@@ -440,6 +440,56 @@ More info at:
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https://github.com/Project-MONAI/monai-deploy-informatics-gateway/blob/develop/docs/api/rest/config.md
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+ ## Deploying in Google Kubernetes Engine
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+
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+ Check you have enough GPU quota in the zone you want to deploy. I have
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+ tested this in europe-west4-a and I had to request to increase the quota
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+ to 1 nvidia-tesla-a100 GPU.
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+
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+ Create a standard cluster and name it monai-deploy-1
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+
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+ > Note you could create an autopilot cluster, so that GPU nodes are
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+ deployed automatically. Be aware that autopilot needs as much quota
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+ as nodes you have multplied by the GPUs you request. With the standard
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+ autopilot cluster configuration, I was getting 11 nodes, and so it was
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+ requesting 11 GPUs, which was way higher than my quota.
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+ Configure your cluster:
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+
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+ ```gcloud container clusters get-credentials monai-deploy-1```
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+
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+ Check your cluster:
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+
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+ ```kubectl cluster-info```
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+
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+ Edit the argo template and add the node selector:
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+
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+ ```
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+ metadata: {}
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+ + nodeSelector:
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+ + cloud.google.com/gke-accelerator: nvidia-tesla-a100
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+ container:
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+ ```
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+
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+ Add a node pool and inside that pool a node with 1 GPU.
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+ Install drivers with:
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+ ```kubectl apply -f https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/master/nvidia-driver-installer/cos/daemonset-preloaded-latest.yaml```
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+ More info at https://cloud.google.com/kubernetes-engine/docs/how-to/gpus#console
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+ Then, you will need a bigger machine for the argo workflows. You need
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+ to create another pool with one node and this time you can select type
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+ e2-standard-2.
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+ Now, you can run all the helm and kubectl commands above.
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+ ```Warning: Using GKE with GPUs will cost you money, be careful with
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+ that. ```
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+
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+
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[0] https://drive.google.com/file/d/1d8Scm3q-kHTqr_-KfnXH0rPnCgKld2Iy/view?usp=sharing
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a DICOM dataset that was converted to DICOM from Medical Decathlon
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training and validation images (see https://github.com/Project-MONAI/monai-deploy/tree/main/deploy/monai-deploy-express#running-a-monai-deploy-workflow)
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