Small edits (#1033)

docs/integrations/accelerate.md

@@ -111,7 +111,7 @@ To augment its automatic logging, ClearML also provides an explicit logging inte
 See more information about explicitly logging information to a ClearML Task:
 * [Models](../clearml_sdk/model_sdk.md#manually-logging-models)
 * [Configuration](../clearml_sdk/task_sdk.md#configuration) (e.g. parameters, configuration files)
-* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or python objects created by a task)
+* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or Python objects created by a task)
 * [Scalars](../clearml_sdk/task_sdk.md#scalars) 
 * [Text/Plots/Debug Samples](../fundamentals/logger.md#manual-reporting)
 

docs/integrations/autokeras.md

@@ -24,7 +24,7 @@ And that's it! This creates a [ClearML Task](../fundamentals/task.md) which capt
 * Scalars (loss, learning rates)
 * Console output
 * General details such as machine details, runtime, creation date etc.
-* Hyperparameters created with standard python packages (such as argparse, click, Python Fire, etc.)
+* Hyperparameters created with standard Python packages (such as argparse, click, Python Fire, etc.)
 * And more
 
 You can view all the task details in the [WebApp](../webapp/webapp_exp_track_visual.md). 
@@ -70,7 +70,7 @@ To augment its automatic logging, ClearML also provides an explicit logging inte
 See more information about explicitly logging information to a ClearML Task:
 * [Models](../clearml_sdk/model_sdk.md#manually-logging-models)
 * [Configuration](../clearml_sdk/task_sdk.md#configuration) (e.g. parameters, configuration files)
-* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or python objects created by a task)
+* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or Python objects created by a task)
 * [Scalars](../clearml_sdk/task_sdk.md#scalars) 
 * [Text/Plots/Debug Samples](../fundamentals/logger.md#manual-reporting)
 

docs/integrations/catboost.md

@@ -24,7 +24,7 @@ And that's it! This creates a [ClearML Task](../fundamentals/task.md) which capt
 * Scalars (loss, learning rates)
 * Console output
 * General details such as machine details, runtime, creation date etc.
-* Hyperparameters created with standard python packages (e.g. argparse, click, Python Fire, etc.)
+* Hyperparameters created with standard Python packages (e.g. argparse, click, Python Fire, etc.)
 * And more
 
 You can view all the task details in the [WebApp](../webapp/webapp_overview.md). 
@@ -68,7 +68,7 @@ To augment its automatic logging, ClearML also provides an explicit logging inte
 See more information about explicitly logging information to a ClearML Task:
 * [Models](../clearml_sdk/model_sdk.md#manually-logging-models)
 * [Configuration](../clearml_sdk/task_sdk.md#configuration) (e.g. parameters, configuration files)
-* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or python objects created by a task)
+* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or Python objects created by a task)
 * [Scalars](../clearml_sdk/task_sdk.md#scalars) 
 * [Text/Plots/Debug Samples](../fundamentals/logger.md#manual-reporting)
 

docs/integrations/click.md

@@ -7,7 +7,7 @@ If you are not already using ClearML, see [Getting Started](../getting_started/d
 instructions.
 :::
 
-[`click`](https://click.palletsprojects.com) is a python package for creating command-line interfaces. ClearML integrates 
+[`click`](https://click.palletsprojects.com) is a Python package for creating command-line interfaces. ClearML integrates 
 seamlessly with `click` and automatically logs its command-line parameters. 
 
 All you have to do is add two lines of code:

docs/integrations/fastai.md

@@ -24,7 +24,7 @@ And that's it! This creates a [ClearML Task](../fundamentals/task.md) which capt
 * Scalars (loss, learning rates)
 * Console output
 * General details such as machine details, runtime, creation date etc.
-* Hyperparameters created with standard python packages (e.g. argparse, click, Python Fire, etc.)
+* Hyperparameters created with standard Python packages (e.g. argparse, click, Python Fire, etc.)
 * And more
 
 You can view all the task details in the [WebApp](../webapp/webapp_overview.md). 
@@ -68,7 +68,7 @@ To augment its automatic logging, ClearML also provides an explicit logging inte
 See more information about explicitly logging information to a ClearML Task:
 * [Models](../clearml_sdk/model_sdk.md#manually-logging-models)
 * [Configuration](../clearml_sdk/task_sdk.md#configuration) (e.g. parameters, configuration files)
-* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or python objects created by a task)
+* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or Python objects created by a task)
 * [Scalars](../clearml_sdk/task_sdk.md#scalars) 
 * [Text/Plots/Debug Samples](../fundamentals/logger.md#manual-reporting)
 

docs/integrations/ignite.md

@@ -25,7 +25,7 @@ task = Task.init(task_name="<task_name>", project_name="<project_name>")
 ```
 
 This will create a [ClearML Task](../fundamentals/task.md) that captures your script's information, including Git details, 
-uncommitted code, python environment, all information logged through `TensorboardLogger`, and more. 
+uncommitted code, Python environment, all information logged through `TensorboardLogger`, and more. 
 
 Visualize all the captured information in the task's page in ClearML's [WebApp](#webapp).
 
@@ -45,7 +45,7 @@ Integrate ClearML with the following steps:
    ```
    
    This creates a [ClearML Task](../fundamentals/task.md) called `ignite` in the `examples` project, which captures your 
-   script's information, including Git details, uncommitted code, python environment. 
+   script's information, including Git details, uncommitted code, Python environment. 
   
    You can also pass the following parameters to the `ClearMLLogger` object:
    * `task_type` – The type of task (see [task types](../fundamentals/task.md#task-types)).

docs/integrations/keras.md

@@ -70,7 +70,7 @@ To augment its automatic logging, ClearML also provides an explicit logging inte
 See more information about explicitly logging information to a ClearML Task:
 * [Models](../clearml_sdk/model_sdk.md#manually-logging-models)
 * [Configuration](../clearml_sdk/task_sdk.md#configuration) (e.g. parameters, configuration files)
-* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or python objects created by a task)
+* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or Python objects created by a task)
 * [Scalars](../clearml_sdk/task_sdk.md#scalars) 
 * [Text/Plots/Debug Samples](../fundamentals/logger.md#manual-reporting)
 

docs/integrations/langchain.md

@@ -14,7 +14,7 @@ class is used to create a ClearML Task to log LangChain assets and metrics.
 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: 
+   uncommitted code, and Python environment: 
    ```python
    from langchain.callbacks import ClearMLCallbackHandler
    from langchain_openai import OpenAI
@@ -60,7 +60,7 @@ To augment its automatic logging, ClearML also provides an explicit logging inte
 See more information about explicitly logging information to a ClearML Task:
 * [Models](../clearml_sdk/model_sdk.md#manually-logging-models)
 * [Configuration](../clearml_sdk/task_sdk.md#configuration) (e.g. parameters, configuration files)
-* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or python objects created by a task)
+* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or Python objects created by a task)
 * [Scalars](../clearml_sdk/task_sdk.md#scalars) 
 * [Text/Plots/Debug Samples](../fundamentals/logger.md#manual-reporting)
 

docs/integrations/lightgbm.md

@@ -69,7 +69,7 @@ To augment its automatic logging, ClearML also provides an explicit logging inte
 See more information about explicitly logging information to a ClearML Task:
 * [Models](../clearml_sdk/model_sdk.md#manually-logging-models)
 * [Configuration](../clearml_sdk/task_sdk.md#configuration) (e.g. parameters, configuration files)
-* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or python objects created by a task)
+* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or Python objects created by a task)
 * [Scalars](../clearml_sdk/task_sdk.md#scalars) 
 * [Text/Plots/Debug Samples](../fundamentals/logger.md#manual-reporting)
 

docs/integrations/megengine.md

@@ -21,7 +21,7 @@ And that's it! This creates a [ClearML Task](../fundamentals/task.md) which capt
 * Source code and uncommitted changes
 * Installed packages
 * MegEngine model files
-* Hyperparameters created with standard python packages (e.g. argparse, click, Python Fire, etc.)
+* Hyperparameters created with standard Python packages (e.g. argparse, click, Python Fire, etc.)
 * Scalars logged to popular frameworks like TensorBoard
 * Console output
 * General details such as machine details, runtime, creation date etc.
@@ -65,7 +65,7 @@ To augment its automatic logging, ClearML also provides an explicit logging inte
 See more information about explicitly logging information to a ClearML Task:
 * [Models](../clearml_sdk/model_sdk.md#manually-logging-models)
 * [Configuration](../clearml_sdk/task_sdk.md#configuration) (e.g. parameters, configuration files)
-* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or python objects created by a task)
+* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or Python objects created by a task)
 * [Scalars](../clearml_sdk/task_sdk.md#scalars) 
 * [Text/Plots/Debug Samples](../fundamentals/logger.md#manual-reporting)
 

docs/integrations/pytorch.md

@@ -65,7 +65,7 @@ To augment its automatic logging, ClearML also provides an explicit logging inte
 See more information about explicitly logging information to a ClearML Task:
 * [Models](../clearml_sdk/model_sdk.md#manually-logging-models)
 * [Configuration](../clearml_sdk/task_sdk.md#configuration) (e.g. parameters, configuration files)
-* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or python objects created by a task)
+* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or Python objects created by a task)
 * [Scalars](../clearml_sdk/task_sdk.md#scalars) 
 * [Text/Plots/Debug Samples](../fundamentals/logger.md#manual-reporting)
 

docs/integrations/pytorch_lightning.md

@@ -95,7 +95,7 @@ To augment its automatic logging, ClearML also provides an explicit logging inte
 See more information about explicitly logging information to a ClearML Task:
 * [Models](../clearml_sdk/model_sdk.md#manually-logging-models)
 * [Configuration](../clearml_sdk/task_sdk.md#configuration) (e.g. parameters, configuration files)
-* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or python objects created by a task)
+* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or Python objects created by a task)
 * [Scalars](../clearml_sdk/task_sdk.md#scalars) 
 * [Text/Plots/Debug Samples](../fundamentals/logger.md#manual-reporting)
 

docs/integrations/scikit_learn.md

@@ -24,7 +24,7 @@ And that's it! This creates a [ClearML Task](../fundamentals/task.md) which capt
 * Joblib model files 
 * Console output
 * General details such as machine details, runtime, creation date etc.
-* Hyperparameters created with standard python packages (e.g. argparse, click, Python Fire, etc.)
+* Hyperparameters created with standard Python packages (e.g. argparse, click, Python Fire, etc.)
 * And more
 
 You can view all the task details in the [WebApp](../webapp/webapp_exp_track_visual.md). 
@@ -63,7 +63,7 @@ To augment its automatic logging, ClearML also provides an explicit logging inte
 See more information about explicitly logging information to a ClearML Task:
 * [Models](../clearml_sdk/model_sdk.md#manually-logging-models)
 * [Configuration](../clearml_sdk/task_sdk.md#configuration) (e.g. parameters, configuration files)
-* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or python objects created by a task)
+* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or Python objects created by a task)
 * [Scalars](../clearml_sdk/task_sdk.md#scalars) 
 * [Text/Plots/Debug Samples](../fundamentals/logger.md#manual-reporting)
 

docs/integrations/seaborn.md

@@ -18,7 +18,7 @@ task = Task.init(task_name="<task_name>", project_name="<project_name>")
 ```
 
 This will create a [ClearML Task](../fundamentals/task.md) that captures your script's information, including Git details,
-uncommitted code, python environment, your `seaborn` plots, and more. View the seaborn plots in the [WebApp](../webapp/webapp_overview.md), 
+uncommitted code, Python environment, your `seaborn` plots, and more. View the seaborn plots in the [WebApp](../webapp/webapp_overview.md), 
 in the task's **Plots** tab.
 
 ![Seaborn plot](../img/integrations_seaborn_plots.png)

docs/integrations/tao.md

@@ -8,7 +8,7 @@ logging metrics, model files, plots, debug samples, and more, so you can gain mo
 
 ## Setup
 
-1. Install the `clearml` python package:
+1. Install the `clearml` Python package:
 
    ```commandline
    pip install clearml

docs/integrations/tensorboard.md

@@ -17,7 +17,7 @@ task = Task.init(task_name="<task_name>", project_name="<project_name>")
 ```
 
 This will create a [ClearML Task](../fundamentals/task.md) that captures your script's information, including Git details,
-uncommitted code, python environment, your TensorBoard metrics, plots, images, and text. 
+uncommitted code, Python environment, your TensorBoard metrics, plots, images, and text. 
 
 View the TensorBoard outputs in the [WebApp](../webapp/webapp_overview.md), in the task's page.
 
@@ -52,7 +52,7 @@ To augment its automatic logging, ClearML also provides an explicit logging inte
 See more information about explicitly logging information to a ClearML Task:
 * [Models](../clearml_sdk/model_sdk.md#manually-logging-models)
 * [Configuration](../clearml_sdk/task_sdk.md#configuration) (e.g. parameters, configuration files)
-* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or python objects created by a task)
+* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or Python objects created by a task)
 * [Scalars](../clearml_sdk/task_sdk.md#scalars) 
 * [Text/Plots/Debug Samples](../fundamentals/logger.md#manual-reporting)
 

docs/integrations/tensorboardx.md

@@ -18,7 +18,7 @@ task = Task.init(task_name="<task_name>", project_name="<project_name>")
 ```
 
 This will create a [ClearML Task](../fundamentals/task.md) that captures your script's information, including Git details,
-uncommitted code, python environment, your TensorboardX metrics, plots, images, and text. 
+uncommitted code, Python environment, your TensorboardX metrics, plots, images, and text. 
 
 View the TensorboardX outputs in the [WebApp](../webapp/webapp_overview.md), in the task's page.
 
@@ -51,7 +51,7 @@ To augment its automatic logging, ClearML also provides an explicit logging inte
 See more information about explicitly logging information to a ClearML Task:
 * [Models](../clearml_sdk/model_sdk.md#manually-logging-models)
 * [Configuration](../clearml_sdk/task_sdk.md#configuration) (e.g. parameters, configuration files)
-* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or python objects created by a task)
+* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or Python objects created by a task)
 * [Scalars](../clearml_sdk/task_sdk.md#scalars) 
 * [Text/Plots/Debug Samples](../fundamentals/logger.md#manual-reporting)
 

docs/integrations/tensorflow.md

@@ -68,7 +68,7 @@ To augment its automatic logging, ClearML also provides an explicit logging inte
 See more information about explicitly logging information to a ClearML Task:
 * [Models](../clearml_sdk/model_sdk.md#manually-logging-models)
 * [Configuration](../clearml_sdk/task_sdk.md#configuration) (e.g. parameters, configuration files)
-* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or python objects created by a task)
+* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or Python objects created by a task)
 * [Scalars](../clearml_sdk/task_sdk.md#scalars) 
 * [Text/Plots/Debug Samples](../fundamentals/logger.md#manual-reporting)
 

docs/integrations/transformers.md

@@ -9,7 +9,7 @@ ClearML automatically logs Transformer's models, parameters, scalars, and more.
 
 All you have to do is install and set up ClearML:
 
-1. Install the `clearml` python package:
+1. Install the `clearml` Python package:
 
    ```commandline
    pip install clearml

docs/integrations/xgboost.md

@@ -25,7 +25,7 @@ And that's it! This creates a [ClearML Task](../fundamentals/task.md) which capt
 * Scalars (loss, learning rates)
 * Console output
 * General details such as machine details, runtime, creation date etc.
-* Hyperparameters created with standard python packages (e.g. argparse, click, Python Fire, etc.)
+* Hyperparameters created with standard Python packages (e.g. argparse, click, Python Fire, etc.)
 * And more
 
 :::tip Logging Plots
@@ -89,7 +89,7 @@ To augment its automatic logging, ClearML also provides an explicit logging inte
 See more information about explicitly logging information to a ClearML Task:
 * [Models](../clearml_sdk/model_sdk.md#manually-logging-models)
 * [Configuration](../clearml_sdk/task_sdk.md#configuration) (e.g. parameters, configuration files)
-* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or python objects created by a task)
+* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or Python objects created by a task)
 * [Scalars](../clearml_sdk/task_sdk.md#scalars) 
 * [Text/Plots/Debug Samples](../fundamentals/logger.md#manual-reporting)
 

docs/integrations/yolov5.md

@@ -11,7 +11,7 @@ built in logger:
 * Turn your newly trained YOLOv5 model into an API with just a few commands using [ClearML Serving](../clearml_serving/clearml_serving.md)
 
 ## Setup
-1. Install the clearml python package:
+1. Install the clearml Python package:
 
    ```commandline
    pip install clearml

docs/integrations/yolov8.md

@@ -22,7 +22,7 @@ segmentation, and classification. Get the most out of YOLOv8 with ClearML:
 
 ## Setup
 
-1. Install the `clearml` python package:
+1. Install the `clearml` Python package:
 
    ```commandline
    pip install clearml