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52 lines
2.1 KiB
Markdown
52 lines
2.1 KiB
Markdown
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---
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title: Kubernetes
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---
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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.
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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.
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## Deployment Options
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You can deploy ClearML Agent onto Kubernetes using one of the following methods:
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1. **ClearML Agent Helm Chart (Recommended)**:
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Use the [ClearML Agent Helm Chart](https://github.com/clearml/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.
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2. **K8s Glue Script**:
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Run a [K8s Glue script](https://github.com/clearml/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.
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## How It Works
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The ClearML Kubernetes-Glue performs the following:
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- Pulls jobs from the ClearML execution queue.
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- Prepares a Kubernetes job based on a provided YAML template.
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- Inside each job pod, the `clearml-agent`:
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- Installs the required environment for the task.
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- Executes and monitors the task process.
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:::important Enterprise Features
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ClearML Enterprise adds advanced Kubernetes features:
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- **Multi-Queue Support**: Service multiple ClearML queues within the same Kubernetes cluster.
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- **Pod-Specific Templates**: Define resource configurations per queue using pod templates.
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For example, you can configure resources for different queues as shown below:
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```yaml
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agentk8sglue:
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queues:
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example_queue_1:
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templateOverrides:
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nodeSelector:
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nvidia.com/gpu.product: A100-SXM4-40GB-MIG-1g.5gb
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resources:
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limits:
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nvidia.com/gpu: 1
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example_queue_2:
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templateOverrides:
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nodeSelector:
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nvidia.com/gpu.product: A100-SXM4-40GB
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resources:
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limits:
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nvidia.com/gpu: 2
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```
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:::
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