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2 Commits

Author SHA1 Message Date
Ettore Di Giacinto
2adddef5fe Address feedback from review
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-01-02 21:34:23 +01:00
majiayu000
d89c7b731a fix: resolve duplicate MCP route registration causing 50% failure rate
Fixes #7772

The issue was caused by duplicate registration of the MCP endpoint
/mcp/v1/chat/completions in both openai.go and localai.go, leading
to a race condition where requests would randomly hit different
handlers with incompatible behaviors.

Changes:
- Removed duplicate MCP route registration from openai.go
- Kept the localai.MCPStreamEndpoint as the canonical handler
- Added all three MCP route patterns for backward compatibility:
  * /v1/mcp/chat/completions
  * /mcp/v1/chat/completions
  * /mcp/chat/completions
- Added comments to clarify route ownership and prevent future conflicts
- Fixed formatting in ui_api.go

The localai.MCPStreamEndpoint handler is more feature-complete as it
supports both streaming and non-streaming modes, while the removed
openai.MCPCompletionEndpoint only supported synchronous requests.

This eliminates the ~50% failure rate where the cogito library would
receive "Invalid http method" errors when internal HTTP requests were
routed to the wrong handler.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Signed-off-by: majiayu000 <1835304752@qq.com>
2026-01-02 21:29:05 +01:00
339 changed files with 5638 additions and 35376 deletions

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@@ -14,7 +14,7 @@ jobs:
steps:
- name: Dependabot metadata
id: metadata
uses: dependabot/fetch-metadata@v2.5.0
uses: dependabot/fetch-metadata@v2.4.0
with:
github-token: "${{ secrets.GITHUB_TOKEN }}"
skip-commit-verification: true

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@@ -33,7 +33,7 @@ jobs:
run: |
CGO_ENABLED=0 make build
- name: rm
uses: appleboy/ssh-action@v1.2.5
uses: appleboy/ssh-action@v1.2.4
with:
host: ${{ secrets.EXPLORER_SSH_HOST }}
username: ${{ secrets.EXPLORER_SSH_USERNAME }}
@@ -53,7 +53,7 @@ jobs:
rm: true
target: ./local-ai
- name: restarting
uses: appleboy/ssh-action@v1.2.5
uses: appleboy/ssh-action@v1.2.4
with:
host: ${{ secrets.EXPLORER_SSH_HOST }}
username: ${{ secrets.EXPLORER_SSH_USERNAME }}

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@@ -16,7 +16,7 @@ jobs:
strategy:
matrix:
include:
- grpc-base-image: ubuntu:24.04
- grpc-base-image: ubuntu:22.04
runs-on: 'ubuntu-latest'
platforms: 'linux/amd64,linux/arm64'
runs-on: ${{matrix.runs-on}}

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@@ -15,7 +15,7 @@ jobs:
strategy:
matrix:
include:
- base-image: intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04
- base-image: intel/oneapi-basekit:2025.2.0-0-devel-ubuntu22.04
runs-on: 'arc-runner-set'
platforms: 'linux/amd64'
runs-on: ${{matrix.runs-on}}
@@ -53,7 +53,7 @@ jobs:
BASE_IMAGE=${{ matrix.base-image }}
context: .
file: ./Dockerfile
tags: quay.io/go-skynet/intel-oneapi-base:24.04
tags: quay.io/go-skynet/intel-oneapi-base:latest
push: true
target: intel
platforms: ${{ matrix.platforms }}

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@@ -1,95 +1,94 @@
---
name: 'build container images tests'
on:
pull_request:
concurrency:
group: ci-${{ github.head_ref || github.ref }}-${{ github.repository }}
cancel-in-progress: true
jobs:
image-build:
uses: ./.github/workflows/image_build.yml
with:
tag-latest: ${{ matrix.tag-latest }}
tag-suffix: ${{ matrix.tag-suffix }}
build-type: ${{ matrix.build-type }}
cuda-major-version: ${{ matrix.cuda-major-version }}
cuda-minor-version: ${{ matrix.cuda-minor-version }}
platforms: ${{ matrix.platforms }}
runs-on: ${{ matrix.runs-on }}
base-image: ${{ matrix.base-image }}
grpc-base-image: ${{ matrix.grpc-base-image }}
makeflags: ${{ matrix.makeflags }}
ubuntu-version: ${{ matrix.ubuntu-version }}
secrets:
dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
# Pushing with all jobs in parallel
# eats the bandwidth of all the nodes
max-parallel: ${{ github.event_name != 'pull_request' && 4 || 8 }}
fail-fast: false
matrix:
include:
- build-type: 'cublas'
cuda-major-version: "12"
cuda-minor-version: "8"
platforms: 'linux/amd64'
tag-latest: 'false'
tag-suffix: '-gpu-nvidia-cuda-12'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
makeflags: "--jobs=3 --output-sync=target"
ubuntu-version: '2404'
- build-type: 'cublas'
cuda-major-version: "13"
cuda-minor-version: "0"
platforms: 'linux/amd64'
tag-latest: 'false'
tag-suffix: '-gpu-nvidia-cuda-13'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:22.04"
makeflags: "--jobs=3 --output-sync=target"
ubuntu-version: '2404'
- build-type: 'hipblas'
platforms: 'linux/amd64'
tag-latest: 'false'
tag-suffix: '-hipblas'
base-image: "rocm/dev-ubuntu-24.04:6.4.4"
grpc-base-image: "ubuntu:24.04"
runs-on: 'ubuntu-latest'
makeflags: "--jobs=3 --output-sync=target"
ubuntu-version: '2404'
- build-type: 'sycl'
platforms: 'linux/amd64'
tag-latest: 'false'
base-image: "intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04"
grpc-base-image: "ubuntu:24.04"
tag-suffix: 'sycl'
runs-on: 'ubuntu-latest'
makeflags: "--jobs=3 --output-sync=target"
ubuntu-version: '2404'
- build-type: 'vulkan'
platforms: 'linux/amd64,linux/arm64'
tag-latest: 'false'
tag-suffix: '-vulkan-core'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
makeflags: "--jobs=4 --output-sync=target"
ubuntu-version: '2404'
- build-type: 'cublas'
cuda-major-version: "13"
cuda-minor-version: "0"
platforms: 'linux/arm64'
tag-latest: 'false'
tag-suffix: '-nvidia-l4t-arm64-cuda-13'
base-image: "ubuntu:24.04"
runs-on: 'ubuntu-24.04-arm'
makeflags: "--jobs=4 --output-sync=target"
skip-drivers: 'false'
ubuntu-version: '2404'
name: 'build container images tests'
on:
pull_request:
concurrency:
group: ci-${{ github.head_ref || github.ref }}-${{ github.repository }}
cancel-in-progress: true
jobs:
image-build:
uses: ./.github/workflows/image_build.yml
with:
tag-latest: ${{ matrix.tag-latest }}
tag-suffix: ${{ matrix.tag-suffix }}
build-type: ${{ matrix.build-type }}
cuda-major-version: ${{ matrix.cuda-major-version }}
cuda-minor-version: ${{ matrix.cuda-minor-version }}
platforms: ${{ matrix.platforms }}
runs-on: ${{ matrix.runs-on }}
base-image: ${{ matrix.base-image }}
grpc-base-image: ${{ matrix.grpc-base-image }}
makeflags: ${{ matrix.makeflags }}
ubuntu-version: ${{ matrix.ubuntu-version }}
secrets:
dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
# Pushing with all jobs in parallel
# eats the bandwidth of all the nodes
max-parallel: ${{ github.event_name != 'pull_request' && 4 || 8 }}
fail-fast: false
matrix:
include:
- build-type: 'cublas'
cuda-major-version: "12"
cuda-minor-version: "0"
platforms: 'linux/amd64'
tag-latest: 'false'
tag-suffix: '-gpu-nvidia-cuda-12'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:22.04"
makeflags: "--jobs=3 --output-sync=target"
ubuntu-version: '2204'
- build-type: 'cublas'
cuda-major-version: "13"
cuda-minor-version: "0"
platforms: 'linux/amd64'
tag-latest: 'false'
tag-suffix: '-gpu-nvidia-cuda-13'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:22.04"
makeflags: "--jobs=3 --output-sync=target"
ubuntu-version: '2204'
- build-type: 'hipblas'
platforms: 'linux/amd64'
tag-latest: 'false'
tag-suffix: '-hipblas'
base-image: "rocm/dev-ubuntu-22.04:6.4.3"
grpc-base-image: "ubuntu:22.04"
runs-on: 'ubuntu-latest'
makeflags: "--jobs=3 --output-sync=target"
ubuntu-version: '2204'
- build-type: 'sycl'
platforms: 'linux/amd64'
tag-latest: 'false'
base-image: "quay.io/go-skynet/intel-oneapi-base:latest"
grpc-base-image: "ubuntu:22.04"
tag-suffix: 'sycl'
runs-on: 'ubuntu-latest'
makeflags: "--jobs=3 --output-sync=target"
ubuntu-version: '2204'
- build-type: 'vulkan'
platforms: 'linux/amd64'
tag-latest: 'false'
tag-suffix: '-vulkan-core'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:22.04"
makeflags: "--jobs=4 --output-sync=target"
ubuntu-version: '2204'
- build-type: 'cublas'
cuda-major-version: "13"
cuda-minor-version: "0"
platforms: 'linux/arm64'
tag-latest: 'false'
tag-suffix: '-nvidia-l4t-arm64-cuda-13'
base-image: "ubuntu:24.04"
runs-on: 'ubuntu-24.04-arm'
makeflags: "--jobs=4 --output-sync=target"
skip-drivers: 'false'
ubuntu-version: '2404'

View File

@@ -1,187 +1,187 @@
---
name: 'build container images'
on:
push:
branches:
- master
tags:
- '*'
concurrency:
group: ci-${{ github.head_ref || github.ref }}-${{ github.repository }}
cancel-in-progress: true
jobs:
hipblas-jobs:
uses: ./.github/workflows/image_build.yml
with:
tag-latest: ${{ matrix.tag-latest }}
tag-suffix: ${{ matrix.tag-suffix }}
build-type: ${{ matrix.build-type }}
cuda-major-version: ${{ matrix.cuda-major-version }}
cuda-minor-version: ${{ matrix.cuda-minor-version }}
platforms: ${{ matrix.platforms }}
runs-on: ${{ matrix.runs-on }}
base-image: ${{ matrix.base-image }}
grpc-base-image: ${{ matrix.grpc-base-image }}
aio: ${{ matrix.aio }}
makeflags: ${{ matrix.makeflags }}
ubuntu-version: ${{ matrix.ubuntu-version }}
ubuntu-codename: ${{ matrix.ubuntu-codename }}
secrets:
dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
matrix:
include:
- build-type: 'hipblas'
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-hipblas'
base-image: "rocm/dev-ubuntu-24.04:6.4.4"
grpc-base-image: "ubuntu:24.04"
runs-on: 'ubuntu-latest'
makeflags: "--jobs=3 --output-sync=target"
aio: "-aio-gpu-hipblas"
ubuntu-version: '2404'
ubuntu-codename: 'noble'
core-image-build:
uses: ./.github/workflows/image_build.yml
with:
tag-latest: ${{ matrix.tag-latest }}
tag-suffix: ${{ matrix.tag-suffix }}
build-type: ${{ matrix.build-type }}
cuda-major-version: ${{ matrix.cuda-major-version }}
cuda-minor-version: ${{ matrix.cuda-minor-version }}
platforms: ${{ matrix.platforms }}
runs-on: ${{ matrix.runs-on }}
aio: ${{ matrix.aio }}
base-image: ${{ matrix.base-image }}
grpc-base-image: ${{ matrix.grpc-base-image }}
makeflags: ${{ matrix.makeflags }}
skip-drivers: ${{ matrix.skip-drivers }}
ubuntu-version: ${{ matrix.ubuntu-version }}
ubuntu-codename: ${{ matrix.ubuntu-codename }}
secrets:
dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
#max-parallel: ${{ github.event_name != 'pull_request' && 2 || 4 }}
matrix:
include:
- build-type: ''
platforms: 'linux/amd64,linux/arm64'
tag-latest: 'auto'
tag-suffix: ''
base-image: "ubuntu:24.04"
runs-on: 'ubuntu-latest'
aio: "-aio-cpu"
makeflags: "--jobs=4 --output-sync=target"
skip-drivers: 'false'
ubuntu-version: '2404'
ubuntu-codename: 'noble'
- build-type: 'cublas'
cuda-major-version: "12"
cuda-minor-version: "8"
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-nvidia-cuda-12'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
makeflags: "--jobs=4 --output-sync=target"
aio: "-aio-gpu-nvidia-cuda-12"
ubuntu-version: '2404'
ubuntu-codename: 'noble'
- build-type: 'cublas'
cuda-major-version: "13"
cuda-minor-version: "0"
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-nvidia-cuda-13'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:22.04"
skip-drivers: 'false'
makeflags: "--jobs=4 --output-sync=target"
aio: "-aio-gpu-nvidia-cuda-13"
ubuntu-version: '2404'
ubuntu-codename: 'noble'
- build-type: 'vulkan'
platforms: 'linux/amd64,linux/arm64'
tag-latest: 'auto'
tag-suffix: '-gpu-vulkan'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
makeflags: "--jobs=4 --output-sync=target"
aio: "-aio-gpu-vulkan"
ubuntu-version: '2404'
ubuntu-codename: 'noble'
- build-type: 'intel'
platforms: 'linux/amd64'
tag-latest: 'auto'
base-image: "intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04"
grpc-base-image: "ubuntu:24.04"
tag-suffix: '-gpu-intel'
runs-on: 'ubuntu-latest'
makeflags: "--jobs=3 --output-sync=target"
aio: "-aio-gpu-intel"
ubuntu-version: '2404'
ubuntu-codename: 'noble'
gh-runner:
uses: ./.github/workflows/image_build.yml
with:
tag-latest: ${{ matrix.tag-latest }}
tag-suffix: ${{ matrix.tag-suffix }}
build-type: ${{ matrix.build-type }}
cuda-major-version: ${{ matrix.cuda-major-version }}
cuda-minor-version: ${{ matrix.cuda-minor-version }}
platforms: ${{ matrix.platforms }}
runs-on: ${{ matrix.runs-on }}
aio: ${{ matrix.aio }}
base-image: ${{ matrix.base-image }}
grpc-base-image: ${{ matrix.grpc-base-image }}
makeflags: ${{ matrix.makeflags }}
skip-drivers: ${{ matrix.skip-drivers }}
ubuntu-version: ${{ matrix.ubuntu-version }}
ubuntu-codename: ${{ matrix.ubuntu-codename }}
secrets:
dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
matrix:
include:
- build-type: 'cublas'
cuda-major-version: "12"
cuda-minor-version: "0"
platforms: 'linux/arm64'
tag-latest: 'auto'
tag-suffix: '-nvidia-l4t-arm64'
base-image: "nvcr.io/nvidia/l4t-jetpack:r36.4.0"
runs-on: 'ubuntu-24.04-arm'
makeflags: "--jobs=4 --output-sync=target"
skip-drivers: 'true'
ubuntu-version: "2204"
ubuntu-codename: 'jammy'
- build-type: 'cublas'
cuda-major-version: "13"
cuda-minor-version: "0"
platforms: 'linux/arm64'
tag-latest: 'auto'
tag-suffix: '-nvidia-l4t-arm64-cuda-13'
base-image: "ubuntu:24.04"
runs-on: 'ubuntu-24.04-arm'
makeflags: "--jobs=4 --output-sync=target"
skip-drivers: 'false'
ubuntu-version: '2404'
ubuntu-codename: 'noble'
name: 'build container images'
on:
push:
branches:
- master
tags:
- '*'
concurrency:
group: ci-${{ github.head_ref || github.ref }}-${{ github.repository }}
cancel-in-progress: true
jobs:
hipblas-jobs:
uses: ./.github/workflows/image_build.yml
with:
tag-latest: ${{ matrix.tag-latest }}
tag-suffix: ${{ matrix.tag-suffix }}
build-type: ${{ matrix.build-type }}
cuda-major-version: ${{ matrix.cuda-major-version }}
cuda-minor-version: ${{ matrix.cuda-minor-version }}
platforms: ${{ matrix.platforms }}
runs-on: ${{ matrix.runs-on }}
base-image: ${{ matrix.base-image }}
grpc-base-image: ${{ matrix.grpc-base-image }}
aio: ${{ matrix.aio }}
makeflags: ${{ matrix.makeflags }}
ubuntu-version: ${{ matrix.ubuntu-version }}
secrets:
dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
matrix:
include:
- build-type: 'hipblas'
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-hipblas'
base-image: "rocm/dev-ubuntu-22.04:6.4.3"
grpc-base-image: "ubuntu:22.04"
runs-on: 'ubuntu-latest'
makeflags: "--jobs=3 --output-sync=target"
aio: "-aio-gpu-hipblas"
ubuntu-version: '2204'
core-image-build:
uses: ./.github/workflows/image_build.yml
with:
tag-latest: ${{ matrix.tag-latest }}
tag-suffix: ${{ matrix.tag-suffix }}
build-type: ${{ matrix.build-type }}
cuda-major-version: ${{ matrix.cuda-major-version }}
cuda-minor-version: ${{ matrix.cuda-minor-version }}
platforms: ${{ matrix.platforms }}
runs-on: ${{ matrix.runs-on }}
aio: ${{ matrix.aio }}
base-image: ${{ matrix.base-image }}
grpc-base-image: ${{ matrix.grpc-base-image }}
makeflags: ${{ matrix.makeflags }}
skip-drivers: ${{ matrix.skip-drivers }}
ubuntu-version: ${{ matrix.ubuntu-version }}
secrets:
dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
#max-parallel: ${{ github.event_name != 'pull_request' && 2 || 4 }}
matrix:
include:
- build-type: ''
platforms: 'linux/amd64,linux/arm64'
tag-latest: 'auto'
tag-suffix: ''
base-image: "ubuntu:22.04"
runs-on: 'ubuntu-latest'
aio: "-aio-cpu"
makeflags: "--jobs=4 --output-sync=target"
skip-drivers: 'false'
ubuntu-version: '2204'
- build-type: 'cublas'
cuda-major-version: "11"
cuda-minor-version: "7"
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-nvidia-cuda-11'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:22.04"
makeflags: "--jobs=4 --output-sync=target"
skip-drivers: 'false'
aio: "-aio-gpu-nvidia-cuda-11"
ubuntu-version: '2204'
- build-type: 'cublas'
cuda-major-version: "12"
cuda-minor-version: "0"
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-nvidia-cuda-12'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:22.04"
skip-drivers: 'false'
makeflags: "--jobs=4 --output-sync=target"
aio: "-aio-gpu-nvidia-cuda-12"
ubuntu-version: '2204'
- build-type: 'cublas'
cuda-major-version: "13"
cuda-minor-version: "0"
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-nvidia-cuda-13'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:22.04"
skip-drivers: 'false'
makeflags: "--jobs=4 --output-sync=target"
aio: "-aio-gpu-nvidia-cuda-13"
ubuntu-version: '2204'
- build-type: 'vulkan'
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-vulkan'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:22.04"
skip-drivers: 'false'
makeflags: "--jobs=4 --output-sync=target"
aio: "-aio-gpu-vulkan"
ubuntu-version: '2204'
- build-type: 'intel'
platforms: 'linux/amd64'
tag-latest: 'auto'
base-image: "quay.io/go-skynet/intel-oneapi-base:latest"
grpc-base-image: "ubuntu:22.04"
tag-suffix: '-gpu-intel'
runs-on: 'ubuntu-latest'
makeflags: "--jobs=3 --output-sync=target"
aio: "-aio-gpu-intel"
ubuntu-version: '2204'
gh-runner:
uses: ./.github/workflows/image_build.yml
with:
tag-latest: ${{ matrix.tag-latest }}
tag-suffix: ${{ matrix.tag-suffix }}
build-type: ${{ matrix.build-type }}
cuda-major-version: ${{ matrix.cuda-major-version }}
cuda-minor-version: ${{ matrix.cuda-minor-version }}
platforms: ${{ matrix.platforms }}
runs-on: ${{ matrix.runs-on }}
aio: ${{ matrix.aio }}
base-image: ${{ matrix.base-image }}
grpc-base-image: ${{ matrix.grpc-base-image }}
makeflags: ${{ matrix.makeflags }}
skip-drivers: ${{ matrix.skip-drivers }}
ubuntu-version: ${{ matrix.ubuntu-version }}
secrets:
dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
matrix:
include:
- build-type: 'cublas'
cuda-major-version: "12"
cuda-minor-version: "0"
platforms: 'linux/arm64'
tag-latest: 'auto'
tag-suffix: '-nvidia-l4t-arm64'
base-image: "nvcr.io/nvidia/l4t-jetpack:r36.4.0"
runs-on: 'ubuntu-24.04-arm'
makeflags: "--jobs=4 --output-sync=target"
skip-drivers: 'true'
ubuntu-version: "2204"
- build-type: 'cublas'
cuda-major-version: "13"
cuda-minor-version: "0"
platforms: 'linux/arm64'
tag-latest: 'auto'
tag-suffix: '-nvidia-l4t-arm64-cuda-13'
base-image: "ubuntu:24.04"
runs-on: 'ubuntu-24.04-arm'
makeflags: "--jobs=4 --output-sync=target"
skip-drivers: 'false'
ubuntu-version: '2404'

View File

@@ -23,7 +23,7 @@ on:
type: string
cuda-minor-version:
description: 'CUDA minor version'
default: "9"
default: "4"
type: string
platforms:
description: 'Platforms'
@@ -61,11 +61,6 @@ on:
required: false
default: '2204'
type: string
ubuntu-codename:
description: 'Ubuntu codename'
required: false
default: 'noble'
type: string
secrets:
dockerUsername:
required: true
@@ -249,7 +244,6 @@ jobs:
MAKEFLAGS=${{ inputs.makeflags }}
SKIP_DRIVERS=${{ inputs.skip-drivers }}
UBUNTU_VERSION=${{ inputs.ubuntu-version }}
UBUNTU_CODENAME=${{ inputs.ubuntu-codename }}
context: .
file: ./Dockerfile
cache-from: type=gha
@@ -278,7 +272,6 @@ jobs:
MAKEFLAGS=${{ inputs.makeflags }}
SKIP_DRIVERS=${{ inputs.skip-drivers }}
UBUNTU_VERSION=${{ inputs.ubuntu-version }}
UBUNTU_CODENAME=${{ inputs.ubuntu-codename }}
context: .
file: ./Dockerfile
cache-from: type=gha

View File

@@ -238,7 +238,7 @@ jobs:
- name: Dependencies
run: |
sudo apt-get update
sudo apt-get install -y build-essential ffmpeg
sudo apt-get install build-essential ffmpeg
sudo apt-get install -y ca-certificates cmake curl patch espeak espeak-ng python3-pip
# Install UV
curl -LsSf https://astral.sh/uv/install.sh | sh
@@ -247,98 +247,3 @@ jobs:
run: |
make --jobs=5 --output-sync=target -C backend/python/coqui
make --jobs=5 --output-sync=target -C backend/python/coqui test
tests-moonshine:
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
run: |
sudo apt-get update
sudo apt-get install -y build-essential ffmpeg
sudo apt-get install -y ca-certificates cmake curl patch python3-pip
# Install UV
curl -LsSf https://astral.sh/uv/install.sh | sh
pip install --user --no-cache-dir grpcio-tools==1.64.1
- name: Test moonshine
run: |
make --jobs=5 --output-sync=target -C backend/python/moonshine
make --jobs=5 --output-sync=target -C backend/python/moonshine test
tests-pocket-tts:
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
run: |
sudo apt-get update
sudo apt-get install -y build-essential ffmpeg
sudo apt-get install -y ca-certificates cmake curl patch python3-pip
# Install UV
curl -LsSf https://astral.sh/uv/install.sh | sh
pip install --user --no-cache-dir grpcio-tools==1.64.1
- name: Test pocket-tts
run: |
make --jobs=5 --output-sync=target -C backend/python/pocket-tts
make --jobs=5 --output-sync=target -C backend/python/pocket-tts test
tests-qwen-tts:
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
run: |
sudo apt-get update
sudo apt-get install -y build-essential ffmpeg
sudo apt-get install -y ca-certificates cmake curl patch python3-pip
# Install UV
curl -LsSf https://astral.sh/uv/install.sh | sh
pip install --user --no-cache-dir grpcio-tools==1.64.1
- name: Test qwen-tts
run: |
make --jobs=5 --output-sync=target -C backend/python/qwen-tts
make --jobs=5 --output-sync=target -C backend/python/qwen-tts test
tests-qwen-asr:
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
run: |
sudo apt-get update
sudo apt-get install -y build-essential ffmpeg sox
sudo apt-get install -y ca-certificates cmake curl patch python3-pip
# Install UV
curl -LsSf https://astral.sh/uv/install.sh | sh
pip install --user --no-cache-dir grpcio-tools==1.64.1
- name: Test qwen-asr
run: |
make --jobs=5 --output-sync=target -C backend/python/qwen-asr
make --jobs=5 --output-sync=target -C backend/python/qwen-asr test
tests-voxcpm:
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
run: |
sudo apt-get update
sudo apt-get install build-essential ffmpeg
sudo apt-get install -y ca-certificates cmake curl patch python3-pip
# Install UV
curl -LsSf https://astral.sh/uv/install.sh | sh
pip install --user --no-cache-dir grpcio-tools==1.64.1
- name: Test voxcpm
run: |
make --jobs=5 --output-sync=target -C backend/python/voxcpm
make --jobs=5 --output-sync=target -C backend/python/voxcpm test

View File

@@ -1,56 +0,0 @@
---
name: 'E2E Backend Tests'
on:
pull_request:
push:
branches:
- master
tags:
- '*'
concurrency:
group: ci-tests-e2e-backend-${{ github.head_ref || github.ref }}-${{ github.repository }}
cancel-in-progress: true
jobs:
tests-e2e-backend:
runs-on: ubuntu-latest
strategy:
matrix:
go-version: ['1.25.x']
steps:
- name: Clone
uses: actions/checkout@v6
with:
submodules: true
- name: Setup Go ${{ matrix.go-version }}
uses: actions/setup-go@v5
with:
go-version: ${{ matrix.go-version }}
cache: false
- name: Display Go version
run: go version
- name: Proto Dependencies
run: |
# Install protoc
curl -L -s https://github.com/protocolbuffers/protobuf/releases/download/v26.1/protoc-26.1-linux-x86_64.zip -o protoc.zip && \
unzip -j -d /usr/local/bin protoc.zip bin/protoc && \
rm protoc.zip
go install google.golang.org/protobuf/cmd/protoc-gen-go@v1.34.2
go install google.golang.org/grpc/cmd/protoc-gen-go-grpc@1958fcbe2ca8bd93af633f11e97d44e567e945af
PATH="$PATH:$HOME/go/bin" make protogen-go
- name: Dependencies
run: |
sudo apt-get update
sudo apt-get install -y build-essential
- name: Test Backend E2E
run: |
PATH="$PATH:$HOME/go/bin" make build-mock-backend test-e2e
- name: Setup tmate session if tests fail
if: ${{ failure() }}
uses: mxschmitt/action-tmate@v3.23
with:
detached: true
connect-timeout-seconds: 180
limit-access-to-actor: true

3
.gitignore vendored
View File

@@ -25,7 +25,6 @@ go-bert
# LocalAI build binary
LocalAI
/local-ai
/local-ai-launcher
# prevent above rules from omitting the helm chart
!charts/*
# prevent above rules from omitting the api/localai folder
@@ -36,8 +35,6 @@ LocalAI
models/*
test-models/
test-dir/
tests/e2e-aio/backends
tests/e2e-aio/models
release/

211
AGENTS.md
View File

@@ -2,163 +2,6 @@
Building and testing the project depends on the components involved and the platform where development is taking place. Due to the amount of context required it's usually best not to try building or testing the project unless the user requests it. If you must build the project then inspect the Makefile in the project root and the Makefiles of any backends that are effected by changes you are making. In addition the workflows in .github/workflows can be used as a reference when it is unclear how to build or test a component. The primary Makefile contains targets for building inside or outside Docker, if the user has not previously specified a preference then ask which they would like to use.
## Building a specified backend
Let's say the user wants to build a particular backend for a given platform. For example let's say they want to build coqui for ROCM/hipblas
- The Makefile has targets like `docker-build-coqui` created with `generate-docker-build-target` at the time of writing. Recently added backends may require a new target.
- At a minimum we need to set the BUILD_TYPE, BASE_IMAGE build-args
- Use .github/workflows/backend.yml as a reference it lists the needed args in the `include` job strategy matrix
- l4t and cublas also requires the CUDA major and minor version
- You can pretty print a command like `DOCKER_MAKEFLAGS=-j$(nproc --ignore=1) BUILD_TYPE=hipblas BASE_IMAGE=rocm/dev-ubuntu-24.04:6.4.4 make docker-build-coqui`
- Unless the user specifies that they want you to run the command, then just print it because not all agent frontends handle long running jobs well and the output may overflow your context
- The user may say they want to build AMD or ROCM instead of hipblas, or Intel instead of SYCL or NVIDIA insted of l4t or cublas. Ask for confirmation if there is ambiguity.
- Sometimes the user may need extra parameters to be added to `docker build` (e.g. `--platform` for cross-platform builds or `--progress` to view the full logs), in which case you can generate the `docker build` command directly.
## Adding a New Backend
When adding a new backend to LocalAI, you need to update several files to ensure the backend is properly built, tested, and registered. Here's a step-by-step guide based on the pattern used for adding backends like `moonshine`:
### 1. Create Backend Directory Structure
Create the backend directory under the appropriate location:
- **Python backends**: `backend/python/<backend-name>/`
- **Go backends**: `backend/go/<backend-name>/`
- **C++ backends**: `backend/cpp/<backend-name>/`
For Python backends, you'll typically need:
- `backend.py` - Main gRPC server implementation
- `Makefile` - Build configuration
- `install.sh` - Installation script for dependencies
- `protogen.sh` - Protocol buffer generation script
- `requirements.txt` - Python dependencies
- `run.sh` - Runtime script
- `test.py` / `test.sh` - Test files
### 2. Add Build Configurations to `.github/workflows/backend.yml`
Add build matrix entries for each platform/GPU type you want to support. Look at similar backends (e.g., `chatterbox`, `faster-whisper`) for reference.
**Placement in file:**
- CPU builds: Add after other CPU builds (e.g., after `cpu-chatterbox`)
- CUDA 12 builds: Add after other CUDA 12 builds (e.g., after `gpu-nvidia-cuda-12-chatterbox`)
- CUDA 13 builds: Add after other CUDA 13 builds (e.g., after `gpu-nvidia-cuda-13-chatterbox`)
**Additional build types you may need:**
- ROCm/HIP: Use `build-type: 'hipblas'` with `base-image: "rocm/dev-ubuntu-24.04:6.4.4"`
- Intel/SYCL: Use `build-type: 'intel'` or `build-type: 'sycl_f16'`/`sycl_f32` with `base-image: "intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04"`
- L4T (ARM): Use `build-type: 'l4t'` with `platforms: 'linux/arm64'` and `runs-on: 'ubuntu-24.04-arm'`
### 3. Add Backend Metadata to `backend/index.yaml`
**Step 3a: Add Meta Definition**
Add a YAML anchor definition in the `## metas` section (around line 2-300). Look for similar backends to use as a template such as `diffusers` or `chatterbox`
**Step 3b: Add Image Entries**
Add image entries at the end of the file, following the pattern of similar backends such as `diffusers` or `chatterbox`. Include both `latest` (production) and `master` (development) tags.
### 4. Update the Makefile
The Makefile needs to be updated in several places to support building and testing the new backend:
**Step 4a: Add to `.NOTPARALLEL`**
Add `backends/<backend-name>` to the `.NOTPARALLEL` line (around line 2) to prevent parallel execution conflicts:
```makefile
.NOTPARALLEL: ... backends/<backend-name>
```
**Step 4b: Add to `prepare-test-extra`**
Add the backend to the `prepare-test-extra` target (around line 312) to prepare it for testing:
```makefile
prepare-test-extra: protogen-python
...
$(MAKE) -C backend/python/<backend-name>
```
**Step 4c: Add to `test-extra`**
Add the backend to the `test-extra` target (around line 319) to run its tests:
```makefile
test-extra: prepare-test-extra
...
$(MAKE) -C backend/python/<backend-name> test
```
**Step 4d: Add Backend Definition**
Add a backend definition variable in the backend definitions section (around line 428-457). The format depends on the backend type:
**For Python backends with root context** (like `faster-whisper`, `coqui`):
```makefile
BACKEND_<BACKEND_NAME> = <backend-name>|python|.|false|true
```
**For Python backends with `./backend` context** (like `chatterbox`, `moonshine`):
```makefile
BACKEND_<BACKEND_NAME> = <backend-name>|python|./backend|false|true
```
**For Go backends**:
```makefile
BACKEND_<BACKEND_NAME> = <backend-name>|golang|.|false|true
```
**Step 4e: Generate Docker Build Target**
Add an eval call to generate the docker-build target (around line 480-501):
```makefile
$(eval $(call generate-docker-build-target,$(BACKEND_<BACKEND_NAME>)))
```
**Step 4f: Add to `docker-build-backends`**
Add `docker-build-<backend-name>` to the `docker-build-backends` target (around line 507):
```makefile
docker-build-backends: ... docker-build-<backend-name>
```
**Determining the Context:**
- If the backend is in `backend/python/<backend-name>/` and uses `./backend` as context in the workflow file, use `./backend` context
- If the backend is in `backend/python/<backend-name>/` but uses `.` as context in the workflow file, use `.` context
- Check similar backends to determine the correct context
### 5. Verification Checklist
After adding a new backend, verify:
- [ ] Backend directory structure is complete with all necessary files
- [ ] Build configurations added to `.github/workflows/backend.yml` for all desired platforms
- [ ] Meta definition added to `backend/index.yaml` in the `## metas` section
- [ ] Image entries added to `backend/index.yaml` for all build variants (latest + development)
- [ ] Tag suffixes match between workflow file and index.yaml
- [ ] Makefile updated with all 6 required changes (`.NOTPARALLEL`, `prepare-test-extra`, `test-extra`, backend definition, docker-build target eval, `docker-build-backends`)
- [ ] No YAML syntax errors (check with linter)
- [ ] No Makefile syntax errors (check with linter)
- [ ] Follows the same pattern as similar backends (e.g., if it's a transcription backend, follow `faster-whisper` pattern)
### 6. Example: Adding a Python Backend
For reference, when `moonshine` was added:
- **Files created**: `backend/python/moonshine/{backend.py, Makefile, install.sh, protogen.sh, requirements.txt, run.sh, test.py, test.sh}`
- **Workflow entries**: 3 build configurations (CPU, CUDA 12, CUDA 13)
- **Index entries**: 1 meta definition + 6 image entries (cpu, cuda12, cuda13 × latest/development)
- **Makefile updates**:
- Added to `.NOTPARALLEL` line
- Added to `prepare-test-extra` and `test-extra` targets
- Added `BACKEND_MOONSHINE = moonshine|python|./backend|false|true`
- Added eval for docker-build target generation
- Added `docker-build-moonshine` to `docker-build-backends`
# Coding style
- The project has the following .editorconfig
@@ -234,57 +77,3 @@ When fixing compilation errors after upstream changes:
- HTTP server uses `server_routes` with HTTP handlers
- Both use the same `server_context` and task queue infrastructure
- gRPC methods: `LoadModel`, `Predict`, `PredictStream`, `Embedding`, `Rerank`, `TokenizeString`, `GetMetrics`, `Health`
## Tool Call Parsing Maintenance
When working on JSON/XML tool call parsing functionality, always check llama.cpp for reference implementation and updates:
### Checking for XML Parsing Changes
1. **Review XML Format Definitions**: Check `llama.cpp/common/chat-parser-xml-toolcall.h` for `xml_tool_call_format` struct changes
2. **Review Parsing Logic**: Check `llama.cpp/common/chat-parser-xml-toolcall.cpp` for parsing algorithm updates
3. **Review Format Presets**: Check `llama.cpp/common/chat-parser.cpp` for new XML format presets (search for `xml_tool_call_format form`)
4. **Review Model Lists**: Check `llama.cpp/common/chat.h` for `COMMON_CHAT_FORMAT_*` enum values that use XML parsing:
- `COMMON_CHAT_FORMAT_GLM_4_5`
- `COMMON_CHAT_FORMAT_MINIMAX_M2`
- `COMMON_CHAT_FORMAT_KIMI_K2`
- `COMMON_CHAT_FORMAT_QWEN3_CODER_XML`
- `COMMON_CHAT_FORMAT_APRIEL_1_5`
- `COMMON_CHAT_FORMAT_XIAOMI_MIMO`
- Any new formats added
### Model Configuration Options
Always check `llama.cpp` for new model configuration options that should be supported in LocalAI:
1. **Check Server Context**: Review `llama.cpp/tools/server/server-context.cpp` for new parameters
2. **Check Chat Params**: Review `llama.cpp/common/chat.h` for `common_chat_params` struct changes
3. **Check Server Options**: Review `llama.cpp/tools/server/server.cpp` for command-line argument changes
4. **Examples of options to check**:
- `ctx_shift` - Context shifting support
- `parallel_tool_calls` - Parallel tool calling
- `reasoning_format` - Reasoning format options
- Any new flags or parameters
### Implementation Guidelines
1. **Feature Parity**: Always aim for feature parity with llama.cpp's implementation
2. **Test Coverage**: Add tests for new features matching llama.cpp's behavior
3. **Documentation**: Update relevant documentation when adding new formats or options
4. **Backward Compatibility**: Ensure changes don't break existing functionality
### Files to Monitor
- `llama.cpp/common/chat-parser-xml-toolcall.h` - Format definitions
- `llama.cpp/common/chat-parser-xml-toolcall.cpp` - Parsing logic
- `llama.cpp/common/chat-parser.cpp` - Format presets and model-specific handlers
- `llama.cpp/common/chat.h` - Format enums and parameter structures
- `llama.cpp/tools/server/server-context.cpp` - Server configuration options
# Documentation
The project documentation is located in `docs/content`. When adding new features or changing existing functionality, it is crucial to update the documentation to reflect these changes. This helps users understand how to use the new capabilities and ensures the documentation stays relevant.
- **Feature Documentation**: If you add a new feature (like a new backend or API endpoint), create a new markdown file in `docs/content/features/` explaining what it is, how to configure it, and how to use it.
- **Configuration**: If you modify configuration options, update the relevant sections in `docs/content/`.
- **Examples**: providing concrete examples (like YAML configuration blocks) is highly encouraged to help users get started quickly.

View File

@@ -78,20 +78,6 @@ LOCALAI_IMAGE_TAG=test LOCALAI_IMAGE=local-ai-aio make run-e2e-aio
We are welcome the contribution of the documents, please open new PR or create a new issue. The documentation is available under `docs/` https://github.com/mudler/LocalAI/tree/master/docs
### Gallery YAML Schema
LocalAI provides a JSON Schema for gallery model YAML files at:
`core/schema/gallery-model.schema.json`
This schema mirrors the internal gallery model configuration and can be used by editors (such as VS Code) to enable autocomplete, validation, and inline documentation when creating or modifying gallery files.
To use it with the YAML language server, add the following comment at the top of a gallery YAML file:
```yaml
# yaml-language-server: $schema=../core/schema/gallery-model.schema.json
```
## Community and Communication
- You can reach out via the Github issue tracker.

View File

@@ -1,7 +1,6 @@
ARG BASE_IMAGE=ubuntu:24.04
ARG BASE_IMAGE=ubuntu:22.04
ARG GRPC_BASE_IMAGE=${BASE_IMAGE}
ARG INTEL_BASE_IMAGE=${BASE_IMAGE}
ARG UBUNTU_CODENAME=noble
FROM ${BASE_IMAGE} AS requirements
@@ -10,7 +9,7 @@ ENV DEBIAN_FRONTEND=noninteractive
RUN apt-get update && \
apt-get install -y --no-install-recommends \
ca-certificates curl wget espeak-ng libgomp1 \
ffmpeg libopenblas0 libopenblas-dev sox && \
ffmpeg && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
@@ -24,7 +23,7 @@ ARG SKIP_DRIVERS=false
ARG TARGETARCH
ARG TARGETVARIANT
ENV BUILD_TYPE=${BUILD_TYPE}
ARG UBUNTU_VERSION=2404
ARG UBUNTU_VERSION=2204
RUN mkdir -p /run/localai
RUN echo "default" > /run/localai/capability
@@ -35,45 +34,11 @@ RUN <<EOT bash
apt-get update && \
apt-get install -y --no-install-recommends \
software-properties-common pciutils wget gpg-agent && \
apt-get install -y libglm-dev cmake libxcb-dri3-0 libxcb-present0 libpciaccess0 \
libpng-dev libxcb-keysyms1-dev libxcb-dri3-dev libx11-dev g++ gcc \
libwayland-dev libxrandr-dev libxcb-randr0-dev libxcb-ewmh-dev \
git python-is-python3 bison libx11-xcb-dev liblz4-dev libzstd-dev \
ocaml-core ninja-build pkg-config libxml2-dev wayland-protocols python3-jsonschema \
clang-format qtbase5-dev qt6-base-dev libxcb-glx0-dev sudo xz-utils mesa-vulkan-drivers
if [ "amd64" = "$TARGETARCH" ]; then
wget "https://sdk.lunarg.com/sdk/download/1.4.335.0/linux/vulkansdk-linux-x86_64-1.4.335.0.tar.xz" && \
tar -xf vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
rm vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
mkdir -p /opt/vulkan-sdk && \
mv 1.4.335.0 /opt/vulkan-sdk/ && \
cd /opt/vulkan-sdk/1.4.335.0 && \
./vulkansdk --no-deps --maxjobs \
vulkan-loader \
vulkan-validationlayers \
vulkan-extensionlayer \
vulkan-tools \
shaderc && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/bin/* /usr/bin/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/lib/* /usr/lib/x86_64-linux-gnu/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/include/* /usr/include/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/share/* /usr/share/ && \
rm -rf /opt/vulkan-sdk
fi
if [ "arm64" = "$TARGETARCH" ]; then
mkdir vulkan && cd vulkan && \
curl -L -o vulkan-sdk.tar.xz https://github.com/mudler/vulkan-sdk-arm/releases/download/1.4.335.0/vulkansdk-ubuntu-24.04-arm-1.4.335.0.tar.xz && \
tar -xvf vulkan-sdk.tar.xz && \
rm vulkan-sdk.tar.xz && \
cd 1.4.335.0 && \
cp -rfv aarch64/bin/* /usr/bin/ && \
cp -rfv aarch64/lib/* /usr/lib/aarch64-linux-gnu/ && \
cp -rfv aarch64/include/* /usr/include/ && \
cp -rfv aarch64/share/* /usr/share/ && \
cd ../.. && \
rm -rf vulkan
fi
ldconfig && \
wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \
apt-get update && \
apt-get install -y \
vulkan-sdk && \
apt-get clean && \
rm -rf /var/lib/apt/lists/* && \
echo "vulkan" > /run/localai/capability
@@ -176,12 +141,13 @@ ENV PATH=/opt/rocm/bin:${PATH}
# The requirements-core target is common to all images. It should not be placed in requirements-core unless every single build will use it.
FROM requirements-drivers AS build-requirements
ARG GO_VERSION=1.25.4
ARG CMAKE_VERSION=3.31.10
ARG GO_VERSION=1.22.6
ARG CMAKE_VERSION=3.26.4
ARG CMAKE_FROM_SOURCE=false
ARG TARGETARCH
ARG TARGETVARIANT
RUN apt-get update && \
apt-get install -y --no-install-recommends \
build-essential \
@@ -238,10 +204,9 @@ WORKDIR /build
# https://community.intel.com/t5/Intel-oneAPI-Math-Kernel-Library/APT-Repository-not-working-signatures-invalid/m-p/1599436/highlight/true#M36143
# This is a temporary workaround until Intel fixes their repository
FROM ${INTEL_BASE_IMAGE} AS intel
ARG UBUNTU_CODENAME=noble
RUN wget -qO - https://repositories.intel.com/gpu/intel-graphics.key | \
gpg --yes --dearmor --output /usr/share/keyrings/intel-graphics.gpg
RUN echo "deb [arch=amd64 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/gpu/ubuntu ${UBUNTU_CODENAME}/lts/2350 unified" > /etc/apt/sources.list.d/intel-graphics.list
RUN echo "deb [arch=amd64 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/gpu/ubuntu jammy/lts/2350 unified" > /etc/apt/sources.list.d/intel-graphics.list
RUN apt-get update && \
apt-get install -y --no-install-recommends \
intel-oneapi-runtime-libs && \

View File

@@ -1,4 +1,4 @@
ARG BASE_IMAGE=ubuntu:24.04
ARG BASE_IMAGE=ubuntu:22.04
FROM ${BASE_IMAGE}

330
Makefile
View File

@@ -1,20 +1,15 @@
# Disable parallel execution for backend builds
.NOTPARALLEL: backends/diffusers backends/llama-cpp backends/piper backends/stablediffusion-ggml backends/whisper backends/faster-whisper backends/silero-vad backends/local-store backends/huggingface backends/rfdetr backends/kitten-tts backends/kokoro backends/chatterbox backends/llama-cpp-darwin backends/neutts build-darwin-python-backend build-darwin-go-backend backends/mlx backends/diffuser-darwin backends/mlx-vlm backends/mlx-audio backends/stablediffusion-ggml-darwin backends/vllm backends/vllm-omni backends/moonshine backends/pocket-tts backends/qwen-tts backends/qwen-asr backends/voxcpm backends/whisperx
GOCMD=go
GOTEST=$(GOCMD) test
GOVET=$(GOCMD) vet
BINARY_NAME=local-ai
LAUNCHER_BINARY_NAME=local-ai-launcher
UBUNTU_VERSION?=2404
UBUNTU_CODENAME?=noble
CUDA_MAJOR_VERSION?=13
CUDA_MINOR_VERSION?=0
GORELEASER?=
export BUILD_TYPE?=
export CUDA_MAJOR_VERSION?=13
export CUDA_MINOR_VERSION?=0
GO_TAGS?=
BUILD_ID?=
@@ -160,17 +155,7 @@ test: test-models/testmodel.ggml protogen-go
########################################################
docker-build-aio:
docker build \
--build-arg MAKEFLAGS="--jobs=5 --output-sync=target" \
--build-arg BASE_IMAGE=$(BASE_IMAGE) \
--build-arg IMAGE_TYPE=$(IMAGE_TYPE) \
--build-arg BUILD_TYPE=$(BUILD_TYPE) \
--build-arg CUDA_MAJOR_VERSION=$(CUDA_MAJOR_VERSION) \
--build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) \
--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
--build-arg GO_TAGS="$(GO_TAGS)" \
-t local-ai:tests -f Dockerfile .
docker build --build-arg MAKEFLAGS="--jobs=5 --output-sync=target" -t local-ai:tests -f Dockerfile .
BASE_IMAGE=local-ai:tests DOCKER_AIO_IMAGE=local-ai-aio:test $(MAKE) docker-aio
e2e-aio:
@@ -189,29 +174,20 @@ run-e2e-aio: protogen-go
########################################################
prepare-e2e:
docker build \
--build-arg IMAGE_TYPE=core \
--build-arg BUILD_TYPE=$(BUILD_TYPE) \
--build-arg BASE_IMAGE=$(BASE_IMAGE) \
--build-arg CUDA_MAJOR_VERSION=$(CUDA_MAJOR_VERSION) \
--build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) \
--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
--build-arg GO_TAGS="$(GO_TAGS)" \
--build-arg MAKEFLAGS="$(DOCKER_MAKEFLAGS)" \
-t localai-tests .
mkdir -p $(TEST_DIR)
cp -rfv $(abspath ./tests/e2e-fixtures)/gpu.yaml $(TEST_DIR)/gpu.yaml
test -e $(TEST_DIR)/ggllm-test-model.bin || wget -q https://huggingface.co/TheBloke/CodeLlama-7B-Instruct-GGUF/resolve/main/codellama-7b-instruct.Q2_K.gguf -O $(TEST_DIR)/ggllm-test-model.bin
docker build --build-arg IMAGE_TYPE=core --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg CUDA_MAJOR_VERSION=12 --build-arg CUDA_MINOR_VERSION=0 -t localai-tests .
run-e2e-image:
docker run -p 5390:8080 -e MODELS_PATH=/models -e THREADS=1 -e DEBUG=true -d --rm -v $(TEST_DIR):/models --name e2e-tests-$(RANDOM) localai-tests
ls -liah $(abspath ./tests/e2e-fixtures)
docker run -p 5390:8080 -e MODELS_PATH=/models -e THREADS=1 -e DEBUG=true -d --rm -v $(TEST_DIR):/models --gpus all --name e2e-tests-$(RANDOM) localai-tests
test-e2e: build-mock-backend prepare-e2e run-e2e-image
test-e2e:
@echo 'Running e2e tests'
BUILD_TYPE=$(BUILD_TYPE) \
LOCALAI_API=http://$(E2E_BRIDGE_IP):5390 \
LOCALAI_API=http://$(E2E_BRIDGE_IP):5390/v1 \
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --flake-attempts $(TEST_FLAKES) -v -r ./tests/e2e
$(MAKE) clean-mock-backend
$(MAKE) teardown-e2e
docker rmi localai-tests
teardown-e2e:
rm -rf $(TEST_DIR) || true
@@ -311,33 +287,19 @@ prepare-test-extra: protogen-python
$(MAKE) -C backend/python/diffusers
$(MAKE) -C backend/python/chatterbox
$(MAKE) -C backend/python/vllm
$(MAKE) -C backend/python/vllm-omni
$(MAKE) -C backend/python/vibevoice
$(MAKE) -C backend/python/moonshine
$(MAKE) -C backend/python/pocket-tts
$(MAKE) -C backend/python/qwen-tts
$(MAKE) -C backend/python/qwen-asr
$(MAKE) -C backend/python/voxcpm
$(MAKE) -C backend/python/whisperx
test-extra: prepare-test-extra
$(MAKE) -C backend/python/transformers test
$(MAKE) -C backend/python/diffusers test
$(MAKE) -C backend/python/chatterbox test
$(MAKE) -C backend/python/vllm test
$(MAKE) -C backend/python/vllm-omni test
$(MAKE) -C backend/python/vibevoice test
$(MAKE) -C backend/python/moonshine test
$(MAKE) -C backend/python/pocket-tts test
$(MAKE) -C backend/python/qwen-tts test
$(MAKE) -C backend/python/qwen-asr test
$(MAKE) -C backend/python/voxcpm test
$(MAKE) -C backend/python/whisperx test
DOCKER_IMAGE?=local-ai
DOCKER_AIO_IMAGE?=local-ai-aio
IMAGE_TYPE?=core
BASE_IMAGE?=ubuntu:24.04
BASE_IMAGE?=ubuntu:22.04
docker:
docker build \
@@ -346,34 +308,24 @@ docker:
--build-arg GO_TAGS="$(GO_TAGS)" \
--build-arg MAKEFLAGS="$(DOCKER_MAKEFLAGS)" \
--build-arg BUILD_TYPE=$(BUILD_TYPE) \
--build-arg CUDA_MAJOR_VERSION=$(CUDA_MAJOR_VERSION) \
--build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) \
--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
-t $(DOCKER_IMAGE) .
docker-cuda12:
docker-cuda11:
docker build \
--build-arg CUDA_MAJOR_VERSION=${CUDA_MAJOR_VERSION} \
--build-arg CUDA_MINOR_VERSION=${CUDA_MINOR_VERSION} \
--build-arg CUDA_MAJOR_VERSION=11 \
--build-arg CUDA_MINOR_VERSION=8 \
--build-arg BASE_IMAGE=$(BASE_IMAGE) \
--build-arg IMAGE_TYPE=$(IMAGE_TYPE) \
--build-arg GO_TAGS="$(GO_TAGS)" \
--build-arg MAKEFLAGS="$(DOCKER_MAKEFLAGS)" \
--build-arg BUILD_TYPE=$(BUILD_TYPE) \
--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
-t $(DOCKER_IMAGE)-cuda-12 .
-t $(DOCKER_IMAGE)-cuda-11 .
docker-aio:
@echo "Building AIO image with base $(BASE_IMAGE) as $(DOCKER_AIO_IMAGE)"
docker build \
--build-arg BASE_IMAGE=$(BASE_IMAGE) \
--build-arg MAKEFLAGS="$(DOCKER_MAKEFLAGS)" \
--build-arg CUDA_MAJOR_VERSION=$(CUDA_MAJOR_VERSION) \
--build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) \
--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
-t $(DOCKER_AIO_IMAGE) -f Dockerfile.aio .
docker-aio-all:
@@ -382,31 +334,66 @@ docker-aio-all:
docker-image-intel:
docker build \
--build-arg BASE_IMAGE=intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04 \
--build-arg BASE_IMAGE=quay.io/go-skynet/intel-oneapi-base:latest \
--build-arg IMAGE_TYPE=$(IMAGE_TYPE) \
--build-arg GO_TAGS="$(GO_TAGS)" \
--build-arg MAKEFLAGS="$(DOCKER_MAKEFLAGS)" \
--build-arg BUILD_TYPE=intel \
--build-arg CUDA_MAJOR_VERSION=$(CUDA_MAJOR_VERSION) \
--build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) \
--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
-t $(DOCKER_IMAGE) .
--build-arg BUILD_TYPE=intel -t $(DOCKER_IMAGE) .
########################################################
## Backends
########################################################
# Pattern rule for standard backends (docker-based)
# This matches all backends that use docker-build-* and docker-save-*
backends/%: docker-build-% docker-save-% build
./local-ai backends install "ocifile://$(abspath ./backend-images/$*.tar)"
# Darwin-specific backends (keep as explicit targets since they have special build logic)
backends/diffusers: docker-build-diffusers docker-save-diffusers build
./local-ai backends install "ocifile://$(abspath ./backend-images/diffusers.tar)"
backends/llama-cpp: docker-build-llama-cpp docker-save-llama-cpp build
./local-ai backends install "ocifile://$(abspath ./backend-images/llama-cpp.tar)"
backends/piper: docker-build-piper docker-save-piper build
./local-ai backends install "ocifile://$(abspath ./backend-images/piper.tar)"
backends/stablediffusion-ggml: docker-build-stablediffusion-ggml docker-save-stablediffusion-ggml build
./local-ai backends install "ocifile://$(abspath ./backend-images/stablediffusion-ggml.tar)"
backends/whisper: docker-build-whisper docker-save-whisper build
./local-ai backends install "ocifile://$(abspath ./backend-images/whisper.tar)"
backends/silero-vad: docker-build-silero-vad docker-save-silero-vad build
./local-ai backends install "ocifile://$(abspath ./backend-images/silero-vad.tar)"
backends/local-store: docker-build-local-store docker-save-local-store build
./local-ai backends install "ocifile://$(abspath ./backend-images/local-store.tar)"
backends/huggingface: docker-build-huggingface docker-save-huggingface build
./local-ai backends install "ocifile://$(abspath ./backend-images/huggingface.tar)"
backends/rfdetr: docker-build-rfdetr docker-save-rfdetr build
./local-ai backends install "ocifile://$(abspath ./backend-images/rfdetr.tar)"
backends/kitten-tts: docker-build-kitten-tts docker-save-kitten-tts build
./local-ai backends install "ocifile://$(abspath ./backend-images/kitten-tts.tar)"
backends/kokoro: docker-build-kokoro docker-save-kokoro build
./local-ai backends install "ocifile://$(abspath ./backend-images/kokoro.tar)"
backends/chatterbox: docker-build-chatterbox docker-save-chatterbox build
./local-ai backends install "ocifile://$(abspath ./backend-images/chatterbox.tar)"
backends/llama-cpp-darwin: build
bash ./scripts/build/llama-cpp-darwin.sh
./local-ai backends install "ocifile://$(abspath ./backend-images/llama-cpp.tar)"
backends/neutts: docker-build-neutts docker-save-neutts build
./local-ai backends install "ocifile://$(abspath ./backend-images/neutts.tar)"
backends/vllm: docker-build-vllm docker-save-vllm build
./local-ai backends install "ocifile://$(abspath ./backend-images/vllm.tar)"
backends/vibevoice: docker-build-vibevoice docker-save-vibevoice build
./local-ai backends install "ocifile://$(abspath ./backend-images/vibevoice.tar)"
build-darwin-python-backend: build
bash ./scripts/build/python-darwin.sh
@@ -436,102 +423,121 @@ backends/stablediffusion-ggml-darwin:
backend-images:
mkdir -p backend-images
# Backend metadata: BACKEND_NAME | DOCKERFILE_TYPE | BUILD_CONTEXT | PROGRESS_FLAG | NEEDS_BACKEND_ARG
# llama-cpp is special - uses llama-cpp Dockerfile and doesn't need BACKEND arg
BACKEND_LLAMA_CPP = llama-cpp|llama-cpp|.|false|false
docker-build-llama-cpp:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:llama-cpp -f backend/Dockerfile.llama-cpp .
# Golang backends
BACKEND_PIPER = piper|golang|.|false|true
BACKEND_LOCAL_STORE = local-store|golang|.|false|true
BACKEND_HUGGINGFACE = huggingface|golang|.|false|true
BACKEND_SILERO_VAD = silero-vad|golang|.|false|true
BACKEND_STABLEDIFFUSION_GGML = stablediffusion-ggml|golang|.|--progress=plain|true
BACKEND_WHISPER = whisper|golang|.|false|true
docker-build-bark-cpp:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:bark-cpp -f backend/Dockerfile.golang --build-arg BACKEND=bark-cpp .
# Python backends with root context
BACKEND_RERANKERS = rerankers|python|.|false|true
BACKEND_TRANSFORMERS = transformers|python|.|false|true
BACKEND_FASTER_WHISPER = faster-whisper|python|.|false|true
BACKEND_COQUI = coqui|python|.|false|true
BACKEND_RFDETR = rfdetr|python|.|false|true
BACKEND_KITTEN_TTS = kitten-tts|python|.|false|true
BACKEND_NEUTTS = neutts|python|.|false|true
BACKEND_KOKORO = kokoro|python|.|false|true
BACKEND_VLLM = vllm|python|.|false|true
BACKEND_VLLM_OMNI = vllm-omni|python|.|false|true
BACKEND_DIFFUSERS = diffusers|python|.|--progress=plain|true
BACKEND_CHATTERBOX = chatterbox|python|.|false|true
BACKEND_VIBEVOICE = vibevoice|python|.|--progress=plain|true
BACKEND_MOONSHINE = moonshine|python|.|false|true
BACKEND_POCKET_TTS = pocket-tts|python|.|false|true
BACKEND_QWEN_TTS = qwen-tts|python|.|false|true
BACKEND_QWEN_ASR = qwen-asr|python|.|false|true
BACKEND_VOXCPM = voxcpm|python|.|false|true
BACKEND_WHISPERX = whisperx|python|.|false|true
docker-build-piper:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:piper -f backend/Dockerfile.golang --build-arg BACKEND=piper .
# Helper function to build docker image for a backend
# Usage: $(call docker-build-backend,BACKEND_NAME,DOCKERFILE_TYPE,BUILD_CONTEXT,PROGRESS_FLAG,NEEDS_BACKEND_ARG)
define docker-build-backend
docker build $(if $(filter-out false,$(4)),$(4)) \
--build-arg BUILD_TYPE=$(BUILD_TYPE) \
--build-arg BASE_IMAGE=$(BASE_IMAGE) \
--build-arg CUDA_MAJOR_VERSION=$(CUDA_MAJOR_VERSION) \
--build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) \
--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
$(if $(filter true,$(5)),--build-arg BACKEND=$(1)) \
-t local-ai-backend:$(1) -f backend/Dockerfile.$(2) $(3)
endef
docker-build-local-store:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:local-store -f backend/Dockerfile.golang --build-arg BACKEND=local-store .
# Generate docker-build targets from backend definitions
define generate-docker-build-target
docker-build-$(word 1,$(subst |, ,$(1))):
$$(call docker-build-backend,$(word 1,$(subst |, ,$(1))),$(word 2,$(subst |, ,$(1))),$(word 3,$(subst |, ,$(1))),$(word 4,$(subst |, ,$(1))),$(word 5,$(subst |, ,$(1))))
endef
docker-build-huggingface:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:huggingface -f backend/Dockerfile.golang --build-arg BACKEND=huggingface .
# Generate all docker-build targets
$(eval $(call generate-docker-build-target,$(BACKEND_LLAMA_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_PIPER)))
$(eval $(call generate-docker-build-target,$(BACKEND_LOCAL_STORE)))
$(eval $(call generate-docker-build-target,$(BACKEND_HUGGINGFACE)))
$(eval $(call generate-docker-build-target,$(BACKEND_SILERO_VAD)))
$(eval $(call generate-docker-build-target,$(BACKEND_STABLEDIFFUSION_GGML)))
$(eval $(call generate-docker-build-target,$(BACKEND_WHISPER)))
$(eval $(call generate-docker-build-target,$(BACKEND_RERANKERS)))
$(eval $(call generate-docker-build-target,$(BACKEND_TRANSFORMERS)))
$(eval $(call generate-docker-build-target,$(BACKEND_FASTER_WHISPER)))
$(eval $(call generate-docker-build-target,$(BACKEND_COQUI)))
$(eval $(call generate-docker-build-target,$(BACKEND_RFDETR)))
$(eval $(call generate-docker-build-target,$(BACKEND_KITTEN_TTS)))
$(eval $(call generate-docker-build-target,$(BACKEND_NEUTTS)))
$(eval $(call generate-docker-build-target,$(BACKEND_KOKORO)))
$(eval $(call generate-docker-build-target,$(BACKEND_VLLM)))
$(eval $(call generate-docker-build-target,$(BACKEND_VLLM_OMNI)))
$(eval $(call generate-docker-build-target,$(BACKEND_DIFFUSERS)))
$(eval $(call generate-docker-build-target,$(BACKEND_CHATTERBOX)))
$(eval $(call generate-docker-build-target,$(BACKEND_VIBEVOICE)))
$(eval $(call generate-docker-build-target,$(BACKEND_MOONSHINE)))
$(eval $(call generate-docker-build-target,$(BACKEND_POCKET_TTS)))
$(eval $(call generate-docker-build-target,$(BACKEND_QWEN_TTS)))
$(eval $(call generate-docker-build-target,$(BACKEND_QWEN_ASR)))
$(eval $(call generate-docker-build-target,$(BACKEND_VOXCPM)))
$(eval $(call generate-docker-build-target,$(BACKEND_WHISPERX)))
docker-build-rfdetr:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:rfdetr -f backend/Dockerfile.python --build-arg BACKEND=rfdetr ./backend
# Pattern rule for docker-save targets
docker-save-%: backend-images
docker save local-ai-backend:$* -o backend-images/$*.tar
docker-build-kitten-tts:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:kitten-tts -f backend/Dockerfile.python --build-arg BACKEND=kitten-tts ./backend
docker-build-backends: docker-build-llama-cpp docker-build-rerankers docker-build-vllm docker-build-vllm-omni docker-build-transformers docker-build-diffusers docker-build-kokoro docker-build-faster-whisper docker-build-coqui docker-build-chatterbox docker-build-vibevoice docker-build-moonshine docker-build-pocket-tts docker-build-qwen-tts docker-build-qwen-asr docker-build-voxcpm docker-build-whisperx
docker-save-kitten-tts: backend-images
docker save local-ai-backend:kitten-tts -o backend-images/kitten-tts.tar
########################################################
### Mock Backend for E2E Tests
########################################################
docker-save-chatterbox: backend-images
docker save local-ai-backend:chatterbox -o backend-images/chatterbox.tar
build-mock-backend: protogen-go
$(GOCMD) build -o tests/e2e/mock-backend/mock-backend ./tests/e2e/mock-backend
docker-save-vibevoice: backend-images
docker save local-ai-backend:vibevoice -o backend-images/vibevoice.tar
clean-mock-backend:
rm -f tests/e2e/mock-backend/mock-backend
docker-build-neutts:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:neutts -f backend/Dockerfile.python --build-arg BACKEND=neutts ./backend
docker-save-neutts: backend-images
docker save local-ai-backend:neutts -o backend-images/neutts.tar
docker-build-kokoro:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:kokoro -f backend/Dockerfile.python --build-arg BACKEND=kokoro ./backend
docker-build-vllm:
docker build --build-arg CUDA_MAJOR_VERSION=$(CUDA_MAJOR_VERSION) --build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:vllm -f backend/Dockerfile.python --build-arg BACKEND=vllm ./backend
docker-save-vllm: backend-images
docker save local-ai-backend:vllm -o backend-images/vllm.tar
docker-save-kokoro: backend-images
docker save local-ai-backend:kokoro -o backend-images/kokoro.tar
docker-save-rfdetr: backend-images
docker save local-ai-backend:rfdetr -o backend-images/rfdetr.tar
docker-save-huggingface: backend-images
docker save local-ai-backend:huggingface -o backend-images/huggingface.tar
docker-save-local-store: backend-images
docker save local-ai-backend:local-store -o backend-images/local-store.tar
docker-build-silero-vad:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:silero-vad -f backend/Dockerfile.golang --build-arg BACKEND=silero-vad .
docker-save-silero-vad: backend-images
docker save local-ai-backend:silero-vad -o backend-images/silero-vad.tar
docker-save-piper: backend-images
docker save local-ai-backend:piper -o backend-images/piper.tar
docker-save-llama-cpp: backend-images
docker save local-ai-backend:llama-cpp -o backend-images/llama-cpp.tar
docker-save-bark-cpp: backend-images
docker save local-ai-backend:bark-cpp -o backend-images/bark-cpp.tar
docker-build-stablediffusion-ggml:
docker build --progress=plain --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) --build-arg CUDA_MAJOR_VERSION=$(CUDA_MAJOR_VERSION) --build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) -t local-ai-backend:stablediffusion-ggml -f backend/Dockerfile.golang --build-arg BACKEND=stablediffusion-ggml .
docker-save-stablediffusion-ggml: backend-images
docker save local-ai-backend:stablediffusion-ggml -o backend-images/stablediffusion-ggml.tar
docker-build-rerankers:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:rerankers -f backend/Dockerfile.python --build-arg BACKEND=rerankers .
docker-build-transformers:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:transformers -f backend/Dockerfile.python --build-arg BACKEND=transformers .
docker-build-diffusers:
docker build --progress=plain --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:diffusers -f backend/Dockerfile.python --build-arg BACKEND=diffusers ./backend
docker-save-diffusers: backend-images
docker save local-ai-backend:diffusers -o backend-images/diffusers.tar
docker-build-whisper:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) --build-arg CUDA_MAJOR_VERSION=$(CUDA_MAJOR_VERSION) --build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) -t local-ai-backend:whisper -f backend/Dockerfile.golang --build-arg BACKEND=whisper .
docker-save-whisper: backend-images
docker save local-ai-backend:whisper -o backend-images/whisper.tar
docker-build-faster-whisper:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:faster-whisper -f backend/Dockerfile.python --build-arg BACKEND=faster-whisper .
docker-build-coqui:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:coqui -f backend/Dockerfile.python --build-arg BACKEND=coqui .
docker-build-bark:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:bark -f backend/Dockerfile.python --build-arg BACKEND=bark .
docker-build-chatterbox:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:chatterbox -f backend/Dockerfile.python --build-arg BACKEND=chatterbox ./backend
docker-build-vibevoice:
docker build --progress=plain --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:vibevoice -f backend/Dockerfile.python --build-arg BACKEND=vibevoice ./backend
docker-build-exllama2:
docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:exllama2 -f backend/Dockerfile.python --build-arg BACKEND=exllama2 .
docker-build-backends: docker-build-llama-cpp docker-build-rerankers docker-build-vllm docker-build-transformers docker-build-diffusers docker-build-kokoro docker-build-faster-whisper docker-build-coqui docker-build-bark docker-build-chatterbox docker-build-vibevoice docker-build-exllama2
########################################################
### END Backends

View File

@@ -51,16 +51,34 @@
**LocalAI** is the free, Open Source OpenAI alternative. LocalAI act as a drop-in replacement REST API that's compatible with OpenAI (Elevenlabs, Anthropic... ) API specifications for local AI inferencing. It allows you to run LLMs, generate images, audio (and not only) locally or on-prem with consumer grade hardware, supporting multiple model families. Does not require GPU. It is created and maintained by [Ettore Di Giacinto](https://github.com/mudler).
## Local Stack Family
## 📚🆕 Local Stack Family
Liking LocalAI? LocalAI is part of an integrated suite of AI infrastructure tools, you might also like:
- **[LocalAGI](https://github.com/mudler/LocalAGI)** - AI agent orchestration platform with OpenAI Responses API compatibility and advanced agentic capabilities
- **[LocalRecall](https://github.com/mudler/LocalRecall)** - MCP/REST API knowledge base system providing persistent memory and storage for AI agents
- 🆕 **[Cogito](https://github.com/mudler/cogito)** - Go library for building intelligent, co-operative agentic software and LLM-powered workflows, focusing on improving results for small, open source language models that scales to any LLM. Powers LocalAGI and LocalAI MCP/Agentic capabilities
- 🆕 **[Wiz](https://github.com/mudler/wiz)** - Terminal-based AI agent accessible via Ctrl+Space keybinding. Portable, local-LLM friendly shell assistant with TUI/CLI modes, tool execution with approval, MCP protocol support, and multi-shell compatibility (zsh, bash, fish)
- 🆕 **[SkillServer](https://github.com/mudler/skillserver)** - Simple, centralized skills database for AI agents via MCP. Manages skills as Markdown files with MCP server integration, web UI for editing, Git synchronization, and full-text search capabilities
🆕 LocalAI is now part of a comprehensive suite of AI tools designed to work together:
<table>
<tr>
<td width="50%" valign="top">
<a href="https://github.com/mudler/LocalAGI">
<img src="https://raw.githubusercontent.com/mudler/LocalAGI/refs/heads/main/webui/react-ui/public/logo_2.png" width="300" alt="LocalAGI Logo">
</a>
</td>
<td width="50%" valign="top">
<h3><a href="https://github.com/mudler/LocalAGI">LocalAGI</a></h3>
<p>A powerful Local AI agent management platform that serves as a drop-in replacement for OpenAI's Responses API, enhanced with advanced agentic capabilities.</p>
</td>
</tr>
<tr>
<td width="50%" valign="top">
<a href="https://github.com/mudler/LocalRecall">
<img src="https://raw.githubusercontent.com/mudler/LocalRecall/refs/heads/main/static/localrecall_horizontal.png" width="300" alt="LocalRecall Logo">
</a>
</td>
<td width="50%" valign="top">
<h3><a href="https://github.com/mudler/LocalRecall">LocalRecall</a></h3>
<p>A REST-ful API and knowledge base management system that provides persistent memory and storage capabilities for AI agents.</p>
</td>
</tr>
</table>
## Screenshots / Video
@@ -93,8 +111,6 @@ Liking LocalAI? LocalAI is part of an integrated suite of AI infrastructure tool
## 💻 Quickstart
> ⚠️ **Note:** The `install.sh` script is currently experiencing issues due to the heavy changes currently undergoing in LocalAI and may produce broken or misconfigured installations. Please use Docker installation (see below) or manual binary installation until [issue #8032](https://github.com/mudler/LocalAI/issues/8032) is resolved.
Run the installer script:
```bash
@@ -112,7 +128,7 @@ For more installation options, see [Installer Options](https://localai.io/instal
> Note: the DMGs are not signed by Apple as quarantined. See https://github.com/mudler/LocalAI/issues/6268 for a workaround, fix is tracked here: https://github.com/mudler/LocalAI/issues/6244
### Containers (Docker, podman, ...)
Or run with docker:
> **💡 Docker Run vs Docker Start**
>
@@ -121,13 +137,13 @@ For more installation options, see [Installer Options](https://localai.io/instal
>
> If you've already run LocalAI before and want to start it again, use: `docker start -i local-ai`
#### CPU only image:
### CPU only image:
```bash
docker run -ti --name local-ai -p 8080:8080 localai/localai:latest
```
#### NVIDIA GPU Images:
### NVIDIA GPU Images:
```bash
# CUDA 13.0
@@ -136,6 +152,9 @@ docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-gp
# CUDA 12.0
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-gpu-nvidia-cuda-12
# CUDA 11.7
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-gpu-nvidia-cuda-11
# NVIDIA Jetson (L4T) ARM64
# CUDA 12 (for Nvidia AGX Orin and similar platforms)
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-nvidia-l4t-arm64
@@ -144,25 +163,25 @@ docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-nv
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-nvidia-l4t-arm64-cuda-13
```
#### AMD GPU Images (ROCm):
### AMD GPU Images (ROCm):
```bash
docker run -ti --name local-ai -p 8080:8080 --device=/dev/kfd --device=/dev/dri --group-add=video localai/localai:latest-gpu-hipblas
```
#### Intel GPU Images (oneAPI):
### Intel GPU Images (oneAPI):
```bash
docker run -ti --name local-ai -p 8080:8080 --device=/dev/dri/card1 --device=/dev/dri/renderD128 localai/localai:latest-gpu-intel
```
#### Vulkan GPU Images:
### Vulkan GPU Images:
```bash
docker run -ti --name local-ai -p 8080:8080 localai/localai:latest-gpu-vulkan
```
#### AIO Images (pre-downloaded models):
### AIO Images (pre-downloaded models):
```bash
# CPU version
@@ -174,6 +193,9 @@ docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-ai
# NVIDIA CUDA 12 version
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-aio-gpu-nvidia-cuda-12
# NVIDIA CUDA 11 version
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-aio-gpu-nvidia-cuda-11
# Intel GPU version
docker run -ti --name local-ai -p 8080:8080 localai/localai:latest-aio-gpu-intel
@@ -239,7 +261,6 @@ Roadmap items: [List of issues](https://github.com/mudler/LocalAI/issues?q=is%3A
- 🔈 [Audio to Text](https://localai.io/features/audio-to-text/) (Audio transcription with `whisper.cpp`)
- 🎨 [Image generation](https://localai.io/features/image-generation)
- 🔥 [OpenAI-alike tools API](https://localai.io/features/openai-functions/)
- ⚡ [Realtime API](https://localai.io/features/openai-realtime/) (Speech-to-speech)
- 🧠 [Embeddings generation for vector databases](https://localai.io/features/embeddings/)
- ✍️ [Constrained grammars](https://localai.io/features/constrained_grammars/)
- 🖼️ [Download Models directly from Huggingface ](https://localai.io/models/)
@@ -258,9 +279,10 @@ LocalAI supports a comprehensive range of AI backends with multiple acceleration
### Text Generation & Language Models
| Backend | Description | Acceleration Support |
|---------|-------------|---------------------|
| **llama.cpp** | LLM inference in C/C++ | CUDA 12/13, ROCm, Intel SYCL, Vulkan, Metal, CPU |
| **llama.cpp** | LLM inference in C/C++ | CUDA 11/12/13, ROCm, Intel SYCL, Vulkan, Metal, CPU |
| **vLLM** | Fast LLM inference with PagedAttention | CUDA 12/13, ROCm, Intel |
| **transformers** | HuggingFace transformers framework | CUDA 12/13, ROCm, Intel, CPU |
| **transformers** | HuggingFace transformers framework | CUDA 11/12/13, ROCm, Intel, CPU |
| **exllama2** | GPTQ inference library | CUDA 12/13 |
| **MLX** | Apple Silicon LLM inference | Metal (M1/M2/M3+) |
| **MLX-VLM** | Apple Silicon Vision-Language Models | Metal (M1/M2/M3+) |
@@ -269,28 +291,28 @@ LocalAI supports a comprehensive range of AI backends with multiple acceleration
|---------|-------------|---------------------|
| **whisper.cpp** | OpenAI Whisper in C/C++ | CUDA 12/13, ROCm, Intel SYCL, Vulkan, CPU |
| **faster-whisper** | Fast Whisper with CTranslate2 | CUDA 12/13, ROCm, Intel, CPU |
| **bark** | Text-to-audio generation | CUDA 12/13, ROCm, Intel |
| **bark-cpp** | C++ implementation of Bark | CUDA, Metal, CPU |
| **coqui** | Advanced TTS with 1100+ languages | CUDA 12/13, ROCm, Intel, CPU |
| **kokoro** | Lightweight TTS model | CUDA 12/13, ROCm, Intel, CPU |
| **chatterbox** | Production-grade TTS | CUDA 12/13, CPU |
| **chatterbox** | Production-grade TTS | CUDA 11/12/13, CPU |
| **piper** | Fast neural TTS system | CPU |
| **kitten-tts** | Kitten TTS models | CPU |
| **silero-vad** | Voice Activity Detection | CPU |
| **neutts** | Text-to-speech with voice cloning | CUDA 12/13, ROCm, CPU |
| **vibevoice** | Real-time TTS with voice cloning | CUDA 12/13, ROCm, Intel, CPU |
| **pocket-tts** | Lightweight CPU-based TTS | CUDA 12/13, ROCm, Intel, CPU |
| **qwen-tts** | High-quality TTS with custom voice, voice design, and voice cloning | CUDA 12/13, ROCm, Intel, CPU |
### Image & Video Generation
| Backend | Description | Acceleration Support |
|---------|-------------|---------------------|
| **stablediffusion.cpp** | Stable Diffusion in C/C++ | CUDA 12/13, Intel SYCL, Vulkan, CPU |
| **diffusers** | HuggingFace diffusion models | CUDA 12/13, ROCm, Intel, Metal, CPU |
| **diffusers** | HuggingFace diffusion models | CUDA 11/12/13, ROCm, Intel, Metal, CPU |
### Specialized AI Tasks
| Backend | Description | Acceleration Support |
|---------|-------------|---------------------|
| **rfdetr** | Real-time object detection | CUDA 12/13, Intel, CPU |
| **rerankers** | Document reranking API | CUDA 12/13, ROCm, Intel, CPU |
| **rerankers** | Document reranking API | CUDA 11/12/13, ROCm, Intel, CPU |
| **local-store** | Vector database | CPU |
| **huggingface** | HuggingFace API integration | API-based |
@@ -298,11 +320,12 @@ LocalAI supports a comprehensive range of AI backends with multiple acceleration
| Acceleration Type | Supported Backends | Hardware Support |
|-------------------|-------------------|------------------|
| **NVIDIA CUDA 11** | llama.cpp, whisper, stablediffusion, diffusers, rerankers, bark, chatterbox | Nvidia hardware |
| **NVIDIA CUDA 12** | All CUDA-compatible backends | Nvidia hardware |
| **NVIDIA CUDA 13** | All CUDA-compatible backends | Nvidia hardware |
| **AMD ROCm** | llama.cpp, whisper, vllm, transformers, diffusers, rerankers, coqui, kokoro, neutts, vibevoice, pocket-tts, qwen-tts | AMD Graphics |
| **Intel oneAPI** | llama.cpp, whisper, stablediffusion, vllm, transformers, diffusers, rfdetr, rerankers, coqui, kokoro, vibevoice, pocket-tts, qwen-tts | Intel Arc, Intel iGPUs |
| **Apple Metal** | llama.cpp, whisper, diffusers, MLX, MLX-VLM | Apple M1/M2/M3+ |
| **AMD ROCm** | llama.cpp, whisper, vllm, transformers, diffusers, rerankers, coqui, kokoro, bark, neutts, vibevoice | AMD Graphics |
| **Intel oneAPI** | llama.cpp, whisper, stablediffusion, vllm, transformers, diffusers, rfdetr, rerankers, exllama2, coqui, kokoro, bark, vibevoice | Intel Arc, Intel iGPUs |
| **Apple Metal** | llama.cpp, whisper, diffusers, MLX, MLX-VLM, bark-cpp | Apple M1/M2/M3+ |
| **Vulkan** | llama.cpp, whisper, stablediffusion | Cross-platform GPUs |
| **NVIDIA Jetson (CUDA 12)** | llama.cpp, whisper, stablediffusion, diffusers, rfdetr | ARM64 embedded AI (AGX Orin, etc.) |
| **NVIDIA Jetson (CUDA 13)** | llama.cpp, whisper, stablediffusion, diffusers, rfdetr | ARM64 embedded AI (DGX Spark) |
@@ -324,10 +347,6 @@ Agentic Libraries:
MCPs:
- https://github.com/mudler/MCPs
OS Assistant:
- https://github.com/mudler/Keygeist - Keygeist is an AI-powered keyboard operator that listens for key combinations and responds with AI-generated text typed directly into your Linux box.
Model galleries
- https://github.com/go-skynet/model-gallery

View File

@@ -1,4 +1,4 @@
ARG BASE_IMAGE=ubuntu:24.04
ARG BASE_IMAGE=ubuntu:22.04
FROM ${BASE_IMAGE} AS builder
ARG BACKEND=rerankers
@@ -12,8 +12,8 @@ ENV CUDA_MINOR_VERSION=${CUDA_MINOR_VERSION}
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETARCH
ARG TARGETVARIANT
ARG GO_VERSION=1.25.4
ARG UBUNTU_VERSION=2404
ARG GO_VERSION=1.22.6
ARG UBUNTU_VERSION=2204
RUN apt-get update && \
apt-get install -y --no-install-recommends \
@@ -40,45 +40,11 @@ RUN <<EOT bash
apt-get update && \
apt-get install -y --no-install-recommends \
software-properties-common pciutils wget gpg-agent && \
apt-get install -y libglm-dev cmake libxcb-dri3-0 libxcb-present0 libpciaccess0 \
libpng-dev libxcb-keysyms1-dev libxcb-dri3-dev libx11-dev g++ gcc \
libwayland-dev libxrandr-dev libxcb-randr0-dev libxcb-ewmh-dev \
git python-is-python3 bison libx11-xcb-dev liblz4-dev libzstd-dev \
ocaml-core ninja-build pkg-config libxml2-dev wayland-protocols python3-jsonschema \
clang-format qtbase5-dev qt6-base-dev libxcb-glx0-dev sudo xz-utils
if [ "amd64" = "$TARGETARCH" ]; then
wget "https://sdk.lunarg.com/sdk/download/1.4.335.0/linux/vulkansdk-linux-x86_64-1.4.335.0.tar.xz" && \
tar -xf vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
rm vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
mkdir -p /opt/vulkan-sdk && \
mv 1.4.335.0 /opt/vulkan-sdk/ && \
cd /opt/vulkan-sdk/1.4.335.0 && \
./vulkansdk --no-deps --maxjobs \
vulkan-loader \
vulkan-validationlayers \
vulkan-extensionlayer \
vulkan-tools \
shaderc && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/bin/* /usr/bin/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/lib/* /usr/lib/x86_64-linux-gnu/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/include/* /usr/include/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/share/* /usr/share/ && \
rm -rf /opt/vulkan-sdk
fi
if [ "arm64" = "$TARGETARCH" ]; then
mkdir vulkan && cd vulkan && \
curl -L -o vulkan-sdk.tar.xz https://github.com/mudler/vulkan-sdk-arm/releases/download/1.4.335.0/vulkansdk-ubuntu-24.04-arm-1.4.335.0.tar.xz && \
tar -xvf vulkan-sdk.tar.xz && \
rm vulkan-sdk.tar.xz && \
cd 1.4.335.0 && \
cp -rfv aarch64/bin/* /usr/bin/ && \
cp -rfv aarch64/lib/* /usr/lib/aarch64-linux-gnu/ && \
cp -rfv aarch64/include/* /usr/include/ && \
cp -rfv aarch64/share/* /usr/share/ && \
cd ../.. && \
rm -rf vulkan
fi
ldconfig && \
wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \
apt-get update && \
apt-get install -y \
vulkan-sdk && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
@@ -182,8 +148,6 @@ EOT
COPY . /LocalAI
RUN git config --global --add safe.directory /LocalAI
RUN cd /LocalAI && make protogen-go && make -C /LocalAI/backend/go/${BACKEND} build
FROM scratch

View File

@@ -1,4 +1,4 @@
ARG BASE_IMAGE=ubuntu:24.04
ARG BASE_IMAGE=ubuntu:22.04
ARG GRPC_BASE_IMAGE=${BASE_IMAGE}
@@ -10,8 +10,7 @@ FROM ${GRPC_BASE_IMAGE} AS grpc
ARG GRPC_MAKEFLAGS="-j4 -Otarget"
ARG GRPC_VERSION=v1.65.0
ARG CMAKE_FROM_SOURCE=false
# CUDA Toolkit 13.x compatibility: CMake 3.31.9+ fixes toolchain detection/arch table issues
ARG CMAKE_VERSION=3.31.10
ARG CMAKE_VERSION=3.26.4
ENV MAKEFLAGS=${GRPC_MAKEFLAGS}
@@ -27,7 +26,7 @@ RUN apt-get update && \
# Install CMake (the version in 22.04 is too old)
RUN <<EOT bash
if [ "${CMAKE_FROM_SOURCE}" = "true" ]; then
if [ "${CMAKE_FROM_SOURCE}}" = "true" ]; then
curl -L -s https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}.tar.gz -o cmake.tar.gz && tar xvf cmake.tar.gz && cd cmake-${CMAKE_VERSION} && ./configure && make && make install
else
apt-get update && \
@@ -51,13 +50,6 @@ RUN git clone --recurse-submodules --jobs 4 -b ${GRPC_VERSION} --depth 1 --shall
rm -rf /build
FROM ${BASE_IMAGE} AS builder
ARG CMAKE_FROM_SOURCE=false
ARG CMAKE_VERSION=3.31.10
# We can target specific CUDA ARCHITECTURES like --build-arg CUDA_DOCKER_ARCH='75;86;89;120'
ARG CUDA_DOCKER_ARCH
ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
ARG CMAKE_ARGS
ENV CMAKE_ARGS=${CMAKE_ARGS}
ARG BACKEND=rerankers
ARG BUILD_TYPE
ENV BUILD_TYPE=${BUILD_TYPE}
@@ -69,8 +61,8 @@ ENV CUDA_MINOR_VERSION=${CUDA_MINOR_VERSION}
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETARCH
ARG TARGETVARIANT
ARG GO_VERSION=1.25.4
ARG UBUNTU_VERSION=2404
ARG GO_VERSION=1.22.6
ARG UBUNTU_VERSION=2204
RUN apt-get update && \
apt-get install -y --no-install-recommends \
@@ -78,7 +70,6 @@ RUN apt-get update && \
ccache git \
ca-certificates \
make \
pkg-config libcurl4-openssl-dev \
curl unzip \
libssl-dev wget && \
apt-get clean && \
@@ -97,45 +88,11 @@ RUN <<EOT bash
apt-get update && \
apt-get install -y --no-install-recommends \
software-properties-common pciutils wget gpg-agent && \
apt-get install -y libglm-dev cmake libxcb-dri3-0 libxcb-present0 libpciaccess0 \
libpng-dev libxcb-keysyms1-dev libxcb-dri3-dev libx11-dev g++ gcc \
libwayland-dev libxrandr-dev libxcb-randr0-dev libxcb-ewmh-dev \
git python-is-python3 bison libx11-xcb-dev liblz4-dev libzstd-dev \
ocaml-core ninja-build pkg-config libxml2-dev wayland-protocols python3-jsonschema \
clang-format qtbase5-dev qt6-base-dev libxcb-glx0-dev sudo xz-utils
if [ "amd64" = "$TARGETARCH" ]; then
wget "https://sdk.lunarg.com/sdk/download/1.4.335.0/linux/vulkansdk-linux-x86_64-1.4.335.0.tar.xz" && \
tar -xf vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
rm vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
mkdir -p /opt/vulkan-sdk && \
mv 1.4.335.0 /opt/vulkan-sdk/ && \
cd /opt/vulkan-sdk/1.4.335.0 && \
./vulkansdk --no-deps --maxjobs \
vulkan-loader \
vulkan-validationlayers \
vulkan-extensionlayer \
vulkan-tools \
shaderc && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/bin/* /usr/bin/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/lib/* /usr/lib/x86_64-linux-gnu/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/include/* /usr/include/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/share/* /usr/share/ && \
rm -rf /opt/vulkan-sdk
fi
if [ "arm64" = "$TARGETARCH" ]; then
mkdir vulkan && cd vulkan && \
curl -L -o vulkan-sdk.tar.xz https://github.com/mudler/vulkan-sdk-arm/releases/download/1.4.335.0/vulkansdk-ubuntu-24.04-arm-1.4.335.0.tar.xz && \
tar -xvf vulkan-sdk.tar.xz && \
rm vulkan-sdk.tar.xz && \
cd 1.4.335.0 && \
cp -rfv aarch64/bin/* /usr/bin/ && \
cp -rfv aarch64/lib/* /usr/lib/aarch64-linux-gnu/ && \
cp -rfv aarch64/include/* /usr/include/ && \
cp -rfv aarch64/share/* /usr/share/ && \
cd ../.. && \
rm -rf vulkan
fi
ldconfig && \
wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \
apt-get update && \
apt-get install -y \
vulkan-sdk && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
@@ -232,7 +189,7 @@ EOT
# Install CMake (the version in 22.04 is too old)
RUN <<EOT bash
if [ "${CMAKE_FROM_SOURCE}" = "true" ]; then
if [ "${CMAKE_FROM_SOURCE}}" = "true" ]; then
curl -L -s https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}.tar.gz -o cmake.tar.gz && tar xvf cmake.tar.gz && cd cmake-${CMAKE_VERSION} && ./configure && make && make install
else
apt-get update && \
@@ -248,30 +205,19 @@ COPY --from=grpc /opt/grpc /usr/local
COPY . /LocalAI
RUN <<'EOT' bash
set -euxo pipefail
if [[ -n "${CUDA_DOCKER_ARCH:-}" ]]; then
CUDA_ARCH_ESC="${CUDA_DOCKER_ARCH//;/\\;}"
export CMAKE_ARGS="${CMAKE_ARGS:-} -DCMAKE_CUDA_ARCHITECTURES=${CUDA_ARCH_ESC}"
echo "CMAKE_ARGS(env) = ${CMAKE_ARGS}"
rm -rf /LocalAI/backend/cpp/llama-cpp-*-build
fi
if [ "${TARGETARCH}" = "arm64" ] || [ "${BUILD_TYPE}" = "hipblas" ]; then
cd /LocalAI/backend/cpp/llama-cpp
make llama-cpp-fallback
make llama-cpp-grpc
make llama-cpp-rpc-server
else
cd /LocalAI/backend/cpp/llama-cpp
make llama-cpp-avx
make llama-cpp-avx2
make llama-cpp-avx512
make llama-cpp-fallback
make llama-cpp-grpc
make llama-cpp-rpc-server
fi
## Otherwise just run the normal build
RUN <<EOT bash
if [ "${TARGETARCH}" = "arm64" ] || [ "${BUILD_TYPE}" = "hipblas" ]; then \
cd /LocalAI/backend/cpp/llama-cpp && make llama-cpp-fallback && \
make llama-cpp-grpc && make llama-cpp-rpc-server; \
else \
cd /LocalAI/backend/cpp/llama-cpp && make llama-cpp-avx && \
make llama-cpp-avx2 && \
make llama-cpp-avx512 && \
make llama-cpp-fallback && \
make llama-cpp-grpc && \
make llama-cpp-rpc-server; \
fi
EOT

View File

@@ -1,4 +1,4 @@
ARG BASE_IMAGE=ubuntu:24.04
ARG BASE_IMAGE=ubuntu:22.04
FROM ${BASE_IMAGE} AS builder
ARG BACKEND=rerankers
@@ -12,7 +12,7 @@ ENV CUDA_MINOR_VERSION=${CUDA_MINOR_VERSION}
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETARCH
ARG TARGETVARIANT
ARG UBUNTU_VERSION=2404
ARG UBUNTU_VERSION=2204
RUN apt-get update && \
apt-get install -y --no-install-recommends \
@@ -54,45 +54,11 @@ RUN <<EOT bash
apt-get update && \
apt-get install -y --no-install-recommends \
software-properties-common pciutils wget gpg-agent && \
apt-get install -y libglm-dev cmake libxcb-dri3-0 libxcb-present0 libpciaccess0 \
libpng-dev libxcb-keysyms1-dev libxcb-dri3-dev libx11-dev g++ gcc \
libwayland-dev libxrandr-dev libxcb-randr0-dev libxcb-ewmh-dev \
git python-is-python3 bison libx11-xcb-dev liblz4-dev libzstd-dev \
ocaml-core ninja-build pkg-config libxml2-dev wayland-protocols python3-jsonschema \
clang-format qtbase5-dev qt6-base-dev libxcb-glx0-dev sudo xz-utils
if [ "amd64" = "$TARGETARCH" ]; then
wget "https://sdk.lunarg.com/sdk/download/1.4.335.0/linux/vulkansdk-linux-x86_64-1.4.335.0.tar.xz" && \
tar -xf vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
rm vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
mkdir -p /opt/vulkan-sdk && \
mv 1.4.335.0 /opt/vulkan-sdk/ && \
cd /opt/vulkan-sdk/1.4.335.0 && \
./vulkansdk --no-deps --maxjobs \
vulkan-loader \
vulkan-validationlayers \
vulkan-extensionlayer \
vulkan-tools \
shaderc && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/bin/* /usr/bin/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/lib/* /usr/lib/x86_64-linux-gnu/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/include/* /usr/include/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/share/* /usr/share/ && \
rm -rf /opt/vulkan-sdk
fi
if [ "arm64" = "$TARGETARCH" ]; then
mkdir vulkan && cd vulkan && \
curl -L -o vulkan-sdk.tar.xz https://github.com/mudler/vulkan-sdk-arm/releases/download/1.4.335.0/vulkansdk-ubuntu-24.04-arm-1.4.335.0.tar.xz && \
tar -xvf vulkan-sdk.tar.xz && \
rm vulkan-sdk.tar.xz && \
cd 1.4.335.0 && \
cp -rfv aarch64/bin/* /usr/bin/ && \
cp -rfv aarch64/lib/* /usr/lib/aarch64-linux-gnu/ && \
cp -rfv aarch64/include/* /usr/include/ && \
cp -rfv aarch64/share/* /usr/share/ && \
cd ../.. && \
rm -rf vulkan
fi
ldconfig && \
wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \
apt-get update && \
apt-get install -y \
vulkan-sdk && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
@@ -176,8 +142,7 @@ RUN if [ "${BUILD_TYPE}" = "hipblas" ]; then \
# Install uv as a system package
RUN curl -LsSf https://astral.sh/uv/install.sh | UV_INSTALL_DIR=/usr/bin sh
ENV PATH="/root/.cargo/bin:${PATH}"
# Increase timeout for uv installs behind slow networks
ENV UV_HTTP_TIMEOUT=180
RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
# Install grpcio-tools (the version in 22.04 is too old)
@@ -190,18 +155,12 @@ RUN <<EOT bash
EOT
COPY backend/python/${BACKEND} /${BACKEND}
COPY backend/backend.proto /${BACKEND}/backend.proto
COPY backend/python/common/ /${BACKEND}/common
COPY scripts/build/package-gpu-libs.sh /package-gpu-libs.sh
COPY python/${BACKEND} /${BACKEND}
COPY backend.proto /${BACKEND}/backend.proto
COPY python/common/ /${BACKEND}/common
RUN cd /${BACKEND} && PORTABLE_PYTHON=true make
# Package GPU libraries into the backend's lib directory
RUN mkdir -p /${BACKEND}/lib && \
TARGET_LIB_DIR="/${BACKEND}/lib" BUILD_TYPE="${BUILD_TYPE}" CUDA_MAJOR_VERSION="${CUDA_MAJOR_VERSION}" \
bash /package-gpu-libs.sh "/${BACKEND}/lib"
FROM scratch
ARG BACKEND=rerankers
COPY --from=builder /${BACKEND}/ /

View File

@@ -46,7 +46,7 @@ The backend system provides language-specific Dockerfiles that handle the build
- **vllm**: High-performance LLM inference
- **mlx**: Apple Silicon optimization
- **diffusers**: Stable Diffusion models
- **Audio**: coqui, faster-whisper, kitten-tts
- **Audio**: bark, coqui, faster-whisper, kitten-tts
- **Vision**: mlx-vlm, rfdetr
- **Specialized**: rerankers, chatterbox, kokoro
@@ -55,6 +55,7 @@ The backend system provides language-specific Dockerfiles that handle the build
- **stablediffusion-ggml**: Stable Diffusion in Go with GGML Cpp backend
- **huggingface**: Hugging Face model integration
- **piper**: Text-to-speech synthesis Golang with C bindings using rhaspy/piper
- **bark-cpp**: Bark TTS models Golang with Cpp bindings
- **local-store**: Vector storage backend
#### C++ Backends (`cpp/`)
@@ -64,7 +65,7 @@ The backend system provides language-specific Dockerfiles that handle the build
## Hardware Acceleration Support
### CUDA (NVIDIA)
- **Versions**: CUDA 12.x, 13.x
- **Versions**: CUDA 11.x, 12.x
- **Features**: cuBLAS, cuDNN, TensorRT optimization
- **Targets**: x86_64, ARM64 (Jetson)
@@ -131,7 +132,8 @@ For ARM64/Mac builds, docker can't be used, and the makefile in the respective b
### Build Types
- **`cpu`**: CPU-only optimization
- **`cublas12`**, **`cublas13`**: CUDA 12.x, 13.x with cuBLAS
- **`cublas11`**: CUDA 11.x with cuBLAS
- **`cublas12`**: CUDA 12.x with cuBLAS
- **`hipblas`**: ROCm with rocBLAS
- **`intel`**: Intel oneAPI optimization
- **`vulkan`**: Vulkan-based acceleration
@@ -208,4 +210,4 @@ When contributing to the backend system:
2. **Add Tests**: Include comprehensive test coverage
3. **Document**: Provide clear usage examples
4. **Optimize**: Consider performance and resource usage
5. **Validate**: Test across different hardware targets
5. **Validate**: Test across different hardware targets

View File

@@ -17,7 +17,6 @@ service Backend {
rpc GenerateVideo(GenerateVideoRequest) returns (Result) {}
rpc AudioTranscription(TranscriptRequest) returns (TranscriptResult) {}
rpc TTS(TTSRequest) returns (Result) {}
rpc TTSStream(TTSRequest) returns (stream Reply) {}
rpc SoundGeneration(SoundGenerationRequest) returns (Result) {}
rpc TokenizeString(PredictOptions) returns (TokenizationResponse) {}
rpc Status(HealthMessage) returns (StatusResponse) {}
@@ -33,8 +32,6 @@ service Backend {
rpc GetMetrics(MetricsRequest) returns (MetricsResponse);
rpc VAD(VADRequest) returns (VADResponse) {}
rpc ModelMetadata(ModelOptions) returns (ModelMetadataResponse) {}
}
// Define the empty request
@@ -299,7 +296,6 @@ message TranscriptSegment {
int64 end = 3;
string text = 4;
repeated int32 tokens = 5;
string speaker = 6;
}
message GenerateImageRequest {
@@ -414,8 +410,3 @@ message Detection {
message DetectResponse {
repeated Detection Detections = 1;
}
message ModelMetadataResponse {
bool supports_thinking = 1;
string rendered_template = 2; // The rendered chat template with enable_thinking=true (empty if not applicable)
}

View File

@@ -70,4 +70,4 @@ target_link_libraries(${TARGET} PRIVATE common llama mtmd ${CMAKE_THREAD_LIBS_IN
target_compile_features(${TARGET} PRIVATE cxx_std_11)
if(TARGET BUILD_INFO)
add_dependencies(${TARGET} BUILD_INFO)
endif()
endif()

View File

@@ -1,5 +1,5 @@
LLAMA_VERSION?=2634ed207a17db1a54bd8df0555bd8499a6ab691
LLAMA_VERSION?=ced765be44ce173c374f295b3c6f4175f8fd109b
LLAMA_REPO?=https://github.com/ggerganov/llama.cpp
CMAKE_ARGS?=
@@ -7,8 +7,7 @@ BUILD_TYPE?=
NATIVE?=false
ONEAPI_VARS?=/opt/intel/oneapi/setvars.sh
TARGET?=--target grpc-server
JOBS?=$(shell nproc 2>/dev/null || sysctl -n hw.ncpu 2>/dev/null || echo 1)
ARCH?=$(shell uname -m)
JOBS?=$(shell nproc)
# Disable Shared libs as we are linking on static gRPC and we can't mix shared and static
CMAKE_ARGS+=-DBUILD_SHARED_LIBS=OFF -DLLAMA_CURL=OFF
@@ -107,21 +106,21 @@ llama-cpp-avx: llama.cpp
cp -rf $(CURRENT_MAKEFILE_DIR)/../llama-cpp $(CURRENT_MAKEFILE_DIR)/../llama-cpp-avx-build
$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../llama-cpp-avx-build purge
$(info ${GREEN}I llama-cpp build info:avx${RESET})
CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=on -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off" $(MAKE) VARIANT="llama-cpp-avx-build" build-llama-cpp-grpc-server
CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=on -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off" $(MAKE) VARIANT="llama-cpp-avx-build" build-llama-cpp-grpc-server
cp -rfv $(CURRENT_MAKEFILE_DIR)/../llama-cpp-avx-build/grpc-server llama-cpp-avx
llama-cpp-fallback: llama.cpp
cp -rf $(CURRENT_MAKEFILE_DIR)/../llama-cpp $(CURRENT_MAKEFILE_DIR)/../llama-cpp-fallback-build
$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../llama-cpp-fallback-build purge
$(info ${GREEN}I llama-cpp build info:fallback${RESET})
CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off" $(MAKE) VARIANT="llama-cpp-fallback-build" build-llama-cpp-grpc-server
CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off" $(MAKE) VARIANT="llama-cpp-fallback-build" build-llama-cpp-grpc-server
cp -rfv $(CURRENT_MAKEFILE_DIR)/../llama-cpp-fallback-build/grpc-server llama-cpp-fallback
llama-cpp-grpc: llama.cpp
cp -rf $(CURRENT_MAKEFILE_DIR)/../llama-cpp $(CURRENT_MAKEFILE_DIR)/../llama-cpp-grpc-build
$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../llama-cpp-grpc-build purge
$(info ${GREEN}I llama-cpp build info:grpc${RESET})
CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_RPC=ON -DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off" TARGET="--target grpc-server --target rpc-server" $(MAKE) VARIANT="llama-cpp-grpc-build" build-llama-cpp-grpc-server
CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_RPC=ON -DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off" TARGET="--target grpc-server --target rpc-server" $(MAKE) VARIANT="llama-cpp-grpc-build" build-llama-cpp-grpc-server
cp -rfv $(CURRENT_MAKEFILE_DIR)/../llama-cpp-grpc-build/grpc-server llama-cpp-grpc
llama-cpp-rpc-server: llama-cpp-grpc

View File

@@ -23,7 +23,6 @@
#include <grpcpp/health_check_service_interface.h>
#include <regex>
#include <atomic>
#include <mutex>
#include <signal.h>
#include <thread>
@@ -83,8 +82,8 @@ static void start_llama_server(server_context& ctx_server) {
// print sample chat example to make it clear which template is used
// LOG_INF("%s: chat template, chat_template: %s, example_format: '%s'\n", __func__,
// common_chat_templates_source(ctx_server.impl->chat_params.tmpls.get()),
// common_chat_format_example(ctx_server.impl->chat_params.tmpls.get(), ctx_server.impl->params_base.use_jinja).c_str(), ctx_server.impl->params_base.default_template_kwargs);
// common_chat_templates_source(ctx_server.impl->chat_templates.get()),
// common_chat_format_example(ctx_server.impl->chat_templates.get(), ctx_server.impl->params_base.use_jinja).c_str(), ctx_server.impl->params_base.default_template_kwargs);
// Keep the chat templates initialized in load_model() so they can be used when UseTokenizerTemplate is enabled
// Templates will only be used conditionally in Predict/PredictStream when UseTokenizerTemplate is true and Messages are provided
@@ -391,9 +390,8 @@ static void params_parse(server_context& /*ctx_server*/, const backend::ModelOpt
// Initialize fit_params options (can be overridden by options)
// fit_params: whether to auto-adjust params to fit device memory (default: true as in llama.cpp)
params.fit_params = true;
// fit_params_target: target margin per device in bytes (default: 1GB per device)
// Initialize as vector with default value for all devices
params.fit_params_target = std::vector<size_t>(llama_max_devices(), 1024 * 1024 * 1024);
// fit_params_target: target margin per device in bytes (default: 1GB)
params.fit_params_target = 1024 * 1024 * 1024;
// fit_params_min_ctx: minimum context size for fit (default: 4096)
params.fit_params_min_ctx = 4096;
@@ -470,28 +468,10 @@ static void params_parse(server_context& /*ctx_server*/, const backend::ModelOpt
} else if (!strcmp(optname, "fit_params_target") || !strcmp(optname, "fit_target")) {
if (optval != NULL) {
try {
// Value is in MiB, can be comma-separated list for multiple devices
// Single value is broadcast across all devices
std::string arg_next = optval_str;
const std::regex regex{ R"([,/]+)" };
std::sregex_token_iterator it{ arg_next.begin(), arg_next.end(), regex, -1 };
std::vector<std::string> split_arg{ it, {} };
if (split_arg.size() >= llama_max_devices()) {
// Too many values provided
continue;
}
if (split_arg.size() == 1) {
// Single value: broadcast to all devices
size_t value_mib = std::stoul(split_arg[0]);
std::fill(params.fit_params_target.begin(), params.fit_params_target.end(), value_mib * 1024 * 1024);
} else {
// Multiple values: set per device
for (size_t i = 0; i < split_arg.size() && i < params.fit_params_target.size(); i++) {
params.fit_params_target[i] = std::stoul(split_arg[i]) * 1024 * 1024;
}
}
// Value is in MiB, convert to bytes
params.fit_params_target = static_cast<size_t>(std::stoi(optval_str)) * 1024 * 1024;
} catch (const std::exception& e) {
// If conversion fails, keep default value (1GB per device)
// If conversion fails, keep default value (1GB)
}
}
} else if (!strcmp(optname, "fit_params_min_ctx") || !strcmp(optname, "fit_ctx")) {
@@ -706,13 +686,13 @@ private:
public:
BackendServiceImpl(server_context& ctx) : ctx_server(ctx) {}
grpc::Status Health(ServerContext* /*context*/, const backend::HealthMessage* /*request*/, backend::Reply* reply) override {
grpc::Status Health(ServerContext* /*context*/, const backend::HealthMessage* /*request*/, backend::Reply* reply) {
// Implement Health RPC
reply->set_message("OK");
return Status::OK;
}
grpc::Status LoadModel(ServerContext* /*context*/, const backend::ModelOptions* request, backend::Result* result) override {
grpc::Status LoadModel(ServerContext* /*context*/, const backend::ModelOptions* request, backend::Result* result) {
// Implement LoadModel RPC
common_params params;
params_parse(ctx_server, request, params);
@@ -729,72 +709,11 @@ public:
LOG_INF("\n");
LOG_INF("%s\n", common_params_get_system_info(params).c_str());
LOG_INF("\n");
// Capture error messages during model loading
struct error_capture {
std::string captured_error;
std::mutex error_mutex;
ggml_log_callback original_callback;
void* original_user_data;
} error_capture_data;
// Get original log callback
llama_log_get(&error_capture_data.original_callback, &error_capture_data.original_user_data);
// Set custom callback to capture errors
llama_log_set([](ggml_log_level level, const char * text, void * user_data) {
auto* capture = static_cast<error_capture*>(user_data);
// Capture error messages
if (level == GGML_LOG_LEVEL_ERROR) {
std::lock_guard<std::mutex> lock(capture->error_mutex);
// Append error message, removing trailing newlines
std::string msg(text);
while (!msg.empty() && (msg.back() == '\n' || msg.back() == '\r')) {
msg.pop_back();
}
if (!msg.empty()) {
if (!capture->captured_error.empty()) {
capture->captured_error.append("; ");
}
capture->captured_error.append(msg);
}
}
// Also call original callback to preserve logging
if (capture->original_callback) {
capture->original_callback(level, text, capture->original_user_data);
}
}, &error_capture_data);
// load the model
bool load_success = ctx_server.load_model(params);
// Restore original log callback
llama_log_set(error_capture_data.original_callback, error_capture_data.original_user_data);
if (!load_success) {
std::string error_msg = "Failed to load model: " + params.model.path;
if (!params.mmproj.path.empty()) {
error_msg += " (with mmproj: " + params.mmproj.path + ")";
}
if (params.speculative.has_dft() && !params.speculative.mparams_dft.path.empty()) {
error_msg += " (with draft model: " + params.speculative.mparams_dft.path + ")";
}
// Add captured error details if available
{
std::lock_guard<std::mutex> lock(error_capture_data.error_mutex);
if (!error_capture_data.captured_error.empty()) {
error_msg += ". Error: " + error_capture_data.captured_error;
} else {
error_msg += ". Model file may not exist or be invalid.";
}
}
result->set_message(error_msg);
if (!ctx_server.load_model(params)) {
result->set_message("Failed loading model");
result->set_success(false);
return grpc::Status(grpc::StatusCode::INTERNAL, error_msg);
return Status::CANCELLED;
}
// Process grammar triggers now that vocab is available
@@ -882,7 +801,7 @@ public:
std::string prompt_str;
std::vector<raw_buffer> files; // Declare files early so it's accessible in both branches
// Handle chat templates when UseTokenizerTemplate is enabled and Messages are provided
if (request->usetokenizertemplate() && request->messages_size() > 0 && ctx_server.impl->chat_params.tmpls != nullptr) {
if (request->usetokenizertemplate() && request->messages_size() > 0 && ctx_server.impl->chat_templates != nullptr) {
// Convert proto Messages to JSON format compatible with oaicompat_chat_params_parse
json body_json;
json messages_json = json::array();
@@ -1261,7 +1180,12 @@ public:
// Use the same approach as server.cpp: call oaicompat_chat_params_parse
// This handles all template application, grammar merging, etc. automatically
// Files extracted from multimodal content in messages will be added to the files vector
// chat_params already contains tmpls, allow_image, and allow_audio set during model loading
// Create parser options with current chat_templates to ensure tmpls is not null
oaicompat_parser_options parser_opt = ctx_server.impl->oai_parser_opt;
parser_opt.tmpls = ctx_server.impl->chat_templates.get(); // Ensure tmpls is set to current chat_templates
// Update allow_image and allow_audio based on current mctx state
parser_opt.allow_image = ctx_server.impl->mctx ? mtmd_support_vision(ctx_server.impl->mctx) : false;
parser_opt.allow_audio = ctx_server.impl->mctx ? mtmd_support_audio(ctx_server.impl->mctx) : false;
// Debug: Log tools before template processing
if (body_json.contains("tools")) {
@@ -1307,7 +1231,7 @@ public:
}
}
json parsed_data = oaicompat_chat_params_parse(body_json, ctx_server.impl->chat_params, files);
json parsed_data = oaicompat_chat_params_parse(body_json, parser_opt, files);
// Debug: Log tools after template processing
if (parsed_data.contains("tools")) {
@@ -1360,7 +1284,7 @@ public:
// If not using chat templates, extract files from image_data/audio_data fields
// (If using chat templates, files were already extracted by oaicompat_chat_params_parse)
if (!request->usetokenizertemplate() || request->messages_size() == 0 || ctx_server.impl->chat_params.tmpls == nullptr) {
if (!request->usetokenizertemplate() || request->messages_size() == 0 || ctx_server.impl->chat_templates == nullptr) {
const auto &images_data = data.find("image_data");
if (images_data != data.end() && images_data->is_array())
{
@@ -1568,7 +1492,7 @@ public:
return grpc::Status::OK;
}
grpc::Status Predict(ServerContext* context, const backend::PredictOptions* request, backend::Reply* reply) override {
grpc::Status Predict(ServerContext* context, const backend::PredictOptions* request, backend::Reply* reply) {
if (params_base.model.path.empty()) {
return grpc::Status(grpc::StatusCode::FAILED_PRECONDITION, "Model not loaded");
}
@@ -1588,7 +1512,7 @@ public:
std::string prompt_str;
std::vector<raw_buffer> files; // Declare files early so it's accessible in both branches
// Handle chat templates when UseTokenizerTemplate is enabled and Messages are provided
if (request->usetokenizertemplate() && request->messages_size() > 0 && ctx_server.impl->chat_params.tmpls != nullptr) {
if (request->usetokenizertemplate() && request->messages_size() > 0 && ctx_server.impl->chat_templates != nullptr) {
// Convert proto Messages to JSON format compatible with oaicompat_chat_params_parse
json body_json;
json messages_json = json::array();
@@ -1992,7 +1916,12 @@ public:
// Use the same approach as server.cpp: call oaicompat_chat_params_parse
// This handles all template application, grammar merging, etc. automatically
// Files extracted from multimodal content in messages will be added to the files vector
// chat_params already contains tmpls, allow_image, and allow_audio set during model loading
// Create parser options with current chat_templates to ensure tmpls is not null
oaicompat_parser_options parser_opt = ctx_server.impl->oai_parser_opt;
parser_opt.tmpls = ctx_server.impl->chat_templates.get(); // Ensure tmpls is set to current chat_templates
// Update allow_image and allow_audio based on current mctx state
parser_opt.allow_image = ctx_server.impl->mctx ? mtmd_support_vision(ctx_server.impl->mctx) : false;
parser_opt.allow_audio = ctx_server.impl->mctx ? mtmd_support_audio(ctx_server.impl->mctx) : false;
// Debug: Log tools before template processing
if (body_json.contains("tools")) {
@@ -2038,7 +1967,7 @@ public:
}
}
json parsed_data = oaicompat_chat_params_parse(body_json, ctx_server.impl->chat_params, files);
json parsed_data = oaicompat_chat_params_parse(body_json, parser_opt, files);
// Debug: Log tools after template processing
if (parsed_data.contains("tools")) {
@@ -2091,7 +2020,7 @@ public:
// If not using chat templates, extract files from image_data/audio_data fields
// (If using chat templates, files were already extracted by oaicompat_chat_params_parse)
if (!request->usetokenizertemplate() || request->messages_size() == 0 || ctx_server.impl->chat_params.tmpls == nullptr) {
if (!request->usetokenizertemplate() || request->messages_size() == 0 || ctx_server.impl->chat_templates == nullptr) {
const auto &images_data = data.find("image_data");
if (images_data != data.end() && images_data->is_array())
{
@@ -2234,7 +2163,7 @@ public:
return grpc::Status::OK;
}
grpc::Status Embedding(ServerContext* context, const backend::PredictOptions* request, backend::EmbeddingResult* embeddingResult) override {
grpc::Status Embedding(ServerContext* context, const backend::PredictOptions* request, backend::EmbeddingResult* embeddingResult) {
if (params_base.model.path.empty()) {
return grpc::Status(grpc::StatusCode::FAILED_PRECONDITION, "Model not loaded");
}
@@ -2329,7 +2258,7 @@ public:
return grpc::Status::OK;
}
grpc::Status Rerank(ServerContext* context, const backend::RerankRequest* request, backend::RerankResult* rerankResult) override {
grpc::Status Rerank(ServerContext* context, const backend::RerankRequest* request, backend::RerankResult* rerankResult) {
if (!params_base.embedding || params_base.pooling_type != LLAMA_POOLING_TYPE_RANK) {
return grpc::Status(grpc::StatusCode::UNIMPLEMENTED, "This server does not support reranking. Start it with `--reranking` and without `--embedding`");
}
@@ -2415,7 +2344,7 @@ public:
return grpc::Status::OK;
}
grpc::Status TokenizeString(ServerContext* /*context*/, const backend::PredictOptions* request, backend::TokenizationResponse* response) override {
grpc::Status TokenizeString(ServerContext* /*context*/, const backend::PredictOptions* request, backend::TokenizationResponse* response) {
if (params_base.model.path.empty()) {
return grpc::Status(grpc::StatusCode::FAILED_PRECONDITION, "Model not loaded");
}
@@ -2438,7 +2367,7 @@ public:
return grpc::Status::OK;
}
grpc::Status GetMetrics(ServerContext* /*context*/, const backend::MetricsRequest* /*request*/, backend::MetricsResponse* response) override {
grpc::Status GetMetrics(ServerContext* /*context*/, const backend::MetricsRequest* /*request*/, backend::MetricsResponse* response) {
// request slots data using task queue
auto rd = ctx_server.get_response_reader();
@@ -2476,47 +2405,6 @@ public:
response->set_prompt_tokens_processed(res_metrics->n_prompt_tokens_processed_total);
return grpc::Status::OK;
}
grpc::Status ModelMetadata(ServerContext* /*context*/, const backend::ModelOptions* /*request*/, backend::ModelMetadataResponse* response) override {
// Check if model is loaded
if (params_base.model.path.empty()) {
return grpc::Status(grpc::StatusCode::FAILED_PRECONDITION, "Model not loaded");
}
// Check if chat templates are initialized
if (ctx_server.impl->chat_params.tmpls == nullptr) {
// If templates are not initialized, we can't detect thinking support
// Return false as default
response->set_supports_thinking(false);
response->set_rendered_template("");
return grpc::Status::OK;
}
// Detect thinking support using llama.cpp's function
bool supports_thinking = common_chat_templates_support_enable_thinking(ctx_server.impl->chat_params.tmpls.get());
response->set_supports_thinking(supports_thinking);
// Render the template with enable_thinking=true so Go code can detect thinking tokens
// This allows reusing existing detection functions in Go
std::string rendered_template = "";
if (params_base.use_jinja) {
// Render the template with enable_thinking=true to see what the actual prompt looks like
common_chat_templates_inputs dummy_inputs;
common_chat_msg msg;
msg.role = "user";
msg.content = "test";
dummy_inputs.messages = {msg};
dummy_inputs.enable_thinking = true;
dummy_inputs.use_jinja = params_base.use_jinja;
const auto rendered = common_chat_templates_apply(ctx_server.impl->chat_params.tmpls.get(), dummy_inputs);
rendered_template = rendered.prompt;
}
response->set_rendered_template(rendered_template);
return grpc::Status::OK;
}
};

View File

@@ -6,7 +6,6 @@
set -e
CURDIR=$(dirname "$(realpath $0)")
REPO_ROOT="${CURDIR}/../../.."
# Create lib directory
mkdir -p $CURDIR/package/lib
@@ -38,15 +37,6 @@ else
exit 1
fi
# Package GPU libraries based on BUILD_TYPE
# The GPU library packaging script will detect BUILD_TYPE and copy appropriate GPU libraries
GPU_LIB_SCRIPT="${REPO_ROOT}/scripts/build/package-gpu-libs.sh"
if [ -f "$GPU_LIB_SCRIPT" ]; then
echo "Packaging GPU libraries for BUILD_TYPE=${BUILD_TYPE:-cpu}..."
source "$GPU_LIB_SCRIPT" "$CURDIR/package/lib"
package_gpu_libs
fi
echo "Packaging completed successfully"
ls -liah $CURDIR/package/
ls -liah $CURDIR/package/lib/

View File

@@ -0,0 +1,51 @@
INCLUDE_PATH := $(abspath ./)
LIBRARY_PATH := $(abspath ./)
AR?=ar
CMAKE_ARGS?=-DGGML_NATIVE=OFF
BUILD_TYPE?=
GOCMD=go
# keep standard at C11 and C++11
CXXFLAGS = -I. -I$(INCLUDE_PATH)/sources/bark.cpp/examples -I$(INCLUDE_PATH)/sources/bark.cpp/encodec.cpp/ggml/include -I$(INCLUDE_PATH)/sources/bark.cpp/spm-headers -I$(INCLUDE_PATH)/sources/bark.cpp -O3 -DNDEBUG -std=c++17 -fPIC
LDFLAGS = -L$(LIBRARY_PATH) -L$(LIBRARY_PATH)/sources/bark.cpp/build/examples -lbark -lstdc++ -lm
# bark.cpp
BARKCPP_REPO?=https://github.com/PABannier/bark.cpp.git
BARKCPP_VERSION?=5d5be84f089ab9ea53b7a793f088d3fbf7247495
# warnings
CXXFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function
## bark.cpp
sources/bark.cpp:
git clone --recursive $(BARKCPP_REPO) sources/bark.cpp && \
cd sources/bark.cpp && \
git checkout $(BARKCPP_VERSION) && \
git submodule update --init --recursive --depth 1 --single-branch
sources/bark.cpp/build/libbark.a: sources/bark.cpp
cd sources/bark.cpp && \
mkdir -p build && \
cd build && \
cmake $(CMAKE_ARGS) .. && \
cmake --build . --config Release
gobark.o:
$(CXX) $(CXXFLAGS) gobark.cpp -o gobark.o -c $(LDFLAGS)
libbark.a: sources/bark.cpp/build/libbark.a gobark.o
cp $(INCLUDE_PATH)/sources/bark.cpp/build/libbark.a ./
$(AR) rcs libbark.a gobark.o
bark-cpp: libbark.a
CGO_LDFLAGS="$(CGO_LDFLAGS)" C_INCLUDE_PATH="$(CURDIR)" LIBRARY_PATH=$(CURDIR) \
$(GOCMD) build -v -ldflags "$(LD_FLAGS)" -tags "$(GO_TAGS)" -o bark-cpp ./
package:
bash package.sh
build: bark-cpp package
clean:
rm -f gobark.o libbark.a

View File

@@ -0,0 +1,85 @@
#include <iostream>
#include <tuple>
#include "bark.h"
#include "gobark.h"
#include "common.h"
#include "ggml.h"
struct bark_context *c;
void bark_print_progress_callback(struct bark_context *bctx, enum bark_encoding_step step, int progress, void *user_data) {
if (step == bark_encoding_step::SEMANTIC) {
printf("\rGenerating semantic tokens... %d%%", progress);
} else if (step == bark_encoding_step::COARSE) {
printf("\rGenerating coarse tokens... %d%%", progress);
} else if (step == bark_encoding_step::FINE) {
printf("\rGenerating fine tokens... %d%%", progress);
}
fflush(stdout);
}
int load_model(char *model) {
// initialize bark context
struct bark_context_params ctx_params = bark_context_default_params();
bark_params params;
params.model_path = model;
// ctx_params.verbosity = verbosity;
ctx_params.progress_callback = bark_print_progress_callback;
ctx_params.progress_callback_user_data = nullptr;
struct bark_context *bctx = bark_load_model(params.model_path.c_str(), ctx_params, params.seed);
if (!bctx) {
fprintf(stderr, "%s: Could not load model\n", __func__);
return 1;
}
c = bctx;
return 0;
}
int tts(char *text,int threads, char *dst ) {
ggml_time_init();
const int64_t t_main_start_us = ggml_time_us();
// generate audio
if (!bark_generate_audio(c, text, threads)) {
fprintf(stderr, "%s: An error occurred. If the problem persists, feel free to open an issue to report it.\n", __func__);
return 1;
}
const float *audio_data = bark_get_audio_data(c);
if (audio_data == NULL) {
fprintf(stderr, "%s: Could not get audio data\n", __func__);
return 1;
}
const int audio_arr_size = bark_get_audio_data_size(c);
std::vector<float> audio_arr(audio_data, audio_data + audio_arr_size);
write_wav_on_disk(audio_arr, dst);
// report timing
{
const int64_t t_main_end_us = ggml_time_us();
const int64_t t_load_us = bark_get_load_time(c);
const int64_t t_eval_us = bark_get_eval_time(c);
printf("\n\n");
printf("%s: load time = %8.2f ms\n", __func__, t_load_us / 1000.0f);
printf("%s: eval time = %8.2f ms\n", __func__, t_eval_us / 1000.0f);
printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us) / 1000.0f);
}
return 0;
}
int unload() {
bark_free(c);
}

View File

@@ -0,0 +1,52 @@
package main
// #cgo CXXFLAGS: -I${SRCDIR}/sources/bark.cpp/ -I${SRCDIR}/sources/bark.cpp/encodec.cpp -I${SRCDIR}/sources/bark.cpp/encodec.cpp/ggml/include -I${SRCDIR}/sources/bark.cpp/examples -I${SRCDIR}/sources/bark.cpp/spm-headers
// #cgo LDFLAGS: -L${SRCDIR}/ -L${SRCDIR}/sources/bark.cpp/build/examples -L${SRCDIR}/sources/bark.cpp/build/encodec.cpp/ggml/src/ -L${SRCDIR}/sources/bark.cpp/build/encodec.cpp/ -lbark -lencodec -lcommon -lggml -lgomp
// #include <gobark.h>
// #include <stdlib.h>
import "C"
import (
"fmt"
"unsafe"
"github.com/mudler/LocalAI/pkg/grpc/base"
pb "github.com/mudler/LocalAI/pkg/grpc/proto"
)
type Bark struct {
base.SingleThread
threads int
}
func (sd *Bark) Load(opts *pb.ModelOptions) error {
sd.threads = int(opts.Threads)
modelFile := C.CString(opts.ModelFile)
defer C.free(unsafe.Pointer(modelFile))
ret := C.load_model(modelFile)
if ret != 0 {
return fmt.Errorf("inference failed")
}
return nil
}
func (sd *Bark) TTS(opts *pb.TTSRequest) error {
t := C.CString(opts.Text)
defer C.free(unsafe.Pointer(t))
dst := C.CString(opts.Dst)
defer C.free(unsafe.Pointer(dst))
threads := C.int(sd.threads)
ret := C.tts(t, threads, dst)
if ret != 0 {
return fmt.Errorf("inference failed")
}
return nil
}

View File

@@ -0,0 +1,8 @@
#ifdef __cplusplus
extern "C" {
#endif
int load_model(char *model);
int tts(char *text,int threads, char *dst );
#ifdef __cplusplus
}
#endif

View File

@@ -0,0 +1,20 @@
package main
// Note: this is started internally by LocalAI and a server is allocated for each model
import (
"flag"
grpc "github.com/mudler/LocalAI/pkg/grpc"
)
var (
addr = flag.String("addr", "localhost:50051", "the address to connect to")
)
func main() {
flag.Parse()
if err := grpc.StartServer(*addr, &Bark{}); err != nil {
panic(err)
}
}

41
backend/go/bark-cpp/package.sh Executable file
View File

@@ -0,0 +1,41 @@
#!/bin/bash
# Script to copy the appropriate libraries based on architecture
# This script is used in the final stage of the Dockerfile
set -e
CURDIR=$(dirname "$(realpath $0)")
# Create lib directory
mkdir -p $CURDIR/package/lib
cp -avrf $CURDIR/bark-cpp $CURDIR/package/
cp -rfv $CURDIR/run.sh $CURDIR/package/
# Detect architecture and copy appropriate libraries
if [ -f "/lib64/ld-linux-x86-64.so.2" ]; then
# x86_64 architecture
echo "Detected x86_64 architecture, copying x86_64 libraries..."
cp -arfLv /lib64/ld-linux-x86-64.so.2 $CURDIR/package/lib/ld.so
cp -arfLv /lib/x86_64-linux-gnu/libc.so.6 $CURDIR/package/lib/libc.so.6
cp -arfLv /lib/x86_64-linux-gnu/libgcc_s.so.1 $CURDIR/package/lib/libgcc_s.so.1
cp -arfLv /lib/x86_64-linux-gnu/libstdc++.so.6 $CURDIR/package/lib/libstdc++.so.6
cp -arfLv /lib/x86_64-linux-gnu/libm.so.6 $CURDIR/package/lib/libm.so.6
cp -arfLv /lib/x86_64-linux-gnu/libgomp.so.1 $CURDIR/package/lib/libgomp.so.1
elif [ -f "/lib/ld-linux-aarch64.so.1" ]; then
# ARM64 architecture
echo "Detected ARM64 architecture, copying ARM64 libraries..."
cp -arfLv /lib/ld-linux-aarch64.so.1 $CURDIR/package/lib/ld.so
cp -arfLv /lib/aarch64-linux-gnu/libc.so.6 $CURDIR/package/lib/libc.so.6
cp -arfLv /lib/aarch64-linux-gnu/libgcc_s.so.1 $CURDIR/package/lib/libgcc_s.so.1
cp -arfLv /lib/aarch64-linux-gnu/libstdc++.so.6 $CURDIR/package/lib/libstdc++.so.6
cp -arfLv /lib/aarch64-linux-gnu/libm.so.6 $CURDIR/package/lib/libm.so.6
cp -arfLv /lib/aarch64-linux-gnu/libgomp.so.1 $CURDIR/package/lib/libgomp.so.1
else
echo "Error: Could not detect architecture"
exit 1
fi
echo "Packaging completed successfully"
ls -liah $CURDIR/package/
ls -liah $CURDIR/package/lib/

13
backend/go/bark-cpp/run.sh Executable file
View File

@@ -0,0 +1,13 @@
#!/bin/bash
set -ex
CURDIR=$(dirname "$(realpath $0)")
export LD_LIBRARY_PATH=$CURDIR/lib:$LD_LIBRARY_PATH
# If there is a lib/ld.so, use it
if [ -f $CURDIR/lib/ld.so ]; then
echo "Using lib/ld.so"
exec $CURDIR/lib/ld.so $CURDIR/bark-cpp "$@"
fi
exec $CURDIR/bark-cpp "$@"

View File

@@ -8,7 +8,7 @@ JOBS?=$(shell nproc --ignore=1)
# stablediffusion.cpp (ggml)
STABLEDIFFUSION_GGML_REPO?=https://github.com/leejet/stable-diffusion.cpp
STABLEDIFFUSION_GGML_VERSION?=e411520407663e1ddf8ff2e5ed4ff3a116fbbc97
STABLEDIFFUSION_GGML_VERSION?=4ff2c8c74bd17c2cfffe3a01be77743fb3efba2f
CMAKE_ARGS+=-DGGML_MAX_NAME=128
@@ -28,12 +28,7 @@ else ifeq ($(BUILD_TYPE),clblas)
CMAKE_ARGS+=-DGGML_CLBLAST=ON -DCLBlast_DIR=/some/path
# If it's hipblas we do have also to set CC=/opt/rocm/llvm/bin/clang CXX=/opt/rocm/llvm/bin/clang++
else ifeq ($(BUILD_TYPE),hipblas)
ROCM_HOME ?= /opt/rocm
ROCM_PATH ?= /opt/rocm
export CXX=$(ROCM_HOME)/llvm/bin/clang++
export CC=$(ROCM_HOME)/llvm/bin/clang
AMDGPU_TARGETS?=gfx803,gfx900,gfx906,gfx908,gfx90a,gfx942,gfx1010,gfx1030,gfx1032,gfx1100,gfx1101,gfx1102,gfx1200,gfx1201
CMAKE_ARGS+=-DSD_HIPBLAS=ON -DGGML_HIPBLAS=ON -DAMDGPU_TARGETS=$(AMDGPU_TARGETS)
CMAKE_ARGS+=-DSD_HIPBLAS=ON -DGGML_HIPBLAS=ON
else ifeq ($(BUILD_TYPE),vulkan)
CMAKE_ARGS+=-DSD_VULKAN=ON -DGGML_VULKAN=ON
else ifeq ($(OS),Darwin)

View File

@@ -6,7 +6,6 @@
set -e
CURDIR=$(dirname "$(realpath $0)")
REPO_ROOT="${CURDIR}/../../.."
# Create lib directory
mkdir -p $CURDIR/package/lib
@@ -51,15 +50,6 @@ else
exit 1
fi
# Package GPU libraries based on BUILD_TYPE
# The GPU library packaging script will detect BUILD_TYPE and copy appropriate GPU libraries
GPU_LIB_SCRIPT="${REPO_ROOT}/scripts/build/package-gpu-libs.sh"
if [ -f "$GPU_LIB_SCRIPT" ]; then
echo "Packaging GPU libraries for BUILD_TYPE=${BUILD_TYPE:-cpu}..."
source "$GPU_LIB_SCRIPT" "$CURDIR/package/lib"
package_gpu_libs
fi
echo "Packaging completed successfully"
ls -liah $CURDIR/package/
ls -liah $CURDIR/package/lib/

View File

@@ -8,7 +8,7 @@ JOBS?=$(shell nproc --ignore=1)
# whisper.cpp version
WHISPER_REPO?=https://github.com/ggml-org/whisper.cpp
WHISPER_CPP_VERSION?=aa1bc0d1a6dfd70dbb9f60c11df12441e03a9075
WHISPER_CPP_VERSION?=e9898ddfb908ffaa7026c66852a023889a5a7202
SO_TARGET?=libgowhisper.so
CMAKE_ARGS+=-DBUILD_SHARED_LIBS=OFF

View File

@@ -130,9 +130,8 @@ func (w *Whisper) AudioTranscription(opts *pb.TranscriptRequest) (pb.TranscriptR
segments := []*pb.TranscriptSegment{}
text := ""
for i := range int(segsLen) {
// segment start/end conversion factor taken from https://github.com/ggml-org/whisper.cpp/blob/master/examples/cli/cli.cpp#L895
s := CppGetSegmentStart(i) * (10000000)
t := CppGetSegmentEnd(i) * (10000000)
s := CppGetSegmentStart(i)
t := CppGetSegmentEnd(i)
txt := strings.Clone(CppGetSegmentText(i))
tokens := make([]int32, CppNTokens(i))

View File

@@ -6,7 +6,6 @@
set -e
CURDIR=$(dirname "$(realpath $0)")
REPO_ROOT="${CURDIR}/../../.."
# Create lib directory
mkdir -p $CURDIR/package/lib
@@ -51,15 +50,6 @@ else
exit 1
fi
# Package GPU libraries based on BUILD_TYPE
# The GPU library packaging script will detect BUILD_TYPE and copy appropriate GPU libraries
GPU_LIB_SCRIPT="${REPO_ROOT}/scripts/build/package-gpu-libs.sh"
if [ -f "$GPU_LIB_SCRIPT" ]; then
echo "Packaging GPU libraries for BUILD_TYPE=${BUILD_TYPE:-cpu}..."
source "$GPU_LIB_SCRIPT" "$CURDIR/package/lib"
package_gpu_libs
fi
echo "Packaging completed successfully"
ls -liah $CURDIR/package/
ls -liah $CURDIR/package/lib/

View File

File diff suppressed because it is too large Load Diff

View File

@@ -16,8 +16,10 @@ The Python backends use a unified build system based on `libbackend.sh` that pro
- **transformers** - Hugging Face Transformers framework (PyTorch-based)
- **vllm** - High-performance LLM inference engine
- **mlx** - Apple Silicon optimized ML framework
- **exllama2** - ExLlama2 quantized models
### Audio & Speech
- **bark** - Text-to-speech synthesis
- **coqui** - Coqui TTS models
- **faster-whisper** - Fast Whisper speech recognition
- **kitten-tts** - Lightweight TTS
@@ -83,7 +85,7 @@ runUnittests
The build system automatically detects and configures for different hardware:
- **CPU** - Standard CPU-only builds
- **CUDA** - NVIDIA GPU acceleration (supports CUDA 12/13)
- **CUDA** - NVIDIA GPU acceleration (supports CUDA 11/12)
- **Intel** - Intel XPU/GPU optimization
- **MLX** - Apple Silicon (M1/M2/M3) optimization
- **HIP** - AMD GPU acceleration
@@ -93,8 +95,8 @@ The build system automatically detects and configures for different hardware:
Backends can specify hardware-specific dependencies:
- `requirements.txt` - Base requirements
- `requirements-cpu.txt` - CPU-specific packages
- `requirements-cublas11.txt` - CUDA 11 packages
- `requirements-cublas12.txt` - CUDA 12 packages
- `requirements-cublas13.txt` - CUDA 13 packages
- `requirements-intel.txt` - Intel-optimized packages
- `requirements-mps.txt` - Apple Silicon packages

View File

@@ -1,18 +1,18 @@
.PHONY: voxcpm
voxcpm:
.PHONY: ttsbark
ttsbark:
bash install.sh
.PHONY: run
run: voxcpm
@echo "Running voxcpm..."
run: ttsbark
@echo "Running bark..."
bash run.sh
@echo "voxcpm run."
@echo "bark run."
.PHONY: test
test: voxcpm
@echo "Testing voxcpm..."
test: ttsbark
@echo "Testing bark..."
bash test.sh
@echo "voxcpm tested."
@echo "bark tested."
.PHONY: protogen-clean
protogen-clean:
@@ -20,4 +20,4 @@ protogen-clean:
.PHONY: clean
clean: protogen-clean
rm -rf venv __pycache__
rm -rf venv __pycache__

View File

@@ -0,0 +1,16 @@
# Creating a separate environment for ttsbark project
```
make ttsbark
```
# Testing the gRPC server
```
<The path of your python interpreter> -m unittest test_ttsbark.py
```
For example
```
/opt/conda/envs/bark/bin/python -m unittest extra/grpc/bark/test_ttsbark.py
``````

View File

@@ -1,6 +1,6 @@
#!/usr/bin/env python3
"""
This is an extra gRPC server of LocalAI for Moonshine transcription
This is an extra gRPC server of LocalAI for Bark TTS
"""
from concurrent import futures
import time
@@ -8,9 +8,11 @@ import argparse
import signal
import sys
import os
from scipy.io.wavfile import write as write_wav
import backend_pb2
import backend_pb2_grpc
import moonshine_onnx
from bark import SAMPLE_RATE, generate_audio, preload_models
import grpc
@@ -27,52 +29,36 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
"""
def Health(self, request, context):
return backend_pb2.Reply(message=bytes("OK", 'utf-8'))
def LoadModel(self, request, context):
model_name = request.Model
try:
print("Preparing models, please wait", file=sys.stderr)
# Store the model name for use in transcription
# Model name format: e.g., "moonshine/tiny"
self.model_name = request.Model
print(f"Model name set to: {self.model_name}", file=sys.stderr)
# download and load all models
preload_models()
except Exception as err:
return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
# Implement your logic here for the LoadModel service
# Replace this with your desired response
return backend_pb2.Result(message="Model loaded successfully", success=True)
def AudioTranscription(self, request, context):
resultSegments = []
text = ""
def TTS(self, request, context):
model = request.model
print(request, file=sys.stderr)
try:
# moonshine_onnx.transcribe returns a list of strings
transcriptions = moonshine_onnx.transcribe(request.dst, self.model_name)
# Combine all transcriptions into a single text
if isinstance(transcriptions, list):
text = " ".join(transcriptions)
# Create segments for each transcription in the list
for id, trans in enumerate(transcriptions):
# Since moonshine doesn't provide timing info, we'll create a single segment
# with id and text, using approximate timing
resultSegments.append(backend_pb2.TranscriptSegment(
id=id,
start=0,
end=0,
text=trans
))
audio_array = None
if model != "":
audio_array = generate_audio(request.text, history_prompt=model)
else:
# Handle case where it's not a list (shouldn't happen, but be safe)
text = str(transcriptions)
resultSegments.append(backend_pb2.TranscriptSegment(
id=0,
start=0,
end=0,
text=text
))
audio_array = generate_audio(request.text)
print("saving to", request.dst, file=sys.stderr)
# save audio to disk
write_wav(request.dst, SAMPLE_RATE, audio_array)
print("saved to", request.dst, file=sys.stderr)
print("tts for", file=sys.stderr)
print(request, file=sys.stderr)
except Exception as err:
print(f"Unexpected {err=}, {type(err)=}", file=sys.stderr)
return backend_pb2.TranscriptResult(segments=[], text="")
return backend_pb2.TranscriptResult(segments=resultSegments, text=text)
return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
return backend_pb2.Result(success=True)
def serve(address):
server = grpc.server(futures.ThreadPoolExecutor(max_workers=MAX_WORKERS),
@@ -110,4 +96,3 @@ if __name__ == "__main__":
args = parser.parse_args()
serve(args.addr)

View File

@@ -16,15 +16,4 @@ if [ "x${BUILD_PROFILE}" == "xintel" ]; then
EXTRA_PIP_INSTALL_FLAGS+=" --upgrade --index-strategy=unsafe-first-match"
fi
# Use python 3.12 for l4t
if [ "x${BUILD_PROFILE}" == "xl4t13" ]; then
PYTHON_VERSION="3.12"
PYTHON_PATCH="12"
PY_STANDALONE_TAG="20251120"
fi
if [ "x${BUILD_PROFILE}" == "xl4t12" ]; then
USE_PIP=true
fi
installRequirements

View File

@@ -0,0 +1,4 @@
transformers
accelerate
torch==2.4.1
torchaudio==2.4.1

View File

@@ -0,0 +1,5 @@
--extra-index-url https://download.pytorch.org/whl/cu118
torch==2.4.1+cu118
torchaudio==2.4.1+cu118
transformers
accelerate

View File

@@ -0,0 +1,4 @@
torch==2.4.1
torchaudio==2.4.1
transformers
accelerate

View File

@@ -0,0 +1,5 @@
--extra-index-url https://download.pytorch.org/whl/rocm6.0
torch==2.4.1+rocm6.0
torchaudio==2.4.1+rocm6.0
transformers
accelerate

View File

@@ -0,0 +1,9 @@
--extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
intel-extension-for-pytorch==2.8.10+xpu
torch==2.3.1+cxx11.abi
torchaudio==2.3.1+cxx11.abi
oneccl_bind_pt==2.3.100+xpu
optimum[openvino]
setuptools
transformers
accelerate

View File

@@ -0,0 +1,4 @@
bark==0.1.5
grpcio==1.76.0
protobuf
certifi

View File

@@ -6,4 +6,4 @@ else
source $backend_dir/../common/libbackend.sh
fi
startBackend $@
startBackend $@

View File

@@ -1,3 +1,6 @@
"""
A test script to test the gRPC service
"""
import unittest
import subprocess
import time
@@ -6,21 +9,29 @@ import backend_pb2_grpc
import grpc
class TestBackendServicer(unittest.TestCase):
"""
TestBackendServicer is the class that tests the gRPC service.
This class contains methods to test the startup and shutdown of the gRPC service.
TestBackendServicer is the class that tests the gRPC service
"""
def setUp(self):
self.service = subprocess.Popen(["python", "backend.py", "--addr", "localhost:50051"])
"""
This method sets up the gRPC service by starting the server
"""
self.service = subprocess.Popen(["python3", "backend.py", "--addr", "localhost:50051"])
time.sleep(10)
def tearDown(self) -> None:
"""
This method tears down the gRPC service by terminating the server
"""
self.service.terminate()
self.service.wait()
def test_server_startup(self):
"""
This method tests if the server starts up successfully
"""
try:
self.setUp()
with grpc.insecure_channel("localhost:50051") as channel:
@@ -41,8 +52,7 @@ class TestBackendServicer(unittest.TestCase):
self.setUp()
with grpc.insecure_channel("localhost:50051") as channel:
stub = backend_pb2_grpc.BackendStub(channel)
# Use a small image generation model for testing
response = stub.LoadModel(backend_pb2.ModelOptions(Model="Tongyi-MAI/Z-Image-Turbo"))
response = stub.LoadModel(backend_pb2.ModelOptions(Model="v2/en_speaker_4"))
self.assertTrue(response.success)
self.assertEqual(response.message, "Model loaded successfully")
except Exception as err:
@@ -51,32 +61,21 @@ class TestBackendServicer(unittest.TestCase):
finally:
self.tearDown()
def test_generate_image(self):
def test_tts(self):
"""
This method tests if image generation works
This method tests if the embeddings are generated successfully
"""
try:
self.setUp()
with grpc.insecure_channel("localhost:50051") as channel:
stub = backend_pb2_grpc.BackendStub(channel)
response = stub.LoadModel(backend_pb2.ModelOptions(Model="Tongyi-MAI/Z-Image-Turbo"))
response = stub.LoadModel(backend_pb2.ModelOptions(Model="v2/en_speaker_4"))
self.assertTrue(response.success)
req = backend_pb2.GenerateImageRequest(
positive_prompt="a cup of coffee on the table",
dst="/tmp/test_output.png",
width=512,
height=512,
step=20,
seed=42additional_information
)
resp = stub.GenerateImage(req)
self.assertTrue(resp.success)
tts_request = backend_pb2.TTSRequest(text="80s TV news production music hit for tonight's biggest story")
tts_response = stub.TTS(tts_request)
self.assertIsNotNone(tts_response)
except Exception as err:
print(err)
self.fail("GenerateImage service failed")
self.fail("TTS service failed")
finally:
self.tearDown()
additional_information
if __name__ == "__main__":
unittest.main()
self.tearDown()

View File

@@ -17,9 +17,4 @@ if [ "x${BUILD_PROFILE}" == "xintel" ]; then
fi
EXTRA_PIP_INSTALL_FLAGS+=" --no-build-isolation"
if [ "x${BUILD_PROFILE}" == "xl4t12" ]; then
USE_PIP=true
fi
installRequirements

View File

@@ -0,0 +1,8 @@
--extra-index-url https://download.pytorch.org/whl/cu118
torch==2.6.0+cu118
torchaudio==2.6.0+cu118
transformers==4.46.3
numpy>=1.24.0,<1.26.0
# https://github.com/mudler/LocalAI/pull/6240#issuecomment-3329518289
chatterbox-tts@git+https://git@github.com/mudler/chatterbox.git@faster
accelerate

View File

@@ -1,6 +1,6 @@
--extra-index-url https://download.pytorch.org/whl/rocm6.4
torch==2.9.1+rocm6.4
torchaudio==2.9.1+rocm6.4
--extra-index-url https://download.pytorch.org/whl/rocm6.0
torch==2.6.0+rocm6.1
torchaudio==2.6.0+rocm6.1
transformers
numpy>=1.24.0,<1.26.0
# https://github.com/mudler/LocalAI/pull/6240#issuecomment-3329518289

View File

@@ -1,5 +0,0 @@
# Build dependencies needed for packages installed from source (e.g., git dependencies)
# When using --no-build-isolation, these must be installed in the venv first
wheel
setuptools
packaging

View File

@@ -1,6 +1,7 @@
--extra-index-url https://download.pytorch.org/whl/xpu
torch
torchaudio
--extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
intel-extension-for-pytorch==2.3.110+xpu
torch==2.3.1+cxx11.abi
torchaudio==2.3.1+cxx11.abi
transformers
numpy>=1.24.0,<1.26.0
# https://github.com/mudler/LocalAI/pull/6240#issuecomment-3329518289

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@@ -1,7 +1,7 @@
#!/usr/bin/env bash
set -euo pipefail
#
#
# use the library by adding the following line to a script:
# source $(dirname $0)/../common/libbackend.sh
#
@@ -206,8 +206,8 @@ function init() {
# getBuildProfile will inspect the system to determine which build profile is appropriate:
# returns one of the following:
# - cublas11
# - cublas12
# - cublas13
# - hipblas
# - intel
function getBuildProfile() {
@@ -392,13 +392,13 @@ function runProtogen() {
# - requirements-${BUILD_TYPE}.txt
# - requirements-${BUILD_PROFILE}.txt
#
# BUILD_PROFILE is a more specific version of BUILD_TYPE, ex: cuda-12 or cuda-13
# BUILD_PROFILE is a more specific version of BUILD_TYPE, ex: cuda-11 or cuda-12
# it can also include some options that we do not have BUILD_TYPES for, ex: intel
#
# NOTE: for BUILD_PROFILE==intel, this function does NOT automatically use the Intel python package index.
# you may want to add the following line to a requirements-intel.txt if you use one:
#
# --index-url https://download.pytorch.org/whl/xpu
# --index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
#
# If you need to add extra flags into the pip install command you can do so by setting the variable EXTRA_PIP_INSTALL_FLAGS
# before calling installRequirements. For example:
@@ -465,14 +465,6 @@ function startBackend() {
if [ "x${PORTABLE_PYTHON}" == "xtrue" ] || [ -x "$(_portable_python)" ]; then
_makeVenvPortable --update-pyvenv-cfg
fi
# Set up GPU library paths if a lib directory exists
# This allows backends to include their own GPU libraries (CUDA, ROCm, etc.)
if [ -d "${EDIR}/lib" ]; then
export LD_LIBRARY_PATH="${EDIR}/lib:${LD_LIBRARY_PATH:-}"
echo "Added ${EDIR}/lib to LD_LIBRARY_PATH for GPU libraries"
fi
if [ ! -z "${BACKEND_FILE:-}" ]; then
exec "${EDIR}/venv/bin/python" "${BACKEND_FILE}" "$@"
elif [ -e "${MY_DIR}/server.py" ]; then

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@@ -1,2 +1,2 @@
--extra-index-url https://download.pytorch.org/whl/rocm6.4
--extra-index-url https://download.pytorch.org/whl/rocm6.0
torch

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@@ -1,4 +1,5 @@
--extra-index-url https://download.pytorch.org/whl/xpu
--extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
intel-extension-for-pytorch==2.8.10+xpu
torch==2.8.0
oneccl_bind_pt==2.8.0+xpu
optimum[openvino]

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@@ -1,4 +1,4 @@
# Creating a separate environment for coqui project
# Creating a separate environment for ttsbark project
```
make coqui

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@@ -1,6 +1,6 @@
#!/usr/bin/env python3
"""
This is an extra gRPC server of LocalAI for Coqui TTS
This is an extra gRPC server of LocalAI for Bark TTS
"""
from concurrent import futures
import time

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@@ -0,0 +1,6 @@
--extra-index-url https://download.pytorch.org/whl/cu118
torch==2.4.1+cu118
torchaudio==2.4.1+cu118
transformers==4.48.3
accelerate
coqui-tts

View File

@@ -1,6 +1,6 @@
--extra-index-url https://download.pytorch.org/whl/rocm6.4
torch==2.8.0+rocm6.4
torchaudio==2.8.0+rocm6.4
--extra-index-url https://download.pytorch.org/whl/rocm6.0
torch==2.4.1+rocm6.0
torchaudio==2.4.1+rocm6.0
transformers==4.48.3
accelerate
coqui-tts

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@@ -1,6 +1,8 @@
--extra-index-url https://download.pytorch.org/whl/xpu
torch==2.8.0+xpu
torchaudio==2.8.0+xpu
--extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
intel-extension-for-pytorch==2.3.110+xpu
torch==2.3.1+cxx11.abi
torchaudio==2.3.1+cxx11.abi
oneccl_bind_pt==2.3.100+xpu
optimum[openvino]
setuptools
transformers==4.48.3

View File

@@ -41,10 +41,6 @@ from optimum.quanto import freeze, qfloat8, quantize
from transformers import T5EncoderModel
from safetensors.torch import load_file
# Import LTX-2 specific utilities
from diffusers.pipelines.ltx2.export_utils import encode_video as ltx2_encode_video
from diffusers import LTX2VideoTransformer3DModel, GGUFQuantizationConfig
_ONE_DAY_IN_SECONDS = 60 * 60 * 24
COMPEL = os.environ.get("COMPEL", "0") == "1"
XPU = os.environ.get("XPU", "0") == "1"
@@ -294,104 +290,6 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
pipe.enable_model_cpu_offload()
return pipe
# LTX2ImageToVideoPipeline - needs img2vid flag, CPU offload, and special handling
if pipeline_type == "LTX2ImageToVideoPipeline":
self.img2vid = True
self.ltx2_pipeline = True
# Check if loading from single file (GGUF)
if fromSingleFile and LTX2VideoTransformer3DModel is not None:
_, single_file_ext = os.path.splitext(modelFile)
if single_file_ext == ".gguf":
# Load transformer from single GGUF file with quantization
transformer_kwargs = {}
quantization_config = GGUFQuantizationConfig(compute_dtype=torchType)
transformer_kwargs["quantization_config"] = quantization_config
transformer = LTX2VideoTransformer3DModel.from_single_file(
modelFile,
config=request.Model, # Use request.Model as the config/model_id
subfolder="transformer",
**transformer_kwargs,
)
# Load pipeline with custom transformer
pipe = load_diffusers_pipeline(
class_name="LTX2ImageToVideoPipeline",
model_id=request.Model,
transformer=transformer,
torch_dtype=torchType,
)
else:
# Single file but not GGUF - use standard single file loading
pipe = load_diffusers_pipeline(
class_name="LTX2ImageToVideoPipeline",
model_id=modelFile,
from_single_file=True,
torch_dtype=torchType,
)
else:
# Standard loading from pretrained
pipe = load_diffusers_pipeline(
class_name="LTX2ImageToVideoPipeline",
model_id=request.Model,
torch_dtype=torchType,
variant=variant
)
if not DISABLE_CPU_OFFLOAD:
pipe.enable_model_cpu_offload()
return pipe
# LTX2Pipeline - text-to-video pipeline, needs txt2vid flag, CPU offload, and special handling
if pipeline_type == "LTX2Pipeline":
self.txt2vid = True
self.ltx2_pipeline = True
# Check if loading from single file (GGUF)
if fromSingleFile and LTX2VideoTransformer3DModel is not None:
_, single_file_ext = os.path.splitext(modelFile)
if single_file_ext == ".gguf":
# Load transformer from single GGUF file with quantization
transformer_kwargs = {}
quantization_config = GGUFQuantizationConfig(compute_dtype=torchType)
transformer_kwargs["quantization_config"] = quantization_config
transformer = LTX2VideoTransformer3DModel.from_single_file(
modelFile,
config=request.Model, # Use request.Model as the config/model_id
subfolder="transformer",
**transformer_kwargs,
)
# Load pipeline with custom transformer
pipe = load_diffusers_pipeline(
class_name="LTX2Pipeline",
model_id=request.Model,
transformer=transformer,
torch_dtype=torchType,
)
else:
# Single file but not GGUF - use standard single file loading
pipe = load_diffusers_pipeline(
class_name="LTX2Pipeline",
model_id=modelFile,
from_single_file=True,
torch_dtype=torchType,
)
else:
# Standard loading from pretrained
pipe = load_diffusers_pipeline(
class_name="LTX2Pipeline",
model_id=request.Model,
torch_dtype=torchType,
variant=variant
)
if not DISABLE_CPU_OFFLOAD:
pipe.enable_model_cpu_offload()
return pipe
# ================================================================
# Dynamic pipeline loading - the default path for most pipelines
# Uses the dynamic loader to instantiate any pipeline by class name
@@ -506,9 +404,6 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
fromSingleFile = request.Model.startswith("http") or request.Model.startswith("/") or local
self.img2vid = False
self.txt2vid = False
self.ltx2_pipeline = False
print(f"LoadModel: PipelineType from request: {request.PipelineType}", file=sys.stderr)
# Load pipeline using dynamic loader
# Special cases that require custom initialization are handled first
@@ -519,8 +414,6 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
torchType=torchType,
variant=variant
)
print(f"LoadModel: After loading - ltx2_pipeline: {self.ltx2_pipeline}, img2vid: {self.img2vid}, txt2vid: {self.txt2vid}, PipelineType: {self.PipelineType}", file=sys.stderr)
if CLIPSKIP and request.CLIPSkip != 0:
self.clip_skip = request.CLIPSkip
@@ -758,20 +651,14 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
try:
prompt = request.prompt
if not prompt:
print(f"GenerateVideo: No prompt provided for video generation.", file=sys.stderr)
return backend_pb2.Result(success=False, message="No prompt provided for video generation")
# Debug: Print raw request values
print(f"GenerateVideo: Raw request values - num_frames: {request.num_frames}, fps: {request.fps}, cfg_scale: {request.cfg_scale}, step: {request.step}", file=sys.stderr)
# Set default values from request or use defaults
num_frames = request.num_frames if request.num_frames > 0 else 81
fps = request.fps if request.fps > 0 else 16
cfg_scale = request.cfg_scale if request.cfg_scale > 0 else 4.0
num_inference_steps = request.step if request.step > 0 else 40
print(f"GenerateVideo: Using values - num_frames: {num_frames}, fps: {fps}, cfg_scale: {cfg_scale}, num_inference_steps: {num_inference_steps}", file=sys.stderr)
# Prepare generation parameters
kwargs = {
"prompt": prompt,
@@ -797,86 +684,9 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
kwargs["end_image"] = load_image(request.end_image)
print(f"Generating video with {kwargs=}", file=sys.stderr)
print(f"GenerateVideo: Pipeline type: {self.PipelineType}, ltx2_pipeline flag: {self.ltx2_pipeline}", file=sys.stderr)
# Generate video frames based on pipeline type
if self.ltx2_pipeline or self.PipelineType in ["LTX2Pipeline", "LTX2ImageToVideoPipeline"]:
# LTX-2 generation with audio (supports both text-to-video and image-to-video)
# Determine if this is text-to-video (no image) or image-to-video (has image)
has_image = bool(request.start_image)
# Remove image-related parameters that might have been added earlier
kwargs.pop("start_image", None)
kwargs.pop("end_image", None)
# LTX2ImageToVideoPipeline uses 'image' parameter for image-to-video
# LTX2Pipeline (text-to-video) doesn't need an image parameter
if has_image:
# Image-to-video: use 'image' parameter
if self.PipelineType == "LTX2ImageToVideoPipeline":
image = load_image(request.start_image)
kwargs["image"] = image
print(f"LTX-2: Using image-to-video mode with image", file=sys.stderr)
else:
# If pipeline type is LTX2Pipeline but we have an image, we can't do image-to-video
return backend_pb2.Result(success=False, message="LTX2Pipeline does not support image-to-video. Use LTX2ImageToVideoPipeline for image-to-video generation.")
else:
# Text-to-video: no image parameter needed
# Ensure no image-related kwargs are present
kwargs.pop("image", None)
print(f"LTX-2: Using text-to-video mode (no image)", file=sys.stderr)
# LTX-2 uses 'frame_rate' instead of 'fps'
frame_rate = float(fps)
kwargs["frame_rate"] = frame_rate
# LTX-2 requires output_type="np" and return_dict=False
kwargs["output_type"] = "np"
kwargs["return_dict"] = False
# Generate video and audio
print(f"LTX-2: Generating with kwargs: {kwargs}", file=sys.stderr)
try:
video, audio = self.pipe(**kwargs)
print(f"LTX-2: Generated video shape: {video.shape}, audio shape: {audio.shape}", file=sys.stderr)
except Exception as e:
print(f"LTX-2: Error during pipe() call: {e}", file=sys.stderr)
traceback.print_exc()
return backend_pb2.Result(success=False, message=f"Error generating video with LTX-2 pipeline: {e}")
# Convert video to uint8 format
video = (video * 255).round().astype("uint8")
video = torch.from_numpy(video)
print(f"LTX-2: Converting video, shape after conversion: {video.shape}", file=sys.stderr)
print(f"LTX-2: Audio sample rate: {self.pipe.vocoder.config.output_sampling_rate}", file=sys.stderr)
print(f"LTX-2: Output path: {request.dst}", file=sys.stderr)
# Use LTX-2's encode_video function which handles audio
try:
ltx2_encode_video(
video[0],
fps=frame_rate,
audio=audio[0].float().cpu(),
audio_sample_rate=self.pipe.vocoder.config.output_sampling_rate,
output_path=request.dst,
)
# Verify file was created and has content
import os
if os.path.exists(request.dst):
file_size = os.path.getsize(request.dst)
print(f"LTX-2: Video file created successfully, size: {file_size} bytes", file=sys.stderr)
if file_size == 0:
return backend_pb2.Result(success=False, message=f"Video file was created but is empty (0 bytes). Check LTX-2 encode_video function.")
else:
return backend_pb2.Result(success=False, message=f"Video file was not created at {request.dst}")
except Exception as e:
print(f"LTX-2: Error encoding video: {e}", file=sys.stderr)
traceback.print_exc()
return backend_pb2.Result(success=False, message=f"Error encoding video: {e}")
return backend_pb2.Result(message="Video generated successfully", success=True)
elif self.PipelineType == "WanPipeline":
if self.PipelineType == "WanPipeline":
# WAN2.2 text-to-video generation
output = self.pipe(**kwargs)
frames = output.frames[0] # WAN2.2 returns frames in this format
@@ -915,23 +725,11 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
output = self.pipe(**kwargs)
frames = output.frames[0]
else:
print(f"GenerateVideo: Pipeline {self.PipelineType} does not match any known video pipeline handler", file=sys.stderr)
return backend_pb2.Result(success=False, message=f"Pipeline {self.PipelineType} does not support video generation")
# Export video (for non-LTX-2 pipelines)
print(f"GenerateVideo: Exporting video to {request.dst} with fps={fps}", file=sys.stderr)
# Export video
export_to_video(frames, request.dst, fps=fps)
# Verify file was created
import os
if os.path.exists(request.dst):
file_size = os.path.getsize(request.dst)
print(f"GenerateVideo: Video file created, size: {file_size} bytes", file=sys.stderr)
if file_size == 0:
return backend_pb2.Result(success=False, message=f"Video file was created but is empty (0 bytes)")
else:
return backend_pb2.Result(success=False, message=f"Video file was not created at {request.dst}")
return backend_pb2.Result(message="Video generated successfully", success=True)
except Exception as err:

View File

@@ -16,12 +16,8 @@ if [ "x${BUILD_PROFILE}" == "xintel" ]; then
EXTRA_PIP_INSTALL_FLAGS+=" --upgrade --index-strategy=unsafe-first-match"
fi
if [ "x${BUILD_PROFILE}" == "xl4t12" ]; then
USE_PIP=true
fi
# Use python 3.12 for l4t
if [ "x${BUILD_PROFILE}" == "xl4t13" ]; then
if [ "x${BUILD_PROFILE}" == "xl4t12" ] || [ "x${BUILD_PROFILE}" == "xl4t13" ]; then
PYTHON_VERSION="3.12"
PYTHON_PATCH="12"
PY_STANDALONE_TAG="20251120"

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@@ -0,0 +1,12 @@
--extra-index-url https://download.pytorch.org/whl/cu118
git+https://github.com/huggingface/diffusers
opencv-python
transformers
torchvision==0.22.1
accelerate
compel
peft
sentencepiece
torch==2.7.1
optimum-quanto
ftfy

View File

@@ -1,6 +1,6 @@
--extra-index-url https://download.pytorch.org/whl/rocm6.4
torch==2.8.0+rocm6.4
torchvision==0.23.0+rocm6.4
--extra-index-url https://download.pytorch.org/whl/rocm6.3
torch==2.7.1+rocm6.3
torchvision==0.22.1+rocm6.3
git+https://github.com/huggingface/diffusers
opencv-python
transformers

View File

@@ -1,6 +1,8 @@
--extra-index-url https://download.pytorch.org/whl/xpu
torch
torchvision
--extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
intel-extension-for-pytorch==2.3.110+xpu
torch==2.5.1+cxx11.abi
torchvision==0.20.1+cxx11.abi
oneccl_bind_pt==2.8.0+xpu
optimum[openvino]
setuptools
git+https://github.com/huggingface/diffusers

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@@ -3,4 +3,3 @@ grpcio==1.76.0
pillow
protobuf
certifi
av

1
backend/python/exllama2/.gitignore vendored Normal file
View File

@@ -0,0 +1 @@
source

View File

@@ -0,0 +1,17 @@
.PHONY: exllama2
exllama2:
bash install.sh
.PHONY: run
run: exllama2
@echo "Running exllama2..."
bash run.sh
@echo "exllama2 run."
.PHONY: protogen-clean
protogen-clean:
$(RM) backend_pb2_grpc.py backend_pb2.py
.PHONY: clean
clean: protogen-clean
$(RM) -r venv source __pycache__

View File

@@ -0,0 +1,143 @@
#!/usr/bin/env python3
import grpc
from concurrent import futures
import time
import backend_pb2
import backend_pb2_grpc
import argparse
import signal
import sys
import os
import glob
from pathlib import Path
import torch
import torch.nn.functional as F
from torch import version as torch_version
from exllamav2.generator import (
ExLlamaV2BaseGenerator,
ExLlamaV2Sampler
)
from exllamav2 import (
ExLlamaV2,
ExLlamaV2Config,
ExLlamaV2Cache,
ExLlamaV2Cache_8bit,
ExLlamaV2Tokenizer,
model_init,
)
_ONE_DAY_IN_SECONDS = 60 * 60 * 24
# If MAX_WORKERS are specified in the environment use it, otherwise default to 1
MAX_WORKERS = int(os.environ.get('PYTHON_GRPC_MAX_WORKERS', '1'))
# Implement the BackendServicer class with the service methods
class BackendServicer(backend_pb2_grpc.BackendServicer):
def Health(self, request, context):
return backend_pb2.Reply(message=bytes("OK", 'utf-8'))
def LoadModel(self, request, context):
try:
model_directory = request.ModelFile
config = ExLlamaV2Config()
config.model_dir = model_directory
config.prepare()
model = ExLlamaV2(config)
cache = ExLlamaV2Cache(model, lazy=True)
model.load_autosplit(cache)
tokenizer = ExLlamaV2Tokenizer(config)
# Initialize generator
generator = ExLlamaV2BaseGenerator(model, cache, tokenizer)
self.generator = generator
generator.warmup()
self.model = model
self.tokenizer = tokenizer
self.cache = cache
except Exception as err:
return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
return backend_pb2.Result(message="Model loaded successfully", success=True)
def Predict(self, request, context):
penalty = 1.15
if request.Penalty != 0.0:
penalty = request.Penalty
settings = ExLlamaV2Sampler.Settings()
settings.temperature = request.Temperature
settings.top_k = request.TopK
settings.top_p = request.TopP
settings.token_repetition_penalty = penalty
settings.disallow_tokens(self.tokenizer, [self.tokenizer.eos_token_id])
tokens = 512
if request.Tokens != 0:
tokens = request.Tokens
output = self.generator.generate_simple(
request.Prompt, settings, tokens)
# Remove prompt from response if present
if request.Prompt in output:
output = output.replace(request.Prompt, "")
return backend_pb2.Result(message=bytes(output, encoding='utf-8'))
def PredictStream(self, request, context):
# Implement PredictStream RPC
# for reply in some_data_generator():
# yield reply
# Not implemented yet
return self.Predict(request, context)
def serve(address):
server = grpc.server(futures.ThreadPoolExecutor(max_workers=MAX_WORKERS),
options=[
('grpc.max_message_length', 50 * 1024 * 1024), # 50MB
('grpc.max_send_message_length', 50 * 1024 * 1024), # 50MB
('grpc.max_receive_message_length', 50 * 1024 * 1024), # 50MB
])
backend_pb2_grpc.add_BackendServicer_to_server(BackendServicer(), server)
server.add_insecure_port(address)
server.start()
print("Server started. Listening on: " + address, file=sys.stderr)
# Define the signal handler function
def signal_handler(sig, frame):
print("Received termination signal. Shutting down...")
server.stop(0)
sys.exit(0)
# Set the signal handlers for SIGINT and SIGTERM
signal.signal(signal.SIGINT, signal_handler)
signal.signal(signal.SIGTERM, signal_handler)
try:
while True:
time.sleep(_ONE_DAY_IN_SECONDS)
except KeyboardInterrupt:
server.stop(0)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Run the gRPC server.")
parser.add_argument(
"--addr", default="localhost:50051", help="The address to bind the server to."
)
args = parser.parse_args()
serve(args.addr)

View File

@@ -0,0 +1,21 @@
#!/bin/bash
set -e
LIMIT_TARGETS="cublas"
EXTRA_PIP_INSTALL_FLAGS="--no-build-isolation"
EXLLAMA2_VERSION=c0ddebaaaf8ffd1b3529c2bb654e650bce2f790f
backend_dir=$(dirname $0)
if [ -d $backend_dir/common ]; then
source $backend_dir/common/libbackend.sh
else
source $backend_dir/../common/libbackend.sh
fi
installRequirements
git clone https://github.com/turboderp/exllamav2 $MY_DIR/source
pushd ${MY_DIR}/source && git checkout -b build ${EXLLAMA2_VERSION} && popd
# This installs exllamav2 in JIT mode so it will compile the appropriate torch extension at runtime
EXLLAMA_NOCOMPILE= uv pip install ${EXTRA_PIP_INSTALL_FLAGS} ${MY_DIR}/source/

View File

@@ -0,0 +1,3 @@
transformers
accelerate
torch==2.4.1

View File

@@ -0,0 +1,4 @@
--extra-index-url https://download.pytorch.org/whl/cu118
torch==2.4.1+cu118
transformers
accelerate

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@@ -0,0 +1,3 @@
torch==2.4.1
transformers
accelerate

View File

@@ -0,0 +1,4 @@
# This is here to trigger the install script to add --no-build-isolation to the uv pip install commands
# exllama2 does not specify it's build requirements per PEP517, so we need to provide some things ourselves
wheel
setuptools

View File

@@ -0,0 +1,5 @@
grpcio==1.76.0
protobuf
certifi
wheel
setuptools

View File

@@ -1,11 +1,11 @@
#!/bin/bash
LIMIT_TARGETS="cublas"
backend_dir=$(dirname $0)
if [ -d $backend_dir/common ]; then
source $backend_dir/common/libbackend.sh
else
source $backend_dir/../common/libbackend.sh
fi
startBackend $@
startBackend $@

View File

View File

@@ -1,6 +1,6 @@
#!/usr/bin/env python3
"""
This is an extra gRPC server of LocalAI for Faster Whisper TTS
This is an extra gRPC server of LocalAI for Bark TTS
"""
from concurrent import futures
import time
@@ -40,7 +40,7 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
device = "mps"
try:
print("Preparing models, please wait", file=sys.stderr)
self.model = WhisperModel(request.Model, device=device, compute_type="default")
self.model = WhisperModel(request.Model, device=device, compute_type="float16")
except Exception as err:
return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
# Implement your logic here for the LoadModel service
@@ -55,12 +55,11 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
id = 0
for segment in segments:
print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
resultSegments.append(backend_pb2.TranscriptSegment(id=id, start=int(segment.start)*1e9, end=int(segment.end)*1e9, text=segment.text))
resultSegments.append(backend_pb2.TranscriptSegment(id=id, start=segment.start, end=segment.end, text=segment.text))
text += segment.text
id += 1
id += 1
except Exception as err:
print(f"Unexpected {err=}, {type(err)=}", file=sys.stderr)
raise err
return backend_pb2.TranscriptResult(segments=resultSegments, text=text)

View File

@@ -0,0 +1,9 @@
--extra-index-url https://download.pytorch.org/whl/cu118
torch==2.4.1+cu118
faster-whisper
opencv-python
accelerate
compel
peft
sentencepiece
optimum-quanto

View File

@@ -1,3 +1,3 @@
--extra-index-url https://download.pytorch.org/whl/rocm6.4
--extra-index-url https://download.pytorch.org/whl/rocm6.0
torch
faster-whisper

View File

@@ -1,4 +1,6 @@
--extra-index-url https://download.pytorch.org/whl/xpu
torch
--extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
intel-extension-for-pytorch==2.3.110+xpu
torch==2.3.1+cxx11.abi
oneccl_bind_pt==2.3.100+xpu
optimum[openvino]
faster-whisper

View File

@@ -16,8 +16,4 @@ if [ "x${BUILD_PROFILE}" == "xintel" ]; then
EXTRA_PIP_INSTALL_FLAGS+=" --upgrade --index-strategy=unsafe-first-match"
fi
if [ "x${BUILD_PROFILE}" == "xl4t12" ]; then
USE_PIP=true
fi
installRequirements

View File

@@ -0,0 +1,7 @@
--extra-index-url https://download.pytorch.org/whl/cu118
torch==2.7.1+cu118
torchaudio==2.7.1+cu118
transformers
accelerate
kokoro
soundfile

View File

@@ -1,6 +1,6 @@
--extra-index-url https://download.pytorch.org/whl/rocm6.4
torch==2.8.0+rocm6.4
torchaudio==2.8.0+rocm6.4
--extra-index-url https://download.pytorch.org/whl/rocm6.3
torch==2.7.1+rocm6.3
torchaudio==2.7.1+rocm6.3
transformers
accelerate
kokoro

View File

@@ -1,6 +1,8 @@
--extra-index-url https://download.pytorch.org/whl/xpu
torch
torchaudio
--extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
intel-extension-for-pytorch==2.8.10+xpu
torch==2.5.1+cxx11.abi
oneccl_bind_pt==2.8.0+xpu
torchaudio==2.5.1+cxx11.abi
optimum[openvino]
setuptools
transformers==4.48.3

View File

@@ -1,16 +0,0 @@
.DEFAULT_GOAL := install
.PHONY: install
install:
bash install.sh
.PHONY: protogen-clean
protogen-clean:
$(RM) backend_pb2_grpc.py backend_pb2.py
.PHONY: clean
clean: protogen-clean
rm -rf venv __pycache__
test: install
bash test.sh

View File

@@ -1,12 +0,0 @@
#!/bin/bash
set -e
backend_dir=$(dirname $0)
if [ -d $backend_dir/common ]; then
source $backend_dir/common/libbackend.sh
else
source $backend_dir/../common/libbackend.sh
fi
installRequirements

View File

@@ -1,12 +0,0 @@
#!/bin/bash
set -e
backend_dir=$(dirname $0)
if [ -d $backend_dir/common ]; then
source $backend_dir/common/libbackend.sh
else
source $backend_dir/../common/libbackend.sh
fi
python3 -m grpc_tools.protoc -I../.. -I./ --python_out=. --grpc_python_out=. backend.proto

View File

@@ -1,4 +0,0 @@
grpcio==1.71.0
protobuf
grpcio-tools
useful-moonshine-onnx@git+https://git@github.com/moonshine-ai/moonshine.git#subdirectory=moonshine-onnx

View File

@@ -1,10 +0,0 @@
#!/bin/bash
backend_dir=$(dirname $0)
if [ -d $backend_dir/common ]; then
source $backend_dir/common/libbackend.sh
else
source $backend_dir/../common/libbackend.sh
fi
startBackend $@

View File

@@ -1,139 +0,0 @@
"""
A test script to test the gRPC service for Moonshine transcription
"""
import unittest
import subprocess
import time
import os
import tempfile
import shutil
import backend_pb2
import backend_pb2_grpc
import grpc
class TestBackendServicer(unittest.TestCase):
"""
TestBackendServicer is the class that tests the gRPC service
"""
def setUp(self):
"""
This method sets up the gRPC service by starting the server
"""
self.service = subprocess.Popen(["python3", "backend.py", "--addr", "localhost:50051"])
time.sleep(10)
def tearDown(self) -> None:
"""
This method tears down the gRPC service by terminating the server
"""
self.service.terminate()
self.service.wait()
def test_server_startup(self):
"""
This method tests if the server starts up successfully
"""
try:
self.setUp()
with grpc.insecure_channel("localhost:50051") as channel:
stub = backend_pb2_grpc.BackendStub(channel)
response = stub.Health(backend_pb2.HealthMessage())
self.assertEqual(response.message, b'OK')
except Exception as err:
print(err)
self.fail("Server failed to start")
finally:
self.tearDown()
def test_load_model(self):
"""
This method tests if the model is loaded successfully
"""
try:
self.setUp()
with grpc.insecure_channel("localhost:50051") as channel:
stub = backend_pb2_grpc.BackendStub(channel)
response = stub.LoadModel(backend_pb2.ModelOptions(Model="moonshine/tiny"))
self.assertTrue(response.success)
self.assertEqual(response.message, "Model loaded successfully")
except Exception as err:
print(err)
self.fail("LoadModel service failed")
finally:
self.tearDown()
def test_audio_transcription(self):
"""
This method tests if audio transcription works successfully
"""
# Create a temporary directory for the audio file
temp_dir = tempfile.mkdtemp()
audio_file = os.path.join(temp_dir, 'audio.wav')
try:
# Download the audio file to the temporary directory
print(f"Downloading audio file to {audio_file}...")
url = "https://cdn.openai.com/whisper/draft-20220913a/micro-machines.wav"
result = subprocess.run(
["wget", "-q", url, "-O", audio_file],
capture_output=True,
text=True
)
if result.returncode != 0:
self.fail(f"Failed to download audio file: {result.stderr}")
# Verify the file was downloaded
if not os.path.exists(audio_file):
self.fail(f"Audio file was not downloaded to {audio_file}")
self.setUp()
with grpc.insecure_channel("localhost:50051") as channel:
stub = backend_pb2_grpc.BackendStub(channel)
# Load the model first
load_response = stub.LoadModel(backend_pb2.ModelOptions(Model="moonshine/tiny"))
self.assertTrue(load_response.success)
# Perform transcription
transcript_request = backend_pb2.TranscriptRequest(dst=audio_file)
transcript_response = stub.AudioTranscription(transcript_request)
# Print the transcribed text for debugging
print(f"Transcribed text: {transcript_response.text}")
print(f"Number of segments: {len(transcript_response.segments)}")
# Verify response structure
self.assertIsNotNone(transcript_response)
self.assertIsNotNone(transcript_response.text)
# Protobuf repeated fields return a sequence, not a list
self.assertIsNotNone(transcript_response.segments)
# Check if segments is iterable (has length)
self.assertGreaterEqual(len(transcript_response.segments), 0)
# Verify the transcription contains the expected text
expected_text = "This is the micro machine man presenting the most midget miniature"
self.assertIn(
expected_text.lower(),
transcript_response.text.lower(),
f"Expected text '{expected_text}' not found in transcription: '{transcript_response.text}'"
)
# If we got segments, verify they have the expected structure
if len(transcript_response.segments) > 0:
segment = transcript_response.segments[0]
self.assertIsNotNone(segment.text)
self.assertIsInstance(segment.id, int)
else:
# Even if no segments, we should have text
self.assertIsNotNone(transcript_response.text)
self.assertGreater(len(transcript_response.text), 0)
except Exception as err:
print(err)
self.fail("AudioTranscription service failed")
finally:
self.tearDown()
# Clean up the temporary directory
if os.path.exists(temp_dir):
shutil.rmtree(temp_dir)

View File

@@ -1,12 +0,0 @@
#!/bin/bash
set -e
backend_dir=$(dirname $0)
if [ -d $backend_dir/common ]; then
source $backend_dir/common/libbackend.sh
else
source $backend_dir/../common/libbackend.sh
fi
runUnittests

View File

@@ -26,12 +26,6 @@ fi
EXTRA_PIP_INSTALL_FLAGS+=" --no-build-isolation"
if [ "x${BUILD_PROFILE}" == "xl4t12" ]; then
USE_PIP=true
fi
git clone https://github.com/neuphonic/neutts-air neutts-air
cp -rfv neutts-air/neuttsair ./

View File

@@ -1,5 +1,5 @@
--extra-index-url https://download.pytorch.org/whl/rocm6.4
torch==2.8.0+rocm6.4
--extra-index-url https://download.pytorch.org/whl/rocm6.3
torch==2.8.0+rocm6.3
transformers==4.56.1
accelerate
librosa==0.11.0

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