58 lines
1.9 KiB
Terraform
58 lines
1.9 KiB
Terraform
 
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# -----------------------------------------------------------------------------
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# EFS for the embedder model cache.
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#
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# Without persistent storage, every cold-start embedder task re-downloads
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# the ~30 MB bge-small model from Hugging Face — slow and rate-limit-risky.
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# EFS lets us share a warm cache across replicas and across restarts.
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#
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# The access point pins ownership to UID/GID 1001, matching the
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# `node-embedder` user baked into apps/embedder/Dockerfile, so files written
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# through the access point are owned correctly.
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# -----------------------------------------------------------------------------
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resource "aws_efs_file_system" "embedder_models" {
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creation_token = "${var.name_prefix}-embedder-models"
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encrypted = true
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# General Purpose performance mode + bursting throughput is plenty for a
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# ~30 MB read-mostly cache. Don't pay for provisioned throughput.
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performance_mode = "generalPurpose"
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throughput_mode = "bursting"
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tags = merge(local.tags, { Name = "${var.name_prefix}-embedder-models" })
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}
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# One mount target per private subnet so any AZ the embedder lands in can
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# reach the file system.
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resource "aws_efs_mount_target" "embedder_models" {
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for_each = toset(var.private_subnet_ids)
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file_system_id = aws_efs_file_system.embedder_models.id
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subnet_id = each.value
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security_groups = [aws_security_group.efs.id]
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}
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# Access point gives the embedder task a chrooted view of the file system,
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# with files always owned by uid/gid 1001 regardless of which task wrote
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# them. Matches the `node-embedder` user in the Dockerfile.
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resource "aws_efs_access_point" "embedder_models" {
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file_system_id = aws_efs_file_system.embedder_models.id
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posix_user {
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uid = 1001
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gid = 1001
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}
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root_directory {
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path = "/models"
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creation_info {
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owner_uid = 1001
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owner_gid = 1001
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permissions = "0755"
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}
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}
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tags = merge(local.tags, { Name = "${var.name_prefix}-embedder-models" })
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}
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