Add XGBoost example docs (#148)

docs/guides/frameworks/xgboost/xgboost_metrics.md

@@ -0,0 +1,34 @@
+---
+title: XGBoost Metric Reporting
+---
+
+The [xgboost_metrics.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/xgboost/xgboost_metrics.py) 
+example demonstrates the integration of ClearML into code that uses XGBoost to train a network on the scikit-learn [iris](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris) 
+classification dataset. ClearML automatically captures models and scalars logged with XGBoost.
+
+When the script runs, it creates a ClearML experiment named `xgboost metric auto reporting`, which is associated with 
+the `examples` project.
+
+## Scalars
+ClearML automatically captures scalars logged with XGBoost, which can be visualized in plots in the 
+ClearML WebApp, in the experiment's **RESULTS > SCALARS** page.
+
+![Scalars](../../../img/examples_xgboost_metric_scalars.png)
+
+## Models
+
+ClearML automatically captures the model logged using the `xgboost.save` method, and saves it as an artifact.
+
+View saved snapshots in the experiment's **ARTIFACTS** tab.
+
+![Artifacts tab](../../../img/examples_xgboost_metric_artifacts.png) 
+
+To view the model details, click the model name in the **ARTIFACTS** page, which will open the model's info tab. Alternatively, download the model.
+
+![Model info panel](../../../img/examples_xgboost_metric_model.png)
+
+## Console
+
+All console output during the script’s execution appears in the experiment’s **RESULTS > CONSOLE** page.
+
+![Console output](../../../img/examples_xgboost_metric_console.png)

docs/guides/frameworks/xgboost/xgboost_sample.md

@@ -1,9 +1,9 @@
 ---
-title: XGBoost
+title: XGBoost and scikit-learn
 ---
 
 The [xgboost_sample.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/xgboost/xgboost_sample.py) 
-example demonstrates integrating **ClearML** into code that trains a network on the scikit-learn [iris](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris) 
+example demonstrates integrating ClearML into code that trains a network on the scikit-learn [iris](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris) 
 classification dataset, using XGBoost to do the following:
 
 * Load a model ([xgboost.Booster.load_model](https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.Booster.load_model))
@@ -14,7 +14,7 @@ classification dataset, using XGBoost to do the following:
 
 And using scikit-learn to score accuracy ([sklearn.metrics.accuracy_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html)).
 
-**ClearML** automatically logs:
+ClearML automatically logs:
 * Input model
 * Output model 
 * Model checkpoints (snapshots) 

sidebars.js

@@ -92,7 +92,7 @@ module.exports = {
                     'Tensorflow': ['guides/frameworks/tensorflow/tensorboard_pr_curve', 'guides/frameworks/tensorflow/tensorboard_toy',
                         'guides/frameworks/tensorflow/tensorflow_mnist', 'guides/frameworks/tensorflow/integration_keras_tuner']
                 },
-                'guides/frameworks/xgboost/xgboost_sample'
+                {'XGBoost': ['guides/frameworks/xgboost/xgboost_sample', 'guides/frameworks/xgboost/xgboost_metrics']}
             ]},
             {'IDEs': ['guides/ide/remote_jupyter_tutorial', 'guides/ide/integration_pycharm', 'guides/ide/google_colab']},
             {'Offline Mode':['guides/set_offline']},