Change headings to title caps (#62)

docs/guides/frameworks/autokeras/integration_autokeras.md

@@ -25,7 +25,7 @@ Once these are logged, they can be visualized in the **ClearML Web UI**.
 If you are not already using **ClearML**, see [Getting Started](/getting_started/ds/best_practices.md).
 :::
 
-## Adding ClearML to code
+## Adding ClearML to Code
 
 Add two lines of code:
 ```python

docs/guides/frameworks/keras/allegro_clearml_keras_tb_example.md

@@ -40,7 +40,7 @@ Text printed to the console for training progress, as well as all other console
 
 ![image](../../../img/keras_colab_01.png)
 
-## Configuration objects
+## Configuration Objects
 
 The configuration appears in **CONFIGURATIONS** **>** **CONFIGURATION OBJECTS** **>** **General**. 
 

docs/guides/frameworks/matplotlib/allegro_clearml_matplotlib_example.md

@@ -21,7 +21,7 @@ The scatter plots appear in the **ClearML Web UI**, in **RESULTS** **>** **PLOTS
 
 ![image](../../../img/examples_matplotlib_example_03.png)
 
-## Debug samples
+## Debug Samples
 
 The images appear in **RESULTS** **>** **DEBUG SAMPLES**. Each debug sample image is associated with a metric.
 

docs/guides/frameworks/matplotlib/matplotlib_example.md

@@ -17,7 +17,7 @@ The scatter plots appear in the **ClearML Web UI**, in **RESULTS** **>** **PLOTS
 
 ![image](../../../img/examples_matplotlib_example_03.png)
 
-## Debug samples
+## Debug Samples
 
 The images appear in **RESULTS** **>** **DEBUG SAMPLES**. Each debug sample image is associated with a metric.
 

docs/guides/frameworks/pytorch ignite/integration_pytorch_ignite.md

@@ -29,7 +29,7 @@ Integrate **ClearML** with the following steps:
         event_name=Events.ITERATION_COMPLETED)
   ```
     
-### ClearMLLogger parameters
+### ClearMLLogger Parameters
 
 The following are the `ClearMLLogger` method parameters:
 
@@ -52,7 +52,7 @@ The following are the `ClearMLLogger` method parameters:
 
 ## Logging 
 
-### Ignite engine output and / or metrics
+### Ignite Engine Output and / or Metrics
 
 To log scalars, Ignite engine's output and / or metrics, use the `OutputHandler`. 
 
@@ -91,7 +91,7 @@ clearml_logger.attach(evaluator,
     event_name=Events.EPOCH_COMPLETED)
 ```
 
-### Optimizer parameters
+### Optimizer Parameters
 
 To log optimizer parameters, use `OptimizerParamsHandler`:
 ```python
@@ -101,7 +101,7 @@ clearml_logger.attach(trainer,
     event_name=Events.ITERATION_STARTED)
 ```
     
-### Model weights
+### Model Weights
 
 To log model weights as scalars, use `WeightsScalarHandler`:
 
@@ -122,7 +122,7 @@ clearml_logger.attach(trainer,
 ```
     
 
-## Model snapshots
+## Model Snapshots
 
 To save input snapshots as **ClearML** artifacts, use `ClearMLSaver`:
 
@@ -137,7 +137,7 @@ handler = Checkpoint(to_save, ClearMLSaver(clearml_logger), n_saved=1,
 validation_evaluator.add_event_handler(Events.EVENT_COMPLETED, handler)
 ```
 
-## Visualizing experiment results
+## Visualizing Experiment Results
 
 When the code with an ignite `ClearMLLogger` object and attached [handlers](https://github.com/pytorch/ignite/blob/master/ignite/contrib/handlers/trains_logger.py)
 runs, the experiment results can be visualized in the **ClearML Web UI**. 
@@ -154,7 +154,7 @@ View the scalars, including training and validation metrics, in the experiment's
 ![image](../../../img/ignite_training.png)
 ![image](../../../img/ignite_validation.png)
 
-### Model snapshots
+### Model Snapshots
 
 To save model snapshots, use `ClearMLServer`.
 

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

@@ -10,17 +10,17 @@ The accuracy, learning rate, and training loss scalars are automatically logged,
 
 ![image](../../../../../img/examples_audio_classification_UrbanSound8K_03.png)
 
-## Debug samples
+## Debug Samples
 
 The audio samples and spectrogram plots are automatically logged and appear in **RESULTS** **>** **DEBUG SAMPLES**.
 
-### Audio samples
+### Audio Samples
 
 ![image](../../../../../img/examples_audio_classification_UrbanSound8K_06.png)
 
 By doubling clicking a thumbnail, you can play an audio sample.
 
-### Spectrogram visualizations
+### Spectrogram Visualizations
 
 ![image](../../../../../img/examples_audio_classification_UrbanSound8K_04.png)
 

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

@@ -11,17 +11,17 @@ demonstrates integrating **ClearML** into a Jupyter Notebook which uses PyTorch
 
 ![image](../../../../../img/examples_audio_preprocessing_example_08.png)
 
-## Debug samples
+## 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
+### Audio Samples
 
 You can play the audio samples by double clicking the audio thumbnail.
 
 ![image](../../../../../img/examples_audio_preprocessing_example_03.png)
 
-### Spectrogram visualizations
+### Spectrogram Visualizations
 
 ![image](../../../../../img/examples_audio_preprocessing_example_06.png)
 ![image](../../../../../img/examples_audio_preprocessing_example_06a.png)

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

@@ -77,7 +77,7 @@ All console output from `Hyper-Parameter Optimization` appears in **RESULTS** ta
 
 ![image](../../../../../img/examples_hyperparameter_search_03.png)
 
-## Experiments comparison
+## 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.
 
@@ -93,31 +93,31 @@ Use the **ClearML Web UI** [experiment comparison](../../../../../webapp/webapp_
 * Plots
 * Debug images
 
-### Side by side hyperparameter value comparison
+### Side by Side Hyperparameter Value Comparison
 
 In the experiment comparison window, **HYPER PARAMETERS** tab, select **Values** in the list (the right of **+ Add Experiment**), and hyperparameter differences appear with a different background color.
 
 ![image](../../../../../img/examples_hyperparameter_search_06.png)
 
-### Metric comparison by hyperparameter
+### Metric Comparison by Hyperparameter
 
 Select **Parallel Coordinates** in the list, click a **Performance Metric**, and then select the checkboxes of the hyperparameters.
 
 ![image](../../../../../img/examples_hyperparameter_search_07.png)
 
-### Scalar values comparison
+### Scalar Values Comparison
 
 In the **SCALARS** tab, select **Last Values**, **Min Values**, or **Max Values**. Value differences appear with a different background color.
 
 ![image](../../../../../img/examples_hyperparameter_search_09.png)
 
-### Scalar series comparison
+### Scalar Series Comparison
 
 Select **Graph** and the scalar series for the jobs appears, where each scalar plot shows the series for all jobs.
 
 ![image](../../../../../img/examples_hyperparameter_search_08.png)
 
-### Debug samples comparison
+### Debug Samples Comparison
 
 In the **DEBUG SAMPLES** tab, debug images appear.
 

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

@@ -17,7 +17,7 @@ The accuracy, accuracy per class, and training loss scalars are automatically lo
 
 ![image](../../../../../img/examples_image_classification_CIFAR10_05.png)
 
-## Debug samples
+## Debug Samples
 
 The image samples are automatically logged and appear in **RESULTS** **>** **DEBUG SAMPLES**.
 

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

@@ -22,13 +22,13 @@ In this pipeline example, the data preprocessing Task and training Task are each
 The data download Task is not a step in the pipeline, see [download_and_split](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/notebooks/table/download_and_split.ipynb).
 :::
     
-## Pipeline controller and steps
+## Pipeline Controller and Steps
 
 In this example, a pipeline controller object is created.
 
     pipe = PipelineController(default_execution_queue='dan_queue', add_pipeline_tags=True)
     
-### Preprocessing step
+### Preprocessing Step
 
 Two preprocessing nodes are added to the pipeline. These steps will run concurrently.
     
@@ -89,7 +89,7 @@ two sets of data are created in the pipeline.
 </details>
    
 
-### Training step
+### Training Step
 
 Each training node depends upon the completion of one preprocessing node. The parameter `parents` is a list of step names indicating all steps that must complete before the new step starts. In this case, `preprocessing_1` must complete before `train_1` begins, and `preprocessing_2` must complete before `train_2` begins.
 
@@ -133,7 +133,7 @@ The ID of a Task whose artifact contains a set of preprocessed data for training
 </details>
    
 
-### Best model step
+### Best Model Step
 
 The best model step depends upon both training nodes completing and takes the two training node Task IDs to override.
 
@@ -168,7 +168,7 @@ The IDs of the training Tasks from the steps named `train_1` and `train_2` are p
 </details>
    
 
-### Pipeline start, wait, and cleanup
+### Pipeline Start, Wait, and Cleanup
 
 Once all steps are added to the pipeline, start it. Wait for it to complete. Finally, cleanup the pipeline processes.
 
@@ -196,7 +196,7 @@ Once all steps are added to the pipeline, start it. Wait for it to complete. Fin
 </details>
 
 
-## Running the pipeline
+## Running the Pipeline
 
 **To run the pipeline:**
 

docs/guides/frameworks/pytorch/pytorch_tensorboard.md

@@ -21,7 +21,7 @@ These scalars, along with the resource utilization plots, which are titled **:mo
 
 ![image](../../../img/examples_pytorch_tensorboard_07.png)
 
-## Debug samples
+## Debug Samples
 
 **ClearML** automatically tracks images and text output to TensorFlow. They appear in **RESULTS** **>** **DEBUG SAMPLES**.
 

docs/guides/frameworks/tensorflow/integration_keras_tuner.md

@@ -40,7 +40,7 @@ When the script runs, it logs:
 
 ![image](../../../img/integration_keras_tuner_06.png)
 
-## Summary of hyperparameter optimization
+## Summary of Hyperparameter Optimization
 
 **ClearML** automatically logs the parameters of each experiment run in the hyperparameter search. They appear in tabular 
 form in **RESULTS** **>** **PLOTS**.
@@ -61,7 +61,7 @@ The model configuration is stored with the model.
 
 ![image](../../../img/integration_keras_tuner_05.png)
 
-## Configuration objects
+## Configuration Objects
 
 ### Hyperparameters
 

docs/guides/frameworks/tensorflow/tensorboard_toy.md

@@ -29,7 +29,7 @@ The `tf.summary.histogram` output appears in **RESULTS** **>** **PLOTS**.
 
 ![image](../../../img/examples_tensorboard_toy_04.png)
 
-## Debug samples
+## Debug Samples
 
 **ClearML** automatically tracks images and text output to TensorFlow. They appear in **RESULTS** **>** **DEBUG SAMPLES**.