add advanced to ToC, add image, add more info to execute_remotely.md

docs/guides/advanced/execute_remotely.md

@@ -7,13 +7,60 @@ script demonstrates the use of the [`execute_remotely`](../../references/sdk/tas
 
 The script does the following: 
 * Trains a simple deep neural network on the PyTorch built-in MNIST dataset.
-* Uses ClearML automatic logging.
+* Uses ClearML's automatic and explicit logging.
 * Creates an experiment named `remote_execution pytorch mnist train`, which is associated with the `examples` project.
 
-When the code is executed, the training runs for one epoch to make sure nothing crashes, then the code passes the `execute_remotely` method
-which terminates the local execution of the code. Execution will switch to remote execution by the agent listening to 
-queue specified in the `queue_name` parameter of the method. 
+## Execution Flow
 
-This feature is especially helpful if you want to run the first epoch locally on your machine to debug and to
-make sure code doesn't crash, and then move to a stronger machine for the entire training.
+The following describes the code's execution flow: 
+1. The training runs for one epoch. 
+1. The code passes the `execute_remotely` method which terminates the local execution of the code.
+1. Execution switches to remote execution by the agent listening to queue specified in the `queue_name` parameter of the method. 
+
+The `execute_remotely` method is especially helpful when running code on a development machine for a few iterations
+to debug and to make sure the code doesn't crash, or setting up an environment. After that, the training can be 
+moved to be executed by a stronger machine.
+
+## Scalars
+
+In the example script's `train` function, the following code explicitly reports scalars to **ClearML**:
+
+```python
+Logger.current_logger().report_scalar(
+    "train", "loss", iteration=(epoch * len(train_loader) + batch_idx), value=loss.item())
+```
+
+In the `test` method, the code explicitly reports `loss` and `accuracy` scalars.
+
+```python
+Logger.current_logger().report_scalar(
+    "test", "loss", iteration=epoch, value=test_loss)
+Logger.current_logger().report_scalar(
+    "test", "accuracy", iteration=epoch, value=(correct / len(test_loader.dataset)))
+```    
+
+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)
+
+## Hyperparameters
+
+ClearML automatically logs command line options defined with `argparse`. They appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS** **>** **Args**.
+
+![image](../../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)
+
+## Artifacts
+
+Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab and in 
+the info panel of the **MODELS** tab.  
+
+
+![image](../../img/examples_remote_execution_artifacts.png)
 

sidebars.js

@@ -57,6 +57,7 @@ module.exports = {
     ],
     guidesSidebar: [
             'guides/guidemain',
+            {'Advanced': ['guides/advanced/execute_remotely', 'guides/advanced/multiple_tasks_single_process']},
             {'Automation': ['guides/automation/manual_random_param_search_example', 'guides/automation/task_piping']},
             {'Data Management': ['guides/data management/data_man_simple', 'guides/data management/data_man_folder_sync', 'guides/data management/data_man_cifar_classification']},
             {'ClearML Task': ['guides/clearml-task/clearml_task_tutorial']},