+
+
+
+Ultralytics' [YOLOv8](https://github.com/ultralytics/ultralytics) is a top modeling repository for object detection,
+segmentation, and classification. Get the most out of YOLOv8 with ClearML:
+* Track every YOLOv8 training run in ClearML
+* Remotely train and monitor your YOLOv8 training runs using [ClearML Agent](../clearml_agent.md)
+* Turn your newly trained YOLOv8 model into an API with just a few commands using [ClearML Serving](../clearml_serving/clearml_serving.md)
+
+## Setup
+
+1. Install the `clearml` python package:
+
+ ```commandline
+ pip install clearml
+ ```
+
+1. To keep track of your experiments and/or data, ClearML needs to communicate to a server. You have 2 server options:
+ * Sign up for free to the [ClearML Hosted Service](https://app.clear.ml/)
+ * Set up your own server, see [here](../deploying_clearml/clearml_server.md).
+1. Connect the ClearML SDK to the server by creating credentials (go to the top right in to UI to **Settings > Workspace > Create new credentials**),
+ then execute the command below and follow the instructions:
+
+ ```commandline
+ clearml-init
+ ```
+
+That’s it! Now, whenever you train a model using YOLOv8, the run will be captured and tracked by ClearML – no additional
+code necessary.
+
+## Training YOLOv8 with ClearML
+
+To enable ClearML experiment tracking, simply install the `clearml` pip package in your execution environment.
+
+```commandline
+pip install clearml>=1.2.0
+```
+
+This will enable integration with the YOLOv8 training script. In every training run from now on, the ClearML experiment
+manager will capture:
+* Source code and uncommitted changes
+* Installed packages
+* [Hyperparameters](../fundamentals/hyperparameters.md)
+* Model files (use [`--save-period n`](https://docs.ultralytics.com/usage/cfg/#modes) to save a checkpoint every `n` epochs)
+* Console output
+* Scalars (e.g. mAP_0.5, mAP_0.5:0.95, precision, recall, losses)
+* General information such as machine details, runtime, creation date etc.
+* All produced plots such as label correlogram and confusion matrix
+* Mosaic per epoch
+* Validation images per epoch
+* And more
+
+All of this is captured into a [ClearML Task](../fundamentals/task.md): a task with your training script's name
+created in a `YOLOv8` ClearML project. To change the task’s name or project, pass the `name` and `project` arguments in one of
+the following ways:
+* Via the SDK:
+
+ ```python
+ from ultralytics import YOLO
+
+ # Initialize YOLO object, load/create YOLOv8 model
+ model = YOLO()
+
+ # Run MODE mode using the custom arguments ARGS
+ model.MODE(name="", project="")
+ ```
+
+* Via the `yolo` CLI:
+
+ ```commandline
+ yolo TASK MODE project=new_project name=new_task_name
+ ```
+
+:::tip project names
+ClearML uses `/` as a delimiter for subprojects: using `example/sample` as a name will create the `sample`
+task within the `example` project.
+:::
+
+You can see all the captured data in the task’s page of the ClearML [WebApp](../webapp/webapp_exp_track_visual.md).
+Additionally, you can view all of your YOLOv8 runs tracked by ClearML in the [Experiments Table](../webapp/webapp_model_table.md).
+Add custom columns to the table, such as mAP values, so you can easily sort and see what is the best performing model.
+You can also select multiple experiments and directly [compare](../webapp/webapp_exp_comparing.md) them.
+
+## Remote Execution
+ClearML logs all the information required to reproduce an experiment on a different machine (installed packages,
+uncommitted changes etc.). The [ClearML Agent](../clearml_agent.md) listens to designated queues and when a task is
+enqueued, the agent pulls it, recreates its execution environment, and runs it, reporting its scalars, plots, etc. to the
+experiment manager.
+
+Deploy a ClearML Agent onto any machine (e.g. a cloud VM, a local GPU machine, your own laptop) by simply running
+the following command on it:
+
+```commandline
+clearml-agent daemon --queue [--docker]
+```
+
+Use the ClearML [Autoscalers](../cloud_autoscaling/autoscaling_overview.md), to help you manage cloud workloads in the
+cloud of your choice (AWS, GCP, Azure) and automatically deploy ClearML agents: the autoscaler automatically spins up and
+shuts down instances as needed, according to a resource budget that you set.
+
+
+### Cloning, Editing, and Enqueuing
+
+ClearML logs all the information required to reproduce an experiment, but you may also want to change a few parameters
+and task details when you re-run an experiment, which you can do through ClearML’s UI.
+
+In order to be able to override parameters via the UI,
+you have to run your code to [create a ClearML Task](../clearml_sdk/task_sdk.md#task-creation), which will log all the
+execution parameters before using the YOLO model. When ClearML re-runs the task remotely, ClearML will override these
+parameters before YOLO comes into play.
+
+For example:
+
+```python
+from clearml import Task
+from ultralytics import YOLO
+
+# Create a ClearML Task
+task = Task.init(
+ project_name="my project",
+ task_name="my yolo task"
+)
+
+# Load a model
+model_variant = "yolov8n"
+# Log "model_variant" parameter to task
+task.set_parameter("model_variant", model_variant)
+
+# Load the YOLOv8 model
+model = YOLO(f'{model_variant}.pt')
+
+# Put all YOLOv8 arguments in a dictionary and pass it to ClearML
+# When the arguments are later changed in UI, they will be overridden here!
+args = dict(data="coco128.yaml", epochs=16)
+task.connect(args)
+
+# Train the model
+# If running remotely, the arguments may be overridden by ClearML if they were changed in the UI
+results = model.train(**args)
+```
+
+:::tip
+Notice that in the script above only the `data` and `epochs` args are passed to ClearML, so only their values can be
+overridden via the UI. You can add all of YOLO's default parameters to the parameter dictionary, so you'll be able to override
+any of them through the UI.
+:::
+
+Use the UI to edit task details, then execute the task
+with the new configuration on a remote machine:
+* Clone the experiment
+* Edit the hyperparameters and/or other details
+* Enqueue the task
+
+The ClearML Agent executing the task will use the new values to [override any hard coded values](../clearml_agent.md).
+
+
diff --git a/sidebars.js b/sidebars.js
index 10ec717b..ae162a07 100644
--- a/sidebars.js
+++ b/sidebars.js
@@ -69,7 +69,7 @@ module.exports = {
{'PyTorch Ignite':['guides/frameworks/pytorch_ignite/integration_pytorch_ignite', 'guides/frameworks/pytorch_ignite/pytorch_ignite_mnist']},
'guides/frameworks/pytorch_lightning/pytorch_lightning_example', 'guides/frameworks/scikit-learn/sklearn_joblib_example',
'guides/frameworks/pytorch/pytorch_tensorboard', 'guides/frameworks/tensorboardx/tensorboardx', 'guides/frameworks/tensorflow/tensorflow_mnist',
- 'integrations/seaborn', 'guides/frameworks/xgboost/xgboost_metrics', 'integrations/yolov5'
+ 'integrations/seaborn', 'guides/frameworks/xgboost/xgboost_metrics', 'integrations/yolov5', 'integrations/yolov8'
]
},
'integrations/storage',