Add MMEngine integration page (#850)

docs/integrations/mmcv.md

@@ -1,13 +1,17 @@
 ---
-title: MMCV
+title: MMCV v1.x
 ---
 
+:::info
+`ClearMLLoggerHook` is supported by `mmcv` =>1.5.1 and <=1.7.0. 
+:::
+
 :::tip
 If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup 
 instructions.
 :::
 
-[MMCV](https://github.com/open-mmlab/mmcv) is a computer vision framework developed by OpenMMLab. You can integrate ClearML into your 
+[MMCV](https://github.com/open-mmlab/mmcv/tree/1.x) is a computer vision framework developed by OpenMMLab. You can integrate ClearML into your 
 code using the `mmcv` package's [`ClearMLLoggerHook`](https://mmcv.readthedocs.io/en/master/_modules/mmcv/runner/hooks/logger/clearml.html)
 class. This class is used to create a ClearML Task and to automatically log metrics. 
 

docs/integrations/mmengine.md

@@ -0,0 +1,70 @@
+---
+title: MMEngine
+---
+
+:::tip
+If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup 
+instructions.
+:::
+
+[MMEngine](https://github.com/open-mmlab/mmengine) is a library for training deep learning models based on PyTorch. 
+MMEngine supports ClearML through a builtin logger: It automatically logs experiment environment information, such as 
+required packages and uncommitted changes, and supports reporting scalars, parameters, and debug samples.
+
+Integrate ClearML with the following steps:
+1. Instantiate a [`ClearMLVisBackend`](https://mmengine.readthedocs.io/en/latest/api/generated/mmengine.visualization.ClearMLVisBackend.html#mmengine.visualization.ClearMLVisBackend) 
+   object. This creates a ClearML Task that logs the experiment’s environment information.   
+   
+   ```python
+   from mmengine.visualization import ClearMLVisBackend
+   vis_backend = ClearMLVisBackend(
+       artifact_suffix=('.py', 'pth'), 
+       init_kwargs=dict(
+           project_name='examples',
+           task_name='OpenMMLab cifar10',
+           output_uri=True
+       )
+   )
+   ```
+   
+   You can specify the following parameters: 
+
+   * `init_kwargs` – A dictionary that contains the arguments to pass to ClearML's [`Task.init()`](../references/sdk/task.md#taskinit).  
+   * `artifact_suffix` – At the end of training, artifacts with these suffixes will be uploaded to the task's `output_uri`. 
+   Defaults to (`.py`, `pth`).  
+
+2. Log experiment parameters using `ClearMLVisBackend.add_config()`. Under the `config` parameter, input a dictionary of parameter key-value pairs.  
+
+    ```python
+    cfg = Config(dict(a=1, b=dict(b1=[0, 1])))
+    vis_backend.add_config(config=cfg)
+    ```
+
+    The parameters will be displayed in the ClearML WebApp, under the experiment’s Hyperparameters
+
+3. Log your experiment’s scalars using either `ClearMLVisBackend.add_scalar()` for single values or `ClearMLVisBackend.add_scalars()` 
+   for multiple values: 
+   
+   ```python
+   vis_backend.add_scalar(name='mAP', value=0.6, step=1)
+   vis_backend.add_scalars(scalar_dict={'loss': 0.1,'acc':0.8}, step=1)
+   ```
+
+   The scalars are displayed in the experiment's Scalars tab.
+
+5. Report images to your experiment using `ClearMLVisBackend.add_image()`. Under the `image` parameter, input the image 
+   to be reported as an `np.ndarray` in RGB format:
+
+   ```python
+   img = np.random.randint(0, 256, size=(10, 10, 3))
+   vis_backend.add_image(name='img.png', image=img, step=1)
+   ```
+   The images will be displayed in the experiment's Debug Samples
+
+5. Once you've finished training, make sure to run `ClearMLVisBackend.close()` so that ClearML can mark the task as 
+   completed. This will also scan the directory for relevant artifacts with the suffixes input when instantiating 
+   `ClearMLVisBackend` and log the artifacts to your experiment. 
+
+    ```python
+    vis_backend.close()
+    ```

sidebars.js

@@ -92,7 +92,8 @@ module.exports = {
                 'integrations/keras', 'integrations/keras_tuner',
                 'integrations/langchain',
                 'integrations/lightgbm', 'integrations/matplotlib',
-                'integrations/megengine', 'integrations/mmcv', 'integrations/monai', 'integrations/tao',
+                'integrations/megengine', 'integrations/monai', 'integrations/tao',
+                {"OpenMMLab":['integrations/mmcv', 'integrations/mmengine']},
                 'integrations/optuna',
                 'integrations/python_fire', 'integrations/pytorch',
                 'integrations/ignite',