From 95dde6ca0cac717d2094114699c11bd1f0d38040 Mon Sep 17 00:00:00 2001 From: allegroai <> Date: Thu, 25 Jan 2024 11:27:56 +0200 Subject: [PATCH] Update README --- README.md | 27 ++++++++++----------------- 1 file changed, 10 insertions(+), 17 deletions(-) diff --git a/README.md b/README.md index 361f4fb..11c5f8d 100644 --- a/README.md +++ b/README.md @@ -78,23 +78,16 @@ 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 - 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 - 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) +**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 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) ### Using the ClearML Agent