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@ -78,20 +78,13 @@ Find Dockerfiles in the [docker](./docker) dir and a helm Chart in https://githu
- Seamless integration with ML/DL experiment manager
- Web UI for customization, scheduling & prioritization of jobs
**Two K8s integration flavours**
- Spin ClearML-Agent as a long-lasting service pod:
- Use [clearml-agent](https://hub.docker.com/r/allegroai/clearml-agent) docker image
- map docker socket into the pod (soon replaced by [podman](https://github.com/containers/podman))
- Allow the clearml-agent to manage sibling dockers
- Benefits: full use of the ClearML scheduling, no need to worry about wrong container images / lost pods etc.
- Downside: sibling containers
- Kubernetes Glue, map ClearML jobs directly to K8s jobs:
- Run the [clearml-k8s glue](https://github.com/allegroai/clearml-agent/blob/master/examples/k8s_glue_example.py) on
**Run the agent in Kubernetes Glue mode an map 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 the [clearml-k8s glue](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 the pod itself the clearml-agent will install the job (experiment) environment and spin and monitor the
- Inside each task pod itself the clearml-agent will install the job (experiment) environment and spin and monitor the
experiment's process
- Benefits: Kubernetes full view of all running jobs in the system
- Downside: No real scheduling (k8s scheduler), no docker image verification (post-mortem only)