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MacRimi
9b5cefb81a
- Immich asks what runs its video and its recognition in one menu, in both modes, and gives the GPU to the server and to Machine learning; AMD uses ROCm - Frigate, Ollama, llama.cpp, Faster Whisper and Piper take the image built for the chosen GPU - The acceleration menu offers only what the host can run - An update or a recreation sends one notification with its result instead of the stop, backup and start of each container - A private bridge with nothing connected is not reported as down - Secondary containers of a stack appear in the App tab with their version and logo; Secure Gateway shows the same update state in both views - A mistyped value in the wizard asks the same question again
140 lines
6.5 KiB
Bash
Executable File
140 lines
6.5 KiB
Bash
Executable File
# Immich ML prerequisites and native GPU setup; no host driver installation.
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validate_immich_ml_profile() {
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ML_CPU_ARGS=(--cores 4)
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ML_MEMORY=4096
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ML_ROOTFS_SIZE=12
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case "$ML_ACCELERATION" in
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cpu) ;;
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rocm)
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ML_RENDER_DEVICE=$(jq -er '.machine_learning.render_device' "$DEPLOYMENT_FILE")
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[[ $ML_RENDER_DEVICE =~ ^/dev/dri/renderD[0-9]+$ && -c $ML_RENDER_DEVICE ]] \
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|| die "$(translate "The selected AMD render device does not exist:") $ML_RENDER_DEVICE"
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[[ $(cat "/sys/class/drm/${ML_RENDER_DEVICE##*/}/device/vendor") == 0x1002 ]] \
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|| die "$(translate "ROCm requires the render device of an AMD GPU")"
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[[ -c /dev/kfd ]] || die "$(translate "ROCm requires /dev/kfd on the host")"
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ML_MEMORY=8192
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# The ROCm image carries the whole AMD runtime and is several times
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# larger than the others.
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ML_ROOTFS_SIZE=40
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;;
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openvino)
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ML_RENDER_DEVICE=$(jq -er '.machine_learning.render_device' "$DEPLOYMENT_FILE")
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[[ $ML_RENDER_DEVICE =~ ^/dev/dri/renderD[0-9]+$ && -c $ML_RENDER_DEVICE ]] \
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|| die "$(translate "The selected Intel render device does not exist:") $ML_RENDER_DEVICE"
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[[ $(cat "/sys/class/drm/${ML_RENDER_DEVICE##*/}/device/vendor") == 0x8086 ]] \
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|| die "$(translate "OpenVINO requires the render device of an Intel GPU")"
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ML_CPU_ARGS=(--cpulimit 4)
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ML_MEMORY=8192
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;;
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cuda)
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command -v nvidia-container-cli >/dev/null 2>&1 \
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|| die "$(translate "CUDA requires the NVIDIA Container Toolkit on the host")"
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command -v nvidia-smi >/dev/null 2>&1 \
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|| die "$(translate "CUDA requires a working NVIDIA driver")"
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local inventory version capability
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inventory=$(nvidia-smi --query-gpu=driver_version,compute_cap --format=csv,noheader) \
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|| die "$(translate "Could not check the NVIDIA GPU")"
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[[ -n $inventory ]] || die "$(translate "No NVIDIA GPU is available")"
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while IFS=, read -r version capability; do
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[[ $version =~ ^[0-9]+\.[0-9]+(\.[0-9]+)?$ ]] \
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|| die "$(translate "Could not read the NVIDIA driver version")"
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(( ${version%%.*} >= 545 )) || die "$(translate "Immich CUDA requires NVIDIA driver 545 or later")"
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capability=${capability//[[:space:]]/}
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[[ $capability =~ ^[0-9]+\.[0-9]+$ ]] \
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|| die "$(translate "Could not read the CUDA compute capability")"
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awk -v value="$capability" 'BEGIN {exit !(value >= 5.2)}' \
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|| die "$(translate "Immich requires CUDA compute capability 5.2 or later")"
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done <<<"$inventory"
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[[ -r $SCRIPT_DIR/nvidia_lxc_mount_lab.sh ]] || die "$(translate "The dynamic NVIDIA hook is missing")"
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ML_CPU_ARGS=(--cores 4)
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ML_MEMORY=8192
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;;
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*) die "$(translate "Machine learning profile not implemented; it is not replaced by CPU:") $ML_ACCELERATION" ;;
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esac
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local cores allocation default_allocation
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default_allocation=cpuset
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[[ $ML_ACCELERATION != openvino ]] || default_allocation=quota
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cores=$(jq -er --argjson fallback "${ML_CPU_ARGS[1]}" '.machine_learning.resources.cores // $fallback' "$DEPLOYMENT_FILE")
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ML_MEMORY=$(jq -er --argjson fallback "$ML_MEMORY" '.machine_learning.resources.memory_mb // $fallback' "$DEPLOYMENT_FILE")
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ML_SWAP=$(jq -er '.machine_learning.resources.swap_mb // 1024' "$DEPLOYMENT_FILE")
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allocation=$(jq -er --arg fallback "$default_allocation" '.machine_learning.resources.cpu_allocation // $fallback' "$DEPLOYMENT_FILE")
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[[ $cores =~ ^[1-9][0-9]*$ && $ML_MEMORY =~ ^[1-9][0-9]*$ && $ML_SWAP =~ ^(0|[1-9][0-9]*)$ ]] \
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|| die "$(translate "Invalid machine learning resources")"
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case "$allocation" in
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quota) ML_CPU_ARGS=(--cpulimit "$cores") ;;
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cpuset)
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[[ $ML_ACCELERATION != openvino ]] || die "$(translate "OpenVINO requires a CPU quota to keep the CPU topology")"
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ML_CPU_ARGS=(--cores "$cores")
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;;
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*) die "$(translate "Invalid machine learning CPU allocation:") $allocation" ;;
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esac
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}
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# Gives one container of the stack the NVIDIA GPU through the dynamic hook.
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# Arguments: VMID CAPABILITIES
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configure_immich_nvidia() {
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# Isolate the shared standalone installer's runtime context from the stack.
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(
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VMID=$1
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CONF="/etc/pve/lxc/${VMID}.conf"
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UNPRIVILEGED_FLAG=1
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DEVICE='{"kind":"nvidia-runtime","runtime_mode":"dynamic"}'
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NVIDIA_GID_ENV=""
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DEVICE_INDEX=0
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while grep -q "^dev${DEVICE_INDEX}:" "$CONF"; do DEVICE_INDEX=$((DEVICE_INDEX + 1)); done
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fragment=$(mktemp)
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trap 'rm -f "$fragment"' EXIT
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jq -nc --arg capabilities "$2" \
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'{environment:[{name:"NVIDIA_DRIVER_CAPABILITIES",value:$capabilities}]}' >"$fragment"
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DEPLOYMENT_FILE=$fragment
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add_character_device() {
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local path=$1 mode gid
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[[ -c $path && $path == /dev/nvidia* ]] || die "$(translate "Invalid NVIDIA device:") $path"
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mode="0$(stat -c %a "$path")"
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gid=$(stat -c %g "$path")
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oci_quiet pct set "$VMID" "--dev${DEVICE_INDEX}" "path=${path},mode=${mode},gid=${gid},deny-write=0"
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DEVICE_INDEX=$((DEVICE_INDEX + 1))
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}
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configure_nvidia_runtime
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)
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}
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configure_immich_ml_gpu() {
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case "$ML_ACCELERATION" in
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openvino)
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oci_quiet pct set "$ML_ID" --dev0 "path=${ML_RENDER_DEVICE},gid=$(stat -c %g "$ML_RENDER_DEVICE"),mode=0660"
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;;
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rocm)
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oci_quiet pct set "$ML_ID" --dev0 "path=${ML_RENDER_DEVICE},gid=$(stat -c %g "$ML_RENDER_DEVICE"),mode=0660"
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oci_quiet pct set "$ML_ID" --dev1 "path=/dev/kfd,gid=$(stat -c %g /dev/kfd),mode=0660"
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;;
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cuda)
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configure_immich_nvidia "$ML_ID" "compute,utility"
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;;
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esac
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}
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validate_immich_ml_runtime() {
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[[ $ML_ACCELERATION != cpu ]] || return 0
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msg_info "$(translate "Checking the GPU of the machine learning container...")"
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oci_quiet pct exec "$ML_ID" -- python -c '
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import ctypes
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import sys
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import onnxruntime as ort
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profile = sys.argv[1]
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if profile == "openvino":
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assert "OpenVINOExecutionProvider" in ort.get_available_providers()
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devices = ort.capi._pybind_state.get_available_openvino_device_ids()
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assert any(device.startswith("GPU") for device in devices), devices
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elif profile == "rocm":
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assert "MIGraphXExecutionProvider" in ort.get_available_providers(), ort.get_available_providers()
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else:
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assert profile == "cuda"
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assert "CUDAExecutionProvider" in ort.get_available_providers()
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driver = ctypes.CDLL("libcuda.so.1")
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assert driver.cuInit(0) == 0, "CUDA driver initialization failed"
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print("Immich ML GPU runtime:", profile, "available; model inference is tested separately")
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' "$ML_ACCELERATION" || return
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msg_ok "$(translate "GPU available for machine learning:") $ML_ACCELERATION"
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}
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