Add Docker support documentation and configuration for backends

This commit is contained in:
2025-09-24 22:15:21 +02:00
parent ba0f877185
commit 2d925b473d
3 changed files with 133 additions and 15 deletions

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@@ -14,6 +14,7 @@
### 🔗 Universal Compatibility
- **OpenAI API Compatible**: Drop-in replacement - route requests by instance name
- **Multi-Backend Support**: Native support for llama.cpp, MLX (Apple Silicon optimized), and vLLM
- **Docker Support**: Run backends in containers
### 🌐 User-Friendly Interface
- **Web Dashboard**: Modern React UI for visual management (unlike CLI-only tools)
@@ -32,6 +33,7 @@
# For llama.cpp: https://github.com/ggml-org/llama.cpp#quick-start
# For MLX on macOS: pip install mlx-lm
# For vLLM: pip install vllm
# Or use Docker - no local installation required
# 2. Download and run llamactl
LATEST_VERSION=$(curl -s https://api.github.com/repos/lordmathis/llamactl/releases/latest | grep '"tag_name":' | sed -E 's/.*"([^"]+)".*/\1/')
@@ -112,6 +114,7 @@ You need `llama-server` from [llama.cpp](https://github.com/ggml-org/llama.cpp)
brew install llama.cpp
# Or build from source - see llama.cpp docs
# Or use Docker - no local installation required
```
**For MLX backend (macOS only):**
@@ -139,9 +142,51 @@ python -m venv vllm-env
source vllm-env/bin/activate
pip install vllm
# For production deployments, consider container-based installation
# Or use Docker - no local installation required
```
## Docker Support
llamactl supports running backends in Docker containers with identical behavior to native execution. This is particularly useful for:
- Production deployments without local backend installation
- Isolating backend dependencies
- GPU-accelerated inference using official Docker images
### Docker Configuration
Enable Docker support using the new structured backend configuration:
```yaml
backends:
llama-cpp:
command: "llama-server"
docker:
enabled: true
image: "ghcr.io/ggml-org/llama.cpp:server"
args: ["run", "--rm", "--network", "host", "--gpus", "all"]
vllm:
command: "vllm"
args: ["serve"]
docker:
enabled: true
image: "vllm/vllm-openai:latest"
args: ["run", "--rm", "--network", "host", "--gpus", "all", "--shm-size", "1g"]
```
### Key Features
- **Host Networking**: Uses `--network host` for seamless port management
- **GPU Support**: Includes `--gpus all` for GPU acceleration
- **Environment Variables**: Configure container environment as needed
- **Flexible Configuration**: Per-backend Docker settings with sensible defaults
### Requirements
- Docker installed and running
- For GPU support: nvidia-docker2 (Linux) or Docker Desktop with GPU support
- No local backend installation required when using Docker
## Configuration
llamactl works out of the box with sensible defaults.
@@ -154,9 +199,27 @@ server:
enable_swagger: false # Enable Swagger UI for API docs
backends:
llama_executable: llama-server # Path to llama-server executable
mlx_lm_executable: mlx_lm.server # Path to mlx_lm.server executable
vllm_executable: vllm # Path to vllm executable
llama-cpp:
command: "llama-server"
args: []
docker:
enabled: false
image: "ghcr.io/ggml-org/llama.cpp:server"
args: ["run", "--rm", "--network", "host", "--gpus", "all"]
environment: {}
vllm:
command: "vllm"
args: ["serve"]
docker:
enabled: false
image: "vllm/vllm-openai:latest"
args: ["run", "--rm", "--network", "host", "--gpus", "all", "--shm-size", "1g"]
environment: {}
mlx:
command: "mlx_lm.server"
args: []
instances:
port_range: [8000, 9000] # Port range for instances

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@@ -20,9 +20,27 @@ server:
enable_swagger: false # Enable Swagger UI for API docs
backends:
llama_executable: llama-server # Path to llama-server executable
mlx_lm_executable: mlx_lm.server # Path to mlx_lm.server executable
vllm_executable: vllm # Path to vllm executable
llama-cpp:
command: "llama-server"
args: []
docker:
enabled: false
image: "ghcr.io/ggml-org/llama.cpp:server"
args: ["run", "--rm", "--network", "host", "--gpus", "all"]
environment: {}
vllm:
command: "vllm"
args: ["serve"]
docker:
enabled: false
image: "vllm/vllm-openai:latest"
args: ["run", "--rm", "--network", "host", "--gpus", "all", "--shm-size", "1g"]
environment: {}
mlx:
command: "mlx_lm.server"
args: []
instances:
port_range: [8000, 9000] # Port range for instances
@@ -90,18 +108,40 @@ server:
- `LLAMACTL_ENABLE_SWAGGER` - Enable Swagger UI (true/false)
### Backend Configuration
```yaml
backends:
llama_executable: "llama-server" # Path to llama-server executable (default: "llama-server")
mlx_lm_executable: "mlx_lm.server" # Path to mlx_lm.server executable (default: "mlx_lm.server")
vllm_executable: "vllm" # Path to vllm executable (default: "vllm")
llama-cpp:
command: "llama-server"
args: []
docker:
enabled: false # Enable Docker runtime (default: false)
image: "ghcr.io/ggml-org/llama.cpp:server"
args: ["run", "--rm", "--network", "host", "--gpus", "all"]
environment: {}
vllm:
command: "vllm"
args: ["serve"]
docker:
enabled: false
image: "vllm/vllm-openai:latest"
args: ["run", "--rm", "--network", "host", "--gpus", "all", "--shm-size", "1g"]
environment: {}
mlx:
command: "mlx_lm.server"
args: []
# MLX does not support Docker
```
**Environment Variables:**
- `LLAMACTL_LLAMA_EXECUTABLE` - Path to llama-server executable
- `LLAMACTL_MLX_LM_EXECUTABLE` - Path to mlx_lm.server executable
- `LLAMACTL_VLLM_EXECUTABLE` - Path to vllm executable
**Backend Configuration Fields:**
- `command`: Executable name/path for the backend
- `args`: Default arguments prepended to all instances
- `docker`: Docker-specific configuration (optional)
- `enabled`: Boolean flag to enable Docker runtime
- `image`: Docker image to use
- `args`: Additional arguments passed to `docker run`
- `environment`: Environment variables for the container (optional)
### Instance Configuration

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@@ -88,6 +88,21 @@ Here are basic example configurations for each backend:
}
```
## Docker Support
Llamactl can run backends in Docker containers. To enable Docker for a backend, add a `docker` section to that backend in your YAML configuration file (e.g. `config.yaml`) as shown below:
```yaml
backends:
vllm:
command: "vllm"
args: ["serve"]
docker:
enabled: true
image: "vllm/vllm-openai:latest"
args: ["run", "--rm", "--network", "host", "--gpus", "all", "--shm-size", "1g"]
```
## Using the API
You can also manage instances via the REST API: