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* feat(ds4): add standalone ds4-worker distributed worker binary Add worker_main.c, a minimal standalone worker that owns a slice of the model's transformer layers and serves activations over ds4's own TCP transport via ds4_dist_run(). It links the same engine objects the backend already builds (including ds4_distributed.o) and has NO gRPC/protobuf dependency, so it builds even on hosts lacking protobuf/grpc dev headers. Launched by `local-ai worker ds4-distributed`. Wire the ds4-worker CMake target (mirrors grpc-server's object/GPU/native handling) and have the Makefile copy + clean the binary alongside grpc-server. Ignore the built ds4-worker artifact. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(ds4): package ds4-worker alongside grpc-server Copy the standalone ds4-worker binary into the backend package (Linux package.sh) and the Darwin OCI tar (ds4-darwin.sh: both the explicit copy and the otool dylib-bundling loop) so distributed workers ship with the backend. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * fix(ds4): tighten ds4-worker integer arg validation to match upstream Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(ds4): wire grpc-server as distributed coordinator Add distributed COORDINATOR support to the ds4 backend's gRPC server. Distributed inference is an engine backend: when LoadModel receives 'ds4_role:coordinator', the process populates ds4_engine_options.distributed (role, layer slice, listen host/port) before ds4_engine_open, then the normal ds4_session_* generation path runs transparently once the worker route covers all layers. - New LoadModel options: ds4_role, ds4_layers (START:END or START:output), ds4_listen (host:port), ds4_route_timeout. - parse_layers_spec() maps the layer spec onto ds4_distributed_layers. - wait_route_ready() blocks generation until ds4_session_distributed_route_ready() reports full coverage (or timeout), gating both Predict and PredictStream; returns UNAVAILABLE on timeout/error. - No ds4_role => g_distributed stays false and wait_route_ready is a no-op, so single-node behavior is unchanged. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * fix(ds4): don't block Status during route wait; validate coordinator opts Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(cli): add ds4-distributed worker exec helper Add the ds4WorkerArgs helper plus findDS4Backend/DS4Distributed.Run that resolve the ds4 backend via the gallery and exec the packaged ds4-worker binary. Unlike worker_llamacpp.go, ds4 bundles its own dynamic loader (lib/ld.so) for glibc compatibility, so when present we exec ds4-worker through that loader with LD_LIBRARY_PATH=<backend>/lib, mirroring backend/cpp/ds4/run.sh; otherwise we exec it directly. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(cli): register the ds4-distributed worker subcommand Wire DS4Distributed into the Worker kong command tree so `local-ai worker ds4-distributed` is available. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * docs(ds4): document layer-split distributed inference Add a ds4 section to the distributed-mode feature docs (coordinator model YAML, manual worker command, layer-range semantics, the 'GGUF on every machine' requirement, coordinator-listens dial direction vs llama.cpp) and a terse Distributed mode section to the ds4 backend agent guide. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * test(ds4): opt-in hardware-gated distributed e2e spec Add a self-contained, opt-in Ginkgo spec to the backend e2e suite that spins a ds4 coordinator (via the packaged run.sh, loaded with ds4_role/ds4_layers/ds4_listen options) plus a ds4-worker process for the upper layers, then uses Eventually to assert a short successful Predict once the layer route forms, before tearing the worker down. Gated by BACKEND_TEST_DS4_DISTRIBUTED=1 (plus the existing BACKEND_BINARY + BACKEND_TEST_MODEL_FILE and optional layer/listen/accel knobs); compiles and skips cleanly with no env, hardware, or model. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * test(ds4): pass coordinator ctx to worker; lowercase error string Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * docs(ds4): note distributed transport is plaintext/unauthenticated Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * style(ds4): replace em dashes in distributed docs/agent/test per repo convention Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * fix(ds4): link ds4-worker with the C++ driver for CUDA/Metal builds The ds4-worker target is built from worker_main.c (C), so CMake linked it with the C driver. The nvcc-built ds4_cuda.o (and Obj-C++ ds4_metal.o) reference the C++ runtime, so the CUDA/Metal builds failed with undefined libstdc++ symbols (std::__throw_length_error). The CPU build passed because ds4_cpu.o is pure C. Force LINKER_LANGUAGE CXX so libstdc++ is linked. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
LocalAI website
LocalAI documentation website
Requirement
In this project, the Docsy theme component is pulled in as a Hugo module, together with other module dependencies:
$ hugo mod graph
hugo: collected modules in 566 ms
hugo: collected modules in 578 ms
github.com/google/docsy-example github.com/google/docsy@v0.5.1-0.20221017155306-99eacb09ffb0
github.com/google/docsy-example github.com/google/docsy/dependencies@v0.5.1-0.20221014161617-be5da07ecff1
github.com/google/docsy/dependencies@v0.5.1-0.20221014161617-be5da07ecff1 github.com/twbs/bootstrap@v4.6.2+incompatible
github.com/google/docsy/dependencies@v0.5.1-0.20221014161617-be5da07ecff1 github.com/FortAwesome/Font-Awesome@v0.0.0-20220831210243-d3a7818c253f
If you want to do SCSS edits and want to publish these, you need to install PostCSS
npm install
Running the website locally
Building and running the site locally requires a recent extended version of Hugo.
You can find out more about how to install Hugo for your environment in our
Getting started guide.
Once you've made your working copy of the site repo, from the repo root folder, run:
hugo server
Running a container locally
You can run docsy-example inside a Docker
container, the container runs with a volume bound to the docsy-example
folder. This approach doesn't require you to install any dependencies other
than Docker Desktop on
Windows and Mac, and Docker Compose
on Linux.
-
Build the docker image
docker-compose build -
Run the built image
docker-compose upNOTE: You can run both commands at once with
docker-compose up --build. -
Verify that the service is working.
Open your web browser and type
http://localhost:1313in your navigation bar, This opens a local instance of the docsy-example homepage. You can now make changes to the docsy example and those changes will immediately show up in your browser after you save.
Cleanup
To stop Docker Compose, on your terminal window, press Ctrl + C.
To remove the produced images run:
docker-compose rm
For more information see the Docker Compose documentation.
Troubleshooting
As you run the website locally, you may run into the following error:
➜ hugo server
INFO 2021/01/21 21:07:55 Using config file:
Building sites … INFO 2021/01/21 21:07:55 syncing static files to /
Built in 288 ms
Error: Error building site: TOCSS: failed to transform "scss/main.scss" (text/x-scss): resource "scss/scss/main.scss_9fadf33d895a46083cdd64396b57ef68" not found in file cache
This error occurs if you have not installed the extended version of Hugo. See this section of the user guide for instructions on how to install Hugo.
Or you may encounter the following error:
➜ hugo server
Error: failed to download modules: binary with name "go" not found
This error occurs if you have not installed the go programming language on your system.
See this section of the user guide for instructions on how to install go.