Update webapp images (#200)

docs/clearml_agent.md

@@ -643,7 +643,7 @@ It's possible to add the Docker container as the base Docker image to a Task (ex
 
 - Using the **ClearML Web UI** - See [Base Docker image](webapp/webapp_exp_tuning.md#base-docker-image) on the "Tuning
   Experiments" page.
-- In the **ClearML** configuration file - Use the **ClearML** configuration file [agent.default_docker](configs/clearml_conf.md#agentdefault_docker)
+- In the ClearML configuration file - Use the ClearML configuration file [agent.default_docker](configs/clearml_conf.md#agentdefault_docker)
   options.
 
 ## Google Colab

docs/clearml_sdk.md

@@ -3,7 +3,7 @@ title: ClearML SDK
 ---
 
 The **ClearML Python Package** supports the [automatic logging](fundamentals/logger.md#automatic-reporting) 
-that documents the experiment for you, and an extensive set of powerful features and functionality you can use to improve experimentation, other workflows, and get more out of **ClearML**. 
+that documents the experiment for you, and an extensive set of powerful features and functionality you can use to improve experimentation, other workflows, and get more out of ClearML. 
 
 The **ClearML Python Package** collects data from scripts including the Git repository (branch, commit ID, and uncommitted changes), working directory and entry point, hyperparameters, initial weights model, model snapshots (checkpoints), output model, other artifacts, metrics, logs, other reported data (from libraries and visualization toolkits), and debug samples. 
 
@@ -13,9 +13,9 @@ In conjunction with the **ClearML Hosted Service** (or self-hosted **ClearML Ser
 
 * [Task](references/sdk/task.md) - The `task` module contains the `task.Task` class which is the code template for all `Task` features and functionality, including collecting data from scripts, storing that data in a `Task` object, automatic bindings with frameworks (TensorFlow/TensorBoard, PyTorch, Keras, Fastai, scikit-learn), libraries (Pandas, Plotly, AutoKeras), and visualization tools (Matplotlib, Seaborn), and a robust set of methods for Task execution, cloning, connecting parameter dictionaries, configurations, models, working with storage, and more.
 
-* [Logger](references/sdk/logger.md) - The `logger` module contains the `logger.Logger` class which is the **ClearML** console log and metric statistics interface, and contains methods for explicit reporting, setting an upload destination in storage for debug samples, logger cache control, and TensorBoard support in addition to **ClearML** automatic TensorBoard logging.
+* [Logger](references/sdk/logger.md) - The `logger` module contains the `logger.Logger` class which is the ClearML console log and metric statistics interface, and contains methods for explicit reporting, setting an upload destination in storage for debug samples, logger cache control, and TensorBoard support in addition to ClearML automatic TensorBoard logging.
 
-* [Model](references/sdk/model_model.md) - The `model` module contains three classes: `model.Model` which represents an existing model in **ClearML** that can be loaded and connected to a Task, `model.InputModel` which represents an existing model that you can load into **ClearML**, and `model.OutputModel` which represents the experiment output model that is always connected to the Task.
+* [Model](references/sdk/model_model.md) - The `model` module contains three classes: `model.Model` which represents an existing model in ClearML that can be loaded and connected to a Task, `model.InputModel` which represents an existing model that you can load into ClearML, and `model.OutputModel` which represents the experiment output model that is always connected to the Task.
 
 * [Automation](references/sdk/automation_controller_pipelinecontroller.md) - The `automation` module contains classes supporting hyperparameter optimization, including Optuna, HpBandSter, grid searching, random searching, you own customized search strategies, and resource budgeting for searches; the AWS autoscaler; pipeline controllers; and Task monitoring.  
 
@@ -25,5 +25,5 @@ In conjunction with the **ClearML Hosted Service** (or self-hosted **ClearML Ser
 
 ## Examples 
 
-**ClearML** example scripts in the [examples folder](https://github.com/allegroai/clearml/tree/master/examples) of the `clearml` GitHub repository. They are pre-loaded in the **ClearML Hosted Service**, and can be viewed, cloned, and edited in the  **ClearML Web UI**, `ClearML Examples` project. The examples are each explained in this documentation's [examples section](guides/main.md). 
+ClearML example scripts in the [examples folder](https://github.com/allegroai/clearml/tree/master/examples) of the `clearml` GitHub repository. They are pre-loaded in the **ClearML Hosted Service**, and can be viewed, cloned, and edited in the  **ClearML Web UI**, `ClearML Examples` project. The examples are each explained in this documentation's [examples section](guides/main.md). 
 

docs/deploying_clearml/clearml_config_for_clearml_server.md

@@ -56,18 +56,18 @@ by setting [configuration options](../configs/clearml_conf.md).
    1. Get ClearML credentials. Open the ClearML Web UI in a browser. On the **SETTINGS > WORKSPACE** page, click 
       **Create new credentials** **>** **Copy to clipboard**.
     
-   1. At the command prompt `Paste copied configuration here:`, copy and paste the **ClearML** credentials.
+   1. At the command prompt `Paste copied configuration here:`, copy and paste the ClearML credentials.
         
         The setup wizard confirms the credentials. 
 
             Detected credentials key="********************" secret="*******"
 
-   1. Enter the **ClearML Server** web server URL, or press **Enter** to accept the default which is detected from the 
+   1. Enter the ClearML Server web server URL, or press **Enter** to accept the default which is detected from the 
       credentials.
     
             WEB Host configured to: [https://app.<your-domain>] 
     
-   1. Enter the **ClearML Server** API server URL, or press **Enter** to accept the default value which is based on the previous response:
+   1. Enter the ClearML Server API server URL, or press **Enter** to accept the default value which is based on the previous response:
     
             API Host configured to: [https://api.<your-domain>] 
     
@@ -110,7 +110,7 @@ These may be localhost, the domain, or a sub-domain of the domain.
         
 **To add ClearML settings to an existing ClearML configuration file:**
         
-1. Open the **ClearML** configuration file for editing. Depending upon the operating system, it is:
+1. Open the ClearML configuration file for editing. Depending upon the operating system, it is:
 
     * Linux - `~/clearml.conf`
     * macOS - `$HOME/clearml.conf`
@@ -131,4 +131,4 @@ These may be localhost, the domain, or a sub-domain of the domain.
 ```
 
 
-1. Save the **ClearML** configuration file. **ClearML** is now configured for the **ClearML Server**.
+1. Save the ClearML configuration file. ClearML is now configured for the **ClearML Server**.

docs/deploying_clearml/clearml_server_aws_ec2_ami.md

@@ -90,7 +90,7 @@ Once deployed, **ClearML Server** exposes the following services:
 
 ### Logging in to the Web-App (UI)
 
-**To log in to the **ClearML** Web-App (UI):**
+**To log in to the ClearML Web-App (UI):**
 
 * If **ClearML Server** was launched from an AWS Community AMI, enter any name.
 * If **ClearML Server** was launched through the AWS Marketplace, enter the preconfigured default login credentials, which 

docs/deploying_clearml/clearml_server_config.md

@@ -235,7 +235,7 @@ To open external access to the Elasticsearch, MongoDB, and Redis ports:
 ### Web Login Authentication
 
 Web login authentication can be configured in the **ClearML Server** in order to permit only users provided 
-with credentials to access the **ClearML** system. Those credentials are a username and password. 
+with credentials to access the ClearML system. Those credentials are a username and password. 
 
 Without web login authentication, **ClearML Server** does not restrict access (by default).
 

docs/deploying_clearml/clearml_server_gcp.md

@@ -2,7 +2,7 @@
 title: Google Cloud Platform
 ---
 
-Deploy **ClearML Server** on the Google Cloud Platform (GCP) using one of the pre-built GCP Custom Images. **ClearML** 
+Deploy **ClearML Server** on the Google Cloud Platform (GCP) using one of the pre-built GCP Custom Images. ClearML 
 provides custom images for each released version of **ClearML Server**. For a list of the pre-built custom images, see 
 [ClearML Server GCP Custom Image](#clearml-server-gcp-custom-image). 
 

docs/deploying_clearml/clearml_server_kubernetes_helm.md

@@ -129,13 +129,13 @@ We recommend using the container ports (``8080``, ``8008``, and ``8081``), or a
 
     For example, `clearml.app.mydomainname.com` points to your node on port `30080`.
     
-    * The record to access the **ClearML** API:
+    * The record to access the ClearML API:
 
             *api.<your domain name>.* 
 
     For example, `clearml.api.mydomainname.com` points to your node on port `30008`.
     
-    * The record to access the **ClearML** file server:
+    * The record to access the ClearML file server:
 
             *files.<your domain name>.*
 

docs/fundamentals/artifacts.md

@@ -74,7 +74,7 @@ This property makes Models a standalone entry that can be used as an artifactory
 When models are saved using certain frameworks (for instance, by calling the `torch.save()` method), ClearML automatically 
 logs the models and all snapshot paths.
 
-![image](../img/fundamentals_artifacts_logging_models.png)
+![image](../img/examples_model_update_model.png)
 
 See automatic model logging examples: 
 * [TF](../guides/frameworks/tensorflow/tensorflow_mnist.md)

docs/fundamentals/hpo.md

@@ -46,7 +46,7 @@ The diagram above demonstrates the typical flow of hyperparameter optimization w
 
 ### Supported Optimizers
 
-The `HyperParameterOptimizer` class contains **ClearML**’s hyperparameter optimization modules. Its modular design enables 
+The `HyperParameterOptimizer` class contains ClearML’s hyperparameter optimization modules. Its modular design enables 
 using different optimizers, including existing software frameworks, enabling simple, accurate, and fast hyperparameter 
 optimization.
 

docs/getting_started/architecture.md

@@ -2,7 +2,7 @@
 title: ClearML Modules
 ---
 
-- **ClearML Python Package** (clearml) for integrating **ClearML** into your existing code-base.
+- **ClearML Python Package** (clearml) for integrating ClearML into your existing code-base.
 - **ClearML Server** (clearml-server) storing experiment, model, and workflow data, and supporting the Web UI experiment manager. It is also the control plane for the MLOps.
 - **ClearML Agent** (clearml-agent) The MLOps orchestration agent. Enabling experiment and workflow reproducibility, and scalability.
 - **ClearML Data** (clearml-data) data management and versioning on top of file-systems/object-storage.

docs/getting_started/ds/ds_second_steps.md

@@ -150,7 +150,7 @@ Once everything is neatly logged and displayed, using the [comparison tool](../.
 
 The experiment table is a powerful tool for creating dashboards and views of your own projects, your team's projects, or the entire development.
 
-![image](../../img/webapp_exp_table_01.png)
+![image](../../img/webapp_experiment_table.png)
 
 
 ### Creating Leaderboards

docs/guides/advanced/execute_remotely.md

@@ -28,12 +28,12 @@ moved to be executed by a stronger machine.
 
 During the execution of the example script, the code does the following:
 * Uses ClearML's automatic and explicit logging.
-* Creates an experiment named `remote_execution pytorch mnist train`, which is associated with the `examples` project.
+* Creates an experiment named `Remote_execution PyTorch MNIST train`, which is associated with the `examples` project.
 
 
 ## Scalars
 
-In the example script's `train` function, the following code explicitly reports scalars to **ClearML**:
+In the example script's `train` function, the following code explicitly reports scalars to ClearML:
 
 ```python
 Logger.current_logger().report_scalar(
@@ -55,24 +55,24 @@ Logger.current_logger().report_scalar(
 These scalars can be visualized in plots, which appear in the ClearML web UI, in the experiment's
 page **>** **RESULTS** **>** **SCALARS**. 
 
-![image](../../img/examples_pytorch_mnist_07.png)
+![Experiment Scalars](../../img/examples_pytorch_mnist_07.png)
 
 ## Hyperparameters
 
 ClearML automatically logs command line options defined with `argparse`. They appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS** **>** **Args**.
 
-![image](../../img/examples_pytorch_mnist_01.png)
+![Experiment hyperparameters](../../img/examples_pytorch_mnist_01.png)
 
 ## Console
 
 Text printed to the console for training progress, as well as all other console output, appear in **RESULTS** **>** **CONSOLE**.
 
-![image](../../img/examples_pytorch_mnist_06.png)
+![Experiment console log](../../img/examples_pytorch_mnist_06.png)
 
 ## Artifacts
 
 Models created by the experiment appear in the experiment’s **ARTIFACTS** tab. ClearML automatically logs and tracks models
 and any snapshots created using PyTorch. 
 
-![image](../../img/examples_remote_execution_artifacts.png)
+![Experiment artifacts](../../img/examples_remote_execution_artifacts.png)
 

docs/guides/advanced/multiple_tasks_single_process.md

@@ -13,13 +13,13 @@ When the script is executed, it should return something like this:
 
 ```text
 ClearML Task: created new task id=5c4d2d3674a94e35b10f04d9d2180l62
-ClearML results page: https://app.community.clear.ml/projects/6835eb7316554c2b933b69638470fe02/experiments/5c4d2d3674a94e35b10f04d9d2180l62/output/log
+ClearML results page: https://app.clear.ml/projects/6835eb7316554c2b933b69638470fe02/experiments/5c4d2d3674a94e35b10f04d9d2180l62/output/log
 ...
 ClearML Task: created new task id=28a84c17a6204b438e1e7a094a234a7f
-ClearML results page: https://app.community.clear.ml/projects/7894eb7316554c4b933a79638473fe02/experiments/28a84c17a6204b438e1e7a094a234a7f/output/log
+ClearML results page: https://app.clear.ml/projects/7894eb7316554c4b933a79638473fe02/experiments/28a84c17a6204b438e1e7a094a234a7f/output/log
 ...
 ClearML Task: created new task id=6d1e253ba0234d32a38sg85013185g46
-ClearML results page: https://app.community.clear.ml/projects/7895eb7316554c4b933a69638470fe02/experiments/6d1e253ba0234d32a38sg85013185g46/output/log
+ClearML results page: https://app.clear.ml/projects/7895eb7316554c4b933a69638470fe02/experiments/6d1e253ba0234d32a38sg85013185g46/output/log
 ```
 
 Notice that three separate tasks with distinct IDs are created, and a link is provided to view the results of each one.  

docs/guides/automation/manual_random_param_search_example.md

@@ -12,7 +12,7 @@ This example accomplishes the automated random parameter search by doing the fol
 script. This experiment must be executed first, so it will be stored in the server, and then it can be accessed, cloned, 
    and modified by another Task. 
 1. Creating a parameter dictionary, which is connected to the Task by calling [Task.connect](../../references/sdk/task.md#connect) 
-   so that the parameters are logged by **ClearML**.
+   so that the parameters are logged by ClearML.
 1. Adding the random search hyperparameters and parameters defining the search (e.g., the experiment name, and number of 
    times to run the experiment).
 1. Creating a Task object referencing the template experiment, `Keras HP optimization base`. See [Task.get_task](../../references/sdk/task.md#taskget_task).

docs/guides/clearml-task/clearml_task_tutorial.md

@@ -43,7 +43,7 @@ Your output should look something like this:
 ```console
 New task created id=2f96ee95b05d4693b360d0fcbb26b727
 Task id=2f96ee95b05d4693b360d0fcbb26b727 sent for execution on queue default
-Execution log at: https://app.community.clear.ml/projects/552d5399112d47029c146d5248570295/experiments/2f96ee95b05d4693b360d0fcbb26b727/output/log
+Execution log at: https://app.clear.ml/projects/552d5399112d47029c146d5248570295/experiments/2f96ee95b05d4693b360d0fcbb26b727/output/log
 ```
 
 

docs/guides/distributed/distributed_pytorch_example.md

@@ -3,7 +3,7 @@ title: PyTorch Distributed
 ---
 
 The [pytorch_distributed_example.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/pytorch_distributed_example.py) 
-script demonstrates integrating **ClearML** into code that uses the [PyTorch Distributed Communications Package](https://pytorch.org/docs/stable/distributed.html) 
+script demonstrates integrating ClearML into code that uses the [PyTorch Distributed Communications Package](https://pytorch.org/docs/stable/distributed.html) 
 (`torch.distributed`). 
 
 The script initializes a main Task and spawns subprocesses, each for an instance of that Task. 
@@ -34,7 +34,7 @@ Task.current_task().upload_artifact(
 
 All of these artifacts appear in the main Task under **ARTIFACTS** **>** **OTHER**.
 
-![image](../../img/examples_pytorch_distributed_example_09.png)
+![Experiment artifacts](../../img/examples_pytorch_distributed_example_09.png)
 
 ## Scalars
 
@@ -54,11 +54,11 @@ Task.current_task().get_logger().report_scalar(
 
 The single scalar plot for loss appears in **RESULTS** **>** **SCALARS**.
 
-![image](../../img/examples_pytorch_distributed_example_08.png)
+![Experiment scalars](../../img/examples_pytorch_distributed_example_08.png)
 
 ## Hyperparameters
 
-**ClearML** automatically logs the argparse command line options. Since the [`Task.connect`](../../references/sdk/task.md#connect) 
+ClearML automatically logs the argparse command line options. Since the [`Task.connect`](../../references/sdk/task.md#connect) 
 method is called on [`Task.current_task`](../../references/sdk/task.md#taskcurrent_task), they are logged in the main Task. A different hyperparameter key is used in each 
 subprocess, so they do not overwrite each other in the main Task.
 
@@ -69,12 +69,12 @@ Task.current_task().connect(param)
 
 All the hyperparameters appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS**.
 
-![image](../../img/examples_pytorch_distributed_example_01.png)
+![Experiment hyperparameters Args](../../img/examples_pytorch_distributed_example_01.png)
 
-![image](../../img/examples_pytorch_distributed_example_01a.png)
+![Experiment hyperparameters General ](../../img/examples_pytorch_distributed_example_01a.png)
 
 ## Console
 
 Output to the console, including the text messages printed from the main Task object and each subprocess appear in **RESULTS** **>** **CONSOLE**.
 
-![image](../../img/examples_pytorch_distributed_example_06.png)
+![Experiment console log](../../img/examples_pytorch_distributed_example_06.png)

docs/guides/frameworks/autokeras/autokeras_imdb_example.md

@@ -20,7 +20,7 @@ which are titled **:monitor: machine**.
 
 ## Hyperparameters
 
-**ClearML** automatically logs TensorFlow Definitions. They appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS** **>** **TF_DEFINE**.
+ClearML automatically logs TensorFlow Definitions. They appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS** **>** **TF_DEFINE**.
 
 ![image](../../../img/examples_keras_16.png)
 

docs/guides/frameworks/autokeras/integration_autokeras.md

@@ -1,11 +1,11 @@
 ---
 title: AutoKeras Integration
 ---
-Integrate **ClearML** into code that uses [autokeras](https://github.com/keras-team/autokeras). Initialize a **ClearML** 
-Task in a code, and **ClearML** automatically logs scalars, plots, and images reported to TensorBoard, Matplotlib, Plotly, 
+Integrate ClearML into code that uses [autokeras](https://github.com/keras-team/autokeras). Initialize a ClearML 
+Task in a code, and ClearML automatically logs scalars, plots, and images reported to TensorBoard, Matplotlib, Plotly, 
 and Seaborn, and all other automatic logging, and explicit reporting added to the code (see [Logging](../../../fundamentals/logger.md)).
 
-**ClearML** allows to:
+ClearML allows to:
 
 * Visualize experiment results in the **ClearML Web UI**.
 * Track and upload models.
@@ -13,7 +13,7 @@ and Seaborn, and all other automatic logging, and explicit reporting added to th
 * Rerun experiments, reproduce experiments on any target machine, and tune experiments.
 * Compare experiments.
 
-See the [AutoKeras](autokeras_imdb_example.md) example, which shows **ClearML** automatically logging: 
+See the [AutoKeras](autokeras_imdb_example.md) example, which shows ClearML automatically logging: 
 * Scalars
 * Hyperparameters
 * The console log 
@@ -22,7 +22,7 @@ See the [AutoKeras](autokeras_imdb_example.md) example, which shows **ClearML**
 Once these are logged, they can be visualized in the **ClearML Web UI**.
  
 :::note
-If you are not already using **ClearML**, see [Getting Started](/getting_started/ds/best_practices.md).
+If you are not already using ClearML, see [Getting Started](/getting_started/ds/best_practices.md).
 :::
 
 ## Adding ClearML to Code
@@ -36,6 +36,6 @@ task = Task.init(project_name="myProject", task_name="myExperiment")
 When the code runs, it initializes a Task in **ClearML Server**. A hyperlink to the experiment's log is output to the console.
 
     CLEARML Task: created new task id=c1f1dc6cf2ee4ec88cd1f6184344ca4e
-    CLEARML results page: https://app.community.clear.ml/projects/1c7a45633c554b8294fa6dcc3b1f2d4d/experiments/c1f1dc6cf2ee4ec88cd1f6184344ca4e/output/log
+    CLEARML results page: https://app.clear.ml/projects/1c7a45633c554b8294fa6dcc3b1f2d4d/experiments/c1f1dc6cf2ee4ec88cd1f6184344ca4e/output/log
 
-Later in the code, define callbacks using TensorBoard, and **ClearML** logs TensorBoard scalars, histograms, and images.
+Later in the code, define callbacks using TensorBoard, and ClearML logs TensorBoard scalars, histograms, and images.

docs/guides/frameworks/fastai/fastai_with_tensorboard.md

@@ -2,16 +2,16 @@
 title: FastAI
 ---
 The [fastai_with_tensorboard_example.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/fastai-v1/fastai_with_tensorboard_example/fastai_with_tensorboard_example.py) 
-example demonstrates the integration of **ClearML** into code that uses fastai v1 and TensorBoard. 
+example demonstrates the integration of ClearML into code that uses fastai v1 and TensorBoard. 
 
 The example code does the following:
 1. Trains a simple deep neural network on the fastai built-in MNIST dataset (see the [fast.ai](https://fastai1.fast.ai) documentation).
-1. Uses the fastai `LearnerTensorboardWriter` callback, and **ClearML** automatically logs TensorBoard through the callback. 
+1. Uses the fastai `LearnerTensorboardWriter` callback, and ClearML automatically logs TensorBoard through the callback. 
 1. During script execution, creates an experiment named `fastai with tensorboard callback`, which is associated with the `examples` project.
 
 ## Scalars
 
-**ClearML** automatically logs the histogram output to TensorBoard. They appear in **RESULTS** **>** **PLOTS**.
+ClearML automatically logs the histogram output to TensorBoard. They appear in **RESULTS** **>** **PLOTS**.
 
 ![image](../../../img/examples_reporting_fastai_01.png)
 

docs/guides/frameworks/keras/jupyter.md

@@ -3,7 +3,7 @@ title: Keras with Matplotlib - Jupyter Notebook
 ---
 
 The [jupyter.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/keras/jupyter.ipynb) example 
-demonstrates **ClearML**'s automatic logging of code running in a Jupyter Notebook that uses Keras and Matplotlib. 
+demonstrates ClearML's automatic logging of code running in a Jupyter Notebook that uses Keras and Matplotlib. 
 
 The example does the following: 
 1. Trains a simple deep neural network on the Keras built-in [MNIST](https://keras.io/api/datasets/mnist/#load_data-function) 
@@ -39,7 +39,7 @@ The example calls Matplotlib methods to log debug sample images. They appear in
 
 ## Hyperparameters
 
-**ClearML** automatically logs TensorFlow Definitions. A parameter dictionary is logged by connecting it to the Task, by 
+ClearML automatically logs TensorFlow Definitions. A parameter dictionary is logged by connecting it to the Task, by 
 calling the [Task.connect](../../../references/sdk/task.md#connect) method. 
 
 ```python

docs/guides/frameworks/keras/keras_tensorboard.md

@@ -2,7 +2,7 @@
 title: Keras with TensorBoard
 ---
 
-The example below demonstrates the integration of **ClearML** into code which uses Keras and TensorBoard.
+The example below demonstrates the integration of ClearML into code which uses Keras and TensorBoard.
 View it in [script](https://github.com/allegroai/clearml/blob/master/examples/frameworks/keras/keras_tensorboard.py)
 or in [Jupyter Notebook](https://github.com/allegroai/clearml/blob/master/examples/frameworks/keras/jupyter_keras_TB_example.ipynb).
 
@@ -35,7 +35,7 @@ Histograms for layer density appear in **RESULTS** **>** **PLOTS**.
 
 ## Hyperparameters
 
-**ClearML** automatically logs command line options generated with `argparse`, and TensorFlow Definitions.
+ClearML automatically logs command line options generated with `argparse`, and TensorFlow Definitions.
 
 Command line options appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS** **>** **Args**.
 

docs/guides/frameworks/matplotlib/matplotlib_example.md

@@ -2,8 +2,8 @@
 title: Matplotlib
 ---
 
-The example below demonstrates integrating **ClearML** into code that uses `matplotlib` to plot scatter diagrams, and 
-show images. **ClearML** automatically logs the diagrams and images. 
+The example below demonstrates integrating ClearML into code that uses `matplotlib` to plot scatter diagrams, and 
+show images. ClearML automatically logs the diagrams and images. 
 
 View the example in [script](https://github.com/allegroai/clearml/blob/master/examples/frameworks/matplotlib/matplotlib_example.py) 
 or in [Jupyter Notebook](https://github.com/allegroai/clearml/blob/master/examples/frameworks/matplotlib/jupyter_matplotlib_example.ipynb).

docs/guides/frameworks/pytorch/notebooks/audio/audio_classification_UrbanSound8K.md

@@ -2,7 +2,7 @@
 title: Audio Classification - Jupyter Notebooks
 ---
 
-The example [audio_classification_UrbanSound8K.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/notebooks/audio/audio_classifier_UrbanSound8K.ipynb) demonstrates integrating **ClearML** into a Jupyter Notebook which uses PyTorch, TensorBoard, and TorchVision to train a neural network on the UrbanSound8K dataset for audio classification. The example calls TensorBoard methods in training and testing to report scalars, audio debug samples, and spectrogram visualizations. The spectrogram visualizations are plotted by calling Matplotlib methods. In the example, we also demonstrate connecting parameters to a Task and logging them. When the script runs, it creates an experiment named `audio classifier` which is associated with the `Audio Example` project.
+The example [audio_classification_UrbanSound8K.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/notebooks/audio/audio_classifier_UrbanSound8K.ipynb) demonstrates integrating ClearML into a Jupyter Notebook which uses PyTorch, TensorBoard, and TorchVision to train a neural network on the UrbanSound8K dataset for audio classification. The example calls TensorBoard methods in training and testing to report scalars, audio debug samples, and spectrogram visualizations. The spectrogram visualizations are plotted by calling Matplotlib methods. In the example, we also demonstrate connecting parameters to a Task and logging them. When the script runs, it creates an experiment named `audio classification UrbanSound8K` which is associated with the `Audio Example` project.
 
 ## Scalars
 
@@ -30,7 +30,7 @@ By doubling clicking a thumbnail, you can view a spectrogram plot in the image v
 
 ## Hyperparameters
 
-**ClearML** automatically logs TensorFlow Definitions. A parameter dictionary is logged by connecting it to the Task using 
+ClearML automatically logs TensorFlow Definitions. A parameter dictionary is logged by connecting it to the Task using 
 a call to the [Task.connect](../../../../../references/sdk/task.md#connect) method. 
 
     configuration_dict = {'number_of_epochs': 10, 'batch_size': 4, 'dropout': 0.25, 'base_lr': 0.001}

docs/guides/frameworks/pytorch/notebooks/audio/audio_preprocessing_example.md

@@ -3,17 +3,17 @@ title: Audio Preprocessing - Jupyter Notebook
 ---
 
 The example [audio_preprocessing_example.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/notebooks/audio/audio_preprocessing_example.ipynb) 
-demonstrates integrating **ClearML** into a Jupyter Notebook which uses PyTorch and preprocesses audio samples. **ClearML** automatically logs spectrogram visualizations reported by calling Matplotlib methods, and audio samples reported by calling TensorBoard methods. In the example, we also demonstrate connecting parameters to a Task and logging them. When the script runs, it creates an experiment named `data pre-processing`, which is associated with the `Audio Example` project.
+demonstrates integrating ClearML into a Jupyter Notebook which uses PyTorch and preprocesses audio samples. ClearML automatically logs spectrogram visualizations reported by calling Matplotlib methods, and audio samples reported by calling TensorBoard methods. In the example, we also demonstrate connecting parameters to a Task and logging them. When the script runs, it creates an experiment named `data pre-processing`, which is associated with the `Audio Example` project.
 
 ## Plots
 
-**ClearML** automatically logs the waveform which the example reports by calling a Matplotlib method. These appear in **RESULTS** **>** **PLOTS**.
+ClearML automatically logs the waveform which the example reports by calling a Matplotlib method. These appear in **RESULTS** **>** **PLOTS**.
 
 ![image](../../../../../img/examples_audio_preprocessing_example_08.png)
 
 ## Debug Samples
 
-**ClearML** automatically logs the audio samples which the example reports by calling TensorBoard methods, and the spectrogram visualizations reported by calling Matplotlib methods. They appear in **RESULTS** **>** **DEBUG SAMPLES**.
+ClearML automatically logs the audio samples which the example reports by calling TensorBoard methods, and the spectrogram visualizations reported by calling Matplotlib methods. They appear in **RESULTS** **>** **DEBUG SAMPLES**.
 
 ### Audio Samples
 

docs/guides/frameworks/pytorch/notebooks/image/hyperparameter_search.md

@@ -3,15 +3,15 @@ title: Image Hyperparameter Optimization - Jupyter Notebook
 ---
 
 [hyperparameter_search.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/notebooks/image/hyperparameter_search.ipynb) 
-demonstrates integrating **ClearML** into a Jupyter Notebook which performs automated hyperparameter optimization. This 
-is an example of **ClearML** automation. It creates a **ClearML** 
+demonstrates integrating ClearML into a Jupyter Notebook which performs automated hyperparameter optimization. This 
+is an example of ClearML automation. It creates a ClearML 
 [HyperParameterOptimizer](../../../../../references/sdk/hpo_optimization_hyperparameteroptimizer.md) 
 object, which is a search controller. The search controller's search strategy optimizer is [OptimizerBOHB](../../../../../references/sdk/hpo_hpbandster_bandster_optimizerbohb.md) 
-The example maximizes total accuracy by finding an optimal batch size, base learning rate, and dropout. **ClearML** 
+The example maximizes total accuracy by finding an optimal batch size, base learning rate, and dropout. ClearML 
 automatically logs the optimization's top performing experiments.
 
 The experiment whose hyperparameters are optimized is named `image_classification_CIFAR10`. It is created by running another 
-**ClearML** example, [image_classification_CIFAR10.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/notebooks/image/image_classification_CIFAR10.ipynb), which must run before `hyperparameter_search.ipynb`.
+ClearML example, [image_classification_CIFAR10.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/notebooks/image/image_classification_CIFAR10.ipynb), which must run before `hyperparameter_search.ipynb`.
 
 When `hyperparameter_search.py` runs, it creates an experiment named `Hyper-Parameter Optimization` which is associated 
 with the `Hyper-Parameter Search` project.
@@ -24,7 +24,7 @@ The optimizer Task, `Hyper-Parameter Optimization`, and the experiments appear i
 
 Scalars for total accuracy and remaining budget by iteration, and a plot of total accuracy by iteration appear in **RESULTS** **>** **SCALARS**. Remaining budget indicates the percentage of total iterations for all jobs left before that total is reached.
 
-These scalars are reported automatically by **ClearML** from `HyperParameterOptimizer` when it runs.
+These scalars are reported automatically by ClearML from `HyperParameterOptimizer` when it runs.
 
 ![image](../../../../../img/examples_hyperparameter_search_04.png)
 
@@ -32,7 +32,7 @@ These scalars are reported automatically by **ClearML** from `HyperParameterOpti
 
 A plot for the optimization of total accuracy by job appears in **RESULTS** **>** **SCALARS**.
 
-This is also reported automatically by **ClearML** when `HyperParameterOptimizer` runs.
+This is also reported automatically by ClearML when `HyperParameterOptimizer` runs.
 
 ![image](../../../../../img/examples_hyperparameter_search_05.png)
 
@@ -79,7 +79,7 @@ All console output from `Hyper-Parameter Optimization` appears in **RESULTS** ta
 
 ## Experiments Comparison
 
-**ClearML** automatically logs each job, meaning each experiment that executes with a set of hyperparameters, separately. Each appears as an individual experiment in the **ClearML Web UI**, where the Task name is `image_classification_CIFAR10` and the hyperparameters appended.
+ClearML automatically logs each job, meaning each experiment that executes with a set of hyperparameters, separately. Each appears as an individual experiment in the **ClearML Web UI**, where the Task name is `image_classification_CIFAR10` and the hyperparameters appended.
 
 For example:
 

docs/guides/frameworks/pytorch/notebooks/image/image_classification_CIFAR10.md

@@ -3,8 +3,8 @@ title: Image Classification - Jupyter Notebook
 ---
 
 The example [image_classification_CIFAR10.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/notebooks/image/image_classification_CIFAR10.ipynb) 
-demonstrates integrating **ClearML** into a Jupyter Notebook, which uses PyTorch, TensorBoard, and TorchVision to train a 
-neural network on the CIFAR10 dataset for image classification. **ClearML** automatically logs the example script's 
+demonstrates integrating ClearML into a Jupyter Notebook, which uses PyTorch, TensorBoard, and TorchVision to train a 
+neural network on the CIFAR10 dataset for image classification. ClearML automatically logs the example script's 
 calls to TensorBoard methods in training and testing which report scalars and image debug samples, as well as the model 
 and console log. In the example, we also demonstrate connecting parameters to a Task and logging them. When the script runs, 
 it creates an experiment named `image_classification_CIFAR10` which is associated with the `Image Example` project.
@@ -29,7 +29,7 @@ By doubling clicking a thumbnail, you can view a spectrogram plot in the image v
 
 ## Hyperparameters
 
-**ClearML** automatically logs TensorFlow Definitions. A parameter dictionary is logged by connecting it to the Task using 
+ClearML automatically logs TensorFlow Definitions. A parameter dictionary is logged by connecting it to the Task using 
 a call to the [Task.connect](../../../../../references/sdk/task.md#connect) method.
 
     configuration_dict = {'number_of_epochs': 3, 'batch_size': 4, 'dropout': 0.25, 'base_lr': 0.001}

docs/guides/frameworks/pytorch/notebooks/table/download_and_preprocessing.md

@@ -2,7 +2,7 @@
 title: Tabular Data Downloading and Preprocessing - Jupyter Notebook
 ---
 
-The [download_and_preprocessing.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/notebooks/table/download_and_preprocessing.ipynb) example demonstrates **ClearML** storing preprocessed tabular data as artifacts, and explicitly reporting the tabular data in the **ClearML Web UI**. When the script runs, it creates an experiment named `tabular preprocessing` which is associated with the `Table Example` project.
+The [download_and_preprocessing.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/notebooks/table/download_and_preprocessing.ipynb) example demonstrates ClearML storing preprocessed tabular data as artifacts, and explicitly reporting the tabular data in the **ClearML Web UI**. When the script runs, it creates an experiment named `tabular preprocessing` which is associated with the `Table Example` project.
 
 This tabular data is prepared for another script, [train_tabular_predictor.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/notebooks/table/train_tabular_predictor.ipynb), which trains a network with it. 
 

docs/guides/frameworks/pytorch/notebooks/table/tabular_training_pipeline.md

@@ -83,7 +83,7 @@ configuration_dict = {
 configuration_dict = task.connect(configuration_dict)  # enabling configuration override by clearml
 ```
 
-**ClearML** tracks and reports each instance of the preprocessing Task.
+ClearML tracks and reports each instance of the preprocessing Task.
 
 The raw data appears as a table in **RESULTS** **>** **PLOTS**.
 
@@ -157,9 +157,9 @@ configuration_dict = {
 configuration_dict = task.connect(configuration_dict)  # enabling configuration override by clearml
    ```
     
-**ClearML** tracks and reports the training step with each instance of the newly cloned and executed training Task.
+ClearML tracks and reports the training step with each instance of the newly cloned and executed training Task.
 
-**ClearML** automatically logs training loss and learning. They appear in **RESULTS** **>** **SCALARS**.
+ClearML automatically logs training loss and learning. They appear in **RESULTS** **>** **SCALARS**.
 
 The following images show one of the two training Tasks.
 

docs/guides/frameworks/pytorch/notebooks/text/text_classification_AG_NEWS.md

@@ -3,8 +3,8 @@ title: Text Classification - Jupyter Notebook
 ---
 
 The example [text_classification_AG_NEWS.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/notebooks/text/text_classification_AG_NEWS.ipynb) 
-demonstrates using Jupyter Notebook for **ClearML**, and the integration of **ClearML** into code which trains a network 
-to classify text in the `torchtext` [AG_NEWS](https://pytorch.org/text/stable/datasets.html#ag-news) dataset, and then applies the model to predict the classification of sample text. **ClearML** automatically logs the scalar and console output by calling TensorBoard methods. In the example, we explicitly log parameters with the Task. When the script runs, it creates an experiment named `text classifier` which is associated with the `Text Example` project.
+demonstrates using Jupyter Notebook for ClearML, and the integration of ClearML into code which trains a network 
+to classify text in the `torchtext` [AG_NEWS](https://pytorch.org/text/stable/datasets.html#ag-news) dataset, and then applies the model to predict the classification of sample text. ClearML automatically logs the scalar and console output by calling TensorBoard methods. In the example, we explicitly log parameters with the Task. When the script runs, it creates an experiment named `text classifier` which is associated with the `Text Example` project.
 
 ## Scalars
 
@@ -14,7 +14,7 @@ Accuracy, learning rate, and training loss appear in **RESULTS** **>** **SCALARS
 
 ## Hyperparameters
 
-**ClearML** automatically logs the command line options, because the example code uses `argparse`. A parameter dictionary 
+ClearML automatically logs the command line options, because the example code uses `argparse`. A parameter dictionary 
 is logged by connecting it to the Task using a call to the [Task.connect](../../../../../references/sdk/task.md#connect) 
 method.
 

docs/guides/frameworks/pytorch/pytorch_abseil.md

@@ -15,7 +15,7 @@ The example script does the following:
   
 ## Scalars
 
-In the example script's `train` function, the following code explicitly reports scalars to **ClearML**:
+In the example script's `train` function, the following code explicitly reports scalars to ClearML:
 
 ```python
 Logger.current_logger().report_scalar(

docs/guides/frameworks/pytorch/pytorch_distributed_example.md

@@ -3,7 +3,7 @@ title: PyTorch Distributed
 ---
 
 The [pytorch_distributed_example.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/pytorch_distributed_example.py) 
-script demonstrates integrating **ClearML** into a code that uses the [PyTorch Distributed Communications Package](https://pytorch.org/docs/stable/distributed.html) 
+script demonstrates integrating ClearML into a code that uses the [PyTorch Distributed Communications Package](https://pytorch.org/docs/stable/distributed.html) 
 (`torch.distributed`). 
 
 The script does the following: 

docs/guides/frameworks/pytorch/pytorch_matplotlib.md

@@ -3,7 +3,7 @@ title: PyTorch with Matplotlib
 ---
 
 The [pytorch_matplotlib.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/pytorch_matplotlib.py) 
-example demonstrates the integration of **ClearML** into code that uses PyTorch and Matplotlib. 
+example demonstrates the integration of ClearML into code that uses PyTorch and Matplotlib. 
 
 The example does the following: 
 * Creates an experiment named `pytorch with matplotlib example`, in the `examples` project.

docs/guides/frameworks/pytorch/pytorch_mnist.md

@@ -14,7 +14,7 @@ The example script does the following:
 
 ## Scalars
 
-In the example script's `train` function, the following code explicitly reports scalars to **ClearML**:
+In the example script's `train` function, the following code explicitly reports scalars to ClearML:
 
 ```python
 Logger.current_logger().report_scalar(

docs/guides/frameworks/pytorch/pytorch_tensorboardx.md

@@ -43,4 +43,4 @@ models and any snapshots created using PyTorch.
 Clicking on the model name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can view 
 the model’s details and access the model.
 
-![image](../../../img/examples_pytorch_tensorboardx_05.png)
+![image](../../../img/examples_pytorch_tensorboardx_model.png)

docs/guides/frameworks/pytorch_ignite/integration_pytorch_ignite.md

@@ -42,7 +42,7 @@ View the scalars in the experiment's page in the **ClearML Web UI**, in **RESULT
 
 ## Model Snapshots
 
-**ClearML** automatically captures the model logged with Torch, and saves it as an artifact. 
+ClearML automatically captures the model logged with Torch, and saves it as an artifact. 
 
 View saved snapshots in the experiment's **ARTIFACTS** tab.
 

docs/guides/frameworks/tensorboardx/tensorboardx.md

@@ -41,5 +41,5 @@ models and any snapshots created using PyTorch.
 Clicking on the model’s name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can 
 view the model’s details and access the model.
 
-![image](../../../img/examples_pytorch_tensorboardx_05.png)
+![image](../../../img/examples_pytorch_tensorboardx_model.png)
 

docs/guides/frameworks/tensorflow/integration_keras_tuner.md

@@ -2,20 +2,20 @@
 title: Keras Tuner Integration
 ---
 
-Integrate **ClearML** into code that uses [Keras Tuner](https://www.tensorflow.org/tutorials/keras/keras_tuner). By 
+Integrate ClearML into code that uses [Keras Tuner](https://www.tensorflow.org/tutorials/keras/keras_tuner). By 
 specifying `ClearMLTunerLogger` (see [kerastuner.py](https://github.com/allegroai/clearml/blob/master/clearml/external/kerastuner.py)) 
-as the Keras Tuner logger, **ClearML** automatically logs scalars and hyperparameter optimization.  
+as the Keras Tuner logger, ClearML automatically logs scalars and hyperparameter optimization.  
 
 ## ClearMLTunerLogger
 
 Take a look at [keras_tuner_cifar.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/kerastuner/keras_tuner_cifar.py) 
-example script, which demonstrates the integration of **ClearML** in a code that uses Keras Tuner. 
+example script, which demonstrates the integration of ClearML in a code that uses Keras Tuner. 
 
 The script does the following:
 1. Creates a `Hyperband` object, which uses Keras Tuner's `Hyperband` tuner. It finds the best hyperparameters to train a 
    network on a CIFAR10 dataset.
 1. When the `Hyperband` object is created, instantiates a `ClearMLTunerLogger` object and assigns it to the `Hyperband` logger.
-The `ClearMLTunerLogger` class provides the required binding for **ClearML** automatic logging. 
+The `ClearMLTunerLogger` class provides the required binding for ClearML automatic logging. 
 
 ```python
 tuner = kt.Hyperband(
@@ -35,21 +35,21 @@ When the script runs, it logs:
 
 ## Scalars
 
-**ClearML** logs the scalars from training each network. They appear in the project's page in the **ClearML web UI**, under
+ClearML logs the scalars from training each network. They appear in the project's page in the **ClearML web UI**, under
 **RESULTS** **>** **SCALARS**.
 
 ![image](../../../img/integration_keras_tuner_06.png)
 
 ## Summary of Hyperparameter Optimization
 
-**ClearML** automatically logs the parameters of each experiment run in the hyperparameter search. They appear in tabular 
+ClearML automatically logs the parameters of each experiment run in the hyperparameter search. They appear in tabular 
 form in **RESULTS** **>** **PLOTS**.
 
 ![image](../../../img/integration_keras_tuner_07.png)
 
 ## Artifacts
 
-**ClearML** automatically stores the output model. It appears in **ARTIFACTS** **>** **Output Model**.
+ClearML automatically stores the output model. It appears in **ARTIFACTS** **>** **Output Model**.
 
 ![image](../../../img/integration_keras_tuner_03.png)
 
@@ -65,7 +65,7 @@ The model configuration is stored with the model.
 
 ### Hyperparameters
 
-**ClearML** automatically logs the TensorFlow Definitions, which appear in **RESULTS** **>** **CONFIGURATION** **>** **HYPER PARAMETERS**.
+ClearML automatically logs the TensorFlow Definitions, which appear in **RESULTS** **>** **CONFIGURATION** **>** **HYPER PARAMETERS**.
 
 ![image](../../../img/integration_keras_tuner_01.png)
 

docs/guides/ide/google_colab.md

@@ -39,9 +39,9 @@ and running, users can send Tasks to be executed on the Google Colab's HW.
    from clearml import Task
    
    Task.set_credentials(
-        api_host="https://api.community.clear.ml", 
-        web_host="https://app.community.clear.ml", 
-        files_host="https://files.community.clear.ml", 
+        api_host="https://api.clear.ml", 
+        web_host="https://app.clear.ml", 
+        files_host="https://files.clear.ml", 
         key='6ZHX9UQMYL874A1NE8', 
         secret='=2h6#%@Y&m*tC!VLEXq&JI7QhZPKuJfbaYD4!uUk(t7=9ENv'
    )

docs/guides/ide/integration_pycharm.md

@@ -27,13 +27,13 @@ the settings in the ClearML configuration file.
 
 **To set ClearML configuration parameters:**
 
-1. In PyCharm, open **Settings** **>** **Tools** **>** **ClearML**.
+1. In PyCharm, open **Settings** **>** **Tools** **>** ClearML.
 
 1. Configure your ClearML server information:
     1. API server (for example: ``http://localhost:8008``)
     1. Web server (for example: ``http://localhost:8080``)
     1. File server  (for example: ``http://localhost:8081``)
     
-1. Add **ClearML** user credentials key/secret.
+1. Add ClearML user credentials key/secret.
 
 ![image](../../img/ide_pycharm_config_params.png)

docs/guides/ide/remote_jupyter_tutorial.md

@@ -70,7 +70,7 @@ Waiting for remote machine allocation [id=35c0af81ae6541589dbae1efb747f388]
 ...Status [in_progress]
 Remote machine allocated
 Setting remote environment [Task id=35c0af81ae6541589dbae1efb747f388]
-Setup process details: https://app.community.clear.ml/projects/60893b87b0b642679fde00db96e90359/experiments/35c0af81ae6541589dbae1efb747f388/output/log
+Setup process details: https://app.clear.ml/projects/60893b87b0b642679fde00db96e90359/experiments/35c0af81ae6541589dbae1efb747f388/output/log
 Waiting for environment setup to complete [usually about 20-30 seconds]
 ```
 

docs/guides/main.md

@@ -5,7 +5,7 @@ slug: /guides
 ---
 
 
-To help learn and use **ClearML**, we provide example scripts that demonstrates how to use ClearML's various features. 
+To help learn and use ClearML, we provide example scripts that demonstrates how to use ClearML's various features. 
 
 Examples scripts are in the [examples](https://github.com/allegroai/clearml/tree/master/examples) folder of the GitHub `clearml` 
 repository. They are also pre-loaded in the **ClearML Server**:

docs/guides/optimization/hyper-parameter-optimization/examples_hyperparam_opt.md

@@ -3,7 +3,7 @@ title: Hyperparameter Optimization
 ---
 
 The [hyper_parameter_optimizer.py](https://github.com/allegroai/clearml/blob/master/examples/optimization/hyper-parameter-optimization/hyper_parameter_optimizer.py) 
-example script demonstrates hyperparameter optimization, which is automated by using **ClearML** 
+example script demonstrates hyperparameter optimization, which is automated by using ClearML 
 
 ## Set the Search Strategy for Optimization
 
@@ -18,13 +18,13 @@ The following search strategies can be used:
   BOHB performs robust and efficient hyperparameter optimization at scale by combining the speed of Hyperband searches 
   with the guidance and guarantees of convergence of Bayesian Optimization.
     
-    **ClearML** implements BOHB for automation with HpBandSter's [bohb.py](https://github.com/automl/HpBandSter/blob/master/hpbandster/optimizers/bohb.py). 
+    ClearML implements BOHB for automation with HpBandSter's [bohb.py](https://github.com/automl/HpBandSter/blob/master/hpbandster/optimizers/bohb.py). 
   For more information about HpBandSter BOHB, see the [HpBandSter](https://automl.github.io/HpBandSter/build/html/index.html) 
   documentation.
      
 * Random uniform sampling of hyperparameter strategy - [automation.RandomSearch](../../../references/sdk/hpo_optimization_randomsearch.md)
 * Full grid sampling strategy of every hyperparameter combination - Grid search [automation.GridSearch](../../../references/sdk/hpo_optimization_gridsearch.md).
-* Custom - Use a custom class and inherit from the **ClearML** automation base strategy class, automation.optimization.SearchStrategy.
+* Custom - Use a custom class and inherit from the ClearML automation base strategy class, automation.optimization.SearchStrategy.
 
 The search strategy class that is chosen will be passed to the [automation.HyperParameterOptimizer](../../../references/sdk/hpo_optimization_hyperparameteroptimizer.md) 
 object later.
@@ -122,7 +122,7 @@ an_optimizer = HyperParameterOptimizer(
     base_task_id=args['template_task_id'],
 ```
 
-Set the hyperparameter ranges to sample, instantiating them as **ClearML** automation objects using [automation.UniformIntegerParameterRange](../../../references/sdk/hpo_parameters_uniformintegerparameterrange.md) 
+Set the hyperparameter ranges to sample, instantiating them as ClearML automation objects using [automation.UniformIntegerParameterRange](../../../references/sdk/hpo_parameters_uniformintegerparameterrange.md) 
 and [automation.DiscreteParameterRange](../../../references/sdk/hpo_parameters_discreteparameterrange.md).
 
 ```python

docs/guides/reporting/3d_plots_reporting.md

@@ -7,7 +7,7 @@ example demonstrates reporting a series as a surface plot and as a 3D scatter pl
 
 When the script runs, it creates an experiment named `3D plot reporting`, which is associated with the `examples` project.
 
-**ClearML** reports these plots in the **ClearML Web UI** **>** experiment page **>** **RESULTS** tab **>** **PLOTS** sub-tab. 
+ClearML reports these plots in the **ClearML Web UI** **>** experiment page **>** **RESULTS** tab **>** **PLOTS** sub-tab. 
 
 ## Surface Plot
 

docs/guides/reporting/artifacts.md

@@ -14,9 +14,9 @@ These artifacts include:
   
 Artifacts can be uploaded and dynamically tracked, or uploaded without tracking. 
 
-Configure **ClearML** for uploading artifacts to any of the supported types of storage, which include local and shared folders, 
+Configure ClearML for uploading artifacts to any of the supported types of storage, which include local and shared folders, 
 S3 buckets, Google Cloud Storage, and Azure Storage ([debug sample storage](../../references/sdk/logger.md#set_default_upload_destination) 
-is different). Configure **ClearML** in any of the following ways:
+is different). Configure ClearML in any of the following ways:
 
 * In the configuration file, set [default_output_uri](../../configs/clearml_conf.md#sdkdevelopment).
 * In code, when [initializing a Task](../../references/sdk/task.md#taskinit), use the `output_uri` parameter.
@@ -24,14 +24,14 @@ is different). Configure **ClearML** in any of the following ways:
 
 When the script runs, it creates an experiment named `artifacts example`, which is associated with the `examples` project. 
 
-**ClearML** reports artifacts in the **ClearML Web UI** **>** experiment details **>** **ARTIFACTS** tab.
+ClearML reports artifacts in the **ClearML Web UI** **>** experiment details **>** **ARTIFACTS** tab.
 
 ![image](../../img/examples_reporting_03.png)
 
 ## Dynamically Tracked Artifacts
 
-Currently, **ClearML** supports uploading and dynamically tracking Pandas DataFrames. Use the [Task.register_artifact](../../references/sdk/task.md#register_artifact)
-method. If the Pandas DataFrame changes, **ClearML** uploads the changes. The updated artifact is associated with the experiment.
+Currently, ClearML supports uploading and dynamically tracking Pandas DataFrames. Use the [Task.register_artifact](../../references/sdk/task.md#register_artifact)
+method. If the Pandas DataFrame changes, ClearML uploads the changes. The updated artifact is associated with the experiment.
 
 For example:
 
@@ -51,7 +51,7 @@ task.register_artifact('train', df, metadata={'counting': 'legs', 'max legs': 69
 ```
 
 By changing the artifact, and calling the [Task.get_registered_artifacts](../../references/sdk/task.md#get_registered_artifacts) 
-method to retrieve it, we can see that **ClearML** tracked the change.
+method to retrieve it, we can see that ClearML tracked the change.
 
 ```python
 # change the artifact object
@@ -62,7 +62,7 @@ Task.current_task().get_registered_artifacts()['train'].sample(frac=0.5, replace
 
 ## Artifacts Without Tracking
 
-**ClearML** supports several types of objects that can be uploaded and are not tracked. Use the [Task.upload_artifact](../../references/sdk/task.md#upload_artifact) 
+ClearML supports several types of objects that can be uploaded and are not tracked. Use the [Task.upload_artifact](../../references/sdk/task.md#upload_artifact) 
 method. 
 
 Artifacts without tracking include:

docs/guides/reporting/hyper_parameters.md

@@ -4,7 +4,7 @@ title: Hyperparameters Reporting
 
 The [hyper_parameters.py](https://github.com/allegroai/clearml/blob/master/examples/reporting/hyper_parameters.py) example 
 script demonstrates: 
-* **ClearML**'s automatic logging of `argparse` command line options and TensorFlow Definitions
+* ClearML's automatic logging of `argparse` command line options and TensorFlow Definitions
 * Logging user-defined hyperparameters with a parameter dictionary and connecting the dictionary to a Task. 
 
 Hyperparameters appear in the **web UI** in the experiment's page, under **CONFIGURATIONS** **>** **HYPER PARAMETERS**. 
@@ -15,7 +15,7 @@ When the script runs, it creates an experiment named `hyper-parameters example`,
 
 ## Argparse Command Line Options
 
-If a code uses argparse and initializes a Task, **ClearML** automatically logs the argparse arguments.
+If a code uses argparse and initializes a Task, ClearML automatically logs the argparse arguments.
  
 ```python
 parser = ArgumentParser()
@@ -34,7 +34,7 @@ Command line options appears in **HYPER PARAMETERS** **>** **Args**.
 
 ## TensorFlow Definitions
 
-**ClearML** automatically logs TensorFlow Definitions, whether they are defined before or after the Task is initialized.
+ClearML automatically logs TensorFlow Definitions, whether they are defined before or after the Task is initialized.
 
 ```python
 flags.DEFINE_string('echo', None, 'Text to echo.')
@@ -55,7 +55,7 @@ TensorFlow Definitions appear in **HYPER PARAMETERS** **>** **TF_DEFINE**.
 ## Parameter Dictionaries
 
 Connect a parameter dictionary to a Task by calling the [Task.connect](../../references/sdk/task.md#connect)
-method, and **ClearML** logs the parameters. **ClearML** also tracks changes to the parameters.
+method, and ClearML logs the parameters. ClearML also tracks changes to the parameters.
 
 ```python
 parameters = {

docs/guides/reporting/manual_matplotlib_reporting.md

@@ -3,7 +3,7 @@ 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**.
+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 
 `examples` project.

docs/guides/reporting/media_reporting.md

@@ -9,11 +9,11 @@ method to upload from:
 * 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](../../fundamentals/artifacts.md#setting-upload-destination) is different). Set credentials for storage in the **ClearML** 
+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](../../fundamentals/artifacts.md#setting-upload-destination) is different). Set credentials for storage in the ClearML 
 [configuration file](../../configs/clearml_conf.md).
 
-**ClearML** reports media in the **ClearML Web UI** **>** experiment details **>** **RESULTS** tab **>** **DEBUG SAMPLES** 
+ClearML reports media in the **ClearML Web UI** **>** experiment details **>** **RESULTS** tab **>** **DEBUG SAMPLES** 
 sub-tab. 
 
 When the script runs, it creates an experiment named `audio and video reporting`, which is associated with the `examples` 

docs/guides/reporting/pandas_reporting.md

@@ -4,10 +4,10 @@ title: Tables Reporting (Pandas and CSV Files)
 
 The [pandas_reporting.py](https://github.com/allegroai/clearml/blob/master/examples/reporting/pandas_reporting.py) example demonstrates reporting tabular data from Pandas DataFrames and CSV files as tables. 
 
-**ClearML** reports these tables in the **ClearML Web UI** **>** experiment details **>** **RESULTS** tab **>** **PLOTS** 
+ClearML reports these tables in the **ClearML Web UI** **>** experiment details **>** **RESULTS** tab **>** **PLOTS** 
 sub-tab. 
 
-When the script runs, it creates an experiment named `pandas table reporting`, which is associated with the `examples` project.
+When the script runs, it creates an experiment named `table reporting`, which is associated with the `examples` project.
 
 ## Reporting Pandas DataFrames as Tables
 

docs/guides/reporting/plotly_reporting.md

@@ -3,9 +3,9 @@ title: Plotly Reporting
 ---
 
 The [plotly_reporting.py](https://github.com/allegroai/clearml/blob/master/examples/reporting/plotly_reporting.py) example 
-demonstrates **ClearML**'s Plotly integration and reporting. 
+demonstrates ClearML's Plotly integration and reporting. 
 
-Report Plotly plots in **ClearML** by calling the [`Logger.report_plotly`](../../references/sdk/logger.md#report_plotly) method, and passing a complex
+Report Plotly plots in ClearML by calling the [`Logger.report_plotly`](../../references/sdk/logger.md#report_plotly) method, and passing a complex
 Plotly figure, using the `figure` parameter. 
 
 In this example, the Plotly figure is created using `plotly.express.scatter` (see [Scatter Plots in Python](https://plotly.com/python/line-and-scatter/) 
@@ -33,7 +33,7 @@ task.get_logger().report_plotly(
 
 When the script runs, it creates an experiment named `plotly reporting`, which is associated with the examples project.
 
-**ClearML** reports Plotly plots in the **ClearML Web UI** **>** experiment details **>** **RESULTS** tab **>** **PLOTS** 
+ClearML reports Plotly plots in the **ClearML Web UI** **>** experiment details **>** **RESULTS** tab **>** **PLOTS** 
 sub-tab.
 
 ![image](../../img/examples_reporting_13.png)

docs/guides/reporting/scalar_reporting.md

@@ -3,7 +3,7 @@ title: Scalars Reporting
 ---
 
 The [scalar_reporting.py](https://github.com/allegroai/clearml/blob/master/examples/reporting/scalar_reporting.py) script
-demonstrates explicit scalar reporting. **ClearML** reports scalars in the **ClearML Web UI** **>** experiment details **>** 
+demonstrates explicit scalar reporting. ClearML reports scalars in the **ClearML Web UI** **>** experiment details **>** 
 **RESULTS** tab **>** **SCALARS** sub-tab. 
 
 When the script runs, it creates an experiment named `scalar reporting`, which is associated with the `examples` project.

docs/guides/reporting/scatter_hist_confusion_mat_reporting.md

@@ -8,7 +8,7 @@ example demonstrates reporting series data in the following 2D formats:
 * [Confusion matrices](#confusion-matrices)
 * [Scatter plots](#2d-scatter-plots) 
 
-**ClearML** reports these tables in the **ClearML Web UI**, experiment details **>** **RESULTS** tab **>** **PLOTS** sub-tab. 
+ClearML reports these tables in the **ClearML Web UI**, experiment details **>** **RESULTS** tab **>** **PLOTS** sub-tab. 
 
 When the script runs, it creates an experiment named `2D plots reporting`, which is associated with the `examples` project.
 

docs/guides/reporting/text_reporting.md

@@ -6,7 +6,7 @@ The [text_reporting.py](https://github.com/allegroai/clearml/blob/master/example
 demonstrates reporting explicit text, by calling the [Logger.report_text](../../references/sdk/logger.md#report_text)
 method. 
 
-**ClearML** reports these tables in the **ClearML Web UI**, experiment details, **RESULTS** tab, **CONSOLE** sub-tab. 
+ClearML reports these tables in the **ClearML Web UI**, experiment details, **RESULTS** tab, **CONSOLE** sub-tab. 
 
 When the script runs, it creates an experiment named `text reporting`, which is associated with the `examples` project.
 

docs/guides/services/aws_autoscaler.md

@@ -134,9 +134,9 @@ and the script prints a hyperlink to the Task's log.
 ```console
 CLEARML Task: created new task id=d0ee5309a9a3471d8802f2561da60dfa
 CLEARML Monitor: GPU monitoring failed getting GPU reading, switching off GPU monitoring
-CLEARML results page: https://app.community.clear.ml/projects/142a598b5d234bebb37a57d692f5689f/experiments/d0ee5309a9a3471d8802f2561da60dfa/output/log
+CLEARML results page: https://app.clear.ml/projects/142a598b5d234bebb37a57d692f5689f/experiments/d0ee5309a9a3471d8802f2561da60dfa/output/log
 Running AWS auto-scaler as a service
-Execution log https://app.community.clear.ml/projects/142a598b5d234bebb37a57d692f5689f/experiments/d0ee5309a9a3471d8802f2561da60dfa/output/log    
+Execution log https://app.clear.ml/projects/142a598b5d234bebb37a57d692f5689f/experiments/d0ee5309a9a3471d8802f2561da60dfa/output/log    
 ```
 
 ### Remote Execution

docs/guides/services/cleanup_service.md

@@ -34,7 +34,7 @@ A new task called `Cleanup Service` is created in the `DevOps` project on your C
 look similar to: 
 ```console
 ClearML Task: created new task id=8126c0af800f4903be07421aa344d7b3
-ClearML results page: https://app.community.clear.ml/projects/608e9039/experiments/81261aa34d7b3/output/log
+ClearML results page: https://app.clear.ml/projects/608e9039/experiments/81261aa34d7b3/output/log
 Cleanup service started
 Starting cleanup
 Deleting 100 tasks

docs/hyperdatasets/task.md

@@ -2,7 +2,7 @@
 title: Tasks
 ---
  
-Hyper-Datasets extend the **ClearML** [**Task**](../fundamentals/task.md) with [Dataviews](dataviews.md)
+Hyper-Datasets extend the ClearML [**Task**](../fundamentals/task.md) with [Dataviews](dataviews.md)
 
 ## Usage 
 

docs/hyperdatasets/webapp/webapp_datasets_frames.md

@@ -7,7 +7,7 @@ of frames and enables viewing SingleFrames and FramesGroups, and edit SingleFram
 Before opening the frame viewer, you can filter the frames by applying [simple](#simple-frame-filtering) or [advanced](#advanced-frame-filtering) 
 filtering logic. 
 
-![Dataset page](../../img/hyperdatasets/frames_01.png)
+![Dataset page](../../img/hyperdatasets/web-app/dataset_simple_adv_02.png)
 
 ## Frame Viewer