Update monitor.md

docs/_tutorials/monitor.md

@@ -11,7 +11,7 @@ In this tutorial, we introduce the DeepSpeed Monitor and provide examples of its
 
 ## Overview
 
-Monitoring model and system metrics during training is vital to ensure hardware resources are fully utilized. The DeepSpeed Monitor enables live logging of metrics through one or more monitoring backends such as PyTorch's [TensorBoard](https://pytorch.org/docs/1.8.0/tensorboard.html), [WandB](https://docs.wandb.ai/quickstart), and simple CSV files.
+Monitoring model and system metrics during training is vital to ensure hardware resources are fully utilized. The DeepSpeed Monitor enables live logging of metrics through one or more monitoring backends such as PyTorch's [TensorBoard](https://pytorch.org/docs/1.8.0/tensorboard.html), [WandB](https://docs.wandb.ai/quickstart), [CometML](https://www.comet.com/docs/v2/guides/quickstart/) and simple CSV files.
 
 Below is a live monitoring view for TensorBoard:
 
@@ -21,6 +21,10 @@ Below is a live monitoring view for WandB:
 
 ![WandB Example Output](/assets/images/wandb_monitor.PNG){: .align-center}
 
+Below is a live monitoring view for CometML
+
+![CometML Example Output](/assets/images/comet_monitor.PNG){: .align-center}
+
 ## Usage
 
 The DeepSpeed Monitor is configured within the deepspeed [configuration file](/docs/config-json/#monitoring-module-tensorboard-wandb-csv). DeepSpeed will automatically monitor key training metrics, including those tracked with the `wall_clock_breakdown` configuration option. In addition, users can log their own custom events and metrics.
@@ -45,6 +49,11 @@ When using DeepSpeed for model training, the Monitor can be configured in the De
     "group": "my_group",
     "project": "my_project"
   }
+  "comet": {
+    "enabled": true,
+    "project": "my_project",
+    "experiment_name": "my_experiment"
+  }
   "csv_monitor": {
     "enabled": true,
     "output_path": "output/ds_logs/",