c731bef759
RL models (DQN/PPO) only need basic CUDA runtime (~2GB), while supervised models need cuDNN (~4GB). Splitting saves ~2GB pull time per RL job. Rename the L4 GPU pool from ci-compile to ci-rl to reflect its actual use. Add DaemonSet image pre-puller to cache training images on GPU nodes. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
47 lines
1.4 KiB
HCL
47 lines
1.4 KiB
HCL
output "cluster_id" {
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description = "ID of the Kapsule cluster"
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value = scaleway_k8s_cluster.foxhunt.id
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}
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output "kubeconfig" {
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description = "Kubeconfig file content for the cluster"
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value = scaleway_k8s_cluster.foxhunt.kubeconfig[0].config_file
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sensitive = true
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}
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output "apiserver_url" {
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description = "URL of the Kubernetes API server"
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value = scaleway_k8s_cluster.foxhunt.apiserver_url
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}
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output "services_pool_id" {
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description = "ID of the services node pool"
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value = scaleway_k8s_pool.services.id
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}
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output "gitlab_pool_id" {
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description = "ID of the GitLab node pool"
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value = var.enable_gitlab_pool ? scaleway_k8s_pool.gitlab[0].id : ""
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}
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output "ci_rl_pool_id" {
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description = "ID of the CI RL node pool (L4)"
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value = var.enable_ci_rl_pool ? scaleway_k8s_pool.ci_rl[0].id : ""
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}
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output "ci_compile_cpu_pool_id" {
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description = "ID of the CPU compile node pool (POP2-HC)"
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value = var.enable_ci_compile_cpu_pool ? scaleway_k8s_pool.ci_compile_cpu[0].id : ""
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}
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output "ci_training_pool_id" {
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description = "ID of the CI training node pool (L40S)"
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value = var.enable_ci_training_pool ? scaleway_k8s_pool.ci_training[0].id : ""
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}
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output "private_network_id" {
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description = "ID of the VPC private network the cluster is attached to"
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value = scaleway_vpc_private_network.foxhunt.id
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}
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