Small edits (#891)

docs/guides/frameworks/megengine/megengine_mnist.md

@@ -50,5 +50,6 @@ The model info panel contains the model details, including:
 ## Console
 
 All console output during the script's execution appears in the experiment's **CONSOLE** page.
+
 ![Console tab](../../../img/examples_megengine_console.png)
 

docs/guides/frameworks/pytorch/pytorch_mnist.md

@@ -9,7 +9,7 @@ The example script does the following:
 * Trains a simple deep neural network on the PyTorch built-in [MNIST](https://pytorch.org/vision/stable/datasets.html#mnist)
   dataset.
 * Creates an experiment named `pytorch mnist train` in the `examples` project.
-* ClearML automatically logs `argparse` command line options, and models (and their snapshots) created by PyTorch
+* ClearML automatically logs `argparse` command line options, and models (and their snapshots) created by PyTorch.
 * Additional metrics are logged by calling [`Logger.report_scalar()`](../../../references/sdk/logger.md#report_scalar).
 
 ## Scalars

docs/guides/frameworks/tensorflow/tensorboard_pr_curve.md

@@ -11,8 +11,8 @@ The example script does the following:
   label of each random color is associated with the normal distribution that generated it.
 * Computes the probability that each color belongs to the class, using three other normal distributions.
 * Generate PR curves using those probabilities. 
-* Creates a summary per class using [tensorboard.plugins.pr_curve.summary](https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/pr_curve/summary.py), 
-* ClearML automatically captures TensorBoard output, TensorFlow Definitions, and output to the console
+* Creates a summary per class using [tensorboard.plugins.pr_curve.summary](https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/pr_curve/summary.py).
+* ClearML automatically captures TensorBoard output, TensorFlow Definitions, and output to the console.
 
 ## Plots
 

docs/guides/ide/remote_jupyter_tutorial.md

@@ -85,7 +85,7 @@ Jupyter Lab URL: http://localhost:8878/?token=ff7e5e8b9e5493a01b1a72530d18181320
 VSCode server available at http://localhost:8898/
 ```
 
-Click on the JupyterLab link, which will open the remote session
+Click on the JupyterLab link, which will open the remote session.
    
 Now, let's execute some code in the remote session!
 

docs/guides/reporting/media_reporting.md

@@ -3,15 +3,16 @@ title: Media Reporting
 ---
 
 The [media_reporting.py](https://github.com/allegroai/clearml/blob/master/examples/reporting/media_reporting.py) example 
-demonstrates reporting (uploading) images, audio, and video. Use the [Logger.report_media](../../references/sdk/logger.md#report_media)
-method to upload from: 
+demonstrates reporting (uploading) images, audio, and video. Use [`Logger.report_media()`](../../references/sdk/logger.md#report_media)
+to upload from: 
 * Local path
 * BytesIO stream
 * URL of media already uploaded to some storage
 
-ClearML uploads media to the bucket specified in the ClearML configuration file or ClearML can be configured for image storage, see [Logger.set_default_upload_destination](../../references/sdk/logger.md#set_default_upload_destination)
-(storage for [artifacts](../../clearml_sdk/task_sdk.md#setting-upload-destination) is different). Set credentials for storage in the ClearML 
-[configuration file](../../configs/clearml_conf.md).
+ClearML uploads media to the bucket specified in the ClearML configuration file. You can configure ClearML for image 
+storage using [`Logger.set_default_upload_destination()`](../../references/sdk/logger.md#set_default_upload_destination) 
+(note that [artifact storage](../../clearml_sdk/task_sdk.md#setting-upload-destination) is handled differently). 
+Set the storage credentials in the [clearml.conf file](../../configs/clearml_conf.md#sdk-section).
 
 ClearML reports media in the **ClearML Web UI** **>** experiment details **>** **DEBUG SAMPLES** 
 tab. 
@@ -21,8 +22,7 @@ project.
 
 ## Reporting (Uploading) Media from a Source by URL
 
-Report by calling the [Logger.report_media](../../references/sdk/logger.md#report_media) 
-method using the `url` parameter.
+Report by using the `url` parameter of [`Logger.report_media()`](../../references/sdk/logger.md#report_media):
 
 ```python
 # report video, an already uploaded video media (url)
@@ -45,7 +45,7 @@ The reported audio can be viewed in the **DEBUG SAMPLES** tab. Click a thumbnail
 
 ## Reporting (Uploading) Media from a Local File
 
-Use the `local_path` parameter.
+Report by using the `local_path` parameter of [`Logger.report_media()`](../../references/sdk/logger.md#report_media):
 
 ```python
 #  report audio, report local media audio file

docs/guides/reporting/text_reporting.md

@@ -3,14 +3,15 @@ title: Text Reporting
 ---
 
 The [text_reporting.py](https://github.com/allegroai/clearml/blob/master/examples/reporting/text_reporting.py) script 
-demonstrates reporting explicit text, by calling the [Logger.report_text](../../references/sdk/logger.md#report_text)
-method. 
+demonstrates reporting explicit text by calling [`Logger.report_text()`](../../references/sdk/logger.md#report_text). 
 
-ClearML reports these tables in the **ClearML Web UI**, experiment details, **CONSOLE** tab. 
+ClearML reports the text in the **ClearML Web UI**, in the experiment's **CONSOLE** tab. 
 
 When the script runs, it creates an experiment named `text reporting` in the `examples` project.
 
-    # report text
-    Logger.current_logger().report_text("hello, this is plain text")
+```python
+# report text
+Logger.current_logger().report_text("hello, this is plain text")
+```
 
 ![image](../../img/examples_reporting_text.png)

docs/guides/ui/tuning_exp.md

@@ -16,7 +16,9 @@ example script.
 
 In the `examples/frameworks/pytorch` directory, run the experiment script:
 
-    python pytorch_mnist.py
+```commandline
+python pytorch_mnist.py
+```
 
 ## Step 2: Clone the Experiment
 
@@ -42,15 +44,12 @@ To demonstrate tuning, change two hyperparameter values.
 
 ## Step 4: Run a Worker Daemon Listening to a Queue
 
-To execute the cloned experiment, use a worker that can run a worker daemon listening to a queue.
+To execute the cloned experiment, use a [ClearML Agent](../../fundamentals/agents_and_queues.md).
 
-:::note
-For more information about workers, worker daemons, and queues, see [Agents and queues](../../fundamentals/agents_and_queues.md).
-:::
-
-Run the worker daemon on the local development machine.
+Run the agent on the local development machine:
 1. Open a terminal session.
 1. Run the following `clearml-agent` command which runs a worker daemon listening to the `default` queue:
+
     ```
     clearml-agent daemon --queue default
     ```
@@ -119,5 +118,4 @@ To compare the original and tuned experiments:
 
 ## Next Steps
 
-* For more information about editing experiments, see [modify experiments](../../webapp/webapp_exp_tuning.md#modifying-experiments) 
-  in the User Interface section.
+* For more information about editing experiments, see [modifying experiments](../../webapp/webapp_exp_tuning.md#modifying-experiments).