackHandler` object:
+   * `task_type` – The type of ClearML task to create (see [task types](../fundamentals/task.md#task-types))
+   * `tags` – A list of tags to add to the task
+   * `visualize` - Set to `True` for ClearML to capture the run's Dependencies and Entities plots to the ClearML task
+   * `complexity_metrics` - Set to `True` to log complexity metrics
+   * `stream_logs` - Set to `True` to stream callback actions to ClearML Parameters.
+1. Use `ClearMLCallbackHandler.flush_tracker()` after each model request to make sure all outputs, including metrics and
    prompts, are logged to ClearML:
 
    ```python

docs/integrations/tao.md

@@ -77,17 +77,17 @@ logging metrics, model files, plots, debug samples, and more, so you can gain mo
    ```
    clearml_config{
        project: "TAO Toolkit ClearML Demo"
-	   task: "detectnet_v2_resnet18_clearml"
-	   tags: "detectnet_v2"
-	   tags: "training"
-	   tags: "resnet18"
-	   tags: "unpruned"
+       task: "detectnet_v2_resnet18_clearml"
+       tags: "detectnet_v2"
+       tags: "training"
+       tags: "resnet18"
+       tags: "unpruned"
    }
    ```
    This configuration may vary depending on the schema the network follows. For more information, see the [NVIDIA documentation](https://docs.nvidia.com/tao/tao-toolkit/text/mlops/clearml.html#configuring-the-clearml-element-in-the-training-spec). 
 
 
-And that's it! Whenever you train a model using TAO toolkit, a [ClearML Task](../fundamentals/task.md) will be created, 
+And that's it! Whenever you train a model using TAO Toolkit, a [ClearML Task](../fundamentals/task.md) will be created, 
 which will capture your model files, training configuration, scalars, debug samples, plots, console output, and more. 
 You can view all of this captured information in the [ClearML Web UI](../webapp/webapp_exp_track_visual.md). 
 

docs/release_notes/ver_1_8.md

@@ -21,7 +21,7 @@ specified and the code is running a preinstalled docker
 **Bug Fixes**
 * Use correct Python version in Poetry init [ClearML Agent GitHub PR #179](https://github.com/allegroai/clearml-agent/pull/179)
 * Fix queue handling in `K8sIntegration` and `k8s_glue_example.py` [ClearML Agent GitHub PR #183](https://github.com/allegroai/clearml-agent/pull/183)
-* Fix `FileNotFoundException` crash in `find_python_executable_for_version` [ClearML Agent GitHub PR #164](https://github.com/allegroai/clearml-agent/issues/164)
+* Fix `FileNotFoundException` crash in `find_python_executable_for_version` [ClearML Agent GitHub issue #164](https://github.com/allegroai/clearml-agent/issues/164)
 * Fix delete temp console pipe log files after Task execution is completed (important for long-lasting services agents to avoid collecting temp files on host machine)
 * Fix `agent.enable_git_ask_pass` does not show in configuration dump
 * Fix `pip` is returned as a pip version if no value exists in `agent.package_manager.pip_version`

docs/user_management/identity_providers.md

@@ -6,7 +6,7 @@ title: Identity Providers
 This feature is available under the ClearML Enterprise plan
 :::
 
-Administrators can seamlessly connect ClearML with their identity service providers to easily implement Single Sign On 
+Administrators can seamlessly connect ClearML with their identity service providers to easily implement single sign-on 
 (SSO). On top of authenticating users, this also allows ClearML to retrieve user information and more. Once an identity 
 provider connection is configured and enabled, the option appears in your server login page.