mirror of
https://github.com/TaterTotterson/microWakeWord-Trainer-Nvidia-Docker.git
synced 2026-08-12 07:55:33 -06:00
Add RTX 50 Blackwell image support
This commit is contained in:
21
.github/workflows/docker-publish.yml
vendored
21
.github/workflows/docker-publish.yml
vendored
@@ -56,3 +56,24 @@ jobs:
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labels: ${{ steps.meta.outputs.labels }}
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cache-from: type=gha,scope=mww-trainer-nvidia-docker
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cache-to: type=gha,mode=max,scope=mww-trainer-nvidia-docker
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- name: Docker metadata (Blackwell)
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id: meta-blackwell
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uses: docker/metadata-action@v5
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with:
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images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
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tags: |
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type=raw,value=blackwell
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type=ref,event=tag,suffix=-blackwell
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- name: Build and push Blackwell image
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uses: docker/build-push-action@v6
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with:
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context: .
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file: dockerfile.blackwell
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platforms: linux/amd64
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push: true
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tags: ${{ steps.meta-blackwell.outputs.tags }}
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labels: ${{ steps.meta-blackwell.outputs.labels }}
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cache-from: type=gha,scope=mww-trainer-nvidia-docker-blackwell
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cache-to: type=gha,mode=max,scope=mww-trainer-nvidia-docker-blackwell
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19
README.md
19
README.md
@@ -25,6 +25,19 @@ Tagged releases also publish matching immutable image tags:
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docker pull ghcr.io/tatertotterson/microwakeword:v5
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```
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RTX 50-series / Blackwell GPUs use a separate image with CUDA 12.8 and a
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Python 3.13 TensorFlow build for `sm_120`:
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```bash
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docker pull ghcr.io/tatertotterson/microwakeword:blackwell
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docker pull ghcr.io/tatertotterson/microwakeword:v5-blackwell
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```
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Use the Blackwell image only for RTX 50-series cards. It includes the
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community-built TensorFlow wheel from
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[chivitiH/tensorflow-blackwell-python313](https://github.com/chivitiH/tensorflow-blackwell-python313),
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which is unofficial and licensed CC BY-NC 4.0.
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---
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## Run The Container
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@@ -39,6 +52,9 @@ docker run -d \
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```
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Use a version tag such as `ghcr.io/tatertotterson/microwakeword:v5` when you want to pin a known release instead of tracking `latest`.
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For RTX 50-series cards, use `ghcr.io/tatertotterson/microwakeword:blackwell`
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or a pinned tag such as `ghcr.io/tatertotterson/microwakeword:v5-blackwell`
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in the same `docker run` command.
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The flags:
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@@ -153,6 +169,8 @@ Personal samples are optional. Training can run with zero personal samples after
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Reviewed negative samples are converted into `/data/work/reviewed_negative_features/` and inserted into the training YAML as a hard-negative feature set when present.
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On RTX 50-series / Blackwell GPUs, the Blackwell Docker image keeps sample generation and augmentation in the normal Python 3.12 trainer environment, then runs only the TensorFlow training/export stage in `/data/.venv-blackwell` with Python 3.13 and the Blackwell-native TensorFlow wheel.
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---
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## Language Support
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@@ -253,3 +271,4 @@ Built on top of:
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- [microWakeWord](https://github.com/kahrendt/microWakeWord)
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- [piper-sample-generator](https://github.com/rhasspy/piper-sample-generator)
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- [tensorflow-blackwell-python313](https://github.com/chivitiH/tensorflow-blackwell-python313) for the optional RTX 50-series / Blackwell image
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112
cli/setup_blackwell_venv
Executable file
112
cli/setup_blackwell_venv
Executable file
@@ -0,0 +1,112 @@
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#!/bin/bash
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set -euo pipefail
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PROGDIR="$(dirname "$(realpath "$0")")"
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ROOTDIR="$(dirname "${PROGDIR}")"
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KNOWN_ARGS=( data-dir force python )
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source "${PROGDIR}/shell.functions"
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if [ ${#UNKNOWN_ARGS[@]} -gt 0 ] ; then
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echo "Unknown argument(s): ${UNKNOWN_ARGS[*]}" >&2
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HELP=true
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fi
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if [ "${HELP}" == "true" ] ; then
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cat <<EOF >&2
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Usage: setup_blackwell_venv [ --data-dir=/data ] [ --force ] [ --python=python3.13 ]
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Creates /data/.venv-blackwell for RTX 50 / Blackwell TensorFlow training.
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Sample generation and augmentation continue to use /data/.venv.
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Environment overrides:
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MWW_BLACKWELL_TF_WHEEL_URL: TensorFlow Blackwell wheel URL.
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EOF
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exit 1
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fi
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[ -n "${DATA_DIR}" ] && DATA_DIR="$(realpath "${DATA_DIR}")"
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[ -d "${DATA_DIR}" ] || {
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echo "Data directory '${DATA_DIR}' doesn't exist." >&2
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exit 1
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}
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PYTHON="${PYTHON:-python3.13}"
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VENV="${DATA_DIR}/.venv-blackwell"
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MARKER="${VENV}/.mww-blackwell-venv"
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TF_WHEEL_URL="${MWW_BLACKWELL_TF_WHEEL_URL:-https://github.com/chivitiH/tensorflow-blackwell-python313/releases/download/v2.22.0-selfbuilt/tensorflow-2.22.0.dev0+selfbuilt-cp313-cp313-linux_x86_64.whl}"
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if ! command -v "${PYTHON}" >/dev/null 2>&1 ; then
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echo "Python 3.13 is required for the Blackwell TensorFlow wheel. Missing: ${PYTHON}" >&2
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exit 1
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fi
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if [ "${FORCE:-false}" != "true" ] && [ -x "${VENV}/bin/python" ] && [ -f "${MARKER}" ] ; then
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echo " Blackwell TensorFlow venv found (skipping setup_blackwell_venv)"
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exit 0
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fi
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echo "===== Setting up Blackwell TensorFlow environment ${VENV} ====="
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rm -rf "${VENV}" || :
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"${PYTHON}" -m venv --upgrade-deps "${VENV}"
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source "${VENV}/bin/activate"
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export PIP_PROGRESS_BAR=off
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export PIP_NO_COLOR=1
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export PIP_QUIET=0
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pip_install() {
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if $VERBOSE ; then
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pip install "$@" || return 1
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else
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{ pip install "$@" || return 1 ; } | stdbuf -i0 -o0 tr -d '[:print:]' | stdbuf -i0 -o0 tr '\n' '.'
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fi
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echo
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}
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echo " ===== Installing Blackwell TensorFlow wheel ====="
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pip_install --upgrade pip setuptools wheel
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pip_install "${TF_WHEEL_URL}"
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echo " ===== Installing microWakeWord training dependencies ====="
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pip_install \
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audiomentations \
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audio_metadata \
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datasets \
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mmap_ninja \
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pymicro-features \
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pyyaml \
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webrtcvad-wheels \
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ai-edge-litert \
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numpy-minmax \
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numpy-rms \
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absl-py \
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"numpy==2.3.5"
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echo " ===== Checking microwakeword ====="
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MWW="${DATA_DIR}/tools/microWakeWord"
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if [ ! -d "${MWW}" ] || [ -n "$(git -C "${MWW}" status --porcelain 2>/dev/null || true)" ] ; then
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rm -rf "${MWW}" || :
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mkdir -p "${DATA_DIR}/tools"
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echo " Cloning micro-wake-word to ${DATA_DIR}/tools"
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git clone https://github.com/TaterTotterson/micro-wake-word "${MWW}" &>/dev/null
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fi
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echo " Installing microwakeword into Blackwell venv"
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pip_install --no-deps -e "${MWW}"
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echo " ===== Testing Blackwell TensorFlow environment ====="
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"${VENV}/bin/python" - <<'PY'
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import tensorflow as tf
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from ai_edge_litert.interpreter import Interpreter
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from microwakeword.data import FeatureHandler
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from microwakeword.inference import Model
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print("TensorFlow:", tf.__version__)
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print("CUDA build:", tf.test.is_built_with_cuda())
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print("GPU:", tf.config.list_physical_devices("GPU"))
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print("microWakeWord Blackwell imports available")
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PY
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touch "${MARKER}"
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echo "Blackwell TensorFlow environment ready: ${VENV}"
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@@ -84,6 +84,21 @@ if [ "${IS_BLACKWELL}" = "true" ]; then
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echo "ℹ️ Using GPU compatibility retries; CPU fallback is ${ALLOW_CPU_FALLBACK} (override with MWW_ALLOW_CPU_FALLBACK=true|false)."
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fi
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BLACKWELL_TF_MODE="${MWW_BLACKWELL_TF:-auto}"
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BLACKWELL_TF_REQUIRED="false"
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BLACKWELL_TF_ACTIVE="false"
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case "${BLACKWELL_TF_MODE,,}" in
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1|true|yes|on|required)
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BLACKWELL_TF_REQUIRED="true"
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;;
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0|false|no|off|disabled)
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BLACKWELL_TF_MODE="disabled"
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;;
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*)
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BLACKWELL_TF_MODE="auto"
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;;
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esac
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# Enable driver-side PTX JIT fallback when ptxas/nvlink are unavailable.
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if [ -z "${XLA_FLAGS:-}" ]; then
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export XLA_FLAGS="--xla_gpu_unsafe_fallback_to_driver_on_ptxas_not_found"
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@@ -238,6 +253,32 @@ fi
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echo " Wrote training_parameters.yaml"
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rm -rf "${WORK_DIR}/trained_models/wakeword"
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if [ "${IS_BLACKWELL}" = "true" ] && [ "${BLACKWELL_TF_MODE}" != "disabled" ]; then
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BLACKWELL_SETUP="${PROGDIR}/setup_blackwell_venv"
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BLACKWELL_PYTHON="${DATA_DIR}/.venv-blackwell/bin/python"
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if [ -x "${BLACKWELL_SETUP}" ] && command -v python3.13 >/dev/null 2>&1; then
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echo "↪️ Preparing Blackwell-native TensorFlow training environment."
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if "${BLACKWELL_SETUP}" --data-dir="${DATA_DIR}"; then
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PYTHON_BIN="${BLACKWELL_PYTHON}"
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BLACKWELL_TF_ACTIVE="true"
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echo "✅ Blackwell TensorFlow training enabled: ${PYTHON_BIN}"
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else
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if [ "${BLACKWELL_TF_REQUIRED}" = "true" ]; then
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echo "❌ Blackwell TensorFlow setup failed and MWW_BLACKWELL_TF is required." >&2
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exit 1
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fi
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echo "⚠️ Blackwell TensorFlow setup failed; continuing with compatibility retries."
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fi
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else
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if [ "${BLACKWELL_TF_REQUIRED}" = "true" ]; then
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echo "❌ Blackwell TensorFlow was required, but python3.13/setup_blackwell_venv is unavailable." >&2
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exit 1
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fi
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echo "ℹ️ Blackwell TensorFlow image support not available; continuing with compatibility retries."
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fi
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fi
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wake_word_filename="$(
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echo "${WAKE_WORD}" \
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| tr '[:upper:]' '[:lower:]' \
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54
dockerfile.blackwell
Normal file
54
dockerfile.blackwell
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# RTX 50 / Blackwell image
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FROM nvidia/cuda:12.8.1-cudnn-devel-ubuntu24.04
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ENV DEBIAN_FRONTEND=noninteractive
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ENV CUDA_HOME=/usr/local/cuda
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ENV PATH=/usr/local/cuda/bin:${PATH}
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ENV LD_LIBRARY_PATH=/usr/local/cuda/lib64:${LD_LIBRARY_PATH}
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ENV MWW_BLACKWELL_IMAGE=1
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ENV MWW_BLACKWELL_TF=auto
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ENV MWW_BLACKWELL_TF_WHEEL_URL=https://github.com/chivitiH/tensorflow-blackwell-python313/releases/download/v2.22.0-selfbuilt/tensorflow-2.22.0.dev0+selfbuilt-cp313-cp313-linux_x86_64.whl
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# System deps. Python 3.12 remains the main trainer/runtime venv, while
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# Python 3.13 is used only for the Blackwell TensorFlow training step.
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RUN apt-get update && apt-get install -y --no-install-recommends \
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software-properties-common ca-certificates curl git wget unzip patch \
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ninja-build nano less \
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&& add-apt-repository -y ppa:deadsnakes/ppa \
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&& apt-get update \
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&& apt-get install -y --no-install-recommends \
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python3.12 python3.12-venv python3.12-dev \
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python3.13 python3.13-venv python3.13-dev \
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python3-pip python-is-python3 \
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&& ldconfig \
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&& rm -rf /var/lib/apt/lists/* \
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&& mkdir -p /data
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# Trainer UI port
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EXPOSE 8789
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# Script root
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WORKDIR /root/mww-scripts
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# Bash environment
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COPY --chown=root:root --chmod=0755 .bashrc /root/
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# Root-level entrypoints
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COPY --chown=root:root --chmod=0755 \
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train_wake_word \
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run.sh \
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trainer_server.py \
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requirements.txt \
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/root/mww-scripts/
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# CLI folder
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COPY --chown=root:root cli/ /root/mww-scripts/cli/
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# Make all CLI scripts executable (avoids "Permission denied")
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RUN chmod -R a+x /root/mww-scripts/cli
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# Static UI for trainer
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COPY --chown=root:root --chmod=0644 static/index.html /root/mww-scripts/static/index.html
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# trainer server
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CMD ["/bin/bash", "-lc", "/root/mww-scripts/run.sh"]
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