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@ -97,26 +97,35 @@ the SSH socket provided in the environment variable.
SSH_AUTH_SOCK=<file_socket> clearml-agent daemon --gpus <your config> --queue <your queue name> --docker
```
## Kubernetes
Agents can be deployed bare-metal or as dockers in a Kubernetes cluster. ClearML Agent adds the missing scheduling
capabilities to Kubernetes, allows for more flexible automation from code, and gives access to all of ClearML Agent's
features.
## Kubernetes
ClearML Agent is deployed onto a Kubernetes cluster through its Kubernetes-Glue which maps ClearML jobs directly to K8s
jobs:
* Use the [ClearML Agent Helm Chart](https://github.com/allegroai/clearml-helm-charts/tree/main/charts/clearml-agent) to
spin an agent pod acting as a controller. Alternatively (less recommended) run a [k8s glue script](https://github.com/allegroai/clearml-agent/blob/master/examples/k8s_glue_example.py)
on a K8S cpu node
* The ClearML K8S glue pulls jobs from the ClearML job execution queue and prepares a K8s job (based on provided yaml
template)
* Inside each job pod the `clearml-agent` will install the ClearML task's environment and run and monitor the experiment's
process
Agents can be deployed bare-metal or as Docker containers in a Kubernetes cluster. ClearML Agent adds missing scheduling capabilities to Kubernetes, enabling more flexible automation from code while leveraging all of ClearML Agent's features.
:::important Enterprise Feature
The ClearML Enterprise plan supports K8S servicing multiple ClearML queues, as well as providing a pod template for each
queue for describing the resources for each pod to use.
ClearML Agent is deployed onto a Kubernetes cluster using **Kubernetes-Glue**, which maps ClearML jobs directly to Kubernetes jobs. This allows seamless task execution and resource allocation across your cluster.
For example, the following configures which resources to use for `example_queue_1` and `example_queue_2`:
### Deployment Options
You can deploy ClearML Agent onto Kubernetes using one of the following methods:
1. **ClearML Agent Helm Chart (Recommended)**:
Use the [ClearML Agent Helm Chart](https://github.com/allegroai/clearml-helm-charts/tree/main/charts/clearml-agent) to spin up an agent pod acting as a controller. This is the recommended and scalable approach.
2. **K8s Glue Script**:
Run a [K8s Glue script](https://github.com/allegroai/clearml-agent/blob/master/examples/k8s_glue_example.py) on a Kubernetes CPU node. This approach is less scalable and typically suited for simpler use cases.
### How It Works
The ClearML Kubernetes-Glue performs the following:
- Pulls jobs from the ClearML execution queue.
- Prepares a Kubernetes job based on a provided YAML template.
- Inside each job pod, the `clearml-agent`:
- Installs the required environment for the task.
- Executes and monitors the experiment process.
:::important Enterprise Features
ClearML Enterprise adds advanced Kubernetes features:
- **Multi-Queue Support**: Service multiple ClearML queues within the same Kubernetes cluster.
- **Pod-Specific Templates**: Define resource configurations per queue using pod templates.
For example, you can configure resources for different queues as shown below:
```yaml
agentk8sglue: