f13b0df6e8
- Switch linker from lld to mold (~2-5x faster linking for large binaries) - Install mold 2.35.1 in CI builder Dockerfile - Update .cargo/config.toml: -fuse-ld=mold - Upgrade CI build pool: L4-1-24G → L40S-1-48G (~2x training throughput) - Increase max_size from 1 to 2 (allows concurrent jobs, fixes scheduling deadlocks) - Update runner resource limits for L40S node (24 vCPU, 96GB) - Update runner-values.yaml comments and .gitlab-ci.yml header Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
129 lines
3.5 KiB
HCL
129 lines
3.5 KiB
HCL
resource "scaleway_vpc_private_network" "foxhunt" {
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name = "${var.cluster_name}-pn"
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region = var.region
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}
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resource "scaleway_k8s_cluster" "foxhunt" {
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name = var.cluster_name
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version = var.k8s_version
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cni = "cilium"
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region = var.region
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delete_additional_resources = true
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private_network_id = scaleway_vpc_private_network.foxhunt.id
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auto_upgrade {
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enable = true
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maintenance_window_start_hour = 4
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maintenance_window_day = "sunday"
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}
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autoscaler_config {
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disable_scale_down = false
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scale_down_delay_after_add = "10m"
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scale_down_unneeded_time = "10m"
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estimator = "binpacking"
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ignore_daemonsets_utilization = true
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}
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}
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resource "scaleway_k8s_pool" "always_on" {
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cluster_id = scaleway_k8s_cluster.foxhunt.id
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name = "always-on"
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node_type = var.always_on_type
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size = 1
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min_size = 1
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max_size = 1
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autoscaling = false
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autohealing = true
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region = var.region
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}
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# GPU pool for ML training (H100 — large VRAM for ensemble training)
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resource "scaleway_k8s_pool" "gpu_training" {
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count = var.enable_gpu_training_pool ? 1 : 0
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cluster_id = scaleway_k8s_cluster.foxhunt.id
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name = "gpu-training"
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node_type = var.gpu_training_type
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size = 1
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min_size = 0
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max_size = var.gpu_training_max_size
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autoscaling = true
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autohealing = true
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region = var.region
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lifecycle {
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ignore_changes = [size]
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}
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}
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# GPU pool for inference during trading (L4 — cost-effective for forward passes)
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# When trading: trading_service + ml_training_service move here via GPU-enabled manifests
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resource "scaleway_k8s_pool" "gpu_inference" {
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count = var.enable_gpu_inference_pool ? 1 : 0
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cluster_id = scaleway_k8s_cluster.foxhunt.id
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name = "gpu-inference"
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node_type = var.gpu_inference_type
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size = 1
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min_size = 0
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max_size = var.gpu_inference_max_size
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autoscaling = true
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autohealing = true
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region = var.region
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lifecycle {
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ignore_changes = [size]
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}
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}
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# GitLab CE node pool (dedicated DEV1-L for GitLab + Runner manager)
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resource "scaleway_k8s_pool" "gitlab" {
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count = var.enable_gitlab_pool ? 1 : 0
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cluster_id = scaleway_k8s_cluster.foxhunt.id
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name = "gitlab"
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node_type = var.gitlab_type
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size = 1
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min_size = 1
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max_size = 1
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autoscaling = false
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autohealing = true
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region = var.region
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}
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# CI build pool (L40S — 48GB VRAM, ~2x L4 throughput, same Ada arch / CUDA CC 89)
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resource "scaleway_k8s_pool" "ci_build" {
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count = var.enable_ci_build_pool ? 1 : 0
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cluster_id = scaleway_k8s_cluster.foxhunt.id
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name = "ci-build"
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node_type = var.ci_build_type
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size = 1
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min_size = 0
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max_size = var.ci_build_max_size
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autoscaling = true
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autohealing = true
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region = var.region
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lifecycle {
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ignore_changes = [size]
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}
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}
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# CPU pool for remote development (DevPod — autoscales to zero)
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resource "scaleway_k8s_pool" "dev" {
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count = var.enable_dev_pool ? 1 : 0
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cluster_id = scaleway_k8s_cluster.foxhunt.id
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name = "gpu-dev"
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node_type = var.dev_pool_type
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size = 1
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min_size = 0
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max_size = var.dev_pool_max_size
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autoscaling = true
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autohealing = true
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wait_for_pool_ready = false
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region = var.region
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lifecycle {
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ignore_changes = [size]
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}
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}
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