Small edits (#144)

docs/clearml_agent.md

@@ -41,7 +41,7 @@ and [configuration options](configs/clearml_conf.md#agent-section).
 ## Installation
 
 :::note
-If **ClearML** was previously configured, follow [this](clearml_agent#adding-clearml-agent-to-a-configuration-file) to add 
+If **ClearML** was previously configured, follow [this](#adding-clearml-agent-to-a-configuration-file) to add 
 ClearML Agent specific configurations
 :::
 

docs/deploying_clearml/clearml_server_aws_ec2_ami.md

@@ -50,7 +50,7 @@ The minimum recommended amount of RAM is 8 GB. For example, a t3.large or t3a.la
 
 1. Open the AWS Marketplace for the [Allegro AI ClearML Server](https://aws.amazon.com/marketplace/pp/B085D8W5NM).
 1. In the heading area, click **Continue to Subscribe**.
-1. **On the Subscribe to software** page, click **Accept Terms**, and then click **Continue to Configuration**.
+1. On the **Subscribe to software** page, click **Accept Terms**, and then click **Continue to Configuration**.
 1. On the **Configure this software** page, complete the following:
 
     1. In the **Fulfillment Option** list, select **64-bit (x86) Amazon Machine Image (AMI)**.

docs/fundamentals/pipelines.md

@@ -155,7 +155,7 @@ def main(pickle_url, mock_parameter='mock'):
     X_train, X_test, y_train, y_test = step_two(data_frame)
     model = step_three(X_train, y_train)
  accuracy = 100 * step_four(model, X_data=X_test, Y_data=y_test)
- print(f‘Accuracy={accuracy}%’)
+ print(f"Accuracy={accuracy}%")
 ```
 
 Notice that the driver is the `main` function, calling ("launching") the different steps. Next we add the decorators over 
@@ -222,7 +222,7 @@ def main(pickle_url, mock_parameter='mock'):
     X_train, X_test, y_train, y_test = step_two(data_frame)
     model = step_three(X_train, y_train)
  accuracy = 100 * step_four(model, X_data=X_test, Y_data=y_test)
- print(f‘Accuracy={accuracy}%’)
+ print(f"Accuracy={accuracy}%")
 ```
 
 We wrap each pipeline component with `@PipelineDecorator.component`, and the main pipeline logic with 

docs/getting_started/mlops/mlops_best_practices.md

@@ -18,23 +18,23 @@ If you are afraid of clutter, use the archive option, and set up your own [clean
 
 ## Clone Tasks
 In order to define a Task in ClearML we have two options
-- Run the actual code with `task.init` call. This will create and auto-populate the Task in CleaML (including Git Repo/Python Packages/ Command line etc.).
+- Run the actual code with `task.init` call. This will create and auto-populate the Task in CleaML (including Git Repo / Python Packages / Command line etc.).
-- Register local/remote code repository with `clearml-task`. See [details](../../apps/clearml_task.md).
+- Register local / remote code repository with `clearml-task`. See [details](../../apps/clearml_task.md).
 
 Once we have a Task in ClearML, we can clone and edit its definitions in the UI, then launch it on one of our nodes with [ClearML Agent](../../clearml_agent.md).
 
 ## Advanced Automation
-- Create daily/weekly cron jobs for retraining best performing models on.
+- Create daily / weekly cron jobs for retraining best performing models on.
 - Create data monitoring & scheduling and launch inference jobs to test performance on any new coming dataset.
 - Once there are two or more experiments that run after another, group them together into a [pipeline](../../fundamentals/pipelines.md).
 
 ## 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.
 
 ## Scale Your Work
-Use [ClearML Agent](../../clearml_agent.md) to scale work. Install the agent machines (Remote or local) and manage
+Use [ClearML Agent](../../clearml_agent.md) to scale work. Install the agent machines (remote or local) and manage
 training workload with it. 
 
 Improve team collaboration by transparent resource monitoring, always know what is running where.

docs/guides/frameworks/fastai/fastai_with_tensorboard.md

@@ -1,5 +1,5 @@
 ---
-title: Fastai
+title: FastAI
 ---
 The [fastai_with_tensorboard.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/fastai/fastai_with_tensorboard.py) 
 example demonstrates the integration of **ClearML** into code that uses fastai and TensorBoard. 

docs/guides/frameworks/megengine/megengine_mnist.md

@@ -1,5 +1,5 @@
 ---
-title: MegEngine MNIST
+title: MegEngine
 ---
 
 The [megengine_mnist.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/megengine/megengine_mnist.py) 

docs/guides/reporting/html_reporting.md

@@ -15,12 +15,14 @@ When the script runs, it creates an experiment named `html samples reporting`, w
 
 ## Reporting HTML URLs
 
-Report HTML by URL, using the `Logger.report_media` method `url` parameter.
+Report HTML by URL, using the [Logger.report_media](../../references/sdk/logger.md#report_media) method's `url` parameter.
 
 See the example script's [report_html_url](https://github.com/allegroai/clearml/blob/master/examples/reporting/html_reporting.py#L16) 
 function, which reports the **ClearML** documentation's home page.
 
-    Logger.current_logger().report_media("html", "url_html", iteration=iteration, url="https://allegro.ai/docs/index.html")
+```python
+Logger.current_logger().report_media("html", "url_html", iteration=iteration, url="https://clear.ml/docs")
+```
 
 ## Reporting HTML Local Files
 

docs/guides/reporting/image_reporting.md

@@ -10,10 +10,10 @@ demonstrates reporting (uploading) images in several formats, including:
 * PIL Image objects
 * Local files. 
 
-**ClearML** uploads images to the bucket specified in the **ClearML** configuration file 
+ClearML uploads images to the bucket specified in the ClearML [configuration file](../../configs/clearml_conf.md), 
-or **ClearML** can be configured for image storage, see [Logger.set_default_upload_destination](../../references/sdk/logger.md#set_default_upload_destination) 
+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](../../fundamentals/artifacts.md#setting-upload-destination) is different). Set credentials for 
-storage in the **ClearML** configuration file.
+storage in the ClearML configuration file.
 
 When the script runs, it creates an experiment named `image reporting`, which is associated with the `examples` project.
 
@@ -48,7 +48,7 @@ Logger.current_logger().report_image(
 )
 ```
 
-**ClearML** reports these images as debug samples in the **ClearML Web UI** **>** experiment details **>** **RESULTS** tab
+ClearML reports these images as debug samples in the **ClearML Web UI** **>** experiment details **>** **RESULTS** tab
 **>** **DEBUG SAMPLES** sub-tab. 
 
 ![image](../../img/examples_reporting_07.png)

docs/guides/reporting/manual_matplotlib_reporting.md

@@ -5,8 +5,8 @@ title: Manual Matplotlib Reporting
 The [matplotlib_manual_reporting.py](https://github.com/allegroai/clearml/blob/master/examples/reporting/matplotlib_manual_reporting.py) 
 example demonstrates reporting using Matplotlib and Seaborn with **ClearML**.
 
-When the script runs, it creates an experiment named "Manual Matplotlib example", which is associated with the 
+When the script runs, it creates an experiment named `Manual Matplotlib example`, which is associated with the 
-examples project.
+`examples` project.
 
 The Matplotlib figure reported by calling the [Logger.report_matplotlib_figure](../../references/sdk/logger.md#report_matplotlib_figure)
 method appears in **RESULTS** **>** **PLOTS**.

docs/hyperdatasets/overview.md

@@ -7,7 +7,7 @@ through parametrized data access and meta-data version control.
 
 The basic premise is that a user-formed query is a full representation of the dataset used by the ML/DL process. 
 
-ClearML Enterprise's hyperdatasets supports rapid prototyping, creating new opportunities such as: 
+ClearML Enterprise's Hyper-Datasets supports rapid prototyping, creating new opportunities such as: 
 * Hyperparameter optimization of the data itself
 * QA/QC pipelining
 * CD/CT (continuous training) during deployment
@@ -28,7 +28,7 @@ These components interact in a way that enables revising data and tracking and a
 
 Frames are the basics units of data in ClearML Enterprise. SingleFrames and FrameGroups make up a Dataset version. 
 Dataset versions can be created, modified, and removed. The different version are recorded and available, 
-so experiments and their data are reproducible and traceable. 
+so experiments, and their data are reproducible and traceable. 
 
 Lastly, Dataviews manage views of the dataset with queries, so the input data to an experiment can be defined from a 
 subset of a Dataset or combinations of Datasets.

sidebars.js

@@ -67,11 +67,11 @@ module.exports = {
             {'Docker': ['guides/docker/extra_docker_shell_script']},
             {'Frameworks': [
                 {'Autokeras': ['guides/frameworks/autokeras/integration_autokeras', 'guides/frameworks/autokeras/autokeras_imdb_example']},
-                {'FastAI': ['guides/frameworks/fastai/fastai_with_tensorboard']},
+                'guides/frameworks/fastai/fastai_with_tensorboard',
                 {'Keras': ['guides/frameworks/keras/jupyter', 'guides/frameworks/keras/keras_tensorboard']},
-                {'LightGBM': ['guides/frameworks/lightgbm/lightgbm_example']},
+                'guides/frameworks/lightgbm/lightgbm_example',
-                {'Matplotlib': ['guides/frameworks/matplotlib/matplotlib_example']},
+                'guides/frameworks/matplotlib/matplotlib_example',
-                {'MegEngine':['guides/frameworks/megengine/megengine_mnist']},
+                'guides/frameworks/megengine/megengine_mnist',
                 {'PyTorch':
                         ['guides/frameworks/pytorch/pytorch_distributed_example', 'guides/frameworks/pytorch/pytorch_matplotlib',
                         'guides/frameworks/pytorch/pytorch_mnist', 'guides/frameworks/pytorch/pytorch_tensorboard', 'guides/frameworks/pytorch/pytorch_tensorboardx',
@@ -85,14 +85,14 @@ module.exports = {
                             ]
                 },
                 {'PyTorch Ignite': ['guides/frameworks/pytorch ignite/integration_pytorch_ignite', 'guides/frameworks/pytorch ignite/pytorch_ignite_mnist']},
-                {'PyTorch Lightning': ['guides/frameworks/pytorch_lightning/pytorch_lightning_example']},
+                'guides/frameworks/pytorch_lightning/pytorch_lightning_example',
                 {'Scikit-Learn': ['guides/frameworks/scikit-learn/sklearn_joblib_example', 'guides/frameworks/scikit-learn/sklearn_matplotlib_example']},
                 {'TensorBoardX': ['guides/frameworks/tensorboardx/tensorboardx', "guides/frameworks/tensorboardx/video_tensorboardx"]},
                 {
                     'Tensorflow': ['guides/frameworks/tensorflow/tensorboard_pr_curve', 'guides/frameworks/tensorflow/tensorboard_toy',
                         'guides/frameworks/tensorflow/tensorflow_mnist', 'guides/frameworks/tensorflow/integration_keras_tuner']
                 },
-                {'XGboost': ['guides/frameworks/xgboost/xgboost_sample']}
+                'guides/frameworks/xgboost/xgboost_sample'
             ]},
             {'IDEs': ['guides/ide/remote_jupyter_tutorial', 'guides/ide/integration_pycharm', 'guides/ide/google_colab']},
             {'Offline Mode':['guides/set_offline']},