Small edits (#870)

docs/clearml_data/data_management_examples/data_man_simple.md

@@ -7,7 +7,7 @@ In this example we'll create a simple dataset and demonstrate basic actions on i
 ## Prerequisites
 1. First, make sure that you have cloned the [clearml](https://github.com/allegroai/clearml) repository. It contains all
 the needed files.
-1. Open terminal and change directory to the cloned repository's examples folder
+1. Open terminal and change directory to the cloned repository's examples folder:
    
     ```
     cd clearml/examples/reporting
@@ -140,7 +140,7 @@ Using ClearML Data, you can create child datasets that inherit the content of ot
    1 files removed
    ```
 
-1. Close and finalize the dataset
+1. Close and finalize the dataset:
 
    ```bash
    clearml-data close

docs/faq.md

@@ -241,7 +241,8 @@ To replace the URL of each model, execute the following commands:
     sudo docker exec -it clearml-mongo /bin/bash
     ```
 
-1. Create the following script inside the Docker shell (as well as the URL protocol if you aren't using `s3`):   
+1. Create the following script inside the Docker shell (as well as the URL protocol if you aren't using `s3`).
+   Make sure to replace `<old-bucket-name>` and `<new-bucket-name>`.   
 
     ```bash
     cat <<EOT >> script.js
@@ -250,7 +251,6 @@ To replace the URL of each model, execute the following commands:
     db.model.save(e);});
     EOT 
     ```
-    Make sure to replace `<old-bucket-name>` and `<new-bucket-name>`.
 
 1. Run the script against the backend DB:
 
@@ -273,7 +273,7 @@ To fix this, the registered URL of each model needs to be replaced with its curr
    sudo docker exec -it clearml-mongo /bin/bash
    ```
 
-1. Create the following script inside the Docker shell:  
+1. Create the following script inside the Docker shell (Make sure to replace `<old-bucket-name>` and `<new-bucket-name>`, as well as the URL protocol prefixes if you aren't using S3):  
     ```bash
     cat <<EOT >> script.js
     db.model.find({uri:{$regex:/^s3/}}).forEach(function(e,i) {
@@ -281,7 +281,6 @@ To fix this, the registered URL of each model needs to be replaced with its curr
     db.model.save(e);});
     EOT 
     ```
-   Make sure to replace `<old-bucket-name>` and `<new-bucket-name>`, as well as the URL protocol prefixes if you aren't using S3.
 1. Run the script against the backend DB:
 
     ```bash

docs/hyperdatasets/dataset.md

@@ -94,37 +94,37 @@ myDataset = DatasetVersion.get_current(dataset_name='myDataset')
 
 ### Deleting Datasets
 
-Use the [`Dataset.delete`](../references/hyperdataset/hyperdataset.md#datasetdelete) method to delete a Dataset.
+Use the [`Dataset.delete`](../references/hyperdataset/hyperdataset.md#datasetdelete) class method to delete a Dataset:
 
-Delete an empty Dataset (no versions).
+* Delete an empty Dataset (no versions):
 
-```python
-Dataset.delete(dataset_name='MyDataset', delete_all_versions=False, force=False)
-```
+  ```python
+  Dataset.delete(dataset_name='MyDataset', delete_all_versions=False, force=False)
+  ```
 
-Delete a Dataset containing only versions whose status is *Draft*.
+* Delete a Dataset containing only versions whose status is *Draft*:
 
-```python
-Dataset.delete(dataset_name='MyDataset', delete_all_versions=True, force=False)
-```    
+  ```python
+  Dataset.delete(dataset_name='MyDataset', delete_all_versions=True, force=False)
+  ```    
 
-Delete a Dataset even if it contains versions whose status is *Published*.
+* Delete a Dataset even if it contains versions whose status is *Published*:
 
-```python
-Dataset.delete(dataset_name='MyDataset', delete_all_versions=True, force=True)
-```
+  ```python
+  Dataset.delete(dataset_name='MyDataset', delete_all_versions=True, force=True)
+  ```
   
-Delete a Dataset and the sources associated with its deleted frames:
+* Delete a Dataset and the sources associated with its deleted frames:
 
-```python
-Dataset.delete(
-  dataset_name='MyDataset', delete_all_versions=True, force=True, delete_sources=True
-)
-```
+  ```python
+  Dataset.delete(
+    dataset_name='MyDataset', delete_all_versions=True, force=True, delete_sources=True
+  )
+  ```
 
-This supports deleting sources located in AWS S3, GCP, and Azure Storage (not local storage). The `delete_sources` 
-parameter is ignored if `delete_all_versions` is `False`. You can view the deletion process' progress by passing 
-`show_progress=True` (`tqdm` required).  
+  This supports deleting sources located in AWS S3, GCP, and Azure Storage (not local storage). The `delete_sources` 
+  parameter is ignored if `delete_all_versions` is `False`. You can view the deletion process' progress by passing 
+  `show_progress=True` (`tqdm` required).  
 
 ### Tagging Datasets
 

docs/integrations/ignite.md

@@ -38,22 +38,22 @@ during training and validation.
 Integrate ClearML with the following steps:
 1. Create a `ClearMLLogger` object:
 
-  ```python
-  from ignite.contrib.handlers.clearml_logger import *
+   ```python
+   from ignite.contrib.handlers.clearml_logger import *
 
-  clearml_logger = ClearMLLogger(task_name="ignite", project_name="examples")
-  ```
-
-  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. 
+   clearml_logger = ClearMLLogger(task_name="ignite", project_name="examples")
+   ```
+   
+   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. 
   
-  You can also pass the following parameters to the `ClearMLLogger` object:
-  * `task_type` – The type of experiment (see [task types](../fundamentals/task.md#task-types)).
-  * `report_freq` – The histogram processing frequency (handles histogram values every X calls to the handler). Affects 
-    `GradsHistHandler` and `WeightsHistHandler` (default: 100).
-  * `histogram_update_freq_multiplier` – The histogram report frequency (report first X histograms and once every X 
-    reports afterwards) (default: 10).
-  * `histogram_granularity` - Histogram sampling granularity (default: 50).
+   You can also pass the following parameters to the `ClearMLLogger` object:
+   * `task_type` – The type of experiment (see [task types](../fundamentals/task.md#task-types)).
+   * `report_freq` – The histogram processing frequency (handles histogram values every X calls to the handler). Affects 
+     `GradsHistHandler` and `WeightsHistHandler` (default: 100).
+   * `histogram_update_freq_multiplier` – The histogram report frequency (report first X histograms and once every X 
+     reports afterwards) (default: 10).
+   * `histogram_granularity` - Histogram sampling granularity (default: 50).
 
 1. Attach the `ClearMLLogger` to output handlers to log metrics: