Small edits (#790)

docs/apps/clearml_param_search.md

@@ -33,8 +33,8 @@ of the optimization results in table and graph forms.
 |`--params-override`|Additional parameters of the base task to override for this parameter search. Use the following JSON format for each parameter: `{"name": "param_name", "value": <new_value>}`. Windows users, see [JSON format note](#json_note).|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />| 
 |`--objective-metric-title`| Objective metric title to maximize/minimize (e.g. 'validation').|<img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />|
 |`--objective-metric-series`| Objective metric series to maximize/minimize (e.g. 'loss').|<img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />|
-|`--objective-metric-sign`| Optimization target, whether to maximize or minimize the value of the objective metric specified. Possible values: "min", "max", "min_global", "max_global". See more information [here](#optimization-objective). |<img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />|
+|`--objective-metric-sign`| Optimization target, whether to maximize or minimize the value of the objective metric specified. Possible values: "min", "max", "min_global", "max_global". For more information, see [Optimization Objective](#optimization-objective). |<img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />|
-|`--optimizer-class`|The optimizer to use. Possible values are: OptimizerOptuna (default), OptimizerBOHB, GridSearch, RandomSearch. See more information [here](../fundamentals/hpo.md#supported-optimizers). |<img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />|
+|`--optimizer-class`|The optimizer to use. Possible values are: OptimizerOptuna (default), OptimizerBOHB, GridSearch, RandomSearch. For more information, see [Supported Optimizers](../fundamentals/hpo.md#supported-optimizers). |<img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />|
 |`--optimization-time-limit`|The maximum time (minutes) for the optimization to run. The default is `None`, indicating no time limit.|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
 |`--compute-time-limit`|The maximum compute time in minutes that experiment can consume. If this time limit is exceeded, all jobs are aborted.|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
 |`--pool-period-min`|The time between two consecutive polls (minutes).|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|

docs/clearml_data/best_practices.md

@@ -42,7 +42,7 @@ Datasets** section.
 
 Attach informative metrics or debug samples to the Dataset itself. Use [`Dataset.get_logger()`](../references/sdk/dataset.md#get_logger)
 to access the dataset's logger object, then add any additional information to the dataset, using the methods
-available with a [logger](../references/sdk/logger.md) object. 
+available with a [`Logger`](../references/sdk/logger.md) object. 
 
 You can add some dataset summaries (like [table reporting](../references/sdk/logger.md#report_table)) to create a preview 
 of the data stored for better visibility, or attach any statistics generated by the data ingestion process. 

docs/clearml_data/clearml_data_sdk.md

@@ -226,7 +226,7 @@ dataset.remove_files(dataset_path="*.csv", recursive=True)
 
 Add informative metrics, plots, or media to the Dataset. Use [`Dataset.get_logger()`](../references/sdk/dataset.md#get_logger)
 to access the dataset's logger object, then add any additional information to the dataset, using the methods
-available with a [logger](../references/sdk/logger.md) object. 
+available with a [`Logger`](../references/sdk/logger.md) object. 
 
 You can add some dataset summaries (like [table reporting](../references/sdk/logger.md#report_table)) to create a preview 
 of the data stored for better visibility, or attach any statistics generated by the data ingestion process. 
@@ -261,8 +261,8 @@ Use the `output_url` parameter to specify storage target, such as S3 / GS / Azur
 * Google Cloud Storage: `gs://bucket-name/folder`
 * Azure Storage: `azure://<account name>.blob.core.windows.net/path/to/file`
 
-By default, the dataset uploads to ClearML's file server. This target storage overrides the `output_uri` value of the 
+By default, the dataset uploads to ClearML's file server. This target storage overrides the `output_uri` value of 
-[`Dataset.create`](#creating-datasets) method.    
+[`Dataset.create()`](#creating-datasets).    
 
 ClearML supports parallel uploading of datasets. Use the `max_workers` parameter to specify the number of threads to use 
 when uploading the dataset. By default, it's the number of your machine's logical cores.
@@ -288,7 +288,7 @@ The folder changes will be reflected in a new dataset version. This method saves
 update (add / remove) files in a dataset.
 
 ## Deleting Datasets
-Delete a dataset using [`Dataset.delete()`](../references/sdk/dataset.md#datasetdelete) method. Input any of the 
+Delete a dataset using the [`Dataset.delete()`](../references/sdk/dataset.md#datasetdelete) class method. Input any of the 
 attributes of the dataset(s) you want to delete, including ID, project name, version, and/or dataset name. Multiple 
 datasets matching the query will raise an exception, unless you pass `entire_dataset=True` and `force=True`. In this 
 case, all matching datasets will be deleted. 

docs/clearml_sdk/task_sdk.md

@@ -41,10 +41,10 @@ task = Task.init(
 )
 ```
 
-Once a task is created, the task object can be accessed from anywhere in the code by calling [`Task.current_task`](../references/sdk/task.md#taskcurrent_task).
+Once a task is created, the task object can be accessed from anywhere in the code by calling [`Task.current_task()`](../references/sdk/task.md#taskcurrent_task).
 
 If multiple tasks need to be created in the same process (for example, for logging multiple manual runs), 
-make sure to close a task, before initializing a new one. To close a task simply call [`Task.close`](../references/sdk/task.md#close) 
+make sure to close a task, before initializing a new one. To close a task simply call [`Task.close()`](../references/sdk/task.md#close) 
 (see example [here](../guides/advanced/multiple_tasks_single_process.md)).
 
 When initializing a task, its project needs to be specified. If the project entered does not exist, it will be created on-the-fly. 
@@ -875,7 +875,7 @@ me = Person('Erik', 5)
 
 params_dictionary = {'epochs': 3, 'lr': 0.4}
 
-task = Task.init(project_name='examples',task_name='argparser')
+task = Task.init(project_name='examples',task_name='python objects')
 
 task.connect(me)
 task.connect(params_dictionary)

docs/clearml_serving/clearml_serving_setup.md

@@ -78,8 +78,8 @@ and the model inference itself will be executed on the Triton Engine container.
 
 ## Advanced Setup - S3/GS/Azure Access (Optional)
 To add access credentials and allow the inference containers to download models from your S3/GS/Azure object-storage, 
-add the respective environment variables to your env files (example.env). See further details on configuring the storage 
+add the respective environment variables to your env files (example.env). For further details, see 
-access [here](../integrations/storage.md#configuring-storage).
+[Configuring Storage](../integrations/storage.md#configuring-storage).
 
 ```
 AWS_ACCESS_KEY_ID

docs/faq.md

@@ -375,7 +375,7 @@ An experiment's name is a user-controlled property, which can be accessed via th
 
 For example, to distinguish between different experiments, you can append the task ID to the task name:
 ```python
-task = Task.init('examples', 'train')
+task = Task.init(project_name='examples', task_name='train')
 task.name += ' {}'.format(task.id)
 ```
 
@@ -625,8 +625,8 @@ For example, the following two scatter2D series are reported on the same plot, b
 ```python
 scatter2d_1 = np.hstack((np.atleast_2d(np.arange(0, 10)).T, np.random.randint(10, size=(10, 1))))
 logger.report_scatter2d(
-    "example_scatter",
+    title="example_scatter",
-    "series_1", 
+    series="series_1", 
     iteration=1, 
     scatter=scatter2d_1,
     xaxis="title x", 
@@ -635,8 +635,8 @@ logger.report_scatter2d(
     
 scatter2d_2 = np.hstack((np.atleast_2d(np.arange(0, 10)).T, np.random.randint(10, size=(10, 1))))
 logger.report_scatter2d(
-    "example_scatter", 
+    title="example_scatter", 
-    "series_2", 
+    series="series_2", 
     iteration=1, 
     scatter=scatter2d_2,
     xaxis="title x", 
@@ -783,7 +783,7 @@ Yes! You can run ClearML in Jupyter Notebooks using either of the following:
 1. You can now use ClearML in your notebook!
    ```python
    # create a task and start training
-   task = Task.init('jupyter project', 'my notebook')
+   task = Task.init(project_name='jupyter project', task_name='my notebook')
    ```
         
 

docs/fundamentals/hyperparameters.md

@@ -74,14 +74,14 @@ When connecting objects to ClearML, users can directly access and modify an obje
 pair in a parameter dictionary).
 
 #### Connecting Configuration Objects
-Configuration objects are dictionaries or configuration files connected to the task using the 
+Configuration objects are dictionaries or configuration files connected to the task using 
-[`Task.connect_configuration`](../references/sdk/task.md#connect_configuration) method. With this method, configuration 
+[`Task.connect_configuration()`](../references/sdk/task.md#connect_configuration). With this method, configuration 
 objects are saved as blobs i.e. ClearML is not aware of their internal structure.
 
 #### Setting and Updating Parameters
-ClearML provides methods to set and update task parameters manually. Use the [`Task.set_parameters`](../references/sdk/task.md#set_parameters) 
+ClearML provides methods to set and update task parameters manually. Use [`Task.set_parameters()`](../references/sdk/task.md#set_parameters) 
-method to define parameters manually. To update the parameters in an experiment, use the [`Task.set_parameters_as_dict`](../references/sdk/task.md#set_parameters_as_dict) 
+to define parameters manually. To update the parameters in an experiment, use [`Task.set_parameters_as_dict()`](../references/sdk/task.md#set_parameters_as_dict). 
-method. The `set_parameters_as_dict` method updates parameters while the `set_parameters` method overrides the parameters.
+The `set_parameters_as_dict` method updates parameters while the `set_parameters` method overrides the parameters.
 
 ClearML does not automatically track changes to explicitly set parameters.
 

docs/guides/advanced/execute_remotely.md

@@ -37,7 +37,10 @@ In the example script's `train` function, the following code explicitly reports
 
 ```python
 Logger.current_logger().report_scalar(
-    "train", "loss", iteration=(epoch * len(train_loader) + batch_idx), value=loss.item()
+    title="train", 
+    series="loss", 
+    iteration=(epoch * len(train_loader) + batch_idx), 
+    value=loss.item()
 )
 ```
 
@@ -45,10 +48,10 @@ In the script's `test` function, the code explicitly reports `loss` and `accurac
 
 ```python
 Logger.current_logger().report_scalar(
-    "test", "loss", iteration=epoch, value=test_loss
+    title="test", series="loss", iteration=epoch, value=test_loss
 )
 Logger.current_logger().report_scalar(
-    "test", "accuracy", iteration=epoch, value=(correct / len(test_loader.dataset))
+    title="test", series="accuracy", iteration=epoch, value=(correct / len(test_loader.dataset))
 )
 ```    
 

docs/guides/pipeline/pipeline_controller.md

@@ -114,7 +114,7 @@ does the following:
 1. Store the data as an artifact named `dataset` using [`Task.upload_artifact()`](../../references/sdk/task.md#upload_artifact):
    ```python
    # add and upload local file containing our toy dataset
-   task.upload_artifact('dataset', artifact_object=local_iris_pkl)
+   task.upload_artifact(name='dataset', artifact_object=local_iris_pkl)
    ```
    
 ## Step 2 - Processing the Data
@@ -146,10 +146,10 @@ does the following:
 1. Generate testing and training sets from the data and store them as artifacts.
    
    ```python
-   task.upload_artifact('X_train', X_train)
+   task.upload_artifact(name='X_train', artifact_object=X_train)
-   task.upload_artifact('X_test', X_test)
+   task.upload_artifact(name='X_test', artifact_object=X_test)
-   task.upload_artifact('y_train', y_train)
+   task.upload_artifact(name='y_train', artifact_object=y_train)
-   task.upload_artifact('y_test', y_test)
+   task.upload_artifact(name='y_test', artifact_object=y_test)
    ```
    
 ## Step 3 - Training the Network

docs/guides/reporting/3d_plots_reporting.md

@@ -11,15 +11,14 @@ ClearML reports these plots in the experiment's **PLOTS** tab.
 
 ## Surface Plot
 
-To plot a series as a surface plot, use the [Logger.report_surface](../../references/sdk/logger.md#report_surface) 
+To plot a series as a surface plot, use [`Logger.report_surface()`](../../references/sdk/logger.md#report_surface):
-method.
 
 ```python
 # report 3d surface
 surface = np.random.randint(10, size=(10, 10))
 Logger.current_logger().report_surface(
-    "example_surface",
+    title="example_surface",
-    "series1",
+    series="series1",
     iteration=iteration,
     matrix=surface,
     xaxis="title X",
@@ -33,14 +32,14 @@ View the reported surface plot in **PLOTS**.
 
 ## 3D Scatter Plot
 
-To plot a series as a 3D scatter plot, use the [Logger.report_scatter3d](../../references/sdk/logger.md#report_scatter3d) 
+To plot a series as a 3D scatter plot, use [`Logger.report_scatter3d()`](../../references/sdk/logger.md#report_scatter3d):
-method.
+
 ```python
 # report 3d scatter plot
 scatter3d = np.random.randint(10, size=(10, 3))
 Logger.current_logger().report_scatter3d(
-    "example_scatter_3d",
+    title="example_scatter_3d",
-    "series_xyz",
+    series="series_xyz",
     iteration=iteration,
     scatter=scatter3d,
     xaxis="title x",

docs/guides/reporting/artifacts.md

@@ -18,7 +18,7 @@ Configure ClearML for uploading artifacts to any of the supported types of stora
 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:
 
-* In the configuration file, set [default_output_uri](../../configs/clearml_conf.md#config_default_output_uri).
+* In the configuration file, set [`default_output_uri`](../../configs/clearml_conf.md#config_default_output_uri).
 * In code, when [initializing a Task](../../references/sdk/task.md#taskinit), use the `output_uri` parameter.
 * In the **ClearML Web UI**, when [modifying an experiment](../../webapp/webapp_exp_tuning.md#output-destination).
 
@@ -26,12 +26,12 @@ When the script runs, it creates an experiment named `artifacts example` in the
 
 ClearML reports artifacts in the **ClearML Web UI** **>** experiment details **>** **ARTIFACTS** tab.
 
-![image](../../img/examples_reporting_03.png)
+![Experiment artifacts](../../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)
+ClearML supports uploading and dynamically tracking Pandas DataFrames. Use [`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.
+to add a DataFrame to a task. If the DataFrame is modified, ClearML will automatically update the changes. 
 
 For example:
 
@@ -47,11 +47,15 @@ df = pd.DataFrame(
 
 # Register Pandas object as artifact to watch
 # (it will be monitored in the background and automatically synced and uploaded)
-task.register_artifact('train', df, metadata={'counting': 'legs', 'max legs': 69}))
+task.register_artifact(
+    name='train', 
+    artifact=df, 
+    metadata={'counting': 'legs', 'max legs': 68}
+)
 ```
 
-By changing the artifact, and calling the [Task.get_registered_artifacts](../../references/sdk/task.md#get_registered_artifacts) 
+By modifying the artifact, and calling [`Task.get_registered_artifacts()`](../../references/sdk/task.md#get_registered_artifacts) 
-method to retrieve it, you can see that ClearML tracked the change.
+to retrieve it, you can see ClearML tracking the changes:
 
 ```python
 # change the artifact object
@@ -78,7 +82,7 @@ Artifacts without tracking include:
 ### Pandas DataFrames
 ```python
 # add and upload pandas.DataFrame (onetime snapshot of the object)
-task.upload_artifact('Pandas', artifact_object=df)
+task.upload_artifact(name='Pandas', artifact_object=df)
 ```
 
 ### Local Files
@@ -86,7 +90,7 @@ task.upload_artifact('Pandas', artifact_object=df)
 ```python
 # add and upload local file artifact
 task.upload_artifact(
-    'local file', 
+    name='local file', 
     artifact_object=os.path.join(
         'data_samples',
         'dancing.jpg'
@@ -97,31 +101,31 @@ task.upload_artifact(
 ### Dictionaries
 ```python
 # add and upload dictionary stored as JSON
-task.upload_artifact('dictionary', df.to_dict())
+task.upload_artifact(name='dictionary', artifact_object=df.to_dict())
 ```
     
 ### Numpy Objects
 ```python
 # add and upload Numpy Object (stored as .npz file)
-task.upload_artifact('Numpy Eye', np.eye(100, 100))
+task.upload_artifact(name='Numpy Eye', artifact_object=np.eye(100, 100))
 ```
     
 ### Image Files
 ```python
 # add and upload Image (stored as .png file)
 im = Image.open(os.path.join('data_samples', 'dancing.jpg'))
-task.upload_artifact('pillow_image', im)
+task.upload_artifact(name='pillow_image', artifact_object=im)
 ```
     
 ### Folders
 ```python
 # add and upload a folder, artifact_object should be the folder path
-task.upload_artifact('local folder', artifact_object=os.path.join('data_samples'))
+task.upload_artifact(name='local folder', artifact_object=os.path.join('data_samples'))
 ```
 
 ### Wildcards
 ```python
 # add and upload a wildcard
-task.upload_artifact('wildcard jpegs', artifact_object=os.path.join('data_samples', '*.jpg'))
+task.upload_artifact(name='wildcard jpegs', artifact_object=os.path.join('data_samples', '*.jpg'))
 ```
     

docs/guides/reporting/clearml_logging_example.md

@@ -53,8 +53,8 @@ scatter2d = np.hstack(
 )
 # report 2d scatter plot with markers
 logger.report_scatter2d(
-    "example_scatter",
+    title="example_scatter",
-    "series_lines+markers",
+    series="series_lines+markers",
     iteration=iteration,
     scatter=scatter2d,
     xaxis="title x",
@@ -73,8 +73,8 @@ To plot a series as a 3D scatter plot, use [`Logger.report_scatter3d()`](../../r
 # report 3d scatter plot
 scatter3d = np.random.randint(10, size=(10, 3))
 logger.report_scatter3d(
-    "example_scatter_3d",
+    title="example_scatter_3d",
-    "series_xyz",
+    series="series_xyz",
     iteration=iteration,
     scatter=scatter3d,
     xaxis="title x",
@@ -91,8 +91,8 @@ To plot a series as a surface plot, use [`Logger.report_surface()`](../../refere
 # report 3d surface
 surface = np.random.randint(10, size=(10, 10))
 logger.report_surface(
-    "example_surface",
+    title="example_surface",
-    "series1",
+    series="series1",
     iteration=iteration,
     matrix=surface,
     xaxis="title X",
@@ -111,8 +111,8 @@ Report confusion matrices by calling [`Logger.report_confusion_matrix()`](../../
 # report confusion matrix
 confusion = np.random.randint(10, size=(10, 10))
 logger.report_confusion_matrix(
-    "example_confusion",
+    title="example_confusion",
-    "ignored",
+    series="ignored",
     iteration=iteration,
     matrix=confusion,
     xaxis="title X",
@@ -131,8 +131,8 @@ To report more than one series on the same plot, use the same `title` argument.
 # report a single histogram
 histogram = np.random.randint(10, size=10)
 logger.report_histogram(
-    "single_histogram",
+    title="single_histogram",
-    "random histogram",
+    series="random histogram",
     iteration=iteration,
     values=histogram,
     xaxis="title x",
@@ -147,16 +147,16 @@ logger.report_histogram(
 histogram1 = np.random.randint(13, size=10)
 histogram2 = histogram * 0.75
 logger.report_histogram(
-    "two_histogram",
+    title="two_histogram",
-    "series 1",
+    series="series 1",
     iteration=iteration,
     values=histogram1,
     xaxis="title x",
     yaxis="title y",
 )
 logger.report_histogram(
-    "two_histogram",
+    title="two_histogram",
-    "series 2",
+    series="series 2",
     iteration=iteration,
     values=histogram2,
     xaxis="title x",
@@ -185,7 +185,7 @@ image_local_copy = StorageManager.get_local_copy(
 ### Audio
 
 ```python
-logger.report_media('audio', 'pink panther', iteration=1, local_path=audio_local_copy)
+logger.report_media(title='audio', series='pink panther', iteration=1, local_path=audio_local_copy)
 ``` 
 
 ![Audio sample](../../img/colab_explicit_reporting_08.png)
@@ -194,8 +194,8 @@ logger.report_media('audio', 'pink panther', iteration=1, local_path=audio_local
 
 ```python
 logger.report_media(
-    "html", 
+    title="html", 
-    "url_html", 
+    series="url_html", 
     iteration=1, 
     url="https://clear.ml/docs/latest/docs/index.html"
 )
@@ -206,7 +206,12 @@ logger.report_media(
 ### Images
 
 ```python
-logger.report_image("image", "image from url", iteration=100, local_path=image_local_copy)
+logger.report_image(
+    title="image", 
+    series="image from url", 
+    iteration=100, 
+    local_path=image_local_copy
+)
 ```
 
 ![Image sample](../../img/colab_explicit_reporting_10.png)
@@ -214,7 +219,12 @@ logger.report_image("image", "image from url", iteration=100, local_path=image_l
 ### Video
 
 ```python
-logger.report_media('video', 'big bunny', iteration=1, local_path=video_local_copy)
+logger.report_media(
+    title='video', 
+    series='big bunny', 
+    iteration=1, 
+    local_path=video_local_copy
+)
 ```
 
 ![Video sample](../../img/colab_explicit_reporting_11.png)

docs/guides/reporting/media_reporting.md

@@ -50,7 +50,9 @@ Use the `local_path` parameter.
 ```python
 #  report audio, report local media audio file
 Logger.current_logger().report_media(
-    'audio', 'tada', iteration=1,
+    title='audio', 
+    series='tada', 
+    iteration=1,
     local_path=os.path.join('data_samples', 'sample.mp3')
 )
 ```

docs/integrations/matplotlib.md

@@ -18,11 +18,17 @@ from clearml import Task
 task = Task.init(task_name="<task_name>", project_name="<project_name>")
 ```
 
-This will create a ClearML Task that captures your script's information, including Git details, uncommitted code, python 
+This will create a ClearML Task that captures:
-environment, your matplotlib visualizations, and more. View these matplotlib plots and images in the [WebApp](../webapp/webapp_exp_track_visual.md), 
+* Git details
+* Source code and uncommitted changes 
+* Installed packages
+* Matplotlib visualizations
+* And more
+
+View captured Matplotlib plots and images in the [WebApp](../webapp/webapp_exp_track_visual.md), 
 in the experiment's **Plots** and **Debug Samples** tabs respectively.
 
-![image](../img/examples_matplotlib_example_01.png)
+![Experiment plots](../img/examples_matplotlib_example_01.png)
 
 ## Automatic Logging Control 
 By default, when ClearML is integrated into your script, it captures all of your matplotlib visualizations. 

docs/webapp/webapp_exp_table.md

@@ -220,7 +220,7 @@ Click <img src="/docs/latest/icons/ico-settings.svg" alt="Setting Gear" classNam
 metrics to view.
 
 In the **Scalars** view, click <img src="/docs/latest/icons/ico-tune.svg" alt="Tune" className="icon size-md" /> to access 
-scalar plot tools (see [here](webapp_exp_track_visual.md#scalar-plot-tools)).
+[scalar plot tools](webapp_exp_track_visual.md#scalar-plot-tools).
 
 
 ## Creating an Experiment Leaderboard