Small changes (#113)

docs/clearml_data/clearml_data_cli.md

@@ -18,8 +18,8 @@ Creates a new dataset. <br/>
 
 |Name|Description|Optional|
 |---|---|---|
-|`--name` |Dataset's name`| <img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" /> |
+|`--name` |Dataset's name| <img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" /> |
-|`--project`|Dataset's project`| <img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" /> |
+|`--project`|Dataset's project| <img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" /> |
 |`--parents`|IDs of the dataset's parents. The dataset inherits all of its parents' content. Multiple parents can be entered, but they are merged in the order they were entered| <img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" /> |
 |`--tags` |Dataset user tags. The dataset can be labeled, which can be useful for organizing datasets| <img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
 

docs/clearml_data/clearml_data_sdk.md

@@ -74,14 +74,14 @@ most recent dataset in the specified project, or the most recent dataset with th
 
 Once a specific dataset object has been obtained, get a local copy of the dataset using one of the following options:
 * [`Dataset.get_local_copy()`](../references/sdk/dataset.md#get_local_copy) - get a read-only local copy of an entire dataset. 
-  This method returns a path to the dataset in local cache (downloading the dataset if it is not already in cache)
+  This method returns a path to the dataset in local cache (downloading the dataset if it is not already in cache).
 * [`Dataset.get_mutable_local_copy()`](../references/sdk/dataset.md#get_mutable_local_copy) - get a writable local copy 
 of an entire dataset. This method downloads the dataset to a specific folder (non-cached), specified with the `target_folder` parameter. If 
 the specified folder already has contents, specify whether to overwrite its contents with the dataset contents, using the `overwrite` parameter.
 
 ## Modifying Datasets
 
-Once a dataset has been created, its contents can be modified and replaced. When your data is changes, you can 
+Once a dataset has been created, its contents can be modified and replaced. When your data is changed, you can 
 add updated files or remove unnecessary files. 
 
 ### add_files()

docs/clearml_data/data_management_examples/data_man_cifar_classification.md

@@ -83,7 +83,10 @@ dataset_project = "dataset_examples"
 
 from clearml import Dataset
 
-dataset_path = Dataset.get(dataset_name=dataset_name, dataset_project=dataset_project).get_local_copy()
+dataset_path = Dataset.get(
+    dataset_name=dataset_name, 
+    dataset_project=dataset_project
+).get_local_copy()
 
 trainset = datasets.CIFAR10(
     root=dataset_path,

docs/clearml_data/data_management_examples/data_man_folder_sync.md

@@ -8,10 +8,8 @@ This example shows how to use the `clearml-data` folder sync function.
 from time to time. When the point of truth is updated, users can call `clearml-data sync` and the 
 changes (file addition, modification, or removal) will be reflected in ClearML.
 
-## Creating Initial Version
-
 ## Prerequisites
-1. First, make sure that you have cloned the [clearml](https://github.com/allegroai/clearml) repository. This contains all
+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

docs/clearml_data/data_management_examples/data_man_python.md

@@ -24,7 +24,9 @@ We first need to obtain a local copy of the CIFAR dataset.
  from clearml import StorageManager
 
  manager = StorageManager()
- dataset_path = manager.get_local_copy(remote_url="https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz")
+ dataset_path = manager.get_local_copy(
+     remote_url="https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
+ )
 ```
 
 This script downloads the data and `dataset_path` contains the path to the downloaded data. 
@@ -83,12 +85,19 @@ demonstrates data ingestion using the dataset created in the first script.
 dataset_name = "cifar_dataset"
 dataset_project = "dataset_examples"
 
-dataset_path = Dataset.get(dataset_name=dataset_name, dataset_project=dataset_project).get_local_copy()
+dataset_path = Dataset.get(
+    dataset_name=dataset_name, 
+    dataset_project=dataset_project
+).get_local_copy()
 ```
 
 The script above gets the dataset and uses the [`Dataset.get_local_copy`](../../references/sdk/dataset.md#get_local_copy) 
-method to return a path to the cached, read-only local dataset. If you need a modifiable copy of the dataset, 
+method to return a path to the cached, read-only local dataset. 
-use `Dataset.get(dataset_name, dataset_project).get_mutable_local_copy(path/to/download)`
+
+If you need a modifiable copy of the dataset, use the following code: 
+```python
+Dataset.get(dataset_name, dataset_project).get_mutable_local_copy("path/to/download")
+```
 
 The script then creates a neural network to train a model to classify images from the dataset that was
 created above.

docs/clearml_data/data_management_examples/data_man_simple.md

@@ -5,7 +5,7 @@ title: Data Management from CLI
 In this example we'll create a simple dataset and demonstrate basic actions on it, using the `clearml-data` CLI. 
 
 ## Prerequisites
-1. First, make sure that you have cloned the [clearml](https://github.com/allegroai/clearml) repository. This contains all
+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
    
@@ -89,13 +89,13 @@ clearml-data - Dataset Management & Versioning CLI
 
 List dataset content: 24d05040f3e14fbfbed8edb1bf08a88c 
 Listing dataset content
-file name                                                        | size       | hash                                                            
+file name                        | size       | hash                                                            
-------------------------------------------------------------------------------------------------------------------------------------------------
+-----------------------------------------------------------------------------------------------------------------
-dancing.jpg                                                      |     40,484 | 78e804c0c1d54da8d67e9d072c1eec514b91f4d1f296cdf9bf16d6e54d63116a
+dancing.jpg                      |     40,484 | 78e804c0c1d54da8d67e9d072c1eec514b91f4d1f296cdf9bf16d6e54d63116a
-data.csv                                                         |     21,440 | b618696f57b822cd2e9b92564a52b3cc93a2206f41df3f022956bb6cfe4e7ad5
+data.csv                         |     21,440 | b618696f57b822cd2e9b92564a52b3cc93a2206f41df3f022956bb6cfe4e7ad5
-picasso.jpg                                                      |    114,573 | 6b3c67ea9ec82b09bd7520dd09dad2f1176347d740fd2042c88720e780691a7c
+picasso.jpg                      |    114,573 | 6b3c67ea9ec82b09bd7520dd09dad2f1176347d740fd2042c88720e780691a7c
-sample.json                                                      |        132 | 9c42a9a978ac7a71873ebd5c65985e613cfaaff1c98f655af0d2ee0246502fd7
+sample.json                      |        132 | 9c42a9a978ac7a71873ebd5c65985e613cfaaff1c98f655af0d2ee0246502fd7
-sample.mp3                                                       |     72,142 | fbb756ae14005420ff00ccdaff99416bebfcea3adb7e30963a69e68e9fbe361b
+sample.mp3                       |     72,142 | fbb756ae14005420ff00ccdaff99416bebfcea3adb7e30963a69e68e9fbe361b
 Total 5 files, 248771 bytes
 ```
 

docs/clearml_data/data_management_examples/workflows.md

@@ -4,9 +4,9 @@ title: Workflows
 
 Take a look at the ClearML Data examples which demonstrate common workflows using the `clearml-data` CLI and the 
 `Dataset` class:
-* [Dataset Management with CLI](data_man_simple.md) - Tutorial for creating, modifying, and consuming dataset with CLI
+* [Dataset Management with CLI](data_man_simple.md) - Tutorial for creating, modifying, and consuming dataset with CLI.
 * [Folder Sync with CLI](data_man_folder_sync.md) - Tutorial for using `clearml-data sync` CLI option to update a dataset according 
   to a local folder.
 * [Dataset Management with CLI and SDK](data_man_cifar_classification.md) - Tutorial for creating a dataset with the CLI
-  then programmatically ingesting the data with the SDK
+  then programmatically ingesting the data with the SDK.
 * [Data Management with Python](data_man_python.md) - Example scripts for creating and consuming a dataset with the SDK. 

docs/getting_started/mlops/mlops_best_practices.md

@@ -30,8 +30,8 @@ Once we have a Task in ClearML, we can clone and edit its definitions in the UI,
 
 ## Manage Your Data
 Use [ClearML Data](../../clearml_data/clearml_data.md) to version your data, then link it to running experiments for easy reproduction.
-Make datasets machine agnostic (i.e. store original dataset in a shared storage location, e.g. shared-folder/S3/Gs/Azure)
+Make datasets machine agnostic (i.e. store original dataset in a shared storage location, e.g. shared-folder/S3/Gs/Azure).
-ClearML Data supports efficient Dataset storage and caching, differentiable & compressed
+ClearML Data supports efficient Dataset storage and caching, differentiable & compressed.
 
 ## Scale Your Work
 Use [ClearML Agent](../../clearml_agent.md) to scale work. Install the agent machines (Remote or local) and manage

docs/getting_started/mlops/mlops_first_steps.md

@@ -121,7 +121,12 @@ Log as many metrics from your processes! It improves visibility on their progres
 Use the Logger class from to report scalars and plots.
 ```python
 from clearml import Logger
-Logger.current_logger().report_scalar(graph='metric', series='variant', value=13.37, iteration=counter)
+Logger.current_logger().report_scalar(
+    graph='metric', 
+    series='variant', 
+    value=13.37, 
+    iteration=counter
+)
 ```
 
 You can later analyze reported scalars
@@ -139,7 +144,11 @@ You can also search and query Tasks in the system.
 Use the `Task.get_tasks` call to retrieve Tasks objects and filter based on the specific values of the Task - status, parameters, metrics and more!
 ```python
 from clearml import Task
-tasks = Task.get_tasks(project_name='examples', task_name='partial_name_match', task_filter={'status': 'in_progress'})
+tasks = Task.get_tasks(
+    project_name='examples', 
+    task_name='partial_name_match', 
+    task_filter={'status': 'in_progress'}
+)
 ```
 
 ## Manage Your Data

docs/guides/datasets/data_man_python.md

@@ -83,12 +83,19 @@ demonstrates data ingestion using the dataset created in the first script.
 dataset_name = "cifar_dataset"
 dataset_project = "dataset_examples"
 
-dataset_path = Dataset.get(dataset_name=dataset_name, dataset_project=dataset_project).get_local_copy()
+dataset_path = Dataset.get(
+    dataset_name=dataset_name, 
+    dataset_project=dataset_project
+).get_local_copy()
 ```
 
 The script above gets the dataset and uses the [`Dataset.get_local_copy`](../../references/sdk/dataset.md#get_local_copy) 
-method to return a path to the cached, read-only local dataset. If you need a modifiable copy of the dataset, 
+method to return a path to the cached, read-only local dataset. 
-use `Dataset.get(dataset_name, dataset_project).get_mutable_local_copy(path/to/download)`
+
+If you need a modifiable copy of the dataset, use the following: 
+```python
+Dataset.get(dataset_name, dataset_project).get_mutable_local_copy("path/to/download")
+```
 
 The script then creates a neural network to train a model to classify images from the dataset that was
 created above.