Small fixes (#131)

docs/apps/clearml_task.md

@@ -37,7 +37,7 @@ A specific branch and commit ID, other than latest commit in master, to be execu
 `--branch <branch_name> --commit <commit_id>` flags.
 If unspecified, `clearml-task` will use the latest commit from the master branch.
 
-### Command line options
+### Command Line Options
 
 <div className="tbl-cmd">
 

docs/guides/clearml-task/clearml_task_tutorial.md

@@ -49,10 +49,10 @@ Task id=2f96ee95b05d4693b360d0fcbb26b727 sent for execution on queue default
 Execution log at: https://app.community.clear.ml/projects/552d5399112d47029c146d5248570295/experiments/2f96ee95b05d4693b360d0fcbb26b727/output/log
 ```
 
-:::note
-**clearml-task** automatically finds the requirements.txt file in remote repositories. 
+:::note Adding Requirements
+`clearml-task` automatically finds the requirements.txt file in remote repositories. 
 If a remote repo does not have such a file, make sure to either add one with all the required Python packages, 
-or add the **`--packages '<package_name>`** flag to the command.
+or add the `--packages "<package_name>"` flag to the command (for example: `--packages "tqdm>=2.1" "scikit-learn"`).
 ::: 
 <br />
 

docs/guides/datasets/data_man_cifar_classification.md

@@ -83,7 +83,9 @@ dataset_project = "dataset_examples"
 
 from clearml import Dataset
 
-dataset_path = Dataset.get(dataset_name=dataset_name, dataset_project=dataset_project).get_local_copy()
+dataset_path = Dataset.get(
+    dataset_name=dataset_name, 
+    dataset_project=dataset_project).get_local_copy()
 
 trainset = datasets.CIFAR10(
     root=dataset_path,

docs/guides/datasets/data_man_python.md

@@ -24,7 +24,9 @@ We first need to obtain a local copy of the CIFAR dataset.
  from clearml import StorageManager
 
  manager = StorageManager()
- dataset_path = manager.get_local_copy(remote_url="https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz")
+ dataset_path = manager.get_local_copy(
+     remote_url="https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
+ )
 ```
 
 This script downloads the data and `dataset_path` contains the path to the downloaded data. 

docs/guides/distributed/distributed_pytorch_example.md

@@ -26,8 +26,12 @@ The example uploads a dictionary as an artifact in the main Task by calling the
 method on [`Task.current_task`](../../references/sdk/task.md#taskcurrent_task) (the main Task). The dictionary contains the [`dist.rank`](https://pytorch.org/docs/stable/distributed.html#torch.distributed.get_rank) 
 of the subprocess, making each unique.
 
-    Task.current_task().upload_artifact(
-        'temp {:02d}'.format(dist.get_rank()), artifact_object={'worker_rank': dist.get_rank()})
+```python
+Task.current_task().upload_artifact(
+    'temp {:02d}'.format(dist.get_rank()), 
+    artifact_object={'worker_rank': dist.get_rank()}
+)
+```
 
 All of these artifacts appear in the main Task under **ARTIFACTS** **>** **OTHER**.
 
@@ -40,8 +44,14 @@ method on `Task.current_task().get_logger`, which is the logger for the main Tas
 with the same title (`loss`), but a different series name (containing the subprocess' `rank`), all loss scalar series are 
 logged together.
 
-    Task.current_task().get_logger().report_scalar(
-        'loss', 'worker {:02d}'.format(dist.get_rank()), value=loss.item(), iteration=i)
+```python
+Task.current_task().get_logger().report_scalar(
+    'loss', 
+    'worker {:02d}'.format(dist.get_rank()), 
+    value=loss.item(), 
+    iteration=i
+)
+```
 
 The single scalar plot for loss appears in **RESULTS** **>** **SCALARS**.
 
@@ -49,12 +59,14 @@ The single scalar plot for loss appears in **RESULTS** **>** **SCALARS**.
 
 ## Hyperparameters
 
-**ClearML** automatically logs the argparse command line options. Since the [Task.connect](../../references/sdk/task#connect) 
-method is called on `Task.current_task`, they are logged in the main Task. A different hyperparameter key is used in each 
+**ClearML** automatically logs the argparse command line options. Since the [`Task.connect`](../../references/sdk/task#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.
 
-    param = {'worker_{}_stuff'.format(dist.get_rank()): 'some stuff ' + str(randint(0, 100))}
-    Task.current_task().connect(param)
+```python
+param = {'worker_{}_stuff'.format(dist.get_rank()): 'some stuff ' + str(randint(0, 100))}
+Task.current_task().connect(param)
+```
 
 All the hyperparameters appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS**.
 

docs/guides/distributed/subprocess_example.md

@@ -14,11 +14,15 @@ which always returns the main Task.
 
 ## Hyperparameters
 
-**ClearML** automatically logs the command line options defined with `argparse`. A parameter dictionary is logged by 
+ClearML automatically logs the command line options defined with `argparse`. A parameter dictionary is logged by 
 connecting it to the Task using a call to the [Task.connect](../../references/sdk/task#connect) method.
 
-    additional_parameters = {'stuff_' + str(randint(0, 100)): 'some stuff ' + str(randint(0, 100))}
-    Task.current_task().connect(additional_parameters)
+```python
+additional_parameters = {
+  'stuff_' + str(randint(0, 100)): 'some stuff ' + str(randint(0, 100))
+}
+Task.current_task().connect(additional_parameters)
+```
 
 Command line options appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS** **>** **Args**.
 

docs/guides/docker/extra_docker_shell_script.md

@@ -19,23 +19,20 @@ a shell script when a docker is started, but before an experiment is run.
 ## Steps 
 
 1. Open your ClearML configuration file for editing. Depending upon your operating system, it is:
-* Linux - ~/clearml.conf
-* Mac - $HOME/clearml.conf
-* Windows - \\User\\<username\>\\clearml.conf
+   * Linux - `~/clearml.conf`
+   * Mac - `$HOME/clearml.conf`
+   * Windows - `\User\<username>\clearml.conf`
 
-When you open up the file, the first line should say: `# CLEARML-AGENT configuration file`
-
-
-1. In the file, search for and go to, "extra_docker_shell_script:", which is where we will be putting our extra script. If
-it is commented out, make sure to uncomment the line. We will use the example script that is already there ["apt-get install -y bindfs", ].
+1. In the file, search for and go to, `extra_docker_shell_script:`, which is where we will be putting our extra script. If
+it is commented out, make sure to uncomment the line. We will use the example script that is already there `["apt-get install -y bindfs", ]`.
    
-1. Search for and go to "docker_force_pull" in the document, and make sure that it is set to "true", so that your docker image will 
-be updated. 
+1. Search for and go to `docker_force_pull` in the document, and make sure that it is set to `true`, so that your docker 
+   image will be updated. 
    
 1. Run the `clearml-agent` in docker mode: `clearml-agent daemon --docker --queue default`. The agent  will use the default 
    Cuda/Nvidia Docker Image. 
 
-1. Enqueue any Clearml Task to the default queue, which the Agent is now listening to. The Agent pulls the Task, and then reproduces it, 
+1. Enqueue any ClearML Task to the `default` queue, which the Agent is now listening to. The Agent pulls the Task, and then reproduces it, 
    and now it will execute the `extra_docker_shell_script` that was put in the configuration file. Then the code will be
    executed in the updated docker container. If we look at the console output in the web UI, the third entry should start
    with `Executing: ['docker', 'run', '-t', '--gpus...'`, and towards the end of the entry, where the downloaded packages are 

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

@@ -18,9 +18,14 @@ Accuracy, learning rate, and training loss appear in **RESULTS** **>** **SCALARS
 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': 6, 'batch_size': 16, 'ngrams': 2, 'base_lr': 1.0}
-    configuration_dict = task.connect(configuration_dict)  # enabling configuration override by clearml
-
+```python
+configuration_dict = {
+    'number_of_epochs': 6, 'batch_size': 16, 'ngrams': 2, 'base_lr': 1.0
+}
+# enabling configuration override by clearml
+configuration_dict = task.connect(configuration_dict)  
+```
+    
 Command line options appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS** **>** **Args**.
 
 ![image](../../../../../img/text_classification_AG_NEWS_01.png)

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

@@ -34,23 +34,14 @@ installed, it attempts to import `OptimizerBOHB`. If `clearml.automation.hpbands
 the `RandomSearch` for the search strategy. 
 
 ```python
-    aSearchStrategy = None
-    
-    if not aSearchStrategy:
-        try:
-            from clearml.optuna import OptimizerOptuna
-            aSearchStrategy = OptimizerOptuna
-        except ImportError as ex:
-            pass
-    
-    if not aSearchStrategy:
-        try:
-            from clearml.automation.hpbandster  import OptimizerBOHB
-            aSearchStrategy = OptimizerBOHB
-        except ImportError as ex:
-            pass
-    
-    if not aSearchStrategy:
+try:
+    from clearml.automation.optuna import OptimizerOptuna  # noqa
+    aSearchStrategy = OptimizerOptuna
+except ImportError as ex:
+    try:
+        from clearml.automation.hpbandster import OptimizerBOHB  # noqa
+        aSearchStrategy = OptimizerBOHB
+    except ImportError as ex:
         logging.getLogger().warning(
             'Apologies, it seems you do not have \'optuna\' or \'hpbandster\' installed, '
             'we will be using RandomSearch strategy instead')
@@ -63,16 +54,16 @@ When the optimization starts, a callback is provided that returns the best perfo
 the `job_complete_callback` function returns the ID of `top_performance_job_id`.
 
 ```python
-    def job_complete_callback(
-        job_id,                 # type: str
-        objective_value,        # type: float
-        objective_iteration,    # type: int
-        job_parameters,         # type: dict
-        top_performance_job_id  # type: str
-    ):
-        print('Job completed!', job_id, objective_value, objective_iteration, job_parameters)
-        if job_id == top_performance_job_id:
-            print('WOOT WOOT we broke the record! Objective reached {}'.format(objective_value))
+def job_complete_callback(
+    job_id,                 # type: str
+    objective_value,        # type: float
+    objective_iteration,    # type: int
+    job_parameters,         # type: dict
+    top_performance_job_id  # type: str
+):
+    print('Job completed!', job_id, objective_value, objective_iteration, job_parameters)
+    if job_id == top_performance_job_id:
+        print('WOOT WOOT we broke the record! Objective reached {}'.format(objective_value))
 ```
 
 ## Initialize the Optimization Task
@@ -86,11 +77,13 @@ When the code runs, it creates an experiment named **Automatic Hyper-Parameter O
 the project **Hyper-Parameter Optimization**, which can be seen in the **ClearML Web UI**. 
  
  ```python
-    # Connecting CLEARML
-    task = Task.init(project_name='Hyper-Parameter Optimization',
-                     task_name='Automatic Hyper-Parameter Optimization',
-                     task_type=Task.TaskTypes.optimizer,
-                     reuse_last_task_id=False)
+# Connecting CLEARML
+task = Task.init(
+    project_name='Hyper-Parameter Optimization',
+    task_name='Automatic Hyper-Parameter Optimization',
+    task_type=Task.TaskTypes.optimizer,
+    reuse_last_task_id=False
+)
 ```
 
 ## Set Up the Arguments
@@ -105,17 +98,17 @@ Since the arguments dictionary is connected to the Task, after the code runs onc
 to optimize a different experiment.
 
 ```python
-    # experiment template to optimize in the hyper-parameter optimization
-    args = {
-        'template_task_id': None,
-        'run_as_service': False,
-    }
-    args = task.connect(args)
+# experiment template to optimize in the hyper-parameter optimization
+args = {
+    'template_task_id': None,
+    'run_as_service': False,
+}
+args = task.connect(args)
     
-    # Get the template task experiment that we want to optimize
-    if not args['template_task_id']:
-        args['template_task_id'] = Task.get_task(
-            project_name='examples', task_name='Keras HP optimization base').id
+# Get the template task experiment that we want to optimize
+if not args['template_task_id']:
+    args['template_task_id'] = Task.get_task(
+        project_name='examples', task_name='Keras HP optimization base').id
 ```
 
 ## Creating the Optimizer Object
@@ -124,9 +117,9 @@ Initialize an [automation.HyperParameterOptimizer](../../../references/sdk/hpo_o
 object, setting the optimization parameters, beginning with the ID of the experiment to optimize.
 
 ```python
-    an_optimizer = HyperParameterOptimizer(
-        # This is the experiment we want to optimize
-        base_task_id=args['template_task_id'],
+an_optimizer = HyperParameterOptimizer(
+    # This is the experiment we want to optimize
+    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) 
@@ -190,24 +183,25 @@ The optimization can run as a service, if the `run_as_service` argument is set t
 running as a service, see [Services Mode](../../../clearml_agent.md#services-mode).
 
 ```python
-    # if we are running as a service, just enqueue ourselves into the services queue and let it run the optimization
-    if args['run_as_service']:
-        # if this code is executed by `clearml-agent` the function call does nothing.
-        # if executed locally, the local process will be terminated, and a remote copy will be executed instead
-        task.execute_remotely(queue_name='services', exit_process=True)
+# if we are running as a service, just enqueue ourselves into the services queue and let it run the optimization
+if args['run_as_service']:
+    # if this code is executed by `clearml-agent` the function call does nothing.
+    # if executed locally, the local process will be terminated, and a remote copy will be executed instead
+    task.execute_remotely(queue_name='services', exit_process=True)
 ```
 
 ## Optimize
 
-The optimizer is ready. Set the report period and start it, providing the callback method to report the best performance.
+The optimizer is ready. Set the report period and [start](../../../references/sdk/hpo_optimization_hyperparameteroptimizer.md#start) 
+it, providing the callback method to report the best performance.
 
 ```python
-    # report every 12 seconds, this is way too often, but we are testing here J
-    an_optimizer.set_report_period(0.2)
-    # start the optimization process, callback function to be called every time an experiment is completed
-    # this function returns immediately
-    an_optimizer.start(job_complete_callback=job_complete_callback)
-    # set the time limit for the optimization process (2 hours)
+# report every 12 seconds, this is way too often, but we are testing here J
+an_optimizer.set_report_period(0.2)
+# start the optimization process, callback function to be called every time an experiment is completed
+# this function returns immediately
+an_optimizer.start(job_complete_callback=job_complete_callback)
+# set the time limit for the optimization process (2 hours)
 ```
 
 Now that it is running: 
@@ -218,15 +212,15 @@ Now that it is running:
 1. Stop the optimizer.
 
 ```python
-    # set the time limit for the optimization process (2 hours)
-    an_optimizer.set_time_limit(in_minutes=90.0)
-    # wait until process is done (notice we are controlling the optimization process in the background)
-    an_optimizer.wait()
-    # optimization is completed, print the top performing experiments id
-    top_exp = an_optimizer.get_top_experiments(top_k=3)
-    print([t.id for t in top_exp])
-    # make sure background optimization stopped
-    an_optimizer.stop()
-    
-    print('We are done, good bye')
+# set the time limit for the optimization process (2 hours)
+an_optimizer.set_time_limit(in_minutes=90.0)
+# wait until process is done (notice we are controlling the optimization process in the background)
+an_optimizer.wait()
+# optimization is completed, print the top performing experiments id
+top_exp = an_optimizer.get_top_experiments(top_k=3)
+print([t.id for t in top_exp])
+# make sure background optimization stopped
+an_optimizer.stop()
+
+print('We are done, good bye')
 ```

docs/guides/reporting/3d_plots_reporting.md

@@ -14,38 +14,40 @@ When the script runs, it creates an experiment named `3D plot reporting`, which
 To plot a series as a surface plot, use the [Logger.report_surface](../../references/sdk/logger.md#report_surface) 
 method.
 
-        # report 3d surface
-        surface = np.random.randint(10, size=(10, 10))
-        Logger.current_logger().report_surface(
-            "example_surface",
-            "series1",
-            iteration=iteration,
-            matrix=surface,
-            xaxis="title X",
-            yaxis="title Y",
-            zaxis="title Z",
-        )
-
+```python
+# report 3d surface
+surface = np.random.randint(10, size=(10, 10))
+Logger.current_logger().report_surface(
+    "example_surface",
+    "series1",
+    iteration=iteration,
+    matrix=surface,
+    xaxis="title X",
+    yaxis="title Y",
+    zaxis="title Z",
+)
+```
 Visualize the reported surface plot in **RESULTS** **>** **PLOTS**.
 
-![image](../../img/examples_reporting_01.png)
+![Surface plot](../../img/examples_reporting_02.png)
 
 ## 3D Scatter Plot
 
 To plot a series as a 3-dimensional scatter plot, use the [Logger.report_scatter3d](../../references/sdk/logger.md#report_scatter3d) 
 method.
-
-    # report 3d scatter plot
-    scatter3d = np.random.randint(10, size=(10, 3))
-    Logger.current_logger().report_scatter3d(
-        "example_scatter_3d",
-        "series_xyz",
-        iteration=iteration,
-        scatter=scatter3d,
-        xaxis="title x",
-        yaxis="title y",
-        zaxis="title z",
-    )
+```python
+# report 3d scatter plot
+scatter3d = np.random.randint(10, size=(10, 3))
+Logger.current_logger().report_scatter3d(
+    "example_scatter_3d",
+    "series_xyz",
+    iteration=iteration,
+    scatter=scatter3d,
+    xaxis="title x",
+    yaxis="title y",
+    zaxis="title z",
+)
+```
 
 Visualize the reported 3D scatter plot in **RESULTS** **>** **PLOTS**.
-![image](../../img/examples_reporting_02.png)
+![3d scatter plot](../../img/examples_reporting_01.png)

docs/guides/reporting/artifacts.md

@@ -38,26 +38,30 @@ method. If the Pandas DataFrame changes, **ClearML** uploads the changes. The up
 
 For example:
 
-    df = pd.DataFrame(
-        {
-            'num_legs': [2, 4, 8, 0],
-            'num_wings': [2, 0, 0, 0],
-            'num_specimen_seen': [10, 2, 1, 8]
-        },
-        index=['falcon', 'dog', 'spider', 'fish']
-    )
+```python
+df = pd.DataFrame(
+    {
+        'num_legs': [2, 4, 8, 0],
+        'num_wings': [2, 0, 0, 0],
+        'num_specimen_seen': [10, 2, 1, 8]
+    },
+    index=['falcon', 'dog', 'spider', 'fish']
+)
 
-    # 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}))
+# 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}))
+```
 
 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.
 
-    # change the artifact object
-    df.sample(frac=0.5, replace=True, random_state=1)
-    # or access it from anywhere using the Task's get_registered_artifacts()
-    Task.current_task().get_registered_artifacts()['train'].sample(frac=0.5, replace=True, random_state=1)
+```python
+# change the artifact object
+df.sample(frac=0.5, replace=True, random_state=1)
+# or access it from anywhere using the Task's get_registered_artifacts()
+Task.current_task().get_registered_artifacts()['train'].sample(frac=0.5, replace=True, random_state=1)
+```
 
 ## Artifacts Without Tracking
 
@@ -75,37 +79,52 @@ Artifacts without tracking include:
 * Wildcards (stored as a ZIP files)
 
 ### Pandas DataFrames
-
-    # add and upload pandas.DataFrame (onetime snapshot of the object)
-    task.upload_artifact('Pandas', artifact_object=df)
+```python
+# add and upload pandas.DataFrame (onetime snapshot of the object)
+task.upload_artifact('Pandas', artifact_object=df)
+```
 
 ### Local Files
 
-    # add and upload local file artifact
-    task.upload_artifact('local file', artifact_object=os.path.join('data_samples', 'dancing.jpg'))
+```python
+# add and upload local file artifact
+task.upload_artifact(
+    'local file', 
+    artifact_object=os.path.join(
+        'data_samples',
+        'dancing.jpg'
+    )
+)
+```
 
 ### Dictionaries
-
-    # add and upload dictionary stored as JSON)
-    task.upload_artifact('dictionary', df.to_dict())
-
+```python
+# add and upload dictionary stored as JSON)
+task.upload_artifact('dictionary', df.to_dict())
+```
+    
 ### Numpy Objects
-
-    # add and upload Numpy Object (stored as .npz file)
-    task.upload_artifact('Numpy Eye', np.eye(100, 100))
-
+```python
+# add and upload Numpy Object (stored as .npz file)
+task.upload_artifact('Numpy Eye', np.eye(100, 100))
+```
+    
 ### Image Files
-
-    # add and upload Image (stored as .png file)
-    im = Image.open(os.path.join('data_samples', 'dancing.jpg'))
-    task.upload_artifact('pillow_image', im)
-
+```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)
+```
+    
 ### Folders
-
-    # add and upload a folder, artifact_object should be the folder path
-    task.upload_artifact('local folder', artifact_object=os.path.join('data_samples'))
+```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'))
+```
 
 ### Wildcards
-
-    # add and upload a wildcard
-    task.upload_artifact('wildcard jpegs', artifact_object=os.path.join('data_samples', '*.jpg'))
+```python
+# add and upload a wildcard
+task.upload_artifact('wildcard jpegs', artifact_object=os.path.join('data_samples', '*.jpg'))
+```
+    

docs/guides/reporting/explicit_reporting.md

@@ -23,8 +23,9 @@ Make a copy of `pytorch_mnist.py` in order to add explicit reporting to it.
 
 * In the local **ClearML** repository, `example` directory.
         
-
-    cp pytorch_mnist.py pytorch_mnist_tutorial.py
+```bash
+cp pytorch_mnist.py pytorch_mnist_tutorial.py
+```
 
 ## Step 1: Setting an Output Destination for Model Checkpoints
 
@@ -42,17 +43,21 @@ In this tutorial, we specify a local folder destination.
 
 In `pytorch_mnist_tutorial.py`, change the code from:
 
-    task = Task.init(project_name='examples', task_name='pytorch mnist train')
+```python
+task = Task.init(project_name='examples', task_name='pytorch mnist train')
+```
 
 to:
 
-    model_snapshots_path = '/mnt/clearml'
-    if not os.path.exists(model_snapshots_path):
-        os.makedirs(model_snapshots_path)
+```python
+model_snapshots_path = '/mnt/clearml'
+if not os.path.exists(model_snapshots_path):
+    os.makedirs(model_snapshots_path)
 
-    task = Task.init(project_name='examples', 
-        task_name='extending automagical ClearML example', 
-        output_uri=model_snapshots_path)
+task = Task.init(project_name='examples', 
+    task_name='extending automagical ClearML example', 
+    output_uri=model_snapshots_path)
+```
 
 When the script runs, **ClearML** creates the following directory structure:
 
@@ -94,83 +99,106 @@ package contains methods for explicit reporting of plots, log text, media, and t
 
 First, create a logger for the Task using the [Task.get_logger](../../references/sdk/task.md#get_logger) 
 method.
-
-    logger = task.get_logger
+```python
+logger = task.get_logger
+```
 
 ### Plot Scalar Metrics
 
 Add scalar metrics using the [Logger.report_scalar](../../references/sdk/logger.md#report_scalar) 
 method to report loss metrics.
 
-    def train(args, model, device, train_loader, optimizer, epoch):
+```python
+def train(args, model, device, train_loader, optimizer, epoch):
     
-        save_loss = []
+    save_loss = []
     
-        model.train()
-        for batch_idx, (data, target) in enumerate(train_loader):
-            data, target = data.to(device), target.to(device)
-            optimizer.zero_grad()
-            output = model(data)
-            loss = F.nll_loss(output, target)
-            loss.backward()
+    model.train()
+    for batch_idx, (data, target) in enumerate(train_loader):
+        data, target = data.to(device), target.to(device)
+        optimizer.zero_grad()
+        output = model(data)
+        loss = F.nll_loss(output, target)
+        loss.backward()
     
-            save_loss.append(loss)
+        save_loss.append(loss)
     
-            optimizer.step()
-            if batch_idx % args.log_interval == 0:
-                print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
-                    epoch, batch_idx * len(data), len(train_loader.dataset),
-                           100. * batch_idx / len(train_loader), loss.item()))
-                # Add manual scalar reporting for loss metrics
-                logger.report_scalar(title='Scalar example {} - epoch'.format(epoch), 
-                    series='Loss', value=loss.item(), iteration=batch_idx)
+        optimizer.step()
+        if batch_idx % args.log_interval == 0:
+            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+                epoch, batch_idx * len(data), len(train_loader.dataset),
+                        100. * batch_idx / len(train_loader), loss.item()))
+            # Add manual scalar reporting for loss metrics
+            logger.report_scalar(title='Scalar example {} - epoch'.format(epoch), 
+                series='Loss', value=loss.item(), iteration=batch_idx)
+```
 
 ### Plot Other (Not Scalar) Data
 
 The script contains a function named `test`, which determines loss and correct for the trained model. We add a histogram 
 and confusion matrix to log them.
 
-    def test(args, model, device, test_loader):
+```python
+def test(args, model, device, test_loader):
     
-        save_test_loss = []
-        save_correct = []
+    save_test_loss = []
+    save_correct = []
     
-        model.eval()
-        test_loss = 0
-        correct = 0
-        with torch.no_grad():
-            for data, target in test_loader:
-                data, target = data.to(device), target.to(device)
-                output = model(data)
-                # sum up batch loss
-                test_loss += F.nll_loss(output, target, reduction='sum').item()
-                # get the index of the max log-probability
-                pred = output.argmax(dim=1, keepdim=True)
-                correct += pred.eq(target.view_as(pred)).sum().item()
+    model.eval()
+    test_loss = 0
+    correct = 0
+    with torch.no_grad():
+        for data, target in test_loader:
+            data, target = data.to(device), target.to(device)
+            output = model(data)
+            # sum up batch loss
+            test_loss += F.nll_loss(output, target, reduction='sum').item()
+            # get the index of the max log-probability
+            pred = output.argmax(dim=1, keepdim=True)
+            correct += pred.eq(target.view_as(pred)).sum().item()
     
-                save_test_loss.append(test_loss)
-                save_correct.append(correct)
+            save_test_loss.append(test_loss)
+            save_correct.append(correct)
     
-        test_loss /= len(test_loader.dataset)
+    test_loss /= len(test_loader.dataset)
     
-        print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
-            test_loss, correct, len(test_loader.dataset),
-            100. * correct / len(test_loader.dataset)))
+    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
+        test_loss, correct, len(test_loader.dataset),
+        100. * correct / len(test_loader.dataset)))
     
-        logger.report_histogram(title='Histogram example', series='correct',
-            iteration=1, values=save_correct, xaxis='Test', yaxis='Correct')
+    logger.report_histogram(
+      title='Histogram example', 
+      series='correct',
+      iteration=1, 
+      values=save_correct, 
+      xaxis='Test', 
+      yaxis='Correct'
+    )
     
-        # Manually report test loss and correct as a confusion matrix
-        matrix = np.array([save_test_loss, save_correct])
-        logger.report_confusion_matrix(title='Confusion matrix example', 
-            series='Test loss / correct', matrix=matrix, iteration=1)
+    # Manually report test loss and correct as a confusion matrix
+    matrix = np.array([save_test_loss, save_correct])
+    logger.report_confusion_matrix(
+      title='Confusion matrix example', 
+      series='Test loss / correct', 
+      matrix=matrix, 
+      iteration=1
+    )
+```
 
 ### Log Text
 
 Extend **ClearML** by explicitly logging text, including errors, warnings, and debugging statements. We use the [Logger.report_text](../../references/sdk/logger.md#report_text) 
 method and its argument `level` to report a debugging message.
 
-    logger.report_text('The default output destination for model snapshots and artifacts is: {}'.format(model_snapshots_path ), level=logging.DEBUG)
+```python
+logger.report_text(
+  'The default output destination for model snapshots and artifacts is: {}'.format(
+    model_snapshots_path
+  ), 
+  level=logging.DEBUG
+)
+```
+
 
 ## Step 3: Registering Artifacts
 
@@ -182,16 +210,25 @@ Currently, **ClearML** supports Pandas DataFrames as registered artifacts.
 In the tutorial script, `test` function, we can assign the test loss and correct data to a Pandas DataFrame object and register 
 that Pandas DataFrame using the [Task.register_artifact](../../references/sdk/task.md#register_artifact) method.
 
-    # Create the Pandas DataFrame
-    test_loss_correct = {
-            'test lost': save_test_loss,
-            'correct': save_correct
-    }
-    df = pd.DataFrame(test_loss_correct, columns=['test lost','correct'])
+```python
+# Create the Pandas DataFrame
+test_loss_correct = {
+        'test lost': save_test_loss,
+        'correct': save_correct
+}
+df = pd.DataFrame(test_loss_correct, columns=['test lost','correct'])
     
-    # Register the test loss and correct as a Pandas DataFrame artifact
-    task.register_artifact('Test_Loss_Correct', df, metadata={'metadata string': 'apple', 
-        'metadata int': 100, 'metadata dict': {'dict string': 'pear', 'dict int': 200}})
+# Register the test loss and correct as a Pandas DataFrame artifact
+task.register_artifact(
+  'Test_Loss_Correct', 
+  df, 
+  metadata={
+    'metadata string': 'apple', 
+    'metadata int': 100, 
+    'metadata dict': {'dict string': 'pear', 'dict int': 200}
+  }
+)
+```
     
 ### Reference the Registered Artifact
 
@@ -201,9 +238,15 @@ In the tutorial script, we add [Task.current_task](../../references/sdk/task.md#
 [Task.get_registered_artifacts](../../references/sdk/task.md#get_registered_artifacts) 
 methods to take a sample.
 
-    # Once the artifact is registered, we can get it and work with it. Here, we sample it.
-    sample = Task.current_task().get_registered_artifacts()['Test_Loss_Correct'].sample(frac=0.5, 
-        replace=True, random_state=1)
+```python
+# Once the artifact is registered, we can get it and work with it. Here, we sample it.
+sample = Task.current_task().get_registered_artifacts()['Test_Loss_Correct'].sample(
+  frac=0.5, 
+  replace=True, 
+  random_state=1
+)
+
+``` 
     
 ## Step 4: Uploading Artifacts
 
@@ -220,10 +263,18 @@ Supported artifacts include:
 In the tutorial script, we upload the loss data as an artifact using the [Task.upload_artifact](../../references/sdk/task.md#upload_artifact) 
 method with metadata specified in the `metadata` parameter.
 
-    # Upload test loss as an artifact. Here, the artifact is numpy array
-    task.upload_artifact('Predictions',artifact_object=np.array(save_test_loss),
-        metadata={'metadata string': 'banana', 'metadata integer': 300,
-        'metadata dictionary': {'dict string': 'orange', 'dict int': 400}})
+```python
+# Upload test loss as an artifact. Here, the artifact is numpy array
+task.upload_artifact(
+  'Predictions',
+  artifact_object=np.array(save_test_loss),
+  metadata={
+    'metadata string': 'banana', 
+    'metadata integer': 300,
+    'metadata dictionary': {'dict string': 'orange', 'dict int': 400}
+  }
+)
+```
 
 ## Additional Information
 

docs/guides/reporting/html_reporting.md

@@ -33,28 +33,41 @@ Report the following using the `Logger.report_media` parameter method `local_pat
 ### Interactive HTML
 
 See the example script's [report_html_periodic_table](https://github.com/allegroai/clearml/blob/master/examples/reporting/html_reporting.py#L26) function, which reports a file created from Bokeh sample data.
-
-    Logger.current_logger().report_media("html", "periodic_html", iteration=iteration, local_path="periodic.html")
+```python
+Logger.current_logger().report_media(
+    "html", "periodic_html", iteration=iteration, local_path="periodic.html"
+)
+```
 
 ### Bokeh GroupBy HTML
 
 See the example script's [report_html_groupby](https://github.com/allegroai/clearml/blob/master/examples/reporting/html_reporting.py#L117) function, which reports a Pandas GroupBy with nested HTML, created from Bokeh sample data.
+```python
+Logger.current_logger().report_media(
+    "html",
+    "pandas_groupby_nested_html",
+    iteration=iteration,
+    local_path="bar_pandas_groupby_nested.html",
+)
 
-    Logger.current_logger().report_media(
-        "html",
-        "pandas_groupby_nested_html",
-        iteration=iteration,
-        local_path="bar_pandas_groupby_nested.html",
-        )
+```
 
 ### Bokeh Graph HTML
 
 See the example script's [report_html_graph](https://github.com/allegroai/clearml/blob/master/examples/reporting/html_reporting.py#L162) function, which reports a Bokeh plot created from Bokeh sample data.
 
-    Logger.current_logger().report_media("html", "Graph_html", iteration=iteration, local_path="graph.html")
+```python
+Logger.current_logger().report_media(
+    "html", "Graph_html", iteration=iteration, local_path="graph.html"
+)
+```
 
 ### Bokeh Image HTML
 
 See the example script's [report_html_image](https://github.com/allegroai/clearml/blob/master/examples/reporting/html_reporting.py#L195) function, which reports an image created from Bokeh sample data.
 
-    Logger.current_logger().report_media("html", "Spectral_html", iteration=iteration, local_path="image.html")
+```python
+Logger.current_logger().report_media(
+    "html", "Spectral_html", iteration=iteration, local_path="image.html"
+)
+```

docs/guides/reporting/hyper_parameters.md

@@ -16,13 +16,17 @@ 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.
-
-    parser = ArgumentParser()
-    parser.add_argument('--argparser_int_value', help='integer value', type=int, default=1)
-    parser.add_argument('--argparser_disabled', action='store_true', default=False, help='disables something')
-    parser.add_argument('--argparser_str_value', help='string value', default='a string')
+ 
+```python
+parser = ArgumentParser()
+parser.add_argument('--argparser_int_value', help='integer value', type=int, default=1)
+parser.add_argument(
+    '--argparser_disabled', action='store_true', default=False, help='disables something'
+)
+parser.add_argument('--argparser_str_value', help='string value', default='a string')
     
-    args = parser.parse_args()
+args = parser.parse_args()
+```
 
 Command line options appears in **HYPER PARAMETERS** **>** **Args**.
 
@@ -32,14 +36,17 @@ Command line options appears in **HYPER PARAMETERS** **>** **Args**.
 
 **ClearML** automatically logs TensorFlow Definitions, whether they are defined before or after the Task is initialized.
 
-    flags.DEFINE_string('echo', None, 'Text to echo.')
-    flags.DEFINE_string('another_str', 'My string', 'A string', module_name='test')
+```python
+flags.DEFINE_string('echo', None, 'Text to echo.')
+flags.DEFINE_string('another_str', 'My string', 'A string', module_name='test')
 
-    task = Task.init(project_name='examples', task_name='hyper-parameters example')
+task = Task.init(project_name='examples', task_name='hyper-parameters example')
     
-    flags.DEFINE_integer('echo3', 3, 'Text to echo.')
+flags.DEFINE_integer('echo3', 3, 'Text to echo.')
     
-    flags.DEFINE_string('echo5', '5', 'Text to echo.', module_name='test')
+flags.DEFINE_string('echo5', '5', 'Text to echo.', module_name='test')
+
+```    
 
 TensorFlow Definitions appear in **HYPER PARAMETERS** **>** **TF_DEFINE**.
 
@@ -50,22 +57,25 @@ TensorFlow Definitions appear in **HYPER PARAMETERS** **>** **TF_DEFINE**.
 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.
 
-    parameters = {
-        'list': [1, 2, 3],
-        'dict': {'a': 1, 'b': 2},
-        'tuple': (1, 2, 3),
-        'int': 3,
-        'float': 2.2,
-        'string': 'my string',
-    }
+```python
+parameters = {
+    'list': [1, 2, 3],
+    'dict': {'a': 1, 'b': 2},
+    'tuple': (1, 2, 3),
+    'int': 3,
+    'float': 2.2,
+    'string': 'my string',
+}
     
-    parameters = task.connect(parameters)
+parameters = task.connect(parameters)
     
-    # adding new parameter after connect (will be logged as well)
-    parameters['new_param'] = 'this is new'
-    
-    # changing the value of a parameter (new value will be stored instead of previous one)
-    parameters['float'] = '9.9'
+# adding new parameter after connect (will be logged as well)
+parameters['new_param'] = 'this is new'
+  
+# changing the value of a parameter (new value will be stored instead of previous one)
+parameters['float'] = '9.9'
+
+```
 
 Parameters from dictionaries connected to Tasks appear in **HYPER PARAMETERS** **>** **General**.
 

docs/guides/reporting/image_reporting.md

@@ -20,27 +20,39 @@ When the script runs, it creates an experiment named `image reporting`, which is
 Report images using several formats by calling the [Logger.report_image](../../references/sdk/logger.md#report_image) 
 method:
 
-        # report image as float image
-        m = np.eye(256, 256, dtype=np.float)
-        Logger.current_logger().report_image("image", "image float", iteration=iteration, image=m)
+```python
+# report image as float image
+m = np.eye(256, 256, dtype=np.float)
+Logger.current_logger().report_image("image", "image float", iteration=iteration, image=m)
         
-        # report image as uint8
-        m = np.eye(256, 256, dtype=np.uint8) * 255
-        Logger.current_logger().report_image("image", "image uint8", iteration=iteration, image=m)
+# report image as uint8
+m = np.eye(256, 256, dtype=np.uint8) * 255
+Logger.current_logger().report_image("image", "image uint8", iteration=iteration, image=m)
         
-        # report image as uint8 RGB
-        m = np.concatenate((np.atleast_3d(m), np.zeros((256, 256, 2), dtype=np.uint8)), axis=2)
-        Logger.current_logger().report_image("image", "image color red", iteration=iteration, image=m)
+# report image as uint8 RGB
+m = np.concatenate((np.atleast_3d(m), np.zeros((256, 256, 2), dtype=np.uint8)), axis=2)
+Logger.current_logger().report_image(
+    "image", 
+    "image color red", 
+    iteration=iteration, 
+    image=m
+)
         
-        # report PIL Image object
-        image_open = Image.open(os.path.join("data_samples", "picasso.jpg"))
-        Logger.current_logger().report_image("image", "image PIL", iteration=iteration, image=image_open)
+# report PIL Image object
+image_open = Image.open(os.path.join("data_samples", "picasso.jpg"))
+Logger.current_logger().report_image(
+    "image", 
+    "image PIL", 
+    iteration=iteration, 
+    image=image_open
+)
+```
 
 **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)
 
-Double click a thumbnail and the image viewer opens.
+Double click a thumbnail, and the image viewer opens.
 
 ![image](../../img/examples_reporting_07a.png)

docs/guides/reporting/media_reporting.md

@@ -24,15 +24,19 @@ project.
 Report by calling the [Logger.report_media](../../references/sdk/logger.md#report_media) 
 method using the `url` parameter.
 
-    # report video, an already uploaded video media (url)
-    Logger.current_logger().report_media(
-        'video', 'big bunny', iteration=1,
-        url='https://test-videos.co.uk/vids/bigbuckbunny/mp4/h264/720/Big_Buck_Bunny_720_10s_1MB.mp4')
+```python
+# report video, an already uploaded video media (url)
+Logger.current_logger().report_media(
+    'video', 'big bunny', iteration=1,
+    url='https://test-videos.co.uk/vids/bigbuckbunny/mp4/h264/720/Big_Buck_Bunny_720_10s_1MB.mp4'
+)
     
-    #  report audio, report an already uploaded audio media (url)
-    Logger.current_logger().report_media(
-        'audio', 'pink panther', iteration=1,
-        url='https://www2.cs.uic.edu/~i101/SoundFiles/PinkPanther30.wav')
+#  report audio, report an already uploaded audio media (url)
+Logger.current_logger().report_media(
+    'audio', 'pink panther', iteration=1,
+    url='https://www2.cs.uic.edu/~i101/SoundFiles/PinkPanther30.wav'
+)
+```
 
 The reported audio can be viewed in the **DEBUG SAMPLES** sub-tab. Double click a thumbnail, and the audio player opens.
 
@@ -43,10 +47,13 @@ The reported audio can be viewed in the **DEBUG SAMPLES** sub-tab. Double click
 
 Use the `local_path` parameter.
 
-    #  report audio, report local media audio file
-    Logger.current_logger().report_media(
-        'audio', 'tada', iteration=1,
-        local_path=os.path.join('data_samples', 'sample.mp3'))
+```python
+#  report audio, report local media audio file
+Logger.current_logger().report_media(
+    'audio', 'tada', iteration=1,
+    local_path=os.path.join('data_samples', 'sample.mp3')
+)
+```
     
 The reported video can be viewed in the **DEBUG SAMPLES** sub-tab. Double click a thumbnail, and the video player opens.
 

docs/guides/reporting/model_config.md

@@ -17,11 +17,12 @@ Connect a configuration file to a Task by calling the [Task.connect_configuratio
 method with the file location and the configuration object's name as arguments. In this example, we connect a JSON file and a YAML file
 to a Task. 
 
-    config_file_json = 'data_samples/sample.json'
-    task.connect_configuration(name="json file", configuration=config_file_json)
-    ...
-    config_file_yaml = 'data_samples/config_yaml.yaml'
-    task.connect_configuration(configuration=config_file_yaml, name="yaml file")        
+```python
+config_file_json = 'data_samples/sample.json'
+task.connect_configuration(name="json file", configuration=config_file_json)
+config_file_yaml = 'data_samples/config_yaml.yaml'
+task.connect_configuration(configuration=config_file_yaml, name="yaml file")
+```
 
 The configuration is logged to the ClearML Task and can be viewed in the **ClearML Web UI** experiment details **>** **CONFIGURATION** tab **>** **CONFIGURATION OBJECTS** 
 section. The contents of the JSON file will appear in the **json file** object, and the contents of the YAML file will appear 
@@ -34,17 +35,21 @@ in the **yaml file** object, as specified in the `name` parameter of the `connec
 Connect a configuration dictionary to a Task by creating a dictionary, and then calling the [Task.connect_configuration](../../references/sdk/task.md#connect_configuration) 
 method with the dictionary and the object name as arguments. After the configuration is connected, **ClearML** tracks changes to it.
 
-    model_config_dict = {
-        'CHANGE ME': 13.37,
-        'dict': {'sub_value': 'string', 'sub_integer': 11},
-        'list_of_ints': [1, 2, 3, 4],
-    }
-    model_config_dict = task.connect_configuration(name='dictionary', configuration=model_config_dict)
-
-    # Update the dictionary after connecting it, and the changes will be tracked as well.
-    model_config_dict['new value'] = 10
-    model_config_dict['CHANGE ME'] *= model_config_dict['new value']
+```python
+model_config_dict = {
+    'CHANGE ME': 13.37,
+    'dict': {'sub_value': 'string', 'sub_integer': 11},
+    'list_of_ints': [1, 2, 3, 4],
+}
+model_config_dict = task.connect_configuration(
+    name='dictionary', 
+    configuration=model_config_dict
+)
 
+# Update the dictionary after connecting it, and the changes will be tracked as well.
+model_config_dict['new value'] = 10
+model_config_dict['CHANGE ME'] *= model_config_dict['new value']
+```
 The configurations are connected to the ClearML Task and can be viewed in the **ClearML Web UI** **>** experiment details **>** **CONFIGURATION** tab **>** 
 **CONFIGURATION OBJECTS** area **>** **dictionary** object.
 
@@ -55,13 +60,16 @@ The configurations are connected to the ClearML Task and can be viewed in the **
 Connect a label enumeration dictionary by creating the dictionary, and then calling the [Task.connect_label_enumeration](../../references/sdk/task.md#connect_label_enumeration) 
 method with the dictionary as an argument.
 
-    # store the label enumeration of the training model
-    labels = {'background': 0, 'cat': 1, 'dog': 2}
-    task.connect_label_enumeration(labels)
+```python
+# store the label enumeration of the training model
+labels = {'background': 0, 'cat': 1, 'dog': 2}
+task.connect_label_enumeration(labels)
+```
 
 Log a local model file.
-    
-    OutputModel().update_weights('my_best_model.bin')
+```python
+OutputModel().update_weights('my_best_model.bin')
+```    
 
 The model which is stored contains the model configuration and the label enumeration. 
 

docs/guides/reporting/pandas_reporting.md

@@ -14,17 +14,24 @@ When the script runs, it creates an experiment named `pandas table reporting`, w
 Report Pandas DataFrames by calling the [Logger.report_table](../../references/sdk/logger.md#report_table) 
 method, and providing the DataFrame in the `table_plot` parameter.
 
-    # Report table - DataFrame with index
-    df = pd.DataFrame(
-        {
-            "num_legs": [2, 4, 8, 0],
-            "num_wings": [2, 0, 0, 0],
-            "num_specimen_seen": [10, 2, 1, 8],
-        },
-        index=["falcon", "dog", "spider", "fish"],
-    )
-    df.index.name = "id"
-    Logger.current_logger().report_table("table pd", "PD with index", iteration=iteration, table_plot=df)
+```python
+# Report table - DataFrame with index
+df = pd.DataFrame(
+    {
+        "num_legs": [2, 4, 8, 0],
+        "num_wings": [2, 0, 0, 0],
+        "num_specimen_seen": [10, 2, 1, 8],
+    },
+    index=["falcon", "dog", "spider", "fish"],
+)
+df.index.name = "id"
+Logger.current_logger().report_table(
+    "table pd", 
+    "PD with index", 
+    iteration=iteration, 
+    table_plot=df
+)
+```
 
 ![image](../../img/examples_reporting_12.png)
 
@@ -32,8 +39,15 @@ method, and providing the DataFrame in the `table_plot` parameter.
 
 Report CSV files by providing the URL location of the CSV file in the `url` parameter. For a local CSV file, use the `csv` parameter.
 
-    # Report table - CSV from path
-    csv_url = "https://raw.githubusercontent.com/plotly/datasets/master/Mining-BTC-180.csv"
-    Logger.current_logger().report_table("table csv", "remote csv", iteration=iteration, url=csv_url)
+```python
+# Report table - CSV from path
+csv_url = "https://raw.githubusercontent.com/plotly/datasets/master/Mining-BTC-180.csv"
+Logger.current_logger().report_table(
+    "table csv", 
+    "remote csv", 
+    iteration=iteration, 
+    url=csv_url
+)
+```
 
 ![image](../../img/examples_reporting_11.png)

docs/guides/reporting/plotly_reporting.md

@@ -11,14 +11,25 @@ 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/) 
 in the Plotly documentation): 
 
-    # Iris dataset
-    df = px.data.iris()
+```python
+# Iris dataset
+df = px.data.iris()
     
-    # create complex plotly figure
-    fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species", marginal_y="rug", marginal_x="histogram")
+# create complex plotly figure
+fig = px.scatter(
+    df, 
+    x="sepal_width", 
+    y="sepal_length", 
+    color="species", 
+    marginal_y="rug", 
+    marginal_x="histogram"
+)
     
-    # report the plotly figure
-    task.get_logger().report_plotly(title="iris", series="sepal", iteration=0, figure=fig)
+# report the plotly figure
+task.get_logger().report_plotly(
+    title="iris", series="sepal", iteration=0, figure=fig
+)
+```
 
 When the script runs, it creates an experiment named `plotly reporting`, which is associated with the examples project.
 

docs/guides/reporting/scalar_reporting.md

@@ -12,14 +12,24 @@ To reports scalars, call the [Logger.report_scalar](../../references/sdk/logger.
 method. To report more than one series on the same plot, use the same `title` argument. For different plots, use different 
 `title` arguments. 
 
-    # report two scalar series on the same graph
-    for i in range(100):
-        Logger.current_logger().report_scalar("unified graph", "series A", iteration=i, value=1./(i+1))
-        Logger.current_logger().report_scalar("unified graph", "series B", iteration=i, value=10./(i+1))
+```python
+# report two scalar series on the same graph
+for i in range(100):
+    Logger.current_logger().report_scalar(
+        "unified graph", "series A", iteration=i, value=1./(i+1)
+    )
+    Logger.current_logger().report_scalar(
+        "unified graph", "series B", iteration=i, value=10./(i+1)
+    )
     
-    # report two scalar series on two different graphs
-    for i in range(100):
-        Logger.current_logger().report_scalar("graph A", "series A", iteration=i, value=1./(i+1))
-        Logger.current_logger().report_scalar("graph B", "series B", iteration=i, value=10./(i+1))
+# report two scalar series on two different graphs
+for i in range(100):
+    Logger.current_logger().report_scalar(
+        "graph A", "series A", iteration=i, value=1./(i+1)
+    )
+    Logger.current_logger().report_scalar(
+        "graph B", "series B", iteration=i, value=10./(i+1)
+    )
+```
 
 ![image](../../img/examples_reporting_14.png)

docs/guides/reporting/scatter_hist_confusion_mat_reporting.md

@@ -19,37 +19,39 @@ method. To report more than one series on the same plot, use same the `title` ar
 `title` arguments. Specify the type of histogram with the `mode` parameter. The `mode` values are `group` (the default), 
 `stack`, and `relative`.
 
-    # report a single histogram
-    histogram = np.random.randint(10, size=10)
-    Logger.current_logger().report_histogram(
-        "single_histogram",
-        "random histogram",
-        iteration=iteration,
-        values=histogram,
-        xaxis="title x",
-        yaxis="title y",
-    )
+```python
+# report a single histogram
+histogram = np.random.randint(10, size=10)
+Logger.current_logger().report_histogram(
+    "single_histogram",
+    "random histogram",
+    iteration=iteration,
+    values=histogram,
+    xaxis="title x",
+    yaxis="title y",
+)
     
-    # report two histograms on the same graph (plot)
-    histogram1 = np.random.randint(13, size=10)
-    histogram2 = histogram * 0.75
-    Logger.current_logger().report_histogram(
-        "two_histogram",
-        "series 1",
-        iteration=iteration,
-        values=histogram1,
-        xaxis="title x",
-        yaxis="title y",
-    )
+# report two histograms on the same graph (plot)
+histogram1 = np.random.randint(13, size=10)
+histogram2 = histogram * 0.75
+Logger.current_logger().report_histogram(
+    "two_histogram",
+    "series 1",
+    iteration=iteration,
+    values=histogram1,
+    xaxis="title x",
+    yaxis="title y",
+)
     
-    Logger.current_logger().report_histogram(
-        "two_histogram",
-        "series 2",
-        iteration=iteration,
-        values=histogram2,
-        xaxis="title x",
-        yaxis="title y",
-        )
+Logger.current_logger().report_histogram(
+    "two_histogram",
+    "series 2",
+    iteration=iteration,
+    values=histogram2,
+    xaxis="title x",
+    yaxis="title y",
+)
+```
 
 ![image](../../img/examples_reporting_15.png)
 
@@ -60,69 +62,75 @@ method. To report more than one series on the same plot, use same the `title` ar
 Report confusion matrices by calling the [Logger.report_matrix](../../references/sdk/logger.md#report_matrix) 
 method.
 
-    # report confusion matrix
-    confusion = np.random.randint(10, size=(10, 10))
-    Logger.current_logger().report_matrix(
-        "example_confusion",
-        "ignored",
-        iteration=iteration,
-        matrix=confusion,
-        xaxis="title X",
-        yaxis="title Y",
-        )
+```python
+# report confusion matrix
+confusion = np.random.randint(10, size=(10, 10))
+Logger.current_logger().report_matrix(
+    "example_confusion",
+    "ignored",
+    iteration=iteration,
+    matrix=confusion,
+    xaxis="title X",
+    yaxis="title Y",
+)
+```
 
 ![image](../../img/examples_reporting_16.png)
 
-    # report confusion matrix with 0,0 is at the top left
-    Logger.current_logger().report_matrix(
-        "example_confusion_0_0_at_top",
-        "ignored",
-        iteration=iteration,
-        matrix=confusion,
-        xaxis="title X",
-        yaxis="title Y",
-        yaxis_reversed=True,
-    )
+```python
+# report confusion matrix with 0,0 is at the top left
+Logger.current_logger().report_matrix(
+    "example_confusion_0_0_at_top",
+    "ignored",
+    iteration=iteration,
+    matrix=confusion,
+    xaxis="title X",
+    yaxis="title Y",
+    yaxis_reversed=True,
+)
+```
 
 ## 2D Scatter Plots
 
 Report 2D scatter plots by calling the [Logger.report_scatter2d](../../references/sdk/logger.md#report_scatter2d) 
 method. Use the `mode` parameter to plot data points with lines (by default), markers, or both lines and markers.
 
-    scatter2d = np.hstack(
-        (np.atleast_2d(np.arange(0, 10)).T, np.random.randint(10, size=(10, 1)))
-    )
+```python
+scatter2d = np.hstack(
+    (np.atleast_2d(np.arange(0, 10)).T, np.random.randint(10, size=(10, 1)))
+)
     
-    # report 2d scatter plot with lines
-    Logger.current_logger().report_scatter2d(
-        "example_scatter",
-        "series_xy",
-        iteration=iteration,
-        scatter=scatter2d,
-        xaxis="title x",
-        yaxis="title y",
-    )
+# report 2d scatter plot with lines
+Logger.current_logger().report_scatter2d(
+    "example_scatter",
+    "series_xy",
+    iteration=iteration,
+    scatter=scatter2d,
+    xaxis="title x",
+    yaxis="title y",
+)
     
-    # report 2d scatter plot with markers
-    Logger.current_logger().report_scatter2d(
-        "example_scatter",
-        "series_markers",
-        iteration=iteration,
-        scatter=scatter2d,
-        xaxis="title x",
-        yaxis="title y",
-        mode='markers'
-    )
+# report 2d scatter plot with markers
+Logger.current_logger().report_scatter2d(
+    "example_scatter",
+    "series_markers",
+    iteration=iteration,
+    scatter=scatter2d,
+    xaxis="title x",
+    yaxis="title y",
+    mode='markers'
+)
     
-    # report 2d scatter plot with lines and markers
-    Logger.current_logger().report_scatter2d(
-        "example_scatter",
-        "series_lines+markers",
-        iteration=iteration,
-        scatter=scatter2d,
-        xaxis="title x",
-        yaxis="title y",
-        mode='lines+markers'
-    )
+# report 2d scatter plot with lines and markers
+Logger.current_logger().report_scatter2d(
+    "example_scatter",
+    "series_lines+markers",
+    iteration=iteration,
+    scatter=scatter2d,
+    xaxis="title x",
+    yaxis="title y",
+    mode='lines+markers'
+)
+```    
 
 ![image](../../img/examples_reporting_17.png)

docs/hyperdatasets/dataviews.md

@@ -342,11 +342,19 @@ one ROI labeled with both `Car` and `largely_occluded` will be input.
 ```python
 myDataView = DataView(iteration_order=IterationOrder.random, iteration_infinite=True)
 
-myDataView.add_query(dataset_name='myDataset', version_name='training', 
-                     roi_query='Car', weight = 1)
+myDataView.add_query(
+    dataset_name='myDataset', 
+    version_name='training', 
+    roi_query='Car', 
+    weight = 1
+)
 
-myDataView.add_query(dataset_name='myDataset', version_name='training', 
-                     roi_query='label.keyword:\"Car\" AND label.keyword:\"largely_occluded\"', weight = 5)
+myDataView.add_query(
+    dataset_name='myDataset', 
+    version_name='training',
+    roi_query='label.keyword:\"Car\" AND label.keyword:\"largely_occluded\"', 
+    weight = 5
+)
 ```
 
 ### Mapping ROI Labels

docs/hyperdatasets/masks.md

@@ -251,6 +251,7 @@ mask value as a list with the RGB values in the `mask_rgb` parameter, and a list
 frame = SingleFrame(
     source='/home/user/woof_meow.jpg',
     preview_uri='https://storage.googleapis.com/kaggle-competitions/kaggle/3362/media/woof_meow.jpg',
+)
     
 frame.add_annotation(mask_rgb=[0, 0, 0], labels=['cat'])
 ```

docs/webapp/webapp_archiving.md

@@ -35,7 +35,7 @@ When archiving an experiment:
 
         * Experiments or models table - Right click the experiment or model **>** **Restore**.
         * Info panel or full screen details view - Click <img src="/docs/latest/icons/ico-bars-menu.svg" alt="Bars menu" className="icon size-sm space-sm" />
-          (menu) **>** **Restore from archive**.
+          (menu) **>** **Restore from Archive**.
 
     * Restore multiple experiments or models from the:
 

docs/webapp/webapp_exp_comparing.md

@@ -126,7 +126,7 @@ Visualize the comparison of scalars, which includes metrics and monitored resour
 
 1. Click the **SCALARS** tab.
 1. In the dropdown menu (upper right of the left sidebar), choose either:
-    * **Last values** (the final or most recent value)
+    * **Last Values** (the final or most recent value)
     * **Min Values** (the minimal values)
     * **Max Values** (the maximal values)
 1. Sort by variant.