mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2026-06-09 07:16:44 +02:00
Todd Malsbary
f478f1b6d7
* Tidy up SYCL doc a bit - Add explicit links to referenced items - Fix spelling errors Signed-off-by: Todd Malsbary <todd.malsbary@intel.com> * Correct documented default for GGML_SYCL_GRAPH The default is ON, not OFF: $ cmake -LAH -B build | grep GGML_SYCL_GRAPH ... GGML_SYCL_GRAPH:BOOL=ON Signed-off-by: Todd Malsbary <todd.malsbary@intel.com> * Move docker instructions from SYCL.md to docker.md This makes them directly accesible from the Quick Start section of the top-level README.md. Signed-off-by: Todd Malsbary <todd.malsbary@intel.com> * Refer to intel.Dockerfile for ARGs and their defaults The defaults are always changing; this avoids accuracy errors from duplicating the information. Signed-off-by: Todd Malsbary <todd.malsbary@intel.com> * Remove mention of Nvidia in SYCL row of backend table This support was removed in 2026.02 - refer to the SYCL.md News. Signed-off-by: Todd Malsbary <todd.malsbary@intel.com> --------- Signed-off-by: Todd Malsbary <todd.malsbary@intel.com>
179 lines
12 KiB
Markdown
179 lines
12 KiB
Markdown
# Docker
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## Prerequisites
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* Docker must be installed and running on your system.
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* Create a folder to store big models & intermediate files (ex. /llama/models)
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## Images
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We have three Docker images available for this project:
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1. `ghcr.io/ggml-org/llama.cpp:full`: This image includes both the `llama-cli` and `llama-completion` executables and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. (platforms: `linux/amd64`, `linux/arm64`, `linux/s390x`)
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2. `ghcr.io/ggml-org/llama.cpp:light`: This image only includes the `llama-cli` and `llama-completion` executables. (platforms: `linux/amd64`, `linux/arm64`, `linux/s390x`)
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3. `ghcr.io/ggml-org/llama.cpp:server`: This image only includes the `llama-server` executable. (platforms: `linux/amd64`, `linux/arm64`, `linux/s390x`)
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Additionally, there the following images, similar to the above:
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- `ghcr.io/ggml-org/llama.cpp:full-cuda`: Same as `full` but compiled with CUDA 12 support. (platforms: `linux/amd64`, `linux/arm64`)
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- `ghcr.io/ggml-org/llama.cpp:full-cuda13`: Same as `full` but compiled with CUDA 13 support. (platforms: `linux/amd64`, `linux/arm64`)
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- `ghcr.io/ggml-org/llama.cpp:light-cuda`: Same as `light` but compiled with CUDA 12 support. (platforms: `linux/amd64`, `linux/arm64`)
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- `ghcr.io/ggml-org/llama.cpp:light-cuda13`: Same as `light` but compiled with CUDA 13 support. (platforms: `linux/amd64`, `linux/arm64`)
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- `ghcr.io/ggml-org/llama.cpp:server-cuda`: Same as `server` but compiled with CUDA 12 support. (platforms: `linux/amd64`, `linux/arm64`)
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- `ghcr.io/ggml-org/llama.cpp:server-cuda13`: Same as `server` but compiled with CUDA 13 support. (platforms: `linux/amd64`, `linux/arm64`)
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- `ghcr.io/ggml-org/llama.cpp:full-rocm`: Same as `full` but compiled with ROCm support. (platforms: `linux/amd64`)
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- `ghcr.io/ggml-org/llama.cpp:light-rocm`: Same as `light` but compiled with ROCm support. (platforms: `linux/amd64`)
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- `ghcr.io/ggml-org/llama.cpp:server-rocm`: Same as `server` but compiled with ROCm support. (platforms: `linux/amd64`)
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- `ghcr.io/ggml-org/llama.cpp:full-musa`: Same as `full` but compiled with MUSA support. (platforms: `linux/amd64`)
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- `ghcr.io/ggml-org/llama.cpp:light-musa`: Same as `light` but compiled with MUSA support. (platforms: `linux/amd64`)
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- `ghcr.io/ggml-org/llama.cpp:server-musa`: Same as `server` but compiled with MUSA support. (platforms: `linux/amd64`)
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- `ghcr.io/ggml-org/llama.cpp:full-intel`: Same as `full` but compiled with SYCL support. (platforms: `linux/amd64`)
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- `ghcr.io/ggml-org/llama.cpp:light-intel`: Same as `light` but compiled with SYCL support. (platforms: `linux/amd64`)
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- `ghcr.io/ggml-org/llama.cpp:server-intel`: Same as `server` but compiled with SYCL support. (platforms: `linux/amd64`)
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- `ghcr.io/ggml-org/llama.cpp:full-vulkan`: Same as `full` but compiled with Vulkan support. (platforms: `linux/amd64`, `linux/arm64`)
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- `ghcr.io/ggml-org/llama.cpp:light-vulkan`: Same as `light` but compiled with Vulkan support. (platforms: `linux/amd64`, `linux/arm64`)
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- `ghcr.io/ggml-org/llama.cpp:server-vulkan`: Same as `server` but compiled with Vulkan support. (platforms: `linux/amd64`, `linux/arm64`)
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- `ghcr.io/ggml-org/llama.cpp:full-openvino`: Same as `full` but compiled with OpenVino support. (platforms: `linux/amd64`)
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- `ghcr.io/ggml-org/llama.cpp:light-openvino`: Same as `light` but compiled with OpenVino support. (platforms: `linux/amd64`)
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- `ghcr.io/ggml-org/llama.cpp:server-openvino`: Same as `server` but compiled with OpenVino support. (platforms: `linux/amd64`)
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- `ghcr.io/ggml-org/llama.cpp:full-s390x`: Identical to `full`, an alias for the `s390x` platform. (platforms: `linux/s390x`)
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- `ghcr.io/ggml-org/llama.cpp:light-s390x`: Identical to `light`, an alias for the `s390x` platform. (platforms: `linux/s390x`)
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- `ghcr.io/ggml-org/llama.cpp:server-s390x`: Identical to `server`, an alias for the `s390x` platform. (platforms: `linux/s390x`)
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The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](../.devops/) and the GitHub Action defined in [.github/workflows/docker.yml](../.github/workflows/docker.yml). If you need different settings (for example, a different CUDA, ROCm or MUSA library, you'll need to build the images locally for now).
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## Usage
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The easiest way to download the models, convert them to ggml and optimize them is with the --all-in-one command which includes the full docker image.
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Replace `/path/to/models` below with the actual path where you downloaded the models.
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```bash
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docker run -v /path/to/models:/models ghcr.io/ggml-org/llama.cpp:full --all-in-one "/models/" 7B
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```
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On completion, you are ready to play!
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```bash
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docker run -v /path/to/models:/models ghcr.io/ggml-org/llama.cpp:full --run -m /models/7B/ggml-model-q4_0.gguf
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docker run -v /path/to/models:/models ghcr.io/ggml-org/llama.cpp:full --run-legacy -m /models/32B/ggml-model-q8_0.gguf -no-cnv -p "Building a mobile app can be done in 15 steps:" -n 512
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```
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or with a light image:
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```bash
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docker run -v /path/to/models:/models --entrypoint /app/llama-cli ghcr.io/ggml-org/llama.cpp:light -m /models/7B/ggml-model-q4_0.gguf
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docker run -v /path/to/models:/models --entrypoint /app/llama-completion ghcr.io/ggml-org/llama.cpp:light -m /models/32B/ggml-model-q8_0.gguf -no-cnv -p "Building a mobile app can be done in 15 steps:" -n 512
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```
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or with a server image:
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```bash
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docker run -v /path/to/models:/models -p 8080:8080 ghcr.io/ggml-org/llama.cpp:server -m /models/7B/ggml-model-q4_0.gguf --port 8080 --host 0.0.0.0 -n 512
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```
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In the above examples, `--entrypoint /app/llama-cli` is specified for clarity, but you can safely omit it since it's the default entrypoint in the container.
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## Docker With CUDA
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Assuming one has the [nvidia-container-toolkit](https://github.com/NVIDIA/nvidia-container-toolkit) properly installed on Linux, or is using a GPU enabled cloud, `cuBLAS` should be accessible inside the container.
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## Building Docker locally
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```bash
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docker build -t local/llama.cpp:full-cuda --target full -f .devops/cuda.Dockerfile .
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docker build -t local/llama.cpp:light-cuda --target light -f .devops/cuda.Dockerfile .
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docker build -t local/llama.cpp:server-cuda --target server -f .devops/cuda.Dockerfile .
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```
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You may want to pass in some different `ARGS`, depending on the CUDA environment supported by your container host, as well as the GPU architecture.
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The defaults are:
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- `CUDA_VERSION` set to `12.8.1`
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- `CUDA_DOCKER_ARCH` set to the cmake build default, which includes all the supported architectures
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The resulting images, are essentially the same as the non-CUDA images:
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1. `local/llama.cpp:full-cuda`: This image includes both the `llama-cli` and `llama-completion` executables and the tools to convert LLaMA models into ggml and convert into 4-bit quantization.
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2. `local/llama.cpp:light-cuda`: This image only includes the `llama-cli` and `llama-completion` executables.
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3. `local/llama.cpp:server-cuda`: This image only includes the `llama-server` executable.
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## Usage
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After building locally, Usage is similar to the non-CUDA examples, but you'll need to add the `--gpus` flag. You will also want to use the `--n-gpu-layers` flag.
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```bash
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docker run --gpus all -v /path/to/models:/models local/llama.cpp:full-cuda --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
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docker run --gpus all -v /path/to/models:/models local/llama.cpp:light-cuda -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
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docker run --gpus all -v /path/to/models:/models local/llama.cpp:server-cuda -m /models/7B/ggml-model-q4_0.gguf --port 8080 --host 0.0.0.0 -n 512 --n-gpu-layers 1
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```
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## Docker With MUSA
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Assuming one has the [mt-container-toolkit](https://developer.mthreads.com/musa/native) properly installed on Linux, `muBLAS` should be accessible inside the container.
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## Building Docker locally
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```bash
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docker build -t local/llama.cpp:full-musa --target full -f .devops/musa.Dockerfile .
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docker build -t local/llama.cpp:light-musa --target light -f .devops/musa.Dockerfile .
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docker build -t local/llama.cpp:server-musa --target server -f .devops/musa.Dockerfile .
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```
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You may want to pass in some different `ARGS`, depending on the MUSA environment supported by your container host, as well as the GPU architecture.
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The defaults are:
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- `MUSA_VERSION` set to `rc4.3.0`
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The resulting images, are essentially the same as the non-MUSA images:
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1. `local/llama.cpp:full-musa`: This image includes both the `llama-cli` and `llama-completion` executables and the tools to convert LLaMA models into ggml and convert into 4-bit quantization.
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2. `local/llama.cpp:light-musa`: This image only includes the `llama-cli` and `llama-completion` executables.
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3. `local/llama.cpp:server-musa`: This image only includes the `llama-server` executable.
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## Usage
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After building locally, Usage is similar to the non-MUSA examples, but you'll need to set `mthreads` as default Docker runtime. This can be done by executing `(cd /usr/bin/musa && sudo ./docker setup $PWD)` and verifying the changes by executing `docker info | grep mthreads` on the host machine. You will also want to use the `--n-gpu-layers` flag.
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```bash
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docker run -v /path/to/models:/models local/llama.cpp:full-musa --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
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docker run -v /path/to/models:/models local/llama.cpp:light-musa -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
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docker run -v /path/to/models:/models local/llama.cpp:server-musa -m /models/7B/ggml-model-q4_0.gguf --port 8080 --host 0.0.0.0 -n 512 --n-gpu-layers 1
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```
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## Docker With SYCL
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## Building Docker locally
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```bash
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docker build -t local/llama.cpp:full-intel --target full -f .devops/intel.Dockerfile .
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docker build -t local/llama.cpp:light-intel --target light -f .devops/intel.Dockerfile .
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docker build -t local/llama.cpp:server-intel --target server -f .devops/intel.Dockerfile .
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```
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You may want to pass in some different `ARGS`, depending on the SYCL environment supported by your container host, as well as the GPU architecture.
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Refer to [.devops/intel.Dockerfile](../.devops/intel.Dockerfile) for the available `ARGS` and their defaults.
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The resulting images, are essentially the same as the non-SYCL images:
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1. `local/llama.cpp:full-intel`: This image includes both the `llama-cli` and `llama-completion` executables and the tools to convert LLaMA models into ggml and convert into 4-bit quantization.
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2. `local/llama.cpp:light-intel`: This image only includes the `llama-cli` and `llama-completion` executables.
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3. `local/llama.cpp:server-intel`: This image only includes the `llama-server` executable.
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## Usage
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After building locally, usage is similar to the non-SYCL examples, but you'll need to add the `--device` flag.
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```bash
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# First, find all the DRI cards
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ls -la /dev/dri
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# Then, pick the card that you want to use (here for e.g. /dev/dri/card0).
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docker run --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card0:/dev/dri/card0 -v /path/to/models:/models local/llama.cpp:full-intel -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 99
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docker run --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card0:/dev/dri/card0 -v /path/to/models:/models local/llama.cpp:light-intel -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 99
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docker run --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card0:/dev/dri/card0 -v /path/to/models:/models local/llama.cpp:server-intel -m /models/7B/ggml-model-q4_0.gguf --port 8080 --host 0.0.0.0 -n 512 --n-gpu-layers 99
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```
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*Notes:*
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- Docker has been tested successfully on native Linux. WSL support has not been verified yet.
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- You may need to install Intel GPU driver on the **host** machine *(Please refer to the [Linux configuration](./backend/SYCL.md#linux) for details)*.
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