Add example links (#57)

docs/fundamentals/agents_and_queues.md

@@ -95,9 +95,9 @@ Some tasks, mainly control (Like a pipeline controller) or services (Like an arc
 This is where the `services-modes` comes into play. An agent running in services-mode will spin multiple tasks at the same time, each Task will register itself as a sub-agent (visible in the workers Tab in the UI).
 Some examples for suitable tasks are:
 
-- [Pipeline controller](https://github.com/allegroai/clearml/blob/master/examples/pipeline/pipeline_controller.py) - Implementing the pipeline scheduling and logic
-- [Hyper-Parameter Optimization](https://github.com/allegroai/clearml/blob/master/examples/optimization/hyper-parameter-optimization/hyper_parameter_optimizer.py) - Implementing an active selection of experiments
-- [Control Service](https://github.com/allegroai/clearml/blob/master/examples/services/aws-autoscaler/aws_autoscaler.py) - AWS Autoscaler for example
-- [External services](https://github.com/allegroai/clearml/blob/master/examples/services/monitoring/slack_alerts.py) - Such as Slack integration alert service
+- [Pipeline controller](../guides/pipeline/pipeline_controller.md) - Implementing the pipeline scheduling and logic
+- [Hyper-Parameter Optimization](../guides/optimization/hyper-parameter-optimization/examples_hyperparam_opt.md) - Implementing an active selection of experiments
+- [Control Service](../guides/services/aws_autoscaler.md) - AWS Autoscaler for example
+- [External services](../guides/services/slack_alerts.md) - Such as Slack integration alert service
 
 By default, [ClearML Server](../deploying_clearml/clearml_server.md) comes with an Agent running on the machine that runs it. It also comes with a Services queue.

docs/fundamentals/artifacts.md

@@ -76,10 +76,14 @@ logs the models and all snapshot paths.
 
 ![image](../img/fundamentals_artifacts_logging_models.png)
 
-See model storage examples, [TF](https://github.com/allegroai/clearml/blob/master/examples/frameworks/tensorflow/tensorflow_mnist.py),
-[PyTorch](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/pytorch_mnist.py),
-[Keras](https://github.com/allegroai/clearml/blob/master/examples/frameworks/keras/keras_tensorboard.py),
-[Scikit-Learn](https://github.com/allegroai/clearml/blob/master/examples/frameworks/scikit-learn/sklearn_joblib_example.py).
+See automatic model logging examples: 
+* [TF](../guides/frameworks/tensorflow/tensorflow_mnist.md)
+* [PyTorch](../guides/frameworks/pytorch/pytorch_mnist.md)
+* [Keras](../guides/frameworks/keras/keras_tensorboard.md)
+* [Scikit-Learn](../guides/frameworks/scikit-learn/sklearn_joblib_example.md)
+* [XGBoost](../guides/frameworks/xgboost/xgboost_sample.md)
+* [FastAI](../guides/frameworks/fastai/fastai_with_tensorboard.md)
+
 
 ### Manual Model Logging 
 
@@ -121,6 +125,7 @@ output_model.update_weights()
 for model weight upload (`registered_uri`).
 * Model Metadata - Model description and iteration number. 
 
+See [Model Configuration](../guides/reporting/model_config.md) example.
 
 ### Using Models
 

docs/fundamentals/hpo.md

@@ -57,7 +57,7 @@ optimization.
 * **BOHB** - `automation.hpbandster.bandster.OptimizerBOHB`. BOHB performs robust and efficient hyperparameter optimization 
   at scale by combining the speed of Hyperband searches with the guidance and guarantees of convergence of Bayesian Optimization. 
   For more information about HpBandSter BOHB, see the [HpBandSter](https://automl.github.io/HpBandSter/build/html/index.html) 
-  documentation.
+  documentation and a [code example](../guides/frameworks/pytorch/notebooks/image/hyperparameter_search.md).
 * **Random** uniform sampling of hyperparameters - `automation.optimization.RandomSearch`.
 * **Full grid** sampling strategy of every hyperparameter combination - `Grid search automation.optimization.GridSearch`.
 * **Custom** - `automation.optimization.SearchStrategy` - Use a custom class and inherit from the ClearML automation base strategy class 

docs/fundamentals/hyperparameters.md

@@ -34,6 +34,8 @@ args = parser.parse_args()
 task = Task.init(project_name="examples",task_name="argparser logging")
 ``` 
 
+See another argparse logging example [here](../guides/reporting/hyper_parameters.md).
+
 ### Click Example
 
 ```python
@@ -55,7 +57,6 @@ hello()
 
 See another code example [here](https://github.com/allegroai/clearml/blob/master/examples/frameworks/click/click_multi_cmd.py).
 
-
 ## Connecting Objects
 
 Users can directly connect objects, such as dictionaries or even custom classes, to Tasks.
@@ -75,6 +76,8 @@ task = Task.init(project_name='examples',task_name='argparser')
 
 task.connect(me)
 ```
+See connecting configuration objects example [here](../guides/reporting/hyper_parameters.md).
+
 
 * Connecting a dictionary:
 ```python
@@ -121,7 +124,11 @@ The CLEARML_LOG_ENVIRONMENT always overrides the clearml.conf file.
 
 ## TF Defines
 
-ClearML automatically captures TFDefine files, which are used as configuration files for Tensorflow.
+ClearML automatically captures TensorFlow definitions, which are used as configuration files for Tensorflow.
+
+See examples of ClearML's automatic logging of TF Defines:
+* [TensorFlow MNIST](../guides/frameworks/tensorflow/tensorflow_mnist.md)
+* [TensorBoard PR Curve](../guides/frameworks/tensorflow/tensorboard_pr_curve.md)
 
 ## Hydra
 

docs/fundamentals/logger.md

@@ -18,7 +18,7 @@ In ClearML, there are four types of reports:
 
 ## Automatic Reporting
 
-ClearML automatically captures metrics reported to tools, such as Tensorboard and Matplotlib, with no additional code
+ClearML automatically captures metrics reported to tools, such as TensorBoard and Matplotlib, with no additional code
 necessary.
 
 In addition, ClearML will capture and log everything written to standard output, from debug messages to errors to 
@@ -28,10 +28,28 @@ GPU, CPU, Memory and Network information is also automatically captured.
 
 ![image](../img/fundamentals_logger_cpu_monitoring.png)
 
-### Supported packages
-- [Tensorboard](https://www.tensorflow.org/tensorboard)
-- [TensorboardX](https://github.com/lanpa/tensorboardX)
-- [matplotlib](https://matplotlib.org/)
+### Supported Packages
+- [TensorBoard](https://www.tensorflow.org/tensorboard)
+- [TensorBoardX](https://github.com/lanpa/tensorboardX)
+- [Matplotlib](https://matplotlib.org/)
+
+### Automatic Reporting Examples
+Check out some of ClearML's automatic reporting examples for supported packages:
+* TensorBoard
+  * [TensorBoard PR Curve](../guides/frameworks/tensorflow/tensorboard_pr_curve.md) - logging TensorBoard outputs and
+    TensorFlow flags
+  * [TensorBoard Toy](../guides/frameworks/tensorflow/tensorboard_toy.md) - logging TensorBoard histograms, scalars, images, text, and 
+    TensorFlow flags
+  * [Tensorboard with PyTorch](../guides/frameworks/pytorch/pytorch_tensorboard.md) - logging TensorBoard scalars, debug samples, and text integrated into 
+    code that uses PyTorch
+* [TensorBoardX](../guides/frameworks/tensorboardx/tensorboardx.md) - logging TensorBoardX scalars, debug 
+  samples, and text in code using PyTorch
+* Matplotlib 
+  * [Matplotlib Script Example](../guides/frameworks/matplotlib/matplotlib_example.md) and [Jupyter Notebook](../guides/frameworks/matplotlib/allegro_clearml_matplotlib_example.md) -
+    logging scatter diagrams plotted with Matplotlib
+  * [Matplotlib with PyTorch](../guides/frameworks/pytorch/pytorch_matplotlib.md) - logging debug images shown 
+    by Matplotlib
+
 
 
 ## Manual Reporting
@@ -46,9 +64,7 @@ The object used for reporting metrics is called **logger** and is obtained by ca
 logger = task.get_logger()
 ```
 
-Check out all the available object types that can be reported in the example [here](../guides/reporting/scalar_reporting.md).
-
-#### Media reporting
+### Media Reporting
 
 ClearML also supports reporting media (such as audio, video and images) for every iteration.
 This section is mostly used for debugging. It's recommended to use [artifacts](artifacts.md#artifacts) for storing script 
@@ -59,4 +75,26 @@ See details in [Logger.report_media](../references/sdk/logger.md#report_media).
 
 ![image](../img/fundamentals_logger_reported_images.png)
 
-Check out the Media Reporting [example](../guides/reporting/media_reporting).
+### Explicit Reporting Examples
+
+Check out ClearML's explicit reporting examples for various types of results:
+- [Text](../guides/reporting/text_reporting.md) 
+- [Scalars](../guides/reporting/scalar_reporting.md)
+- Plots 
+    - [2d plots](../guides/reporting/scatter_hist_confusion_mat_reporting.md)
+      - Histograms
+      - Confusion matrices
+      - Scatter plots
+    - [3d plots](../guides/reporting/3d_plots_reporting.md)
+      - Surface plots
+      - Scatter plots
+    - [Tables](../guides/reporting/pandas_reporting.md)
+        - Pandas DataFrames 
+        - CSV file
+    - [Matplotlib figures](../guides/reporting/manual_matplotlib_reporting.md)
+    - [Plotly figures](../guides/reporting/plotly_reporting.md)
+- Debug Samples
+    - [Images](../guides/reporting/image_reporting.md)
+    - [HTML](../guides/reporting/html_reporting.md)
+    - [Media - images, audio, video](../guides/reporting/media_reporting.md)
+- Explicit reporting in Jupyter Notebook [example](../guides/reporting/clearml_logging_example.md)

docs/fundamentals/pipelines.md

@@ -66,4 +66,8 @@ Custom pipelines usually involve cloning template tasks, modifying their paramet
 them to queues (for execution by [agents](../clearml_agent.md)). It's possible to create custom logic that controls inputs 
 (e.g. overriding hyperparameters and artifacts) and acts upon task outputs.
 
-See an example of a custom pipeline [here](../guides/automation/task_piping.md).
+See examples of custom pipelines: 
+   * [Task Piping](../guides/automation/task_piping.md)
+   * [Manual Random Parameter Search](../guides/automation/manual_random_param_search_example.md)
+
+

docs/guides/storage/examples_storagehelper.md

@@ -5,9 +5,9 @@ title: Storage Examples
 This page describes storage examples using the [StorageManager](../../references/sdk/storage.md) 
 class. The storage examples include:
 
-* [Downloading a file](#downloading_storagemanager) - Get an object from storage.
-* [Uploading a file](#uploading_storagemanager) - Upload an object.
-* [Setting cache limits](#cache) - Set the maximum number of objects.
+* [Downloading a file](#downloading-a-file) - Get an object from storage.
+* [Uploading a file](#uploading-a-file) - Upload an object.
+* [Setting cache limits](#setting-cache-limits) - Set the maximum number of objects.
 
 :::note 
 `StorageManager` supports http(s), S3, Google Cloud Storage, Azure, and file system folders.
@@ -26,6 +26,10 @@ method, and specify the destination location as the `remote_url` argument:
     
     manager.get_local_copy(remote_url="s3://MyBucket/MyFolder/file.zip")
 
+:::note
+Zip and tar.gz files will be automatically extracted to cache. This can be controlled with the`extract_archive` flag.
+:::
+
 To download a file to a specific context in cache, specify the name of the context as the `cache_context` argument:
 
     manager.get_local_copy(remote_url="s3://MyBucket/MyFolder/file.ext", cache_context="test")
@@ -34,7 +38,6 @@ To download a non-compressed file, set the `extract_archive` argument to `False`
 
     manager.get_local_copy(remote_url="s3://MyBucket/MyFolder/file.ext", extract_archive=False)
 
-<a class="tr_top_negative" name="uploading_storagemanager"></a>
 
 ### Uploading a file
 

docs/integrations/storage.md

@@ -99,31 +99,11 @@ It's also possible to specify credentials for a specific bucket.
 
 ## Storage Manager
 
-ClearML Offers a package to manage downloading, uploading and caching of content directly from code.
+ClearML offers the [StorageManager](../references/sdk/storage.md) class to manage downloading, uploading, and caching of 
+content directly from code.
 
-### Uploading files
-To upload a file using storage manager, just run the following line specifying the path to a local file or folder, and the
-remote destination.
-```python
-from clearml import StorageManager
+See [Storage Examples](../guides/storage/examples_storagehelper.md).
 
-StorageManager.upload_file(local_file='path_to_file',remote_url='s3://my_bucket')
-```
-
-
-### Downloading files
-To download files into cache, run the following line, specifying the remote destination's URL.
-```python
-StorageManager.get_local_copy(remote_url='s3://my_bucket/path_to_file')
-```
-
-:::note
-Zip and tar.gz files will be automatically extracted to cache. This can be controlled with the`extract_archive` flag.
-:::
-
-### Controling cache file limit
-It's possible to control the maximum cache size by limiting the number of files it stores.
-This is done by calling the ```StorageManager.set_cache_file_limit()``` method.
 
 ## Caching
 ClearML also manages a cache of all downloaded content so nothing is duplicated, and code won't need to download the same

sidebars.js

@@ -87,7 +87,7 @@ module.exports = {
                     ]
                 },
                 {'Scikit-Learn': ['guides/frameworks/scikit-learn/sklearn_joblib_example', 'guides/frameworks/scikit-learn/sklearn_matplotlib_example']},
-                {'TensorboardX': ['guides/frameworks/tensorboardx/tensorboardx']},
+                {'TensorBoardX': ['guides/frameworks/tensorboardx/tensorboardx']},
                 {
                     'Tensorflow': ['guides/frameworks/tensorflow/tensorboard_pr_curve', 'guides/frameworks/tensorflow/tensorboard_toy',
                         'guides/frameworks/tensorflow/tensorflow_mnist', 'guides/frameworks/tensorflow/integration_keras_tuner']